SGP-1: Prediction and Validation of Homologous Genes Based on Sequence Alignments

PubMed Central

Wiehe, Thomas; Gebauer-Jung, Steffi; Mitchell-Olds, Thomas; Guigó, Roderic

2001-01-01

Conventional methods of gene prediction rely on the recognition of DNA-sequence signals, the coding potential or the comparison of a genomic sequence with a cDNA, EST, or protein database. Reasons for limited accuracy in many circumstances are species-specific training and the incompleteness of reference databases. Lately, comparative genome analysis has attracted increasing attention. Several analysis tools that are based on human/mouse comparisons are already available. Here, we present a program for the prediction of protein-coding genes, termed SGP-1 (Syntenic Gene Prediction), which is based on the similarity of homologous genomic sequences. In contrast to most existing tools, the accuracy of SGP-1 depends little on species-specific properties such as codon usage or the nucleotide distribution. SGP-1 may therefore be applied to nonstandard model organisms in vertebrates as well as in plants, without the need for extensive parameter training. In addition to predicting genes in large-scale genomic sequences, the program may be useful to validate gene structure annotations from databases. To this end, SGP-1 output also contains comparisons between predicted and annotated gene structures in HTML format. The program can be accessed via a Web server at http://soft.ice.mpg.de/sgp-1. The source code, written in ANSI C, is available on request from the authors. PMID:11544202

Tertiary structure prediction and identification of druggable pocket in the cancer biomarker – Osteopontin-c

PubMed Central

2014-01-01

Background Osteopontin (Eta, secreted sialoprotein 1, opn) is secreted from different cell types including cancer cells. Three splice variant forms namely osteopontin-a, osteopontin-b and osteopontin-c have been identified. The main astonishing feature is that osteopontin-c is found to be elevated in almost all types of cancer cells. This was the vital point to consider it for sequence analysis and structure predictions which provide ample chances for prognostic, therapeutic and preventive cancer research. Methods Osteopontin-c gene sequence was determined from Breast Cancer sample and was translated to protein sequence. It was then analyzed using various software and web tools for binding pockets, docking and druggability analysis. Due to the lack of homological templates, tertiary structure was predicted using ab-initio method server – I-TASSER and was evaluated after refinement using web tools. Refined structure was compared with known bone sialoprotein electron microscopic structure and docked with CD44 for binding analysis and binding pockets were identified for drug designing. Results Signal sequence of about sixteen amino acid residues was identified using signal sequence prediction servers. Due to the absence of known structures of similar proteins, three dimensional structure of osteopontin-c was predicted using I-TASSER server. The predicted structure was refined with the help of SUMMA server and was validated using SAVES server. Molecular dynamic analysis was carried out using GROMACS software. The final model was built and was used for docking with CD44. Druggable pockets were identified using pocket energies. Conclusions The tertiary structure of osteopontin-c was predicted successfully using the ab-initio method and the predictions showed that osteopontin-c is of fibrous nature comparable to firbronectin. Docking studies showed the significant similarities of QSAET motif in the interaction of CD44 and osteopontins between the normal and splice variant forms of osteopontins and binding pockets analyses revealed several pockets which paved the way to the identification of a druggable pocket. PMID:24401206

ComplexContact: a web server for inter-protein contact prediction using deep learning.

PubMed

Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo

2018-05-22

ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

eShadow: A tool for comparing closely related sequences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ovcharenko, Ivan; Boffelli, Dario; Loots, Gabriela G.

2004-01-15

Primate sequence comparisons are difficult to interpret due to the high degree of sequence similarity shared between such closely related species. Recently, a novel method, phylogenetic shadowing, has been pioneered for predicting functional elements in the human genome through the analysis of multiple primate sequence alignments. We have expanded this theoretical approach to create a computational tool, eShadow, for the identification of elements under selective pressure in multiple sequence alignments of closely related genomes, such as in comparisons of human to primate or mouse to rat DNA. This tool integrates two different statistical methods and allows for the dynamic visualizationmore » of the resulting conservation profile. eShadow also includes a versatile optimization module capable of training the underlying Hidden Markov Model to differentially predict functional sequences. This module grants the tool high flexibility in the analysis of multiple sequence alignments and in comparing sequences with different divergence rates. Here, we describe the eShadow comparative tool and its potential uses for analyzing both multiple nucleotide and protein alignments to predict putative functional elements. The eShadow tool is publicly available at http://eshadow.dcode.org/« less

Genotypic tropism testing by massively parallel sequencing: qualitative and quantitative analysis.

PubMed

Däumer, Martin; Kaiser, Rolf; Klein, Rolf; Lengauer, Thomas; Thiele, Bernhard; Thielen, Alexander

2011-05-13

Inferring viral tropism from genotype is a fast and inexpensive alternative to phenotypic testing. While being highly predictive when performed on clonal samples, sensitivity of predicting CXCR4-using (X4) variants drops substantially in clinical isolates. This is mainly attributed to minor variants not detected by standard bulk-sequencing. Massively parallel sequencing (MPS) detects single clones thereby being much more sensitive. Using this technology we wanted to improve genotypic prediction of coreceptor usage. Plasma samples from 55 antiretroviral-treated patients tested for coreceptor usage with the Monogram Trofile Assay were sequenced with standard population-based approaches. Fourteen of these samples were selected for further analysis with MPS. Tropism was predicted from each sequence with geno2pheno[coreceptor]. Prediction based on bulk-sequencing yielded 59.1% sensitivity and 90.9% specificity compared to the trofile assay. With MPS, 7600 reads were generated on average per isolate. Minorities of sequences with high confidence in CXCR4-usage were found in all samples, irrespective of phenotype. When using the default false-positive-rate of geno2pheno[coreceptor] (10%), and defining a minority cutoff of 5%, the results were concordant in all but one isolate. The combination of MPS and coreceptor usage prediction results in a fast and accurate alternative to phenotypic assays. The detection of X4-viruses in all isolates suggests that coreceptor usage as well as fitness of minorities is important for therapy outcome. The high sensitivity of this technology in combination with a quantitative description of the viral population may allow implementing meaningful cutoffs for predicting response to CCR5-antagonists in the presence of X4-minorities.

RNAstructure: software for RNA secondary structure prediction and analysis.

PubMed

Reuter, Jessica S; Mathews, David H

2010-03-15

To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

Classification of viral zoonosis through receptor pattern analysis.

PubMed

Bae, Se-Eun; Son, Hyeon Seok

2011-04-13

Viral zoonosis, the transmission of a virus from its primary vertebrate reservoir species to humans, requires ubiquitous cellular proteins known as receptor proteins. Zoonosis can occur not only through direct transmission from vertebrates to humans, but also through intermediate reservoirs or other environmental factors. Viruses can be categorized according to genotype (ssDNA, dsDNA, ssRNA and dsRNA viruses). Among them, the RNA viruses exhibit particularly high mutation rates and are especially problematic for this reason. Most zoonotic viruses are RNA viruses that change their envelope proteins to facilitate binding to various receptors of host species. In this study, we sought to predict zoonotic propensity through the analysis of receptor characteristics. We hypothesized that the major barrier to interspecies virus transmission is that receptor sequences vary among species--in other words, that the specific amino acid sequence of the receptor determines the ability of the viral envelope protein to attach to the cell. We analysed host-cell receptor sequences for their hydrophobicity/hydrophilicity characteristics. We then analysed these properties for similarities among receptors of different species and used a statistical discriminant analysis to predict the likelihood of transmission among species. This study is an attempt to predict zoonosis through simple computational analysis of receptor sequence differences. Our method may be useful in predicting the zoonotic potential of newly discovered viral strains.

Draft versus finished sequence data for DNA and protein diagnostic signature development

PubMed Central

Gardner, Shea N.; Lam, Marisa W.; Smith, Jason R.; Torres, Clinton L.; Slezak, Tom R.

2005-01-01

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10−3–10−5 (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures. PMID:16243783

Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.

PubMed

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2015-01-01

Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

Novel proteases from the genome of the carnivorous plant Drosera capensis: structural prediction and comparative analysis

PubMed Central

Butts, Carter T.; Bierma, Jan C.; Martin, Rachel W.

2016-01-01

In his 1875 monograph on insectivorous plants, Darwin described the feeding reactions of Drosera flypaper traps and predicted that their secretions contained a “ferment” similar to mammalian pepsin, an aspartic protease. Here we report a high-quality draft genome sequence for the cape sundew, Drosera capensis, the first genome of a carnivorous plant from order Caryophyllales, which also includes the Venus flytrap (Dionaea) and the tropical pitcher plants (Nepenthes). This species was selected in part for its hardiness and ease of cultivation, making it an excellent model organism for further investigations of plant carnivory. Analysis of predicted protein sequences yields genes encoding proteases homologous to those found in other plants, some of which display sequence and structural features that suggest novel functionalities. Because the sequence similarity to proteins of known structure is in most cases too low for traditional homology modeling, 3D structures of representative proteases are predicted using comparative modeling with all-atom refinement. Although the overall folds and active residues for these proteins are conserved, we find structural and sequence differences consistent with a diversity of substrate recognition patterns. Finally, we predict differences in substrate specificities using in silico experiments, providing targets for structure/function studies of novel enzymes with biological and technological significance. PMID:27353064

dbWGFP: a database and web server of human whole-genome single nucleotide variants and their functional predictions.

PubMed

Wu, Jiaxin; Wu, Mengmeng; Li, Lianshuo; Liu, Zhuo; Zeng, Wanwen; Jiang, Rui

2016-01-01

The recent advancement of the next generation sequencing technology has enabled the fast and low-cost detection of all genetic variants spreading across the entire human genome, making the application of whole-genome sequencing a tendency in the study of disease-causing genetic variants. Nevertheless, there still lacks a repository that collects predictions of functionally damaging effects of human genetic variants, though it has been well recognized that such predictions play a central role in the analysis of whole-genome sequencing data. To fill this gap, we developed a database named dbWGFP (a database and web server of human whole-genome single nucleotide variants and their functional predictions) that contains functional predictions and annotations of nearly 8.58 billion possible human whole-genome single nucleotide variants. Specifically, this database integrates 48 functional predictions calculated by 17 popular computational methods and 44 valuable annotations obtained from various data sources. Standalone software, user-friendly query services and free downloads of this database are available at http://bioinfo.au.tsinghua.edu.cn/dbwgfp. dbWGFP provides a valuable resource for the analysis of whole-genome sequencing, exome sequencing and SNP array data, thereby complementing existing data sources and computational resources in deciphering genetic bases of human inherited diseases. © The Author(s) 2016. Published by Oxford University Press.

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

PubMed Central

Rodriguez-Rivas, Juan; Marsili, Simone; Juan, David; Valencia, Alfonso

2016-01-01

Protein–protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein–protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein–protein interfaces. We show that sequence-based coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein–protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequence-based accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach. PMID:27965389

Conservation of coevolving protein interfaces bridges prokaryote-eukaryote homologies in the twilight zone.

PubMed

Rodriguez-Rivas, Juan; Marsili, Simone; Juan, David; Valencia, Alfonso

2016-12-27

Protein-protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein-protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein-protein interfaces. We show that sequence-based coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein-protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequence-based accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.

GI-POP: a combinational annotation and genomic island prediction pipeline for ongoing microbial genome projects.

PubMed

Lee, Chi-Ching; Chen, Yi-Ping Phoebe; Yao, Tzu-Jung; Ma, Cheng-Yu; Lo, Wei-Cheng; Lyu, Ping-Chiang; Tang, Chuan Yi

2013-04-10

Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project. Copyright © 2012 Elsevier B.V. All rights reserved.

Local backbone structure prediction of proteins

PubMed Central

De Brevern, Alexandre G.; Benros, Cristina; Gautier, Romain; Valadié, Hélène; Hazout, Serge; Etchebest, Catherine

2004-01-01

Summary A statistical analysis of the PDB structures has led us to define a new set of small 3D structural prototypes called Protein Blocks (PBs). This structural alphabet includes 16 PBs, each one is defined by the (φ, Ψ) dihedral angles of 5 consecutive residues. The amino acid distributions observed in sequence windows encompassing these PBs are used to predict by a Bayesian approach the local 3D structure of proteins from the sole knowledge of their sequences. LocPred is a software which allows the users to submit a protein sequence and performs a prediction in terms of PBs. The prediction results are given both textually and graphically. PMID:15724288

Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction.

PubMed

Huang, Ying; Chen, Shi-Yi; Deng, Feilong

2016-01-01

In silico analysis of DNA sequences is an important area of computational biology in the post-genomic era. Over the past two decades, computational approaches for ab initio prediction of gene structure from genome sequence alone have largely facilitated our understanding on a variety of biological questions. Although the computational prediction of protein-coding genes has already been well-established, we are also facing challenges to robustly find the non-coding RNA genes, such as miRNA and lncRNA. Two main aspects of ab initio gene prediction include the computed values for describing sequence features and used algorithm for training the discriminant function, and by which different combinations are employed into various bioinformatic tools. Herein, we briefly review these well-characterized sequence features in eukaryote genomes and applications to ab initio gene prediction. The main purpose of this article is to provide an overview to beginners who aim to develop the related bioinformatic tools.

Domain fusion analysis by applying relational algebra to protein sequence and domain databases

PubMed Central

Truong, Kevin; Ikura, Mitsuhiko

2003-01-01

Background Domain fusion analysis is a useful method to predict functionally linked proteins that may be involved in direct protein-protein interactions or in the same metabolic or signaling pathway. As separate domain databases like BLOCKS, PROSITE, Pfam, SMART, PRINTS-S, ProDom, TIGRFAMs, and amalgamated domain databases like InterPro continue to grow in size and quality, a computational method to perform domain fusion analysis that leverages on these efforts will become increasingly powerful. Results This paper proposes a computational method employing relational algebra to find domain fusions in protein sequence databases. The feasibility of this method was illustrated on the SWISS-PROT+TrEMBL sequence database using domain predictions from the Pfam HMM (hidden Markov model) database. We identified 235 and 189 putative functionally linked protein partners in H. sapiens and S. cerevisiae, respectively. From scientific literature, we were able to confirm many of these functional linkages, while the remainder offer testable experimental hypothesis. Results can be viewed at . Conclusion As the analysis can be computed quickly on any relational database that supports standard SQL (structured query language), it can be dynamically updated along with the sequence and domain databases, thereby improving the quality of predictions over time. PMID:12734020

Power law tails in phylogenetic systems.

PubMed

Qin, Chongli; Colwell, Lucy J

2018-01-23

Covariance analysis of protein sequence alignments uses coevolving pairs of sequence positions to predict features of protein structure and function. However, current methods ignore the phylogenetic relationships between sequences, potentially corrupting the identification of covarying positions. Here, we use random matrix theory to demonstrate the existence of a power law tail that distinguishes the spectrum of covariance caused by phylogeny from that caused by structural interactions. The power law is essentially independent of the phylogenetic tree topology, depending on just two parameters-the sequence length and the average branch length. We demonstrate that these power law tails are ubiquitous in the large protein sequence alignments used to predict contacts in 3D structure, as predicted by our theory. This suggests that to decouple phylogenetic effects from the interactions between sequence distal sites that control biological function, it is necessary to remove or down-weight the eigenvectors of the covariance matrix with largest eigenvalues. We confirm that truncating these eigenvectors improves contact prediction.

Regulatory sequence analysis tools.

PubMed

van Helden, Jacques

2003-07-01

The web resource Regulatory Sequence Analysis Tools (RSAT) (http://rsat.ulb.ac.be/rsat) offers a collection of software tools dedicated to the prediction of regulatory sites in non-coding DNA sequences. These tools include sequence retrieval, pattern discovery, pattern matching, genome-scale pattern matching, feature-map drawing, random sequence generation and other utilities. Alternative formats are supported for the representation of regulatory motifs (strings or position-specific scoring matrices) and several algorithms are proposed for pattern discovery. RSAT currently holds >100 fully sequenced genomes and these data are regularly updated from GenBank.

Analysis of the origin of predictability in human communications

NASA Astrophysics Data System (ADS)

Zhang, Lin; Liu, Yani; Wu, Ye; Xiao, Jinghua

2014-01-01

Human behaviors in daily life can be traced by their communications via electronic devices. E-mails, short messages and cell-phone calls can be used to investigate the predictability of communication partners’ patterns, because these three are the most representative and common behaviors in daily communications. In this paper, we show that all the three manners have apparent predictability in partners’ patterns, and moreover, the short message users’ sequences have the highest predictability among the three. We also reveal that people with fewer communication partners have higher predictability. Finally, we investigate the origin of predictability, which comes from two aspects: one is the intrinsic pattern in the partners sequence, that is, people have the preference of communicating with a fixed partner after another fixed one. The other aspect is the burst, which is communicating with the same partner several times in a row. The high burst in short message communication pattern is one of the main reasons for its high predictability, the intrinsic pattern in e-mail partners sequence is the main reason for its predictability, and the predictability of cell-phone call partners sequence comes from both aspects.

Prefrontal neural correlates of memory for sequences.

PubMed

Averbeck, Bruno B; Lee, Daeyeol

2007-02-28

The sequence of actions appropriate to solve a problem often needs to be discovered by trial and error and recalled in the future when faced with the same problem. Here, we show that when monkeys had to discover and then remember a sequence of decisions across trials, ensembles of prefrontal cortex neurons reflected the sequence of decisions the animal would make throughout the interval between trials. This signal could reflect either an explicit memory process or a sequence-planning process that begins far in advance of the actual sequence execution. This finding extended to error trials such that, when the neural activity during the intertrial interval specified the wrong sequence, the animal also attempted to execute an incorrect sequence. More specifically, we used a decoding analysis to predict the sequence the monkey was planning to execute at the end of the fore-period, just before sequence execution. When this analysis was applied to error trials, we were able to predict where in the sequence the error would occur, up to three movements into the future. This suggests that prefrontal neural activity can retain information about sequences between trials, and that regardless of whether information is remembered correctly or incorrectly, the prefrontal activity veridically reflects the animal's action plan.

A comparative analysis of soft computing techniques for gene prediction.

PubMed

Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

2013-07-01

The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

BlockLogo: visualization of peptide and sequence motif conservation

PubMed Central

Olsen, Lars Rønn; Kudahl, Ulrich Johan; Simon, Christian; Sun, Jing; Schönbach, Christian; Reinherz, Ellis L.; Zhang, Guang Lan; Brusic, Vladimir

2013-01-01

BlockLogo is a web-server application for visualization of protein and nucleotide fragments, continuous protein sequence motifs, and discontinuous sequence motifs using calculation of block entropy from multiple sequence alignments. The user input consists of a multiple sequence alignment, selection of motif positions, type of sequence, and output format definition. The output has BlockLogo along with the sequence logo, and a table of motif frequencies. We deployed BlockLogo as an online application and have demonstrated its utility through examples that show visualization of T-cell epitopes and B-cell epitopes (both continuous and discontinuous). Our additional example shows a visualization and analysis of structural motifs that determine specificity of peptide binding to HLA-DR molecules. The BlockLogo server also employs selected experimentally validated prediction algorithms to enable on-the-fly prediction of MHC binding affinity to 15 common HLA class I and class II alleles as well as visual analysis of discontinuous epitopes from multiple sequence alignments. It enables the visualization and analysis of structural and functional motifs that are usually described as regular expressions. It provides a compact view of discontinuous motifs composed of distant positions within biological sequences. BlockLogo is available at: http://research4.dfci.harvard.edu/cvc/blocklogo/ and http://methilab.bu.edu/blocklogo/ PMID:24001880

Prediction and phylogenetic analysis of mammalian short interspersed elements (SINEs).

PubMed

Rogozin, I B; Mayorov, V I; Lavrentieva, M V; Milanesi, L; Adkison, L R

2000-09-01

The presence of repetitive elements can create serious problems for sequence analysis, especially in the case of homology searches in nucleotide sequence databases. Repetitive elements should be treated carefully by using special programs and databases. In this paper, various aspects of SINE (short interspersed repetitive element) identification, analysis and evolution are discussed.

Free energy minimization to predict RNA secondary structures and computational RNA design.

PubMed

Churkin, Alexander; Weinbrand, Lina; Barash, Danny

2015-01-01

Determining the RNA secondary structure from sequence data by computational predictions is a long-standing problem. Its solution has been approached in two distinctive ways. If a multiple sequence alignment of a collection of homologous sequences is available, the comparative method uses phylogeny to determine conserved base pairs that are more likely to form as a result of billions of years of evolution than by chance. In the case of single sequences, recursive algorithms that compute free energy structures by using empirically derived energy parameters have been developed. This latter approach of RNA folding prediction by energy minimization is widely used to predict RNA secondary structure from sequence. For a significant number of RNA molecules, the secondary structure of the RNA molecule is indicative of its function and its computational prediction by minimizing its free energy is important for its functional analysis. A general method for free energy minimization to predict RNA secondary structures is dynamic programming, although other optimization methods have been developed as well along with empirically derived energy parameters. In this chapter, we introduce and illustrate by examples the approach of free energy minimization to predict RNA secondary structures.

Protein domain analysis of genomic sequence data reveals regulation of LRR related domains in plant transpiration in Ficus.

PubMed

Lang, Tiange; Yin, Kangquan; Liu, Jinyu; Cao, Kunfang; Cannon, Charles H; Du, Fang K

2014-01-01

Predicting protein domains is essential for understanding a protein's function at the molecular level. However, up till now, there has been no direct and straightforward method for predicting protein domains in species without a reference genome sequence. In this study, we developed a functionality with a set of programs that can predict protein domains directly from genomic sequence data without a reference genome. Using whole genome sequence data, the programming functionality mainly comprised DNA assembly in combination with next-generation sequencing (NGS) assembly methods and traditional methods, peptide prediction and protein domain prediction. The proposed new functionality avoids problems associated with de novo assembly due to micro reads and small single repeats. Furthermore, we applied our functionality for the prediction of leucine rich repeat (LRR) domains in four species of Ficus with no reference genome, based on NGS genomic data. We found that the LRRNT_2 and LRR_8 domains are related to plant transpiration efficiency, as indicated by the stomata index, in the four species of Ficus. The programming functionality established in this study provides new insights for protein domain prediction, which is particularly timely in the current age of NGS data expansion.

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

PubMed Central

2017-01-01

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis. PMID:29104927

Whole-exome sequencing analysis of Waardenburg syndrome in a Chinese family.

PubMed

Chen, Dezhong; Zhao, Na; Wang, Jing; Li, Zhuoyu; Wu, Changxin; Fu, Jie; Xiao, Han

2017-01-01

Waardenburg syndrome (WS) is a dominantly inherited, genetically heterogeneous auditory-pigmentary syndrome characterized by non-progressive sensorineural hearing loss and iris discoloration. By whole-exome sequencing (WES), we identified a nonsense mutation (c.598C>T) in PAX3 gene, predicted to be disease causing by in silico analysis. This is the first report of genetically diagnosed case of WS PAX3 c.598C>T nonsense mutation in Chinese ethnic origin by WES and in silico functional prediction methods.

Whole-exome sequencing analysis of Waardenburg syndrome in a Chinese family

PubMed Central

Chen, Dezhong; Zhao, Na; Wang, Jing; Li, Zhuoyu; Wu, Changxin; Fu, Jie; Xiao, Han

2017-01-01

Waardenburg syndrome (WS) is a dominantly inherited, genetically heterogeneous auditory-pigmentary syndrome characterized by non-progressive sensorineural hearing loss and iris discoloration. By whole-exome sequencing (WES), we identified a nonsense mutation (c.598C>T) in PAX3 gene, predicted to be disease causing by in silico analysis. This is the first report of genetically diagnosed case of WS PAX3 c.598C>T nonsense mutation in Chinese ethnic origin by WES and in silico functional prediction methods. PMID:28690861

A generalized analysis of hydrophobic and loop clusters within globular protein sequences

PubMed Central

Eudes, Richard; Le Tuan, Khanh; Delettré, Jean; Mornon, Jean-Paul; Callebaut, Isabelle

2007-01-01

Background Hydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet. Results The structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities). Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 %) reveals preferences for α-helices or β-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that β-strands and especially α-helices are generally over-predicted within the limits of typical β and α hydrophobic clusters. Conclusion The dictionary of hydrophobic clusters described here can help the HCA user to interpret and compare the HCA plots of globular protein sequences, as well as provides an original fundamental insight into the structural bricks of protein folds. Moreover, the novel loop cluster analysis brings additional information for secondary structure prediction on the whole sequence through a generalized cluster analysis (GCA), and not only on regular secondary structures. Such information lays the foundations for developing a new and original tool for secondary structure prediction. PMID:17210072

Pfarao: a web application for protein family analysis customized for cytoskeletal and motor proteins (CyMoBase).

PubMed

Odronitz, Florian; Kollmar, Martin

2006-11-29

Annotation of protein sequences of eukaryotic organisms is crucial for the understanding of their function in the cell. Manual annotation is still by far the most accurate way to correctly predict genes. The classification of protein sequences, their phylogenetic relation and the assignment of function involves information from various sources. This often leads to a collection of heterogeneous data, which is hard to track. Cytoskeletal and motor proteins consist of large and diverse superfamilies comprising up to several dozen members per organism. Up to date there is no integrated tool available to assist in the manual large-scale comparative genomic analysis of protein families. Pfarao (Protein Family Application for Retrieval, Analysis and Organisation) is a database driven online working environment for the analysis of manually annotated protein sequences and their relationship. Currently, the system can store and interrelate a wide range of information about protein sequences, species, phylogenetic relations and sequencing projects as well as links to literature and domain predictions. Sequences can be imported from multiple sequence alignments that are generated during the annotation process. A web interface allows to conveniently browse the database and to compile tabular and graphical summaries of its content. We implemented a protein sequence-centric web application to store, organize, interrelate, and present heterogeneous data that is generated in manual genome annotation and comparative genomics. The application has been developed for the analysis of cytoskeletal and motor proteins (CyMoBase) but can easily be adapted for any protein.

A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies.

PubMed

Thakur, Shalabh; Guttman, David S

2016-06-30

Comparative analysis of whole genome sequence data from closely related prokaryotic species or strains is becoming an increasingly important and accessible approach for addressing both fundamental and applied biological questions. While there are number of excellent tools developed for performing this task, most scale poorly when faced with hundreds of genome sequences, and many require extensive manual curation. We have developed a de-novo genome analysis pipeline (DeNoGAP) for the automated, iterative and high-throughput analysis of data from comparative genomics projects involving hundreds of whole genome sequences. The pipeline is designed to perform reference-assisted and de novo gene prediction, homolog protein family assignment, ortholog prediction, functional annotation, and pan-genome analysis using a range of proven tools and databases. While most existing methods scale quadratically with the number of genomes since they rely on pairwise comparisons among predicted protein sequences, DeNoGAP scales linearly since the homology assignment is based on iteratively refined hidden Markov models. This iterative clustering strategy enables DeNoGAP to handle a very large number of genomes using minimal computational resources. Moreover, the modular structure of the pipeline permits easy updates as new analysis programs become available. DeNoGAP integrates bioinformatics tools and databases for comparative analysis of a large number of genomes. The pipeline offers tools and algorithms for annotation and analysis of completed and draft genome sequences. The pipeline is developed using Perl, BioPerl and SQLite on Ubuntu Linux version 12.04 LTS. Currently, the software package accompanies script for automated installation of necessary external programs on Ubuntu Linux; however, the pipeline should be also compatible with other Linux and Unix systems after necessary external programs are installed. DeNoGAP is freely available at https://sourceforge.net/projects/denogap/ .

In silico study of breast cancer associated gene 3 using LION Target Engine and other tools.

PubMed

León, Darryl A; Cànaves, Jaume M

2003-12-01

Sequence analysis of individual targets is an important step in annotation and validation. As a test case, we investigated human breast cancer associated gene 3 (BCA3) with LION Target Engine and with other bioinformatics tools. LION Target Engine confirmed that the BCA3 gene is located on 11p15.4 and that the two most likely splice variants (lacking exon 3 and exons 3 and 5, respectively) exist. Based on our manual curation of sequence data, it is proposed that an additional variant (missing only exon 5) published in a public sequence repository, is a prediction artifact. A significant number of new orthologs were also identified, and these were the basis for a high-quality protein secondary structure prediction. Moreover, our research confirmed several distinct functional domains as described in earlier reports. Sequence conservation from multiple sequence alignments, splice variant identification, secondary structure predictions, and predicted phosphorylation sites suggest that the removal of interaction sites through alternative splicing might play a modulatory role in BCA3. This in silico approach shows the depth and relevance of an analysis that can be accomplished by including a variety of publicly available tools with an integrated and customizable life science informatics platform.

Predicted secondary structure similarity in the absence of primary amino acid sequence homology: hepatitis B virus open reading frames.

PubMed Central

Schaeffer, E; Sninsky, J J

1984-01-01

Proteins that are related evolutionarily may have diverged at the level of primary amino acid sequence while maintaining similar secondary structures. Computer analysis has been used to compare the open reading frames of the hepatitis B virus to those of the woodchuck hepatitis virus at the level of amino acid sequence, and to predict the relative hydrophilic character and the secondary structure of putative polypeptides. Similarity is seen at the levels of relative hydrophilicity and secondary structure, in the absence of sequence homology. These data reinforce the proposal that these open reading frames encode viral proteins. Computer analysis of this type can be more generally used to establish structural similarities between proteins that do not share obvious sequence homology as well as to assess whether an open reading frame is fortuitous or codes for a protein. PMID:6585835

GWFASTA: server for FASTA search in eukaryotic and microbial genomes.

PubMed

Issac, Biju; Raghava, G P S

2002-09-01

Similarity searches are a powerful method for solving important biological problems such as database scanning, evolutionary studies, gene prediction, and protein structure prediction. FASTA is a widely used sequence comparison tool for rapid database scanning. Here we describe the GWFASTA server that was developed to assist the FASTA user in similarity searches against partially and/or completely sequenced genomes. GWFASTA consists of more than 60 microbial genomes, eight eukaryote genomes, and proteomes of annotatedgenomes. Infact, it provides the maximum number of databases for similarity searching from a single platform. GWFASTA allows the submission of more than one sequence as a single query for a FASTA search. It also provides integrated post-processing of FASTA output, including compositional analysis of proteins, multiple sequences alignment, and phylogenetic analysis. Furthermore, it summarizes the search results organism-wise for prokaryotes and chromosome-wise for eukaryotes. Thus, the integration of different tools for sequence analyses makes GWFASTA a powerful toolfor biologists.

Sequence and analysis of chromosome 4 of the plant Arabidopsis thaliana.

PubMed

Mayer, K; Schüller, C; Wambutt, R; Murphy, G; Volckaert, G; Pohl, T; Düsterhöft, A; Stiekema, W; Entian, K D; Terryn, N; Harris, B; Ansorge, W; Brandt, P; Grivell, L; Rieger, M; Weichselgartner, M; de Simone, V; Obermaier, B; Mache, R; Müller, M; Kreis, M; Delseny, M; Puigdomenech, P; Watson, M; Schmidtheini, T; Reichert, B; Portatelle, D; Perez-Alonso, M; Boutry, M; Bancroft, I; Vos, P; Hoheisel, J; Zimmermann, W; Wedler, H; Ridley, P; Langham, S A; McCullagh, B; Bilham, L; Robben, J; Van der Schueren, J; Grymonprez, B; Chuang, Y J; Vandenbussche, F; Braeken, M; Weltjens, I; Voet, M; Bastiaens, I; Aert, R; Defoor, E; Weitzenegger, T; Bothe, G; Ramsperger, U; Hilbert, H; Braun, M; Holzer, E; Brandt, A; Peters, S; van Staveren, M; Dirske, W; Mooijman, P; Klein Lankhorst, R; Rose, M; Hauf, J; Kötter, P; Berneiser, S; Hempel, S; Feldpausch, M; Lamberth, S; Van den Daele, H; De Keyser, A; Buysshaert, C; Gielen, J; Villarroel, R; De Clercq, R; Van Montagu, M; Rogers, J; Cronin, A; Quail, M; Bray-Allen, S; Clark, L; Doggett, J; Hall, S; Kay, M; Lennard, N; McLay, K; Mayes, R; Pettett, A; Rajandream, M A; Lyne, M; Benes, V; Rechmann, S; Borkova, D; Blöcker, H; Scharfe, M; Grimm, M; Löhnert, T H; Dose, S; de Haan, M; Maarse, A; Schäfer, M; Müller-Auer, S; Gabel, C; Fuchs, M; Fartmann, B; Granderath, K; Dauner, D; Herzl, A; Neumann, S; Argiriou, A; Vitale, D; Liguori, R; Piravandi, E; Massenet, O; Quigley, F; Clabauld, G; Mündlein, A; Felber, R; Schnabl, S; Hiller, R; Schmidt, W; Lecharny, A; Aubourg, S; Chefdor, F; Cooke, R; Berger, C; Montfort, A; Casacuberta, E; Gibbons, T; Weber, N; Vandenbol, M; Bargues, M; Terol, J; Torres, A; Perez-Perez, A; Purnelle, B; Bent, E; Johnson, S; Tacon, D; Jesse, T; Heijnen, L; Schwarz, S; Scholler, P; Heber, S; Francs, P; Bielke, C; Frishman, D; Haase, D; Lemcke, K; Mewes, H W; Stocker, S; Zaccaria, P; Bevan, M; Wilson, R K; de la Bastide, M; Habermann, K; Parnell, L; Dedhia, N; Gnoj, L; Schutz, K; Huang, E; Spiegel, L; Sehkon, M; Murray, J; Sheet, P; Cordes, M; Abu-Threideh, J; Stoneking, T; Kalicki, J; Graves, T; Harmon, G; Edwards, J; Latreille, P; Courtney, L; Cloud, J; Abbott, A; Scott, K; Johnson, D; Minx, P; Bentley, D; Fulton, B; Miller, N; Greco, T; Kemp, K; Kramer, J; Fulton, L; Mardis, E; Dante, M; Pepin, K; Hillier, L; Nelson, J; Spieth, J; Ryan, E; Andrews, S; Geisel, C; Layman, D; Du, H; Ali, J; Berghoff, A; Jones, K; Drone, K; Cotton, M; Joshu, C; Antonoiu, B; Zidanic, M; Strong, C; Sun, H; Lamar, B; Yordan, C; Ma, P; Zhong, J; Preston, R; Vil, D; Shekher, M; Matero, A; Shah, R; Swaby, I K; O'Shaughnessy, A; Rodriguez, M; Hoffmann, J; Till, S; Granat, S; Shohdy, N; Hasegawa, A; Hameed, A; Lodhi, M; Johnson, A; Chen, E; Marra, M; Martienssen, R; McCombie, W R

1999-12-16

The higher plant Arabidopsis thaliana (Arabidopsis) is an important model for identifying plant genes and determining their function. To assist biological investigations and to define chromosome structure, a coordinated effort to sequence the Arabidopsis genome was initiated in late 1996. Here we report one of the first milestones of this project, the sequence of chromosome 4. Analysis of 17.38 megabases of unique sequence, representing about 17% of the genome, reveals 3,744 protein coding genes, 81 transfer RNAs and numerous repeat elements. Heterochromatic regions surrounding the putative centromere, which has not yet been completely sequenced, are characterized by an increased frequency of a variety of repeats, new repeats, reduced recombination, lowered gene density and lowered gene expression. Roughly 60% of the predicted protein-coding genes have been functionally characterized on the basis of their homology to known genes. Many genes encode predicted proteins that are homologous to human and Caenorhabditis elegans proteins.

Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

NASA Astrophysics Data System (ADS)

Champeimont, Raphaël; Laine, Elodie; Hu, Shuang-Wei; Penin, Francois; Carbone, Alessandra

2016-05-01

A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

PubMed

Wan, Cen; Lees, Jonathan G; Minneci, Federico; Orengo, Christine A; Jones, David T

2017-10-01

Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

Domain fusion analysis by applying relational algebra to protein sequence and domain databases.

PubMed

Truong, Kevin; Ikura, Mitsuhiko

2003-05-06

Domain fusion analysis is a useful method to predict functionally linked proteins that may be involved in direct protein-protein interactions or in the same metabolic or signaling pathway. As separate domain databases like BLOCKS, PROSITE, Pfam, SMART, PRINTS-S, ProDom, TIGRFAMs, and amalgamated domain databases like InterPro continue to grow in size and quality, a computational method to perform domain fusion analysis that leverages on these efforts will become increasingly powerful. This paper proposes a computational method employing relational algebra to find domain fusions in protein sequence databases. The feasibility of this method was illustrated on the SWISS-PROT+TrEMBL sequence database using domain predictions from the Pfam HMM (hidden Markov model) database. We identified 235 and 189 putative functionally linked protein partners in H. sapiens and S. cerevisiae, respectively. From scientific literature, we were able to confirm many of these functional linkages, while the remainder offer testable experimental hypothesis. Results can be viewed at http://calcium.uhnres.utoronto.ca/pi. As the analysis can be computed quickly on any relational database that supports standard SQL (structured query language), it can be dynamically updated along with the sequence and domain databases, thereby improving the quality of predictions over time.

Prediction during statistical learning, and implications for the implicit/explicit divide

PubMed Central

Dale, Rick; Duran, Nicholas D.; Morehead, J. Ryan

2012-01-01

Accounts of statistical learning, both implicit and explicit, often invoke predictive processes as central to learning, yet practically all experiments employ non-predictive measures during training. We argue that the common theoretical assumption of anticipation and prediction needs clearer, more direct evidence for it during learning. We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events. Predictive tendencies in participants were measured using their computer mouse, the trajectories of which served as a means of tapping into predictive behavior while participants were exposed to very short and simple sequences of events. A total of 143 participants were randomly assigned to stimulus sequences along a continuum of regularity. Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction. We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning. PMID:22723817

Joint analysis of bacterial DNA methylation, predicted promoter and regulation motifs for biological significance

USDA-ARS?s Scientific Manuscript database

Advances in long-read, single molecule real-time sequencing technology and analysis software over the last two years has enabled the efficient production of closed bacterial genome sequences. However, consistent annotation of these genomes has lagged behind the ability to create them, while the avai...

Isolation and in silico analysis of a novel H+-pyrophosphatase gene orthologue from the halophytic grass Leptochloa fusca

NASA Astrophysics Data System (ADS)

Rauf, Muhammad; Saeed, Nasir A.; Habib, Imran; Ahmed, Moddassir; Shahzad, Khurram; Mansoor, Shahid; Ali, Rashid

2017-02-01

Structure prediction can provide information about function and active sites of protein which helps to design new functional proteins. H+-pyrophosphatase is transmembrane protein involved in establishing proton motive force for active transport of Na+ across membrane by Na+/H+ antiporters. A full length novel H+-pyrophosphatase gene was isolated from halophytic grass Leptochloa fusca using RT-PCR and RACE method. Full length LfVP1 gene sequence of 2292 nucleotides encodes protein of 764 amino acids. DNA and protein sequences were used for characterization using bioinformatics tools. Various important potential sites were predicted by PROSITE webserver. Primary structural analysis showed LfVP1 as stable protein and Grand average hydropathy (GRAVY) indicated that LfVP1 protein has good hydrosolubility. Secondary structure analysis showed that LfVP1 protein sequence contains significant proportion of alpha helix and random coil. Protein membrane topology suggested the presence of 14 transmembrane domains and presence of catalytic domain in TM3. Three dimensional structure from LfVP1 protein sequence also indicated the presence of 14 transmembrane domains and hydrophobicity surface model showed amino acid hydrophobicity. Ramachandran plot showed that 98% amino acid residues were predicted in the favored region.

Genome Sequences of Three Cluster AU Arthrobacter Phages, Caterpillar, Nightmare, and Teacup

PubMed Central

Adair, Tamarah L.; Stowe, Emily; Pizzorno, Marie C.; Krukonis, Gregory; Harrison, Melinda; Garlena, Rebecca A.; Russell, Daniel A.; Jacobs-Sera, Deborah

2017-01-01

ABSTRACT Caterpillar, Nightmare, and Teacup are cluster AU siphoviral phages isolated from enriched soil on Arthrobacter sp. strain ATCC 21022. These genomes are 58 kbp long with an average G+C content of 50%. Sequence analysis predicts 86 to 92 protein-coding genes, including a large number of small proteins with predicted transmembrane domains. PMID:29122860

3D RNA and functional interactions from evolutionary couplings

PubMed Central

Weinreb, Caleb; Riesselman, Adam; Ingraham, John B.; Gross, Torsten; Sander, Chris; Marks, Debora S.

2016-01-01

Summary Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces research on their structure and functional interactions. We mine the evolutionary sequence record to derive precise information about function and structure of RNAs and RNA-protein complexes. As in protein structure prediction, we use maximum entropy global probability models of sequence co-variation to infer evolutionarily constrained nucleotide-nucleotide interactions within RNA molecules, and nucleotide-amino acid interactions in RNA-protein complexes. The predicted contacts allow all-atom blinded 3D structure prediction at good accuracy for several known RNA structures and RNA-protein complexes. For unknown structures, we predict contacts in 160 non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings help identify important functional interactions, e.g., at switch points in riboswitches and at a complex nucleation site in HIV. Aided by accelerating sequence accumulation, evolutionary coupling analysis can accelerate the discovery of functional interactions and 3D structures involving RNA. PMID:27087444

Coevolutionary modeling of protein sequences: Predicting structure, function, and mutational landscapes

NASA Astrophysics Data System (ADS)

Weigt, Martin

Over the last years, biological research has been revolutionized by experimental high-throughput techniques, in particular by next-generation sequencing technology. Unprecedented amounts of data are accumulating, and there is a growing request for computational methods unveiling the information hidden in raw data, thereby increasing our understanding of complex biological systems. Statistical-physics models based on the maximum-entropy principle have, in the last few years, played an important role in this context. To give a specific example, proteins and many non-coding RNA show a remarkable degree of structural and functional conservation in the course of evolution, despite a large variability in amino acid sequences. We have developed a statistical-mechanics inspired inference approach - called Direct-Coupling Analysis - to link this sequence variability (easy to observe in sequence alignments, which are available in public sequence databases) to bio-molecular structure and function. In my presentation I will show, how this methodology can be used (i) to infer contacts between residues and thus to guide tertiary and quaternary protein structure prediction and RNA structure prediction, (ii) to discriminate interacting from non-interacting protein families, and thus to infer conserved protein-protein interaction networks, and (iii) to reconstruct mutational landscapes and thus to predict the phenotypic effect of mutations. References [1] M. Figliuzzi, H. Jacquier, A. Schug, O. Tenaillon and M. Weigt ''Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1'', Mol. Biol. Evol. (2015), doi: 10.1093/molbev/msv211 [2] E. De Leonardis, B. Lutz, S. Ratz, S. Cocco, R. Monasson, A. Schug, M. Weigt ''Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction'', Nucleic Acids Research (2015), doi: 10.1093/nar/gkv932 [3] F. Morcos, A. Pagnani, B. Lunt, A. Bertolino, D. Marks, C. Sander, R. Zecchina, J.N. Onuchic, T. Hwa, M. Weigt, ''Direct-coupling analysis of residue co-evolution captures native contacts across many protein families'', Proc. Natl. Acad. Sci. 108, E1293-E1301 (2011).

Computational identification of developmental enhancers:conservation and function of transcription factor binding-site clustersin drosophila melanogaster and drosophila psedoobscura

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berman, Benjamin P.; Pfeiffer, Barret D.; Laverty, Todd R.

2004-08-06

The identification of sequences that control transcription in metazoans is a major goal of genome analysis. In a previous study, we demonstrated that searching for clusters of predicted transcription factor binding sites could discover active regulatory sequences, and identified 37 regions of the Drosophila melanogaster genome with high densities of predicted binding sites for five transcription factors involved in anterior-posterior embryonic patterning. Nine of these clusters overlapped known enhancers. Here, we report the results of in vivo functional analysis of 27 remaining clusters. We generated transgenic flies carrying each cluster attached to a basal promoter and reporter gene, and assayedmore » embryos for reporter gene expression. Six clusters are enhancers of adjacent genes: giant, fushi tarazu, odd-skipped, nubbin, squeeze and pdm2; three drive expression in patterns unrelated to those of neighboring genes; the remaining 18 do not appear to have enhancer activity. We used the Drosophila pseudoobscura genome to compare patterns of evolution in and around the 15 positive and 18 false-positive predictions. Although conservation of primary sequence cannot distinguish true from false positives, conservation of binding-site clustering accurately discriminates functional binding-site clusters from those with no function. We incorporated conservation of binding-site clustering into a new genome-wide enhancer screen, and predict several hundred new regulatory sequences, including 85 adjacent to genes with embryonic patterns. Measuring conservation of sequence features closely linked to function--such as binding-site clustering--makes better use of comparative sequence data than commonly used methods that examine only sequence identity.« less

In silico analysis of subtilisin from Glaciozyma antarctica PI12

NASA Astrophysics Data System (ADS)

Mustafha, Siti Mardhiah; Murad, Abdul Munir Abdul; Mahadi, Nor Muhammad; Kamaruddin, Shazilah; Bakar, Farah Diba Abu

2015-09-01

Subtilisin constitute as a major player in industrial enzymes that has a wide range of application especially in the detergent industry. In this study, a cDNA encoding for subtilisin (GaSUBT) was extracted from the psychrophilic yeast, Glaciozyma antarctica PI12, PCR amplified and sequenced. Various bioinformatics tools were used to characterize the GaSUBT. GaSUBT contains 1587 bp nucleotides encoding for 529 amino acids. The predicted molecular weight of the deduced protein is 55.34 kDa with an isoelectric point of 6.25. GaSUBT was predicted to possess a signal peptide and pro-peptide consisting of a peptidase inhibitor I9 sequence. From the sequence alignment analysis of deduced amino acids with other subtilisins in the NCBI database showed that the sequences surrounding the catalytic triad that forms the catalytic domain are well conserved.

Protein Sequence Annotation Tool (PSAT): A centralized web-based meta-server for high-throughput sequence annotations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leung, Elo; Huang, Amy; Cadag, Eithon

In this study, we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resultingmore » functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequencebased genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less

Protein Sequence Annotation Tool (PSAT): A centralized web-based meta-server for high-throughput sequence annotations

DOE PAGES

Leung, Elo; Huang, Amy; Cadag, Eithon; ...

2016-01-20

In this study, we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resultingmore » functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequencebased genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.

PubMed

Song, Jiangning; Burrage, Kevin; Yuan, Zheng; Huber, Thomas

2006-03-09

The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

Pfarao: a web application for protein family analysis customized for cytoskeletal and motor proteins (CyMoBase)

PubMed Central

Odronitz, Florian; Kollmar, Martin

2006-01-01

Background Annotation of protein sequences of eukaryotic organisms is crucial for the understanding of their function in the cell. Manual annotation is still by far the most accurate way to correctly predict genes. The classification of protein sequences, their phylogenetic relation and the assignment of function involves information from various sources. This often leads to a collection of heterogeneous data, which is hard to track. Cytoskeletal and motor proteins consist of large and diverse superfamilies comprising up to several dozen members per organism. Up to date there is no integrated tool available to assist in the manual large-scale comparative genomic analysis of protein families. Description Pfarao (Protein Family Application for Retrieval, Analysis and Organisation) is a database driven online working environment for the analysis of manually annotated protein sequences and their relationship. Currently, the system can store and interrelate a wide range of information about protein sequences, species, phylogenetic relations and sequencing projects as well as links to literature and domain predictions. Sequences can be imported from multiple sequence alignments that are generated during the annotation process. A web interface allows to conveniently browse the database and to compile tabular and graphical summaries of its content. Conclusion We implemented a protein sequence-centric web application to store, organize, interrelate, and present heterogeneous data that is generated in manual genome annotation and comparative genomics. The application has been developed for the analysis of cytoskeletal and motor proteins (CyMoBase) but can easily be adapted for any protein. PMID:17134497

Analysis of functional redundancies within the Arabidopsis TCP transcription factor family.

PubMed

Danisman, Selahattin; van Dijk, Aalt D J; Bimbo, Andrea; van der Wal, Froukje; Hennig, Lars; de Folter, Stefan; Angenent, Gerco C; Immink, Richard G H

2013-12-01

Analyses of the functions of TEOSINTE-LIKE1, CYCLOIDEA, and PROLIFERATING CELL FACTOR1 (TCP) transcription factors have been hampered by functional redundancy between its individual members. In general, putative functionally redundant genes are predicted based on sequence similarity and confirmed by genetic analysis. In the TCP family, however, identification is impeded by relatively low overall sequence similarity. In a search for functionally redundant TCP pairs that control Arabidopsis leaf development, this work performed an integrative bioinformatics analysis, combining protein sequence similarities, gene expression data, and results of pair-wise protein-protein interaction studies for the 24 members of the Arabidopsis TCP transcription factor family. For this, the work completed any lacking gene expression and protein-protein interaction data experimentally and then performed a comprehensive prediction of potential functional redundant TCP pairs. Subsequently, redundant functions could be confirmed for selected predicted TCP pairs by genetic and molecular analyses. It is demonstrated that the previously uncharacterized class I TCP19 gene plays a role in the control of leaf senescence in a redundant fashion with TCP20. Altogether, this work shows the power of combining classical genetic and molecular approaches with bioinformatics predictions to unravel functional redundancies in the TCP transcription factor family.

Analysis of functional redundancies within the Arabidopsis TCP transcription factor family

PubMed Central

Danisman, Selahattin; de Folter, Stefan; Immink, Richard G. H.

2013-01-01

Analyses of the functions of TEOSINTE-LIKE1, CYCLOIDEA, and PROLIFERATING CELL FACTOR1 (TCP) transcription factors have been hampered by functional redundancy between its individual members. In general, putative functionally redundant genes are predicted based on sequence similarity and confirmed by genetic analysis. In the TCP family, however, identification is impeded by relatively low overall sequence similarity. In a search for functionally redundant TCP pairs that control Arabidopsis leaf development, this work performed an integrative bioinformatics analysis, combining protein sequence similarities, gene expression data, and results of pair-wise protein–protein interaction studies for the 24 members of the Arabidopsis TCP transcription factor family. For this, the work completed any lacking gene expression and protein–protein interaction data experimentally and then performed a comprehensive prediction of potential functional redundant TCP pairs. Subsequently, redundant functions could be confirmed for selected predicted TCP pairs by genetic and molecular analyses. It is demonstrated that the previously uncharacterized class I TCP19 gene plays a role in the control of leaf senescence in a redundant fashion with TCP20. Altogether, this work shows the power of combining classical genetic and molecular approaches with bioinformatics predictions to unravel functional redundancies in the TCP transcription factor family. PMID:24129704

Evolutionary conservation analysis increases the colocalization of predicted exonic splicing enhancers in the BRCA1 gene with missense sequence changes and in-frame deletions, but not polymorphisms

PubMed Central

Pettigrew, Christopher; Wayte, Nicola; Lovelock, Paul K; Tavtigian, Sean V; Chenevix-Trench, Georgia; Spurdle, Amanda B; Brown, Melissa A

2005-01-01

Introduction Aberrant pre-mRNA splicing can be more detrimental to the function of a gene than changes in the length or nature of the encoded amino acid sequence. Although predicting the effects of changes in consensus 5' and 3' splice sites near intron:exon boundaries is relatively straightforward, predicting the possible effects of changes in exonic splicing enhancers (ESEs) remains a challenge. Methods As an initial step toward determining which ESEs predicted by the web-based tool ESEfinder in the breast cancer susceptibility gene BRCA1 are likely to be functional, we have determined their evolutionary conservation and compared their location with known BRCA1 sequence variants. Results Using the default settings of ESEfinder, we initially detected 669 potential ESEs in the coding region of the BRCA1 gene. Increasing the threshold score reduced the total number to 464, while taking into consideration the proximity to splice donor and acceptor sites reduced the number to 211. Approximately 11% of these ESEs (23/211) either are identical at the nucleotide level in human, primates, mouse, cow, dog and opossum Brca1 (conserved) or are detectable by ESEfinder in the same position in the Brca1 sequence (shared). The frequency of conserved and shared predicted ESEs between human and mouse is higher in BRCA1 exons (2.8 per 100 nucleotides) than in introns (0.6 per 100 nucleotides). Of conserved or shared putative ESEs, 61% (14/23) were predicted to be affected by sequence variants reported in the Breast Cancer Information Core database. Applying the filters described above increased the colocalization of predicted ESEs with missense changes, in-frame deletions and unclassified variants predicted to be deleterious to protein function, whereas they decreased the colocalization with known polymorphisms or unclassified variants predicted to be neutral. Conclusion In this report we show that evolutionary conservation analysis may be used to improve the specificity of an ESE prediction tool. This is the first report on the prediction of the frequency and distribution of ESEs in the BRCA1 gene, and it is the first reported attempt to predict which ESEs are most likely to be functional and therefore which sequence variants in ESEs are most likely to be pathogenic. PMID:16280041

Predicting PDZ domain mediated protein interactions from structure

PubMed Central

2013-01-01

Background PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. Results We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. Conclusions We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW. PMID:23336252

Network Analysis of Sequence-Function Relationships and Exploration of Sequence Space of TEM β-Lactamases.

PubMed

Zeil, Catharina; Widmann, Michael; Fademrecht, Silvia; Vogel, Constantin; Pleiss, Jürgen

2016-05-01

The Lactamase Engineering Database (www.LacED.uni-stuttgart.de) was developed to facilitate the classification and analysis of TEM β-lactamases. The current version contains 474 TEM variants. Two hundred fifty-nine variants form a large scale-free network of highly connected point mutants. The network was divided into three subnetworks which were enriched by single phenotypes: one network with predominantly 2be and two networks with 2br phenotypes. Fifteen positions were found to be highly variable, contributing to the majority of the observed variants. Since it is expected that a considerable fraction of the theoretical sequence space is functional, the currently sequenced 474 variants represent only the tip of the iceberg of functional TEM β-lactamase variants which form a huge natural reservoir of highly interconnected variants. Almost 50% of the variants are part of a quartet. Thus, two single mutations that result in functional enzymes can be combined into a functional protein. Most of these quartets consist of the same phenotype, or the mutations are additive with respect to the phenotype. By predicting quartets from triplets, 3,916 unknown variants were constructed. Eighty-seven variants complement multiple quartets and therefore have a high probability of being functional. The construction of a TEM β-lactamase network and subsequent analyses by clustering and quartet prediction are valuable tools to gain new insights into the viable sequence space of TEM β-lactamases and to predict their phenotype. The highly connected sequence space of TEM β-lactamases is ideally suited to network analysis and demonstrates the strengths of network analysis over tree reconstruction methods. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

The landscape of transposable elements in the finished genome of the fungal wheat pathogen Mycosphaerella graminicola

USDA-ARS?s Scientific Manuscript database

Repetitive sequence analysis has become an integral part of genome sequencing projects in addition to gene identification and annotation. Identification of repeats is important not only because it improves gene prediction, but also because of the role that repetitive sequences play in determining th...

Effects of stacking sequence on impact damage resistance and residual strength for quasi-isotropic laminates

NASA Technical Reports Server (NTRS)

Dost, Ernest F.; Ilcewicz, Larry B.; Avery, William B.; Coxon, Brian R.

1991-01-01

Residual strength of an impacted composite laminate is dependent on details of the damage state. Stacking sequence was varied to judge its effect on damage caused by low-velocity impact. This was done for quasi-isotropic layups of a toughened composite material. Experimental observations on changes in the impact damage state and postimpact compressive performance were presented for seven different laminate stacking sequences. The applicability and limitations of analysis compared to experimental results were also discussed. Postimpact compressive behavior was found to be a strong function of the laminate stacking sequence. This relationship was found to depend on thickness, stacking sequence, size, and location of sublaminates that comprise the impact damage state. The postimpact strength for specimens with a relatively symmetric distribution of damage through the laminate thickness was accurately predicted by models that accounted for sublaminate stability and in-plane stress redistribution. An asymmetric distribution of damage in some laminate stacking sequences tended to alter specimen stability. Geometrically nonlinear finite element analysis was used to predict this behavior.

Quantitative analysis and prediction of G-quadruplex forming sequences in double-stranded DNA

PubMed Central

Kim, Minji; Kreig, Alex; Lee, Chun-Ying; Rube, H. Tomas; Calvert, Jacob; Song, Jun S.; Myong, Sua

2016-01-01

Abstract G-quadruplex (GQ) is a four-stranded DNA structure that can be formed in guanine-rich sequences. GQ structures have been proposed to regulate diverse biological processes including transcription, replication, translation and telomere maintenance. Recent studies have demonstrated the existence of GQ DNA in live mammalian cells and a significant number of potential GQ forming sequences in the human genome. We present a systematic and quantitative analysis of GQ folding propensity on a large set of 438 GQ forming sequences in double-stranded DNA by integrating fluorescence measurement, single-molecule imaging and computational modeling. We find that short minimum loop length and the thymine base are two main factors that lead to high GQ folding propensity. Linear and Gaussian process regression models further validate that the GQ folding potential can be predicted with high accuracy based on the loop length distribution and the nucleotide content of the loop sequences. Our study provides important new parameters that can inform the evaluation and classification of putative GQ sequences in the human genome. PMID:27095201

[Comparative analysis of clustered regularly interspaced short palindromic repeats (CRISPRs) loci in the genomes of halophilic archaea].

PubMed

Zhang, Fan; Zhang, Bing; Xiang, Hua; Hu, Songnian

2009-11-01

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a widespread system that provides acquired resistance against phages in bacteria and archaea. Here we aim to genome-widely analyze the CRISPR in extreme halophilic archaea, of which the whole genome sequences are available at present time. We used bioinformatics methods including alignment, conservation analysis, GC content and RNA structure prediction to analyze the CRISPR structures of 7 haloarchaeal genomes. We identified the CRISPR structures in 5 halophilic archaea and revealed a conserved palindromic motif in the flanking regions of these CRISPR structures. In addition, we found that the repeat sequences of large CRISPR structures in halophilic archaea were greatly conserved, and two types of predicted RNA secondary structures derived from the repeat sequences were likely determined by the fourth base of the repeat sequence. Our results support the proposal that the leader sequence may function as recognition site by having palindromic structures in flanking regions, and the stem-loop secondary structure formed by repeat sequences may function in mediating the interaction between foreign genetic elements and CAS-encoded proteins.

Prediction of Spontaneous Protein Deamidation from Sequence-Derived Secondary Structure and Intrinsic Disorder.

PubMed

Lorenzo, J Ramiro; Alonso, Leonardo G; Sánchez, Ignacio E

2015-01-01

Asparagine residues in proteins undergo spontaneous deamidation, a post-translational modification that may act as a molecular clock for the regulation of protein function and turnover. Asparagine deamidation is modulated by protein local sequence, secondary structure and hydrogen bonding. We present NGOME, an algorithm able to predict non-enzymatic deamidation of internal asparagine residues in proteins in the absence of structural data, using sequence-based predictions of secondary structure and intrinsic disorder. Compared to previous algorithms, NGOME does not require three-dimensional structures yet yields better predictions than available sequence-only methods. Four case studies of specific proteins show how NGOME may help the user identify deamidation-prone asparagine residues, often related to protein gain of function, protein degradation or protein misfolding in pathological processes. A fifth case study applies NGOME at a proteomic scale and unveils a correlation between asparagine deamidation and protein degradation in yeast. NGOME is freely available as a webserver at the National EMBnet node Argentina, URL: http://www.embnet.qb.fcen.uba.ar/ in the subpage "Protein and nucleic acid structure and sequence analysis".

Computational identification of developmental enhancers:conservation and function of transcription factor binding-site clustersin drosophila melanogaster and drosophila psedoobscura

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berman, Benjamin P.; Pfeiffer, Barret D.; Laverty, Todd R.

2004-08-06

Background The identification of sequences that control transcription in metazoans is a major goal of genome analysis. In a previous study, we demonstrated that searching for clusters of predicted transcription factor binding sites could discover active regulatory sequences, and identified 37 regions of the Drosophila melanogaster genome with high densities of predicted binding sites for five transcription factors involved in anterior-posterior embryonic patterning. Nine of these clusters overlapped known enhancers. Here, we report the results of in vivo functional analysis of 27 remaining clusters. Results We generated transgenic flies carrying each cluster attached to a basal promoter and reporter gene,more » and assayed embryos for reporter gene expression. Six clusters are enhancers of adjacent genes: giant, fushi tarazu, odd-skipped, nubbin, squeeze and pdm2; three drive expression in patterns unrelated to those of neighboring genes; the remaining 18 do not appear to have enhancer activity. We used the Drosophila pseudoobscura genome to compare patterns of evolution in and around the 15 positive and 18 false-positive predictions. Although conservation of primary sequence cannot distinguish true from false positives, conservation of binding-site clustering accurately discriminates functional binding-site clusters from those with no function. We incorporated conservation of binding-site clustering into a new genome-wide enhancer screen, and predict several hundred new regulatory sequences, including 85 adjacent to genes with embryonic patterns. Conclusions Measuring conservation of sequence features closely linked to function - such as binding-site clustering - makes better use of comparative sequence data than commonly used methods that examine only sequence identity.« less

A Guide to Analyzing Message-Response Sequences and Group Interaction Patterns in Computer-Mediated Communication

ERIC Educational Resources Information Center

Jeong, Allan

2005-01-01

This paper proposes a set of methods and a framework for evaluating, modeling, and predicting group interactions in computer-mediated communication. The method of sequential analysis is described along with specific software tools and techniques to facilitate the analysis of message-response sequences. In addition, the Dialogic Theory and its…

bpRNA: large-scale automated annotation and analysis of RNA secondary structure.

PubMed

Danaee, Padideh; Rouches, Mason; Wiley, Michelle; Deng, Dezhong; Huang, Liang; Hendrix, David

2018-05-09

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here, we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, 'bpRNA-1m', of over 100 000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

Dipeptide Sequence Determination: Analyzing Phenylthiohydantoin Amino Acids by HPLC

NASA Astrophysics Data System (ADS)

Barton, Janice S.; Tang, Chung-Fei; Reed, Steven S.

2000-02-01

Amino acid composition and sequence determination, important techniques for characterizing peptides and proteins, are essential for predicting conformation and studying sequence alignment. This experiment presents improved, fundamental methods of sequence analysis for an upper-division biochemistry laboratory. Working in pairs, students use the Edman reagent to prepare phenylthiohydantoin derivatives of amino acids for determination of the sequence of an unknown dipeptide. With a single HPLC technique, students identify both the N-terminal amino acid and the composition of the dipeptide. This method yields good precision of retention times and allows use of a broad range of amino acids as components of the dipeptide. Students learn fundamental principles and techniques of sequence analysis and HPLC.

Improved serial analysis of V1 ribosomal sequence tags (SARST-V1) provides a rapid, comprehensive, sequence-based characterization of bacterial diversity and community composition.

PubMed

Yu, Zhongtang; Yu, Marie; Morrison, Mark

2006-04-01

Serial analysis of ribosomal sequence tags (SARST) is a recently developed technology that can generate large 16S rRNA gene (rrs) sequence data sets from microbiomes, but there are numerous enzymatic and purification steps required to construct the ribosomal sequence tag (RST) clone libraries. We report here an improved SARST method, which still targets the V1 hypervariable region of rrs genes, but reduces the number of enzymes, oligonucleotides, reagents, and technical steps needed to produce the RST clone libraries. The new method, hereafter referred to as SARST-V1, was used to examine the eubacterial diversity present in community DNA recovered from the microbiome resident in the ovine rumen. The 190 sequenced clones contained 1055 RSTs and no less than 236 unique phylotypes (based on > or = 95% sequence identity) that were assigned to eight different eubacterial phyla. Rarefaction and monomolecular curve analyses predicted that the complete RST clone library contains 99% of the 353 unique phylotypes predicted to exist in this microbiome. When compared with ribosomal intergenic spacer analysis (RISA) of the same community DNA sample, as well as a compilation of nine previously published conventional rrs clone libraries prepared from the same type of samples, the RST clone library provided a more comprehensive characterization of the eubacterial diversity present in rumen microbiomes. As such, SARST-V1 should be a useful tool applicable to comprehensive examination of diversity and composition in microbiomes and offers an affordable, sequence-based method for diversity analysis.

SeqAPASS (Sequence Alignment to Predict Across Species Susceptibility) software and documentation

EPA Science Inventory

SeqAPASS is a software application facilitates rapid and streamlined, yet transparent, comparisons of the similarity of toxicologically-significant molecular targets across species. The present application facilitates analysis of primary amino acid sequence similarity (including ...

Molecular cloning and analysis of Schizosaccharomyces pombe Reb1p: sequence-specific recognition of two sites in the far upstream rDNA intergenic spacer.

PubMed Central

Zhao, A; Guo, A; Liu, Z; Pape, L

1997-01-01

The coding sequences for a Schizosaccharomyces pombe sequence-specific DNA binding protein, Reb1p, have been cloned. The predicted S. pombe Reb1p is 24-29% identical to mouse TTF-1 (transcription termination factor-1) and Saccharomyces cerevisiae REB1 protein, both of which direct termination of RNA polymerase I catalyzed transcripts. The S.pombe Reb1 cDNA encodes a predicted polypeptide of 504 amino acids with a predicted molecular weight of 58.4 kDa. The S. pombe Reb1p is unusual in that the bipartite DNA binding motif identified originally in S.cerevisiae and Klyveromyces lactis REB1 proteins is uninterrupted and thus S.pombe Reb1p may contain the smallest natural REB1 homologous DNA binding domain. Its genomic coding sequences were shown to be interrupted by two introns. A recombinant histidine-tagged Reb1 protein bearing the rDNA binding domain has two homologous, sequence-specific binding sites in the S. pomber DNA intergenic spacer, located between 289 and 480 nt downstream of the end of the approximately 25S rRNA coding sequences. Each binding site is 13-14 bp downstream of two of the three proposed in vivo termination sites. The core of this 17 bp site, AGGTAAGGGTAATGCAC, is specifically protected by Reb1p in footprinting analysis. PMID:9016645

Interactive computer programs for the graphic analysis of nucleotide sequence data.

PubMed Central

Luckow, V A; Littlewood, R K; Rownd, R H

1984-01-01

A group of interactive computer programs have been developed which aid in the collection and graphical analysis of nucleotide and protein sequence data. The programs perform the following basic functions: a) enter, edit, list, and rearrange sequence data; b) permit automatic entry of nucleotide sequence data directly from an autoradiograph into the computer; c) search for restriction sites or other specified patterns and plot a linear or circular restriction map, or print their locations; d) plot base composition; e) analyze homology between sequences by plotting a two-dimensional graphic matrix; and f) aid in plotting predicted secondary structures of RNA molecules. PMID:6546437

Genome-wide comparative analysis of four Indian Drosophila species.

PubMed

Mohanty, Sujata; Khanna, Radhika

2017-12-01

Comparative analysis of multiple genomes of closely or distantly related Drosophila species undoubtedly creates excitement among evolutionary biologists in exploring the genomic changes with an ecology and evolutionary perspective. We present herewith the de novo assembled whole genome sequences of four Drosophila species, D. bipectinata, D. takahashii, D. biarmipes and D. nasuta of Indian origin using Next Generation Sequencing technology on an Illumina platform along with their detailed assembly statistics. The comparative genomics analysis, e.g. gene predictions and annotations, functional and orthogroup analysis of coding sequences and genome wide SNP distribution were performed. The whole genome of Zaprionus indianus of Indian origin published earlier by us and the genome sequences of previously sequenced 12 Drosophila species available in the NCBI database were included in the analysis. The present work is a part of our ongoing genomics project of Indian Drosophila species.

Comparative analysis of grapevine whole-genome gene predictions, functional annotation, categorization and integration of the predicted gene sequences

PubMed Central

2012-01-01

Background The first draft assembly and gene prediction of the grapevine genome (8X base coverage) was made available to the scientific community in 2007, and functional annotation was developed on this gene prediction. Since then additional Sanger sequences were added to the 8X sequences pool and a new version of the genomic sequence with superior base coverage (12X) was produced. Results In order to more efficiently annotate the function of the genes predicted in the new assembly, it is important to build on as much of the previous work as possible, by transferring 8X annotation of the genome to the 12X version. The 8X and 12X assemblies and gene predictions of the grapevine genome were compared to answer the question, “Can we uniquely map 8X predicted genes to 12X predicted genes?” The results show that while the assemblies and gene structure predictions are too different to make a complete mapping between them, most genes (18,725) showed a one-to-one relationship between 8X predicted genes and the last version of 12X predicted genes. In addition, reshuffled genomic sequence structures appeared. These highlight regions of the genome where the gene predictions need to be taken with caution. Based on the new grapevine gene functional annotation and in-depth functional categorization, twenty eight new molecular networks have been created for VitisNet while the existing networks were updated. Conclusions The outcomes of this study provide a functional annotation of the 12X genes, an update of VitisNet, the system of the grapevine molecular networks, and a new functional categorization of genes. Data are available at the VitisNet website (http://www.sdstate.edu/ps/research/vitis/pathways.cfm). PMID:22554261

GDAP: a web tool for genome-wide protein disulfide bond prediction.

PubMed

O'Connor, Brian D; Yeates, Todd O

2004-07-01

The Genomic Disulfide Analysis Program (GDAP) provides web access to computationally predicted protein disulfide bonds for over one hundred microbial genomes, including both bacterial and achaeal species. In the GDAP process, sequences of unknown structure are mapped, when possible, to known homologous Protein Data Bank (PDB) structures, after which specific distance criteria are applied to predict disulfide bonds. GDAP also accepts user-supplied protein sequences and subsequently queries the PDB sequence database for the best matches, scans for possible disulfide bonds and returns the results to the client. These predictions are useful for a variety of applications and have previously been used to show a dramatic preference in certain thermophilic archaea and bacteria for disulfide bonds within intracellular proteins. Given the central role these stabilizing, covalent bonds play in such organisms, the predictions available from GDAP provide a rich data source for designing site-directed mutants with more stable thermal profiles. The GDAP web application is a gateway to this information and can be used to understand the role disulfide bonds play in protein stability both in these unusual organisms and in sequences of interest to the individual researcher. The prediction server can be accessed at http://www.doe-mbi.ucla.edu/Services/GDAP.

First Pass Annotation of Promoters on Human Chromosome 22

PubMed Central

Scherf, Matthias; Klingenhoff, Andreas; Frech, Kornelie; Quandt, Kerstin; Schneider, Ralf; Grote, Korbinian; Frisch, Matthias; Gailus-Durner, Valérie; Seidel, Alexander; Brack-Werner, Ruth; Werner, Thomas

2001-01-01

The publication of the first almost complete sequence of a human chromosome (chromosome 22) is a major milestone in human genomics. Together with the sequence, an excellent annotation of genes was published which certainly will serve as an information resource for numerous future projects. We noted that the annotation did not cover regulatory regions; in particular, no promoter annotation has been provided. Here we present an analysis of the complete published chromosome 22 sequence for promoters. A recent breakthrough in specific in silico prediction of promoter regions enabled us to attempt large-scale prediction of promoter regions on chromosome 22. Scanning of sequence databases revealed only 20 experimentally verified promoters, of which 10 were correctly predicted by our approach. Nearly 40% of our 465 predicted promoter regions are supported by the currently available gene annotation. Promoter finding also provides a biologically meaningful method for “chromosomal scaffolding”, by which long genomic sequences can be divided into segments starting with a gene. As one example, the combination of promoter region prediction with exon/intron structure predictions greatly enhances the specificity of de novo gene finding. The present study demonstrates that it is possible to identify promoters in silico on the chromosomal level with sufficient reliability for experimental planning and indicates that a wealth of information about regulatory regions can be extracted from current large-scale (megabase) sequencing projects. Results are available on-line at http://genomatix.gsf.de/chr22/. PMID:11230158

Correlation approach to identify coding regions in DNA sequences

NASA Technical Reports Server (NTRS)

Ossadnik, S. M.; Buldyrev, S. V.; Goldberger, A. L.; Havlin, S.; Mantegna, R. N.; Peng, C. K.; Simons, M.; Stanley, H. E.

1994-01-01

Recently, it was observed that noncoding regions of DNA sequences possess long-range power-law correlations, whereas coding regions typically display only short-range correlations. We develop an algorithm based on this finding that enables investigators to perform a statistical analysis on long DNA sequences to locate possible coding regions. The algorithm is particularly successful in predicting the location of lengthy coding regions. For example, for the complete genome of yeast chromosome III (315,344 nucleotides), at least 82% of the predictions correspond to putative coding regions; the algorithm correctly identified all coding regions larger than 3000 nucleotides, 92% of coding regions between 2000 and 3000 nucleotides long, and 79% of coding regions between 1000 and 2000 nucleotides. The predictive ability of this new algorithm supports the claim that there is a fundamental difference in the correlation property between coding and noncoding sequences. This algorithm, which is not species-dependent, can be implemented with other techniques for rapidly and accurately locating relatively long coding regions in genomic sequences.

Genome analysis of the foxtail millet pathogen Sclerospora graminicola reveals the complex effector repertoire of graminicolous downy mildews.

PubMed

Kobayashi, Michie; Hiraka, Yukie; Abe, Akira; Yaegashi, Hiroki; Natsume, Satoshi; Kikuchi, Hideko; Takagi, Hiroki; Saitoh, Hiromasa; Win, Joe; Kamoun, Sophien; Terauchi, Ryohei

2017-11-22

Downy mildew, caused by the oomycete pathogen Sclerospora graminicola, is an economically important disease of Gramineae crops including foxtail millet (Setaria italica). Plants infected with S. graminicola are generally stunted and often undergo a transformation of flower organs into leaves (phyllody or witches' broom), resulting in serious yield loss. To establish the molecular basis of downy mildew disease in foxtail millet, we carried out whole-genome sequencing and an RNA-seq analysis of S. graminicola. Sequence reads were generated from S. graminicola using an Illumina sequencing platform and assembled de novo into a draft genome sequence comprising approximately 360 Mbp. Of this sequence, 73% comprised repetitive elements, and a total of 16,736 genes were predicted from the RNA-seq data. The predicted genes included those encoding effector-like proteins with high sequence similarity to those previously identified in other oomycete pathogens. Genes encoding jacalin-like lectin-domain-containing secreted proteins were enriched in S. graminicola compared to other oomycetes. Of a total of 1220 genes encoding putative secreted proteins, 91 significantly changed their expression levels during the infection of plant tissues compared to the sporangia and zoospore stages of the S. graminicola lifecycle. We established the draft genome sequence of a downy mildew pathogen that infects Gramineae plants. Based on this sequence and our transcriptome analysis, we generated a catalog of in planta-induced candidate effector genes, providing a solid foundation from which to identify the effectors causing phyllody.

Performance comparison of the Prophecy (forecasting) Algorithm in FFT form for unseen feature and time-series prediction

NASA Astrophysics Data System (ADS)

Jaenisch, Holger; Handley, James

2013-06-01

We introduce a generalized numerical prediction and forecasting algorithm. We have previously published it for malware byte sequence feature prediction and generalized distribution modeling for disparate test article analysis. We show how non-trivial non-periodic extrapolation of a numerical sequence (forecast and backcast) from the starting data is possible. Our ancestor-progeny prediction can yield new options for evolutionary programming. Our equations enable analytical integrals and derivatives to any order. Interpolation is controllable from smooth continuous to fractal structure estimation. We show how our generalized trigonometric polynomial can be derived using a Fourier transform.

RNAseq versus genome-predicted transcriptomes: a large population of novel transcripts identified in an Illumina-454 Hydra transcriptome.

PubMed

Wenger, Yvan; Galliot, Brigitte

2013-03-25

Evolutionary studies benefit from deep sequencing technologies that generate genomic and transcriptomic sequences from a variety of organisms. Genome sequencing and RNAseq have complementary strengths. In this study, we present the assembly of the most complete Hydra transcriptome to date along with a comparative analysis of the specific features of RNAseq and genome-predicted transcriptomes currently available in the freshwater hydrozoan Hydra vulgaris. To produce an accurate and extensive Hydra transcriptome, we combined Illumina and 454 Titanium reads, giving the primacy to Illumina over 454 reads to correct homopolymer errors. This strategy yielded an RNAseq transcriptome that contains 48'909 unique sequences including splice variants, representing approximately 24'450 distinct genes. Comparative analysis to the available genome-predicted transcriptomes identified 10'597 novel Hydra transcripts that encode 529 evolutionarily-conserved proteins. The annotation of 170 human orthologs points to critical functions in protein biosynthesis, FGF and TOR signaling, vesicle transport, immunity, cell cycle regulation, cell death, mitochondrial metabolism, transcription and chromatin regulation. However, a majority of these novel transcripts encodes short ORFs, at least 767 of them corresponding to pseudogenes. This RNAseq transcriptome also lacks 11'270 predicted transcripts that correspond either to silent genes or to genes expressed below the detection level of this study. We established a simple and powerful strategy to combine Illumina and 454 reads and we produced, with genome assistance, an extensive and accurate Hydra transcriptome. The comparative analysis of the RNAseq transcriptome with genome-predicted transcriptomes lead to the identification of large populations of novel as well as missing transcripts that might reflect Hydra-specific evolutionary events.

RNAseq versus genome-predicted transcriptomes: a large population of novel transcripts identified in an Illumina-454 Hydra transcriptome

PubMed Central

2013-01-01

Background Evolutionary studies benefit from deep sequencing technologies that generate genomic and transcriptomic sequences from a variety of organisms. Genome sequencing and RNAseq have complementary strengths. In this study, we present the assembly of the most complete Hydra transcriptome to date along with a comparative analysis of the specific features of RNAseq and genome-predicted transcriptomes currently available in the freshwater hydrozoan Hydra vulgaris. Results To produce an accurate and extensive Hydra transcriptome, we combined Illumina and 454 Titanium reads, giving the primacy to Illumina over 454 reads to correct homopolymer errors. This strategy yielded an RNAseq transcriptome that contains 48’909 unique sequences including splice variants, representing approximately 24’450 distinct genes. Comparative analysis to the available genome-predicted transcriptomes identified 10’597 novel Hydra transcripts that encode 529 evolutionarily-conserved proteins. The annotation of 170 human orthologs points to critical functions in protein biosynthesis, FGF and TOR signaling, vesicle transport, immunity, cell cycle regulation, cell death, mitochondrial metabolism, transcription and chromatin regulation. However, a majority of these novel transcripts encodes short ORFs, at least 767 of them corresponding to pseudogenes. This RNAseq transcriptome also lacks 11’270 predicted transcripts that correspond either to silent genes or to genes expressed below the detection level of this study. Conclusions We established a simple and powerful strategy to combine Illumina and 454 reads and we produced, with genome assistance, an extensive and accurate Hydra transcriptome. The comparative analysis of the RNAseq transcriptome with genome-predicted transcriptomes lead to the identification of large populations of novel as well as missing transcripts that might reflect Hydra-specific evolutionary events. PMID:23530871

Prediction of glutathionylation sites in proteins using minimal sequence information and their experimental validation.

PubMed

Pal, Debojyoti; Sharma, Deepak; Kumar, Mukesh; Sandur, Santosh K

2016-09-01

S-glutathionylation of proteins plays an important role in various biological processes and is known to be protective modification during oxidative stress. Since, experimental detection of S-glutathionylation is labor intensive and time consuming, bioinformatics based approach is a viable alternative. Available methods require relatively longer sequence information, which may prevent prediction if sequence information is incomplete. Here, we present a model to predict glutathionylation sites from pentapeptide sequences. It is based upon differential association of amino acids with glutathionylated and non-glutathionylated cysteines from a database of experimentally verified sequences. This data was used to calculate position dependent F-scores, which measure how a particular amino acid at a particular position may affect the likelihood of glutathionylation event. Glutathionylation-score (G-score), indicating propensity of a sequence to undergo glutathionylation, was calculated using position-dependent F-scores for each amino-acid. Cut-off values were used for prediction. Our model returned an accuracy of 58% with Matthew's correlation-coefficient (MCC) value of 0.165. On an independent dataset, our model outperformed the currently available model, in spite of needing much less sequence information. Pentapeptide motifs having high abundance among glutathionylated proteins were identified. A list of potential glutathionylation hotspot sequences were obtained by assigning G-scores and subsequent Protein-BLAST analysis revealed a total of 254 putative glutathionable proteins, a number of which were already known to be glutathionylated. Our model predicted glutathionylation sites in 93.93% of experimentally verified glutathionylated proteins. Outcome of this study may assist in discovering novel glutathionylation sites and finding candidate proteins for glutathionylation.

Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

PubMed Central

Burger, Lukas; van Nimwegen, Erik

2008-01-01

Accurate and large-scale prediction of protein–protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners. PMID:18277381

Analysis of Physicochemical and Structural Properties Determining HIV-1 Coreceptor Usage

PubMed Central

Bozek, Katarzyna; Lengauer, Thomas; Sierra, Saleta; Kaiser, Rolf; Domingues, Francisco S.

2013-01-01

The relationship of HIV tropism with disease progression and the recent development of CCR5-blocking drugs underscore the importance of monitoring virus coreceptor usage. As an alternative to costly phenotypic assays, computational methods aim at predicting virus tropism based on the sequence and structure of the V3 loop of the virus gp120 protein. Here we present a numerical descriptor of the V3 loop encoding its physicochemical and structural properties. The descriptor allows for structure-based prediction of HIV tropism and identification of properties of the V3 loop that are crucial for coreceptor usage. Use of the proposed descriptor for prediction results in a statistically significant improvement over the prediction based solely on V3 sequence with 3 percentage points improvement in AUC and 7 percentage points in sensitivity at the specificity of the 11/25 rule (95%). We additionally assessed the predictive power of the new method on clinically derived ‘bulk’ sequence data and obtained a statistically significant improvement in AUC of 3 percentage points over sequence-based prediction. Furthermore, we demonstrated the capacity of our method to predict therapy outcome by applying it to 53 samples from patients undergoing Maraviroc therapy. The analysis of structural features of the loop informative of tropism indicates the importance of two loop regions and their physicochemical properties. The regions are located on opposite strands of the loop stem and the respective features are predominantly charge-, hydrophobicity- and structure-related. These regions are in close proximity in the bound conformation of the loop potentially forming a site determinant for the coreceptor binding. The method is available via server under http://structure.bioinf.mpi-inf.mpg.de/. PMID:23555214

The prediction of human exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames

DOE Office of Scientific and Technical Information (OSTI.GOV)

Solovyev, V.V.; Salamov, A.A.; Lawrence, C.B.

1994-12-31

Discriminant analysis is applied to the problem of recognition 5`-, internal and 3`-exons in human DNA sequences. Specific recognition functions were developed for revealing exons of particular types. The method based on a splice site prediction algorithm that uses the linear Fisher discriminant to combine the information about significant triplet frequencies of various functional parts of splice site regions and preferences of oligonucleotide in protein coding and nation regions. The accuracy of our splice site recognition function is about 97%. A discriminant function for 5`-exon prediction includes hexanucleotide composition of upstream region, triplet composition around the ATG codon, ORF codingmore » potential, donor splice site potential and composition of downstream introit region. For internal exon prediction, we combine in a discriminant function the characteristics describing the 5`- intron region, donor splice site, coding region, acceptor splice site and Y-intron region for each open reading frame flanked by GT and AG base pairs. The accuracy of precise internal exon recognition on a test set of 451 exon and 246693 pseudoexon sequences is 77% with a specificity of 79% and a level of pseudoexon ORF prediction of 99.96%. The recognition quality computed at the level of individual nucleotides is 89%, for exon sequences and 98% for intron sequences. A discriminant function for 3`-exon prediction includes octanucleolide composition of upstream nation region, triplet composition around the stop codon, ORF coding potential, acceptor splice site potential and hexanucleotide composition of downstream region. We unite these three discriminant functions in exon predicting program FEX (find exons). FEX exactly predicts 70% of 1016 exons from the test of 181 complete genes with specificity 73%, and 89% exons are exactly or partially predicted. On the average, 85% of nucleotides were predicted accurately with specificity 91%.« less

Antimicrobial activity predictors benchmarking analysis using shuffled and designed synthetic peptides.

PubMed

Porto, William F; Pires, Állan S; Franco, Octavio L

2017-08-07

The antimicrobial activity prediction tools aim to help the novel antimicrobial peptides (AMP) sequences discovery, utilizing machine learning methods. Such approaches have gained increasing importance in the generation of novel synthetic peptides by means of rational design techniques. This study focused on predictive ability of such approaches to determine the antimicrobial sequence activities, which were previously characterized at the protein level by in vitro studies. Using four web servers and one standalone software, we evaluated 78 sequences generated by the so-called linguistic model, being 40 designed and 38 shuffled sequences, with ∼60 and ∼25% of identity to AMPs, respectively. The ab initio molecular modelling of such sequences indicated that the structure does not affect the predictions, as both sets present similar structures. Overall, the systems failed on predicting shuffled versions of designed peptides, as they are identical in AMPs composition, which implies in accuracies below 30%. The prediction accuracy is negatively affected by the low specificity of all systems here evaluated, as they, on the other hand, reached 100% of sensitivity. Our results suggest that complementary approaches with high specificity, not necessarily high accuracy, should be developed to be used together with the current systems, overcoming their limitations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach

PubMed Central

Meyer, Pablo; Siwo, Geoffrey; Zeevi, Danny; Sharon, Eilon; Norel, Raquel; Segal, Eran; Stolovitzky, Gustavo; Siwo, Geoffrey; Rider, Andrew K.; Tan, Asako; Pinapati, Richard S.; Emrich, Scott; Chawla, Nitesh; Ferdig, Michael T.; Tung, Yi-An; Chen, Yong-Syuan; Chen, Mei-Ju May; Chen, Chien-Yu; Knight, Jason M.; Sahraeian, Sayed Mohammad Ebrahim; Esfahani, Mohammad Shahrokh; Dreos, Rene; Bucher, Philipp; Maier, Ezekiel; Saeys, Yvan; Szczurek, Ewa; Myšičková, Alena; Vingron, Martin; Klein, Holger; Kiełbasa, Szymon M.; Knisley, Jeff; Bonnell, Jeff; Knisley, Debra; Kursa, Miron B.; Rudnicki, Witold R.; Bhattacharjee, Madhuchhanda; Sillanpää, Mikko J.; Yeung, James; Meysman, Pieter; Rodríguez, Aminael Sánchez; Engelen, Kristof; Marchal, Kathleen; Huang, Yezhou; Mordelet, Fantine; Hartemink, Alexander; Pinello, Luca; Yuan, Guo-Cheng

2013-01-01

The Gene Promoter Expression Prediction challenge consisted of predicting gene expression from promoter sequences in a previously unknown experimentally generated data set. The challenge was presented to the community in the framework of the sixth Dialogue for Reverse Engineering Assessments and Methods (DREAM6), a community effort to evaluate the status of systems biology modeling methodologies. Nucleotide-specific promoter activity was obtained by measuring fluorescence from promoter sequences fused upstream of a gene for yellow fluorescence protein and inserted in the same genomic site of yeast Saccharomyces cerevisiae. Twenty-one teams submitted results predicting the expression levels of 53 different promoters from yeast ribosomal protein genes. Analysis of participant predictions shows that accurate values for low-expressed and mutated promoters were difficult to obtain, although in the latter case, only when the mutation induced a large change in promoter activity compared to the wild-type sequence. As in previous DREAM challenges, we found that aggregation of participant predictions provided robust results, but did not fare better than the three best algorithms. Finally, this study not only provides a benchmark for the assessment of methods predicting activity of a specific set of promoters from their sequence, but it also shows that the top performing algorithm, which used machine-learning approaches, can be improved by the addition of biological features such as transcription factor binding sites. PMID:23950146

Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.

PubMed

Liu, Bin; Wu, Hao; Zhang, Deyuan; Wang, Xiaolong; Chou, Kuo-Chen

2017-02-21

To expedite the pace in conducting genome/proteome analysis, we have developed a Python package called Pse-Analysis. The powerful package can automatically complete the following five procedures: (1) sample feature extraction, (2) optimal parameter selection, (3) model training, (4) cross validation, and (5) evaluating prediction quality. All the work a user needs to do is to input a benchmark dataset along with the query biological sequences concerned. Based on the benchmark dataset, Pse-Analysis will automatically construct an ideal predictor, followed by yielding the predicted results for the submitted query samples. All the aforementioned tedious jobs can be automatically done by the computer. Moreover, the multiprocessing technique was adopted to enhance computational speed by about 6 folds. The Pse-Analysis Python package is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/Pse-Analysis/, and can be directly run on Windows, Linux, and Unix.

PreCisIon: PREdiction of CIS-regulatory elements improved by gene's positION.

PubMed

Elati, Mohamed; Nicolle, Rémy; Junier, Ivan; Fernández, David; Fekih, Rim; Font, Julio; Képès, François

2013-02-01

Conventional approaches to predict transcriptional regulatory interactions usually rely on the definition of a shared motif sequence on the target genes of a transcription factor (TF). These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices, which may match large numbers of sites and produce an unreliable list of target genes. To improve the prediction of binding sites, we propose to additionally use the unrelated knowledge of the genome layout. Indeed, it has been shown that co-regulated genes tend to be either neighbors or periodically spaced along the whole chromosome. This study demonstrates that respective gene positioning carries significant information. This novel type of information is combined with traditional sequence information by a machine learning algorithm called PreCisIon. To optimize this combination, PreCisIon builds a strong gene target classifier by adaptively combining weak classifiers based on either local binding sequence or global gene position. This strategy generically paves the way to the optimized incorporation of any future advances in gene target prediction based on local sequence, genome layout or on novel criteria. With the current state of the art, PreCisIon consistently improves methods based on sequence information only. This is shown by implementing a cross-validation analysis of the 20 major TFs from two phylogenetically remote model organisms. For Bacillus subtilis and Escherichia coli, respectively, PreCisIon achieves on average an area under the receiver operating characteristic curve of 70 and 60%, a sensitivity of 80 and 70% and a specificity of 60 and 56%. The newly predicted gene targets are demonstrated to be functionally consistent with previously known targets, as assessed by analysis of Gene Ontology enrichment or of the relevant literature and databases.

Sequence features of viral and human Internal Ribosome Entry Sites predictive of their activity

PubMed Central

Elias-Kirma, Shani; Nir, Ronit; Segal, Eran

2017-01-01

Translation of mRNAs through Internal Ribosome Entry Sites (IRESs) has emerged as a prominent mechanism of cellular and viral initiation. It supports cap-independent translation of select cellular genes under normal conditions, and in conditions when cap-dependent translation is inhibited. IRES structure and sequence are believed to be involved in this process. However due to the small number of IRESs known, there have been no systematic investigations of the determinants of IRES activity. With the recent discovery of thousands of novel IRESs in human and viruses, the next challenge is to decipher the sequence determinants of IRES activity. We present the first in-depth computational analysis of a large body of IRESs, exploring RNA sequence features predictive of IRES activity. We identified predictive k-mer features resembling IRES trans-acting factor (ITAF) binding motifs across human and viral IRESs, and found that their effect on expression depends on their sequence, number and position. Our results also suggest that the architecture of retroviral IRESs differs from that of other viruses, presumably due to their exposure to the nuclear environment. Finally, we measured IRES activity of synthetically designed sequences to confirm our prediction of increasing activity as a function of the number of short IRES elements. PMID:28922394

BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers.

PubMed

Abo, Ryan P; Ducar, Matthew; Garcia, Elizabeth P; Thorner, Aaron R; Rojas-Rudilla, Vanesa; Lin, Ling; Sholl, Lynette M; Hahn, William C; Meyerson, Matthew; Lindeman, Neal I; Van Hummelen, Paul; MacConaill, Laura E

2015-02-18

Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for 'targeted' resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a 'kmer' strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Molecular Cloning and Characterization of cDNA Encoding a Putative Stress-Induced Heat-Shock Protein from Camelus dromedarius

PubMed Central

Elrobh, Mohamed S.; Alanazi, Mohammad S.; Khan, Wajahatullah; Abduljaleel, Zainularifeen; Al-Amri, Abdullah; Bazzi, Mohammad D.

2011-01-01

Heat shock proteins are ubiquitous, induced under a number of environmental and metabolic stresses, with highly conserved DNA sequences among mammalian species. Camelus dromedaries (the Arabian camel) domesticated under semi-desert environments, is well adapted to tolerate and survive against severe drought and high temperatures for extended periods. This is the first report of molecular cloning and characterization of full length cDNA of encoding a putative stress-induced heat shock HSPA6 protein (also called HSP70B′) from Arabian camel. A full-length cDNA (2417 bp) was obtained by rapid amplification of cDNA ends (RACE) and cloned in pET-b expression vector. The sequence analysis of HSPA6 gene showed 1932 bp-long open reading frame encoding 643 amino acids. The complete cDNA sequence of the Arabian camel HSPA6 gene was submitted to NCBI GeneBank (accession number HQ214118.1). The BLAST analysis indicated that C. dromedaries HSPA6 gene nucleotides shared high similarity (77–91%) with heat shock gene nucleotide of other mammals. The deduced 643 amino acid sequences (accession number ADO12067.1) showed that the predicted protein has an estimated molecular weight of 70.5 kDa with a predicted isoelectric point (pI) of 6.0. The comparative analyses of camel HSPA6 protein sequences with other mammalian heat shock proteins (HSPs) showed high identity (80–94%). Predicted camel HSPA6 protein structure using Protein 3D structural analysis high similarities with human and mouse HSPs. Taken together, this study indicates that the cDNA sequences of HSPA6 gene and its amino acid and protein structure from the Arabian camel are highly conserved and have similarities with other mammalian species. PMID:21845074

A manually annotated Actinidia chinensis var. chinensis (kiwifruit) genome highlights the challenges associated with draft genomes and gene prediction in plants.

PubMed

Pilkington, Sarah M; Crowhurst, Ross; Hilario, Elena; Nardozza, Simona; Fraser, Lena; Peng, Yongyan; Gunaseelan, Kularajathevan; Simpson, Robert; Tahir, Jibran; Deroles, Simon C; Templeton, Kerry; Luo, Zhiwei; Davy, Marcus; Cheng, Canhong; McNeilage, Mark; Scaglione, Davide; Liu, Yifei; Zhang, Qiong; Datson, Paul; De Silva, Nihal; Gardiner, Susan E; Bassett, Heather; Chagné, David; McCallum, John; Dzierzon, Helge; Deng, Cecilia; Wang, Yen-Yi; Barron, Lorna; Manako, Kelvina; Bowen, Judith; Foster, Toshi M; Erridge, Zoe A; Tiffin, Heather; Waite, Chethi N; Davies, Kevin M; Grierson, Ella P; Laing, William A; Kirk, Rebecca; Chen, Xiuyin; Wood, Marion; Montefiori, Mirco; Brummell, David A; Schwinn, Kathy E; Catanach, Andrew; Fullerton, Christina; Li, Dawei; Meiyalaghan, Sathiyamoorthy; Nieuwenhuizen, Niels; Read, Nicola; Prakash, Roneel; Hunter, Don; Zhang, Huaibi; McKenzie, Marian; Knäbel, Mareike; Harris, Alastair; Allan, Andrew C; Gleave, Andrew; Chen, Angela; Janssen, Bart J; Plunkett, Blue; Ampomah-Dwamena, Charles; Voogd, Charlotte; Leif, Davin; Lafferty, Declan; Souleyre, Edwige J F; Varkonyi-Gasic, Erika; Gambi, Francesco; Hanley, Jenny; Yao, Jia-Long; Cheung, Joey; David, Karine M; Warren, Ben; Marsh, Ken; Snowden, Kimberley C; Lin-Wang, Kui; Brian, Lara; Martinez-Sanchez, Marcela; Wang, Mindy; Ileperuma, Nadeesha; Macnee, Nikolai; Campin, Robert; McAtee, Peter; Drummond, Revel S M; Espley, Richard V; Ireland, Hilary S; Wu, Rongmei; Atkinson, Ross G; Karunairetnam, Sakuntala; Bulley, Sean; Chunkath, Shayhan; Hanley, Zac; Storey, Roy; Thrimawithana, Amali H; Thomson, Susan; David, Charles; Testolin, Raffaele; Huang, Hongwen; Hellens, Roger P; Schaffer, Robert J

2018-04-16

Most published genome sequences are drafts, and most are dominated by computational gene prediction. Draft genomes typically incorporate considerable sequence data that are not assigned to chromosomes, and predicted genes without quality confidence measures. The current Actinidia chinensis (kiwifruit) 'Hongyang' draft genome has 164 Mb of sequences unassigned to pseudo-chromosomes, and omissions have been identified in the gene models. A second genome of an A. chinensis (genotype Red5) was fully sequenced. This new sequence resulted in a 554.0 Mb assembly with all but 6 Mb assigned to pseudo-chromosomes. Pseudo-chromosomal comparisons showed a considerable number of translocation events have occurred following a whole genome duplication (WGD) event some consistent with centromeric Robertsonian-like translocations. RNA sequencing data from 12 tissues and ab initio analysis informed a genome-wide manual annotation, using the WebApollo tool. In total, 33,044 gene loci represented by 33,123 isoforms were identified, named and tagged for quality of evidential support. Of these 3114 (9.4%) were identical to a protein within 'Hongyang' The Kiwifruit Information Resource (KIR v2). Some proportion of the differences will be varietal polymorphisms. However, as most computationally predicted Red5 models required manual re-annotation this proportion is expected to be small. The quality of the new gene models was tested by fully sequencing 550 cloned 'Hort16A' cDNAs and comparing with the predicted protein models for Red5 and both the original 'Hongyang' assembly and the revised annotation from KIR v2. Only 48.9% and 63.5% of the cDNAs had a match with 90% identity or better to the original and revised 'Hongyang' annotation, respectively, compared with 90.9% to the Red5 models. Our study highlights the need to take a cautious approach to draft genomes and computationally predicted genes. Our use of the manual annotation tool WebApollo facilitated manual checking and correction of gene models enabling improvement of computational prediction. This utility was especially relevant for certain types of gene families such as the EXPANSIN like genes. Finally, this high quality gene set will supply the kiwifruit and general plant community with a new tool for genomics and other comparative analysis.

Heterogeneous Suppression of Sequential Effects in Random Sequence Generation, but Not in Operant Learning.

PubMed

Shteingart, Hanan; Loewenstein, Yonatan

2016-01-01

There is a long history of experiments in which participants are instructed to generate a long sequence of binary random numbers. The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper, we used generalized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, we used logistic regression analysis in order to characterize the temporal sequence of participants' choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seems irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect are a monotonous decreasing function of the delay, yet these individual sequential effects are largely averaged out in a population analysis because of heterogeneity. The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation. Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in the random sequence generation task, different participants adopt different cognitive strategies to suppress sequential dependencies when generating the "random" sequences.

Predictive Rate-Distortion for Infinite-Order Markov Processes

NASA Astrophysics Data System (ADS)

Marzen, Sarah E.; Crutchfield, James P.

2016-06-01

Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on finite sequences cannot capture all of their past dependencies. Spectral arguments confirm a popular intuition: algorithms that cluster finite-length sequences fail dramatically when the underlying process has long-range temporal correlations and can fail even for processes generated by finite-memory hidden Markov models. We circumvent the curse of dimensionality in rate-distortion analysis of finite- and infinite-order processes by casting predictive rate-distortion objective functions in terms of the forward- and reverse-time causal states of computational mechanics. Examples demonstrate that the resulting algorithms yield substantial improvements.

Comparison of predicted binders in Rhipicephalus (Boophilus) microplus intestine protein variants Bm86 Campo Grande strain, Bm86 and Bm95.

PubMed

Andreotti, Renato; Pedroso, Marisela S; Caetano, Alexandre R; Martins, Natália F

2008-01-01

This paper reports the sequence analysis of Bm86 Campo Grande strain comparing it with Bm86 and Bm95 antigens from the preparations TickGardPLUS and Gavac, respectively. The PCR product was cloned into pMOSBlue and sequenced. The secondary structure prediction tool PSIPRED was used to calculate alpha helices and beta strand contents of the predicted polypeptide. The hydrophobicity profile was calculated using the algorithms from the Hopp and Woods method, in addition to identification of potential MHC class-I binding regions in the antigens. Pair-wise alignment revealed that the similarity between Bm86 Campo Grande strain and Bm86 is 0.2% higher than that between Bm86 Campo Grande strain and Bm95 antigens. The identities were 96.5% and 96.3% respectively. Major suggestive differences in hydrophobicity were predicted among the sequences in two specific regions.

Can natural proteins designed with 'inverted' peptide sequences adopt native-like protein folds?

PubMed

Sridhar, Settu; Guruprasad, Kunchur

2014-01-01

We have carried out a systematic computational analysis on a representative dataset of proteins of known three-dimensional structure, in order to evaluate whether it would possible to 'swap' certain short peptide sequences in naturally occurring proteins with their corresponding 'inverted' peptides and generate 'artificial' proteins that are predicted to retain native-like protein fold. The analysis of 3,967 representative proteins from the Protein Data Bank revealed 102,677 unique identical inverted peptide sequence pairs that vary in sequence length between 5-12 and 18 amino acid residues. Our analysis illustrates with examples that such 'artificial' proteins may be generated by identifying peptides with 'similar structural environment' and by using comparative protein modeling and validation studies. Our analysis suggests that natural proteins may be tolerant to accommodating such peptides.

The MIGenAS integrated bioinformatics toolkit for web-based sequence analysis

PubMed Central

Rampp, Markus; Soddemann, Thomas; Lederer, Hermann

2006-01-01

We describe a versatile and extensible integrated bioinformatics toolkit for the analysis of biological sequences over the Internet. The web portal offers convenient interactive access to a growing pool of chainable bioinformatics software tools and databases that are centrally installed and maintained by the RZG. Currently, supported tasks comprise sequence similarity searches in public or user-supplied databases, computation and validation of multiple sequence alignments, phylogenetic analysis and protein–structure prediction. Individual tools can be seamlessly chained into pipelines allowing the user to conveniently process complex workflows without the necessity to take care of any format conversions or tedious parsing of intermediate results. The toolkit is part of the Max-Planck Integrated Gene Analysis System (MIGenAS) of the Max Planck Society available at (click ‘Start Toolkit’). PMID:16844980

RNA-SSPT: RNA Secondary Structure Prediction Tools.

PubMed

Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; Din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

2013-01-01

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

RNA-SSPT: RNA Secondary Structure Prediction Tools

PubMed Central

Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

2013-01-01

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes. PMID:24250115

BayesPI-BAR: a new biophysical model for characterization of regulatory sequence variations

PubMed Central

Wang, Junbai; Batmanov, Kirill

2015-01-01

Sequence variations in regulatory DNA regions are known to cause functionally important consequences for gene expression. DNA sequence variations may have an essential role in determining phenotypes and may be linked to disease; however, their identification through analysis of massive genome-wide sequencing data is a great challenge. In this work, a new computational pipeline, a Bayesian method for protein–DNA interaction with binding affinity ranking (BayesPI-BAR), is proposed for quantifying the effect of sequence variations on protein binding. BayesPI-BAR uses biophysical modeling of protein–DNA interactions to predict single nucleotide polymorphisms (SNPs) that cause significant changes in the binding affinity of a regulatory region for transcription factors (TFs). The method includes two new parameters (TF chemical potentials or protein concentrations and direct TF binding targets) that are neglected by previous methods. The new method is verified on 67 known human regulatory SNPs, of which 47 (70%) have predicted true TFs ranked in the top 10. Importantly, the performance of BayesPI-BAR, which uses principal component analysis to integrate multiple predictions from various TF chemical potentials, is found to be better than that of existing programs, such as sTRAP and is-rSNP, when evaluated on the same SNPs. BayesPI-BAR is a publicly available tool and is able to carry out parallelized computation, which helps to investigate a large number of TFs or SNPs and to detect disease-associated regulatory sequence variations in the sea of genome-wide noncoding regions. PMID:26202972

First draft genome sequencing of indole acetic acid producing and plant growth promoting fungus Preussia sp. BSL10.

PubMed

Khan, Abdul Latif; Asaf, Sajjad; Khan, Abdur Rahim; Al-Harrasi, Ahmed; Al-Rawahi, Ahmed; Lee, In-Jung

2016-05-10

Preussia sp. BSL10, family Sporormiaceae, was actively producing phytohormone (indole-3-acetic acid) and extra-cellular enzymes (phosphatases and glucosidases). The fungus was also promoting the growth of arid-land tree-Boswellia sacra. Looking at such prospects of this fungus, we sequenced its draft genome for the first time. The Illumina based sequence analysis reveals an approximate genome size of 31.4Mbp for Preussia sp. BSL10. Based on ab initio gene prediction, total 32,312 coding sequences were annotated consisting of 11,967 coding genes, pseudogenes, and 221 tRNA genes. Furthermore, 321 carbohydrate-active enzymes were predicted and classified into many functional families. Copyright © 2016 Elsevier B.V. All rights reserved.

Uniform, optimal signal processing of mapped deep-sequencing data.

PubMed

Kumar, Vibhor; Muratani, Masafumi; Rayan, Nirmala Arul; Kraus, Petra; Lufkin, Thomas; Ng, Huck Hui; Prabhakar, Shyam

2013-07-01

Despite their apparent diversity, many problems in the analysis of high-throughput sequencing data are merely special cases of two general problems, signal detection and signal estimation. Here we adapt formally optimal solutions from signal processing theory to analyze signals of DNA sequence reads mapped to a genome. We describe DFilter, a detection algorithm that identifies regulatory features in ChIP-seq, DNase-seq and FAIRE-seq data more accurately than assay-specific algorithms. We also describe EFilter, an estimation algorithm that accurately predicts mRNA levels from as few as 1-2 histone profiles (R ∼0.9). Notably, the presence of regulatory motifs in promoters correlates more with histone modifications than with mRNA levels, suggesting that histone profiles are more predictive of cis-regulatory mechanisms. We show by applying DFilter and EFilter to embryonic forebrain ChIP-seq data that regulatory protein identification and functional annotation are feasible despite tissue heterogeneity. The mathematical formalism underlying our tools facilitates integrative analysis of data from virtually any sequencing-based functional profile.

Bacterial diversity in permanently cold and alkaline ikaite columns from Greenland.

PubMed

Schmidt, Mariane; Priemé, Anders; Stougaard, Peter

2006-12-01

Bacterial diversity in alkaline (pH 10.4) and permanently cold (4 degrees C) ikaite tufa columns from the Ikka Fjord, SW Greenland, was investigated using growth characterization of cultured bacterial isolates with Terminal-restriction fragment length polymorphism (T-RFLP) and sequence analysis of bacterial 16S rRNA gene fragments. More than 200 bacterial isolates were characterized with respect to pH and temperature tolerance, and it was shown that the majority were cold-active alkaliphiles. T-RFLP analysis revealed distinct bacterial communities in different fractions of three ikaite columns, and, along with sequence analysis, it showed the presence of rich and diverse bacterial communities. Rarefaction analysis showed that the 109 sequenced clones in the 16S rRNA gene library represented between 25 and 65% of the predicted species richness in the three ikaite columns investigated. Phylogenetic analysis of the 16S rRNA gene sequences revealed many sequences with similarity to alkaliphilic or psychrophilic bacteria, and showed that 33% of the cloned sequences and 33% of the cultured bacteria showed less than 97% sequence identity to known sequences in databases, and may therefore represent yet unknown species.

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis.

PubMed

Liu, Bin; Wang, Xiaolong; Lin, Lei; Dong, Qiwen; Wang, Xuan

2008-12-01

Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences. In this paper, a novel building block of proteins called Top-n-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-n-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-n-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-n-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-n-grams and LSA gives significantly better results compared to related methods. The method based on Top-n-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-n-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.

Predicting Protein-Protein Interactions by Combing Various Sequence-Derived.

PubMed

Zhao, Xiao-Wei; Ma, Zhi-Qiang; Yin, Ming-Hao

2011-09-20

Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

PubMed

Srinivasulu, Yerukala Sathipati; Wang, Jyun-Rong; Hsu, Kai-Ti; Tsai, Ming-Ju; Charoenkwan, Phasit; Huang, Wen-Lin; Huang, Hui-Ling; Ho, Shinn-Ying

2015-01-01

Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes

PubMed Central

2015-01-01

Background Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. Results This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. Conclusions The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes. PMID:26681483

Global analysis of bacterial transcription factors to predict cellular target processes.

PubMed

Doerks, Tobias; Andrade, Miguel A; Lathe, Warren; von Mering, Christian; Bork, Peer

2004-03-01

Whole-genome sequences are now available for >100 bacterial species, giving unprecedented power to comparative genomics approaches. We have applied genome-context methods to predict target processes that are regulated by transcription factors (TFs). Of 128 orthologous groups of proteins annotated as TFs, to date, 36 are functionally uncharacterized; in our analysis we predict a probable cellular target process or biochemical pathway for half of these functionally uncharacterized TFs.

Community and gene composition of a human dental plaque microbiota obtained by metagenomic sequencing

PubMed Central

Xie, G.; Chain, P.S.G.; Lo, C.; Liu, K-L.; Gans, J.; Merritt, J.; Qi, F.

2010-01-01

SUMMARY Human dental plaque is a complex microbial community containing an estimated 700 to 19,000 species/phylotypes. Despite numerous studies analysing species richness in healthy and diseased human subjects, the true genomic composition of the human dental plaque microbiota remains unknown. Here we report a metagenomic analysis of a healthy human plaque sample using a combination of second-generation sequencing platforms. A total of 860 million base pairs of non-human sequences were generated. Various analysis tools revealed the presence of 12 well-characterized phyla, members of the TM-7 and BRC1 clade, and sequences that could not be classified. Both pathogens and opportunistic pathogens were identified, supporting the ecological plaque hypothesis for oral diseases. Mapping the metagenomic reads to sequenced reference genomes demonstrated that 4% of the reads could be assigned to the sequenced species. Preliminary annotation identified genes belonging to all known functional categories. Interestingly, although 73% of the total assembled contig sequences were predicted to code for proteins, only 51% of them could be assigned a functional role. Furthermore, ~ 2.8% of the total predicted genes coded for proteins involved in resistance to antibiotics and toxic compounds, suggesting that the oral cavity is an important reservoir for antimicrobial resistance. PMID:21040513

Community and gene composition of a human dental plaque microbiota obtained by metagenomic sequencing.

PubMed

Xie, G; Chain, P S G; Lo, C-C; Liu, K-L; Gans, J; Merritt, J; Qi, F

2010-12-01

Human dental plaque is a complex microbial community containing an estimated 700 to 19,000 species/phylotypes. Despite numerous studies analysing species richness in healthy and diseased human subjects, the true genomic composition of the human dental plaque microbiota remains unknown. Here we report a metagenomic analysis of a healthy human plaque sample using a combination of second-generation sequencing platforms. A total of 860 million base pairs of non-human sequences were generated. Various analysis tools revealed the presence of 12 well-characterized phyla, members of the TM-7 and BRC1 clade, and sequences that could not be classified. Both pathogens and opportunistic pathogens were identified, supporting the ecological plaque hypothesis for oral diseases. Mapping the metagenomic reads to sequenced reference genomes demonstrated that 4% of the reads could be assigned to the sequenced species. Preliminary annotation identified genes belonging to all known functional categories. Interestingly, although 73% of the total assembled contig sequences were predicted to code for proteins, only 51% of them could be assigned a functional role. Furthermore, ~2.8% of the total predicted genes coded for proteins involved in resistance to antibiotics and toxic compounds, suggesting that the oral cavity is an important reservoir for antimicrobial resistance. © 2010 John Wiley & Sons A/S.

Sequencing, annotation and comparative analysis of nine BACs of giant panda (Ailuropoda melanoleuca).

PubMed

Zheng, Yang; Cai, Jing; Li, JianWen; Li, Bo; Lin, Runmao; Tian, Feng; Wang, XiaoLing; Wang, Jun

2010-01-01

A 10-fold BAC library for giant panda was constructed and nine BACs were selected to generate finish sequences. These BACs could be used as a validation resource for the de novo assembly accuracy of the whole genome shotgun sequencing reads of giant panda newly generated by the Illumina GA sequencing technology. Complete sanger sequencing, assembly, annotation and comparative analysis were carried out on the selected BACs of a joint length 878 kb. Homologue search and de novo prediction methods were used to annotate genes and repeats. Twelve protein coding genes were predicted, seven of which could be functionally annotated. The seven genes have an average gene size of about 41 kb, an average coding size of about 1.2 kb and an average exon number of 6 per gene. Besides, seven tRNA genes were found. About 27 percent of the BAC sequence is composed of repeats. A phylogenetic tree was constructed using neighbor-join algorithm across five species, including giant panda, human, dog, cat and mouse, which reconfirms dog as the most related species to giant panda. Our results provide detailed sequence and structure information for new genes and repeats of giant panda, which will be helpful for further studies on the giant panda.

Secondary structure prediction and structure-specific sequence analysis of single-stranded DNA.

PubMed

Dong, F; Allawi, H T; Anderson, T; Neri, B P; Lyamichev, V I

2001-08-01

DNA sequence analysis by oligonucleotide binding is often affected by interference with the secondary structure of the target DNA. Here we describe an approach that improves DNA secondary structure prediction by combining enzymatic probing of DNA by structure-specific 5'-nucleases with an energy minimization algorithm that utilizes the 5'-nuclease cleavage sites as constraints. The method can identify structural differences between two DNA molecules caused by minor sequence variations such as a single nucleotide mutation. It also demonstrates the existence of long-range interactions between DNA regions separated by >300 nt and the formation of multiple alternative structures by a 244 nt DNA molecule. The differences in the secondary structure of DNA molecules revealed by 5'-nuclease probing were used to design structure-specific probes for mutation discrimination that target the regions of structural, rather than sequence, differences. We also demonstrate the performance of structure-specific 'bridge' probes complementary to non-contiguous regions of the target molecule. The structure-specific probes do not require the high stringency binding conditions necessary for methods based on mismatch formation and permit mutation detection at temperatures from 4 to 37 degrees C. Structure-specific sequence analysis is applied for mutation detection in the Mycobacterium tuberculosis katG gene and for genotyping of the hepatitis C virus.

Signal peptide discrimination and cleavage site identification using SVM and NN.

PubMed

Kazemian, H B; Yusuf, S A; White, K

2014-02-01

About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd.

Species classifier choice is a key consideration when analysing low-complexity food microbiome data.

PubMed

Walsh, Aaron M; Crispie, Fiona; O'Sullivan, Orla; Finnegan, Laura; Claesson, Marcus J; Cotter, Paul D

2018-03-20

The use of shotgun metagenomics to analyse low-complexity microbial communities in foods has the potential to be of considerable fundamental and applied value. However, there is currently no consensus with respect to choice of species classification tool, platform, or sequencing depth. Here, we benchmarked the performances of three high-throughput short-read sequencing platforms, the Illumina MiSeq, NextSeq 500, and Ion Proton, for shotgun metagenomics of food microbiota. Briefly, we sequenced six kefir DNA samples and a mock community DNA sample, the latter constructed by evenly mixing genomic DNA from 13 food-related bacterial species. A variety of bioinformatic tools were used to analyse the data generated, and the effects of sequencing depth on these analyses were tested by randomly subsampling reads. Compositional analysis results were consistent between the platforms at divergent sequencing depths. However, we observed pronounced differences in the predictions from species classification tools. Indeed, PERMANOVA indicated that there was no significant differences between the compositional results generated by the different sequencers (p = 0.693, R 2  = 0.011), but there was a significant difference between the results predicted by the species classifiers (p = 0.01, R 2  = 0.127). The relative abundances predicted by the classifiers, apart from MetaPhlAn2, were apparently biased by reference genome sizes. Additionally, we observed varying false-positive rates among the classifiers. MetaPhlAn2 had the lowest false-positive rate, whereas SLIMM had the greatest false-positive rate. Strain-level analysis results were also similar across platforms. Each platform correctly identified the strains present in the mock community, but accuracy was improved slightly with greater sequencing depth. Notably, PanPhlAn detected the dominant strains in each kefir sample above 500,000 reads per sample. Again, the outputs from functional profiling analysis using SUPER-FOCUS were generally accordant between the platforms at different sequencing depths. Finally, and expectedly, metagenome assembly completeness was significantly lower on the MiSeq than either on the NextSeq (p = 0.03) or the Proton (p = 0.011), and it improved with increased sequencing depth. Our results demonstrate a remarkable similarity in the results generated by the three sequencing platforms at different sequencing depths, and, in fact, the choice of bioinformatics methodology had a more evident impact on results than the choice of sequencer did.

Coupling detrended fluctuation analysis for multiple warehouse-out behavioral sequences

NASA Astrophysics Data System (ADS)

Yao, Can-Zhong; Lin, Ji-Nan; Zheng, Xu-Zhou

2017-01-01

Interaction patterns among different warehouses could make the warehouse-out behavioral sequences less predictable. We firstly take a coupling detrended fluctuation analysis on the warehouse-out quantity, and find that the multivariate sequences exhibit significant coupling multifractal characteristics regardless of the types of steel products. Secondly, we track the sources of multifractal warehouse-out sequences by shuffling and surrogating original ones, and we find that fat-tail distribution contributes more to multifractal features than the long-term memory, regardless of types of steel products. From perspective of warehouse contribution, some warehouses steadily contribute more to multifractal than other warehouses. Finally, based on multiscale multifractal analysis, we propose Hurst surface structure to investigate coupling multifractal, and show that multiple behavioral sequences exhibit significant coupling multifractal features that emerge and usually be restricted within relatively greater time scale interval.

[Prediction of ETA oligopeptides antagonists from Glycine max based on in silico proteolysis].

PubMed

Qiao, Lian-Sheng; Jiang, Lu-di; Luo, Gang-Gang; Lu, Fang; Chen, Yan-Kun; Wang, Ling-Zhi; Li, Gong-Yu; Zhang, Yan-Ling

2017-02-01

Oligopeptides are one of the the key pharmaceutical effective constituents of traditional Chinese medicine(TCM). Systematic study on composition and efficacy of TCM oligopeptides is essential for the analysis of material basis and mechanism of TCM. In this study, the potential anti-hypertensive oligopeptides from Glycine max and their endothelin receptor A (ETA) antagonistic activity were discovered and predicted based on in silico technologies.Main protein sequences of G. max were collected and oligopeptides were obtained using in silico gastrointestinal tract proteolysis. Then, the pharmacophore of ETA antagonistic peptides was constructed and included one hydrophobic feature, one ionizable negative feature, one ring aromatic feature and five excluded volumes. Meanwhile, three-dimensional structure of ETA was developed by homology modeling methods for further docking studies. According to docking analysis and consensus score, the key amino acid of GLN165 was identified for ETA antagonistic activity. And 27 oligopeptides from G. max were predicted as the potential ETA antagonists by pharmacophore and docking studies.In silico proteolysis could be used to analyze the protein sequences from TCM. According to combination of in silico proteolysis and molecular simulation, the biological activities of oligopeptides could be predicted rapidly based on the known TCM protein sequence. It might provide the methodology basis for rapidly and efficiently implementing the mechanism analysis of TCM oligopeptides. Copyright© by the Chinese Pharmaceutical Association.

Prediction of Drug-Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures.

PubMed

Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Zhou, Yong; An, Ji-Yong

2017-07-05

Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

Gene discovery in the hamster: a comparative genomics approach for gene annotation by sequencing of hamster testis cDNAs

PubMed Central

Oduru, Sreedhar; Campbell, Janee L; Karri, SriTulasi; Hendry, William J; Khan, Shafiq A; Williams, Simon C

2003-01-01

Background Complete genome annotation will likely be achieved through a combination of computer-based analysis of available genome sequences combined with direct experimental characterization of expressed regions of individual genomes. We have utilized a comparative genomics approach involving the sequencing of randomly selected hamster testis cDNAs to begin to identify genes not previously annotated on the human, mouse, rat and Fugu (pufferfish) genomes. Results 735 distinct sequences were analyzed for their relatedness to known sequences in public databases. Eight of these sequences were derived from previously unidentified genes and expression of these genes in testis was confirmed by Northern blotting. The genomic locations of each sequence were mapped in human, mouse, rat and pufferfish, where applicable, and the structure of their cognate genes was derived using computer-based predictions, genomic comparisons and analysis of uncharacterized cDNA sequences from human and macaque. Conclusion The use of a comparative genomics approach resulted in the identification of eight cDNAs that correspond to previously uncharacterized genes in the human genome. The proteins encoded by these genes included a new member of the kinesin superfamily, a SET/MYND-domain protein, and six proteins for which no specific function could be predicted. Each gene was expressed primarily in testis, suggesting that they may play roles in the development and/or function of testicular cells. PMID:12783626

Draft Genome Sequence of Thermotoga maritima A7A Reconstructed from Metagenomic Sequencing Analysis of a Hydrocarbon Reservoir in the Bass Strait, Australia

PubMed Central

Sutcliffe, Brodie; Rosewarne, Carly P.; Greenfield, Paul; Li, Dongmei

2013-01-01

The draft genome sequence of Thermotoga maritima A7A was obtained from a metagenomic assembly obtained from a high-temperature hydrocarbon reservoir in the Gippsland Basin, Australia. The organism is predicted to be a motile anaerobe with an array of catabolic enzymes for the degradation of numerous carbohydrates. PMID:24009120

BrEPS 2.0: Optimization of sequence pattern prediction for enzyme annotation.

PubMed

Dudek, Christian-Alexander; Dannheim, Henning; Schomburg, Dietmar

2017-01-01

The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step. Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature. BrEPS is part of the Braunschweig Enzyme Database (BRENDA) and is available on a completely redesigned website and as download. The database can be downloaded and used with the BrEPScmd command line tool for large scale sequence analysis. The BrEPS website and downloads for the database creation tool, command line tool and database are freely accessible at http://breps.tu-bs.de.

BrEPS 2.0: Optimization of sequence pattern prediction for enzyme annotation

PubMed Central

Schomburg, Dietmar

2017-01-01

The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step. Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature. BrEPS is part of the Braunschweig Enzyme Database (BRENDA) and is available on a completely redesigned website and as download. The database can be downloaded and used with the BrEPScmd command line tool for large scale sequence analysis. The BrEPS website and downloads for the database creation tool, command line tool and database are freely accessible at http://breps.tu-bs.de. PMID:28750104

Sequence-based analysis of pQBR103; a representative of a unique, transfer-proficient mega plasmid resident in the microbial community of sugar beet

PubMed Central

Tett, Adrian; Spiers, Andrew J; Crossman, Lisa C; Ager, Duane; Ciric, Lena; Dow, J Maxwell; Fry, John C; Harris, David; Lilley, Andrew; Oliver, Anna; Parkhill, Julian; Quail, Michael A; Rainey, Paul B; Saunders, Nigel J; Seeger, Kathy; Snyder, Lori AS; Squares, Rob; Thomas, Christopher M; Turner, Sarah L; Zhang, Xue-Xian; Field, Dawn; Bailey, Mark J

2009-01-01

The plasmid pQBR103 was found within Pseudomonas populations colonizing the leaf and root surfaces of sugar beet plants growing at Wytham, Oxfordshire, UK. At 425 kb it is the largest self-transmissible plasmid yet sequenced from the phytosphere. It is known to enhance the competitive fitness of its host, and parts of the plasmid are known to be actively transcribed in the plant environment. Analysis of the complete sequence of this plasmid predicts a coding sequence (CDS)-rich genome containing 478 CDSs and an exceptional degree of genetic novelty; 80% of predicted coding sequences cannot be ascribed a function and 60% are orphans. Of those to which function could be assigned, 40% bore greatest similarity to sequences from Pseudomonas spp, and the majority of the remainder showed similarity to other c-proteobacterial genera and plasmids. pQBR103 has identifiable regions presumed responsible for replication and partitioning, but despite being tra+ lacks the full complement of any previously described conjugal transfer functions. The DNA sequence provided few insights into the functional significance of plant-induced transcriptional regions, but suggests that 14% of CDSs may be expressed (11 CDSs with functional annotation and 54 without), further highlighting the ecological importance of these novel CDSs. Comparative analysis indicates that pQBR103 shares significant regions of sequence with other plasmids isolated from sugar beet plants grown at the same geographic location. These plasmid sequences indicate there is more novelty in the mobile DNA pool accessible to phytosphere pseudomonas than is currently appreciated or understood. PMID:18043644

Bioinformatic prediction of G protein-coupled receptor encoding sequences from the transcriptome of the foreleg, including the Haller’s organ, of the cattle tick, Rhipicephalus australis

PubMed Central

Munoz, Sergio; Guerrero, Felix D.; Kellogg, Anastasia; Heekin, Andrew M.

2017-01-01

The cattle tick of Australia, Rhipicephalus australis, is a vector for microbial parasites that cause serious bovine diseases. The Haller’s organ, located in the tick’s forelegs, is crucial for host detection and mating. To facilitate the development of new technologies for better control of this agricultural pest, we aimed to sequence and annotate the transcriptome of the R. australis forelegs and associated tissues, including the Haller's organ. As G protein-coupled receptors (GPCRs) are an important family of eukaryotic proteins studied as pharmaceutical targets in humans, we prioritized the identification and classification of the GPCRs expressed in the foreleg tissues. The two forelegs from adult R. australis were excised, RNA extracted, and pyrosequenced with 454 technology. Reads were assembled into unigenes and annotated by sequence similarity. Python scripts were written to find open reading frames (ORFs) from each unigene. These ORFs were analyzed by different GPCR prediction approaches based on sequence alignments, support vector machines, hidden Markov models, and principal component analysis. GPCRs consistently predicted by multiple methods were further studied by phylogenetic analysis and 3D homology modeling. From 4,782 assembled unigenes, 40,907 possible ORFs were predicted. Using Blastp, Pfam, GPCRpred, TMHMM, and PCA-GPCR, a basic set of 46 GPCR candidates were compiled and a phylogenetic tree was constructed. With further screening of tertiary structures predicted by RaptorX, 6 likely GPCRs emerged and the strongest candidate was classified by PCA-GPCR to be a GABAB receptor. PMID:28231302

Bioinformatic prediction of G protein-coupled receptor encoding sequences from the transcriptome of the foreleg, including the Haller's organ, of the cattle tick, Rhipicephalus australis.

PubMed

Munoz, Sergio; Guerrero, Felix D; Kellogg, Anastasia; Heekin, Andrew M; Leung, Ming-Ying

2017-01-01

The cattle tick of Australia, Rhipicephalus australis, is a vector for microbial parasites that cause serious bovine diseases. The Haller's organ, located in the tick's forelegs, is crucial for host detection and mating. To facilitate the development of new technologies for better control of this agricultural pest, we aimed to sequence and annotate the transcriptome of the R. australis forelegs and associated tissues, including the Haller's organ. As G protein-coupled receptors (GPCRs) are an important family of eukaryotic proteins studied as pharmaceutical targets in humans, we prioritized the identification and classification of the GPCRs expressed in the foreleg tissues. The two forelegs from adult R. australis were excised, RNA extracted, and pyrosequenced with 454 technology. Reads were assembled into unigenes and annotated by sequence similarity. Python scripts were written to find open reading frames (ORFs) from each unigene. These ORFs were analyzed by different GPCR prediction approaches based on sequence alignments, support vector machines, hidden Markov models, and principal component analysis. GPCRs consistently predicted by multiple methods were further studied by phylogenetic analysis and 3D homology modeling. From 4,782 assembled unigenes, 40,907 possible ORFs were predicted. Using Blastp, Pfam, GPCRpred, TMHMM, and PCA-GPCR, a basic set of 46 GPCR candidates were compiled and a phylogenetic tree was constructed. With further screening of tertiary structures predicted by RaptorX, 6 likely GPCRs emerged and the strongest candidate was classified by PCA-GPCR to be a GABAB receptor.

cnvScan: a CNV screening and annotation tool to improve the clinical utility of computational CNV prediction from exome sequencing data.

PubMed

Samarakoon, Pubudu Saneth; Sorte, Hanne Sørmo; Stray-Pedersen, Asbjørg; Rødningen, Olaug Kristin; Rognes, Torbjørn; Lyle, Robert

2016-01-14

With advances in next generation sequencing technology and analysis methods, single nucleotide variants (SNVs) and indels can be detected with high sensitivity and specificity in exome sequencing data. Recent studies have demonstrated the ability to detect disease-causing copy number variants (CNVs) in exome sequencing data. However, exonic CNV prediction programs have shown high false positive CNV counts, which is the major limiting factor for the applicability of these programs in clinical studies. We have developed a tool (cnvScan) to improve the clinical utility of computational CNV prediction in exome data. cnvScan can accept input from any CNV prediction program. cnvScan consists of two steps: CNV screening and CNV annotation. CNV screening evaluates CNV prediction using quality scores and refines this using an in-house CNV database, which greatly reduces the false positive rate. The annotation step provides functionally and clinically relevant information using multiple source datasets. We assessed the performance of cnvScan on CNV predictions from five different prediction programs using 64 exomes from Primary Immunodeficiency (PIDD) patients, and identified PIDD-causing CNVs in three individuals from two different families. In summary, cnvScan reduces the time and effort required to detect disease-causing CNVs by reducing the false positive count and providing annotation. This improves the clinical utility of CNV detection in exome data.

Comparative genomic analysis reveals a novel mitochondrial isoform of human rTS protein and unusual phylogenetic distribution of the rTS gene

PubMed Central

Liang, Ping; Nair, Jayakumar R; Song, Lei; McGuire, John J; Dolnick, Bruce J

2005-01-01

Background The rTS gene (ENOSF1), first identified in Homo sapiens as a gene complementary to the thymidylate synthase (TYMS) mRNA, is known to encode two protein isoforms, rTSα and rTSβ. The rTSβ isoform appears to be an enzyme responsible for the synthesis of signaling molecules involved in the down-regulation of thymidylate synthase, but the exact cellular functions of rTS genes are largely unknown. Results Through comparative genomic sequence analysis, we predicted the existence of a novel protein isoform, rTS, which has a 27 residue longer N-terminus by virtue of utilizing an alternative start codon located upstream of the start codon in rTSβ. We observed that a similar extended N-terminus could be predicted in all rTS genes for which genomic sequences are available and the extended regions are conserved from bacteria to human. Therefore, we reasoned that the protein with the extended N-terminus might represent an ancestral form of the rTS protein. Sequence analysis strongly predicts a mitochondrial signal sequence in the extended N-terminal of human rTSγ, which is absent in rTSβ. We confirmed the existence of rTS in human mitochondria experimentally by demonstrating the presence of both rTSγ and rTSβ proteins in mitochondria isolated by subcellular fractionation. In addition, our comprehensive analysis of rTS orthologous sequences reveals an unusual phylogenetic distribution of this gene, which suggests the occurrence of one or more horizontal gene transfer events. Conclusion The presence of two rTS isoforms in mitochondria suggests that the rTS signaling pathway may be active within mitochondria. Our report also presents an example of identifying novel protein isoforms and for improving gene annotation through comparative genomic analysis. PMID:16162288

Using information content and base frequencies to distinguish mutations from genetic polymorphisms in splice junction recognition sites.

PubMed

Rogan, P K; Schneider, T D

1995-01-01

Predicting the effects of nucleotide substitutions in human splice sites has been based on analysis of consensus sequences. We used a graphic representation of sequence conservation and base frequency, the sequence logo, to demonstrate that a change in a splice acceptor of hMSH2 (a gene associated with familial nonpolyposis colon cancer) probably does not reduce splicing efficiency. This confirms a population genetic study that suggested that this substitution is a genetic polymorphism. The information theory-based sequence logo is quantitative and more sensitive than the corresponding splice acceptor consensus sequence for detection of true mutations. Information analysis may potentially be used to distinguish polymorphisms from mutations in other types of transcriptional, translational, or protein-coding motifs.

Prediction of Protein Structure by Template-Based Modeling Combined with the UNRES Force Field.

PubMed

Krupa, Paweł; Mozolewska, Magdalena A; Joo, Keehyoung; Lee, Jooyoung; Czaplewski, Cezary; Liwo, Adam

2015-06-22

A new approach to the prediction of protein structures that uses distance and backbone virtual-bond dihedral angle restraints derived from template-based models and simulations with the united residue (UNRES) force field is proposed. The approach combines the accuracy and reliability of template-based methods for the segments of the target sequence with high similarity to those having known structures with the ability of UNRES to pack the domains correctly. Multiplexed replica-exchange molecular dynamics with restraints derived from template-based models of a given target, in which each restraint is weighted according to the accuracy of the prediction of the corresponding section of the molecule, is used to search the conformational space, and the weighted histogram analysis method and cluster analysis are applied to determine the families of the most probable conformations, from which candidate predictions are selected. To test the capability of the method to recover template-based models from restraints, five single-domain proteins with structures that have been well-predicted by template-based methods were used; it was found that the resulting structures were of the same quality as the best of the original models. To assess whether the new approach can improve template-based predictions with incorrectly predicted domain packing, four such targets were selected from the CASP10 targets; for three of them the new approach resulted in significantly better predictions compared with the original template-based models. The new approach can be used to predict the structures of proteins for which good templates can be found for sections of the sequence or an overall good template can be found for the entire sequence but the prediction quality is remarkably weaker in putative domain-linker regions.

The complete genome sequence and genetic analysis of ΦCA82 a novel uncultured microphage from the turkey gastrointestinal system

PubMed Central

2011-01-01

The genomic DNA sequence of a novel enteric uncultured microphage, ΦCA82 from a turkey gastrointestinal system was determined utilizing metagenomics techniques. The entire circular, single-stranded nucleotide sequence of the genome was 5,514 nucleotides. The ΦCA82 genome is quite different from other microviruses as indicated by comparisons of nucleotide similarity, predicted protein similarity, and functional classifications. Only three genes showed significant similarity to microviral proteins as determined by local alignments using BLAST analysis. ORF1 encoded a predicted phage F capsid protein that was phylogenetically most similar to the Microviridae ΦMH2K member's major coat protein. The ΦCA82 genome also encoded a predicted minor capsid protein (ORF2) and putative replication initiation protein (ORF3) most similar to the microviral bacteriophage SpV4. The distant evolutionary relationship of ΦCA82 suggests that the divergence of this novel turkey microvirus from other microviruses may reflect unique evolutionary pressures encountered within the turkey gastrointestinal system. PMID:21714899

Sequence co-evolution gives 3D contacts and structures of protein complexes

PubMed Central

Hopf, Thomas A; Schärfe, Charlotta P I; Rodrigues, João P G L M; Green, Anna G; Kohlbacher, Oliver; Sander, Chris; Bonvin, Alexandre M J J; Marks, Debora S

2014-01-01

Protein–protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein–protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein–protein interaction networks and used for interaction predictions at residue resolution. DOI: http://dx.doi.org/10.7554/eLife.03430.001 PMID:25255213

Predicting protein-protein interactions by combing various sequence- derived features into the general form of Chou's Pseudo amino acid composition.

PubMed

Zhao, Xiao-Wei; Ma, Zhi-Qiang; Yin, Ming-Hao

2012-05-01

Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.

Contingency Table Browser - prediction of early stage protein structure.

PubMed

Kalinowska, Barbara; Krzykalski, Artur; Roterman, Irena

2015-01-01

The Early Stage (ES) intermediate represents the starting structure in protein folding simulations based on the Fuzzy Oil Drop (FOD) model. The accuracy of FOD predictions is greatly dependent on the accuracy of the chosen intermediate. A suitable intermediate can be constructed using the sequence-structure relationship information contained in the so-called contingency table - this table expresses the likelihood of encountering various structural motifs for each tetrapeptide fragment in the amino acid sequence. The limited accuracy with which such structures could previously be predicted provided the motivation for a more indepth study of the contingency table itself. The Contingency Table Browser is a tool which can visualize, search and analyze the table. Our work presents possible applications of Contingency Table Browser, among them - analysis of specific protein sequences from the point of view of their structural ambiguity.

Function-based classification of carbohydrate-active enzymes by recognition of short, conserved peptide motifs.

PubMed

Busk, Peter Kamp; Lange, Lene

2013-06-01

Functional prediction of carbohydrate-active enzymes is difficult due to low sequence identity. However, similar enzymes often share a few short motifs, e.g., around the active site, even when the overall sequences are very different. To exploit this notion for functional prediction of carbohydrate-active enzymes, we developed a simple algorithm, peptide pattern recognition (PPR), that can divide proteins into groups of sequences that share a set of short conserved sequences. When this method was used on 118 glycoside hydrolase 5 proteins with 9% average pairwise identity and representing four characterized enzymatic functions, 97% of the proteins were sorted into groups correlating with their enzymatic activity. Furthermore, we analyzed 8,138 glycoside hydrolase 13 proteins including 204 experimentally characterized enzymes with 28 different functions. There was a 91% correlation between group and enzyme activity. These results indicate that the function of carbohydrate-active enzymes can be predicted with high precision by finding short, conserved motifs in their sequences. The glycoside hydrolase 61 family is important for fungal biomass conversion, but only a few proteins of this family have been functionally characterized. Interestingly, PPR divided 743 glycoside hydrolase 61 proteins into 16 subfamilies useful for targeted investigation of the function of these proteins and pinpointed three conserved motifs with putative importance for enzyme activity. Furthermore, the conserved sequences were useful for cloning of new, subfamily-specific glycoside hydrolase 61 proteins from 14 fungi. In conclusion, identification of conserved sequence motifs is a new approach to sequence analysis that can predict carbohydrate-active enzyme functions with high precision.

Predicting human activities in sequences of actions in RGB-D videos

NASA Astrophysics Data System (ADS)

Jardim, David; Nunes, Luís.; Dias, Miguel

2017-03-01

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.

Genome survey sequencing of red swamp crayfish Procambarus clarkii.

PubMed

Shi, Linlin; Yi, Shaokui; Li, Yanhe

2018-06-21

Red swamp crayfish, Procambarus clarkii, presently is an important aquatic commercial species in China. The crayfish is a hot area of research focus, and its genetic improvement is quite urgent for the crayfish aquaculture in China. However, the knowledge of its genomic landscape is limited. In this study, a survey of P. clarkii genome was investigated based on Illumina's Solexa sequencing platform. Meanwhile, its genome size was estimated using flow cytometry. Interestingly, the genome size estimated is about 8.50 Gb by flow cytometry and 1.86 Gb with genome survey sequencing. Based on the assembled genome sequences, total of 136,962 genes and 152,268 exons were predicted, and the predicted genes ranged from 150 to 12,807 bp in length. The survey sequences could help accelerate the progress of gene discovery involved in genetic diversity and evolutionary analysis, even though it could not successfully applied for estimation of P. clarkii genome size.

Sequence stratigraphy of the Triassic in the Barentsz Sea

DOE Office of Scientific and Technical Information (OSTI.GOV)

Skjold, L.JU.; Van Veen, P.M.; Gjelberg, J.

1990-05-01

A regional study of the Triassic in the Barentsz Sea (20-32{degree}E, 71-74{degree}N) revealed sequences that correlate seismically for hundreds of kilometers. Recent offshore drilling results enabled them to establish a biostratigraphic time framework. Comparisons with information from onshore outcrops (such as the Svalbard Archipelago) aided the piecing together of these superregional sequences. Seismic character analysis identified three units with composite progradational patterns (Induan, Olenekian, and Anisian). Fluvial, deltaic, and marine deposits can be distinguished and located relative to the paleocoastlines. Corresponding downlap surfaces suggest the development of condensed intervals, predicted to consist of organic-rich source rocks, as was later confirmedmore » by drilling. Regional predictions based on this sequence-stratigraphic approach have proved valuable when correlating and evaluating well information. The sequences identified also help define third-order sea level curves for the area; these improve published curves thought to have global significance.« less

REPPER—repeats and their periodicities in fibrous proteins

PubMed Central

Gruber, Markus; Söding, Johannes; Lupas, Andrei N.

2005-01-01

REPPER (REPeats and their PERiodicities) is an integrated server that detects and analyzes regions with short gapless repeats in protein sequences or alignments. It finds periodicities by Fourier Transform (FTwin) and internal similarity analysis (REPwin). FTwin assigns numerical values to amino acids that reflect certain properties, for instance hydrophobicity, and gives information on corresponding periodicities. REPwin uses self-alignments and displays repeats that reveal significant internal similarities. Both programs use a sliding window to ensure that different periodic regions within the same protein are detected independently. FTwin and REPwin are complemented by secondary structure prediction (PSIPRED) and coiled coil prediction (COILS), making the server a versatile analysis tool for sequences of fibrous proteins. REPPER is available at . PMID:15980460

Purification, developmental expression, and in silico characterization of α-amylase inhibitor from Echinochloa frumentacea.

PubMed

Panwar, Priyankar; Verma, A K; Dubey, Ashutosh

2018-05-01

Barnyard ( Echinochloa frumentacea ) and finger ( Eleusine coracana ) millet growing at northwestern Himalaya were explored for the α-amylase inhibitor (α-AI). The mature seeds of barnyard millet variety PRJ1 had maximum α-AI activity which increases in different developmental stage. α-AI was purified up to 22.25-fold from barnyard millet variety PRJ1. Semi-quantitative PCR of different developmental stages of barnyard millet seeds showed increased levels of the transcript from 7 to 28 days. Sequence analysis revealed that it contained 315 bp nucleotide which encodes 104 amino acid sequence with molecular weight 10.72 kDa. The predicted 3D structure of α-AI was 86.73% similar to a bifunctional inhibitor of ragi. In silico analysis of 71 α-AI protein sequences were carried out for biochemical features, homology search, multiple sequence alignment, phylogenetic tree construction, motif, and superfamily distribution of protein sequences. Analysis of multiple sequence alignment revealed the existence of conserved regions NPLP[S/G]CRWYVV[S/Q][Q/R]TCG[V/I] throughout sequences. Superfam analysis revealed that α-AI protein sequences were distributed among seven different superfamilies.

A time series based sequence prediction algorithm to detect activities of daily living in smart home.

PubMed

Marufuzzaman, M; Reaz, M B I; Ali, M A M; Rahman, L F

2015-01-01

The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included. The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge. The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED. Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.

Genome sequencing and comparative genomics of honey bee microsporidia, Nosema apis reveal novel insights into host-parasite interactions.

PubMed

Chen, Yan ping; Pettis, Jeffery S; Zhao, Yan; Liu, Xinyue; Tallon, Luke J; Sadzewicz, Lisa D; Li, Renhua; Zheng, Huoqing; Huang, Shaokang; Zhang, Xuan; Hamilton, Michele C; Pernal, Stephen F; Melathopoulos, Andony P; Yan, Xianghe; Evans, Jay D

2013-07-05

The microsporidia parasite Nosema contributes to the steep global decline of honey bees that are critical pollinators of food crops. There are two species of Nosema that have been found to infect honey bees, Nosema apis and N. ceranae. Genome sequencing of N. apis and comparative genome analysis with N. ceranae, a fully sequenced microsporidia species, reveal novel insights into host-parasite interactions underlying the parasite infections. We applied the whole-genome shotgun sequencing approach to sequence and assemble the genome of N. apis which has an estimated size of 8.5 Mbp. We predicted 2,771 protein- coding genes and predicted the function of each putative protein using the Gene Ontology. The comparative genomic analysis led to identification of 1,356 orthologs that are conserved between the two Nosema species and genes that are unique characteristics of the individual species, thereby providing a list of virulence factors and new genetic tools for studying host-parasite interactions. We also identified a highly abundant motif in the upstream promoter regions of N. apis genes. This motif is also conserved in N. ceranae and other microsporidia species and likely plays a role in gene regulation across the microsporidia. The availability of the N. apis genome sequence is a significant addition to the rapidly expanding body of microsprodian genomic data which has been improving our understanding of eukaryotic genome diversity and evolution in a broad sense. The predicted virulent genes and transcriptional regulatory elements are potential targets for innovative therapeutics to break down the life cycle of the parasite.

Prediction of enhancer-promoter interactions via natural language processing.

PubMed

Zeng, Wanwen; Wu, Mengmeng; Jiang, Rui

2018-05-09

Precise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput. We propose a novel computational framework EP2vec to assay three-dimensional genomic interactions. We first extract sequence embedding features, defined as fixed-length vector representations learned from variable-length sequences using an unsupervised deep learning method in natural language processing. Then, we train a classifier to predict EPIs using the learned representations in supervised way. Experimental results demonstrate that EP2vec obtains F1 scores ranging from 0.841~ 0.933 on different datasets, which outperforms existing methods. We prove the robustness of sequence embedding features by carrying out sensitivity analysis. Besides, we identify motifs that represent cell line-specific information through analysis of the learned sequence embedding features by adopting attention mechanism. Last, we show that even superior performance with F1 scores 0.889~ 0.940 can be achieved by combining sequence embedding features and experimental features. EP2vec sheds light on feature extraction for DNA sequences of arbitrary lengths and provides a powerful approach for EPIs identification.

The complete genome sequence of a south Indian isolate of Rice tungro spherical virus reveals evidence of genetic recombination between distinct isolates.

PubMed

Sailaja, B; Anjum, Najreen; Patil, Yogesh K; Agarwal, Surekha; Malathi, P; Krishnaveni, D; Balachandran, S M; Viraktamath, B C; Mangrauthia, Satendra K

2013-12-01

In this study, complete genome of a south Indian isolate of Rice tungro spherical virus (RTSV) from Andhra Pradesh (AP) was sequenced, and the predicted amino acid sequence was analysed. The RTSV RNA genome consists of 12,171 nt without the poly(A) tail, encoding a putative typical polyprotein of 3,470 amino acids. Furthermore, cleavage sites and sequence motifs of the polyprotein were predicted. Multiple alignment with other RTSV isolates showed a nucleotide sequence identity of 95% to east Indian isolates and 90% to Philippines isolates. A phylogenetic tree based on complete genome sequence showed that Indian isolates clustered together, while Vt6 and PhilA isolates of Philippines formed two separate clusters. Twelve recombination events were detected in RNA genome of RTSV using the Recombination Detection Program version 3. Recombination analysis suggested significant role of 5' end and central region of genome in virus evolution. Further, AP and Odisha isolates appeared as important RTSV isolates involved in diversification of this virus in India through recombination phenomenon. The new addition of complete genome of first south Indian isolate provided an opportunity to establish the molecular evolution of RTSV through recombination analysis and phylogenetic relationship.

[Two novel pathogenic mutations of GAN gene identified in a patient with giant axonal neuropathy].

PubMed

Wang, Juan; Ma, Qingwen; Cai, Qin; Liu, Yanna; Wang, Wei; Ren, Zhaorui

2016-06-01

To explore the disease-causing mutations in a patient suspected for giant axonal neuropathy(GAN). Target sequence capture sequencing was used to screen potential mutations in genomic DNA extracted from peripheral blood sample of the patient. Sanger sequencing was applied to confirm the detected mutation. The mutation was verified among 400 GAN alleles from 200 healthy individuals by Sanger sequencing. The function of the mutations was predicted by bioinformatics analysis. The patient was identified as a compound heterozygote carrying two novel pathogenic GAN mutations, i.e., c.778G>T (p.Glu260Ter) and c.277G>A (p.Gly93Arg). Sanger sequencing confirmed that the c.778G>T (p.Glu260Ter) mutation was inherited from his father, while c.277G>A (p.Gly93Arg) was inherited from his mother. The same mutations was not found in the 200 healthy individuals. Bioinformatics analysis predicted that the two mutations probably caused functional abnormality of gigaxonin. Two novel GAN mutations were detected in a patient with GAN. Both mutations are pathogenic and can cause abnormalities of gigaxonin structure and function, leading to pathogenesis of GAN. The results may also offer valuable information for similar diseases.

Structure prediction of Fe(II) 2-oxoglutarate dioxygenase from a psychrophilic yeast Glaciozyma antarctica PI12

NASA Astrophysics Data System (ADS)

Yusof, Nik Yusnoraini; Bakar, Farah Diba Abu; Mahadi, Nor Muhammad; Raih, Mohd Firdaus; Murad, Abdul Munir Abdul

2015-09-01

A cDNA encoding Fe(II) 2-oxoglutarate (2OG) dependent dioxygenases was isolated from psychrophilic yeast, Glaciozyma antarctica PI12. We have successfully amplified 1,029 bp cDNA sequence that encodes 342 amino acid with predicted molecular weight 38 kDa. The prediction protein was analysed using various bioinformatics tools to explore the properties of the protein. Based on a BLAST search analysis, the Fe2OX amino acid sequence showed 61% identity to the sequence of oxoglutarate/iron-dependent oxygenase from Rhodosporidium toruloides NP11. SignalP prediction showed that the Fe2OX protein contains no putative signal peptide, which suggests that this enzyme most probably localised intracellularly.The structure of Fe2OX was predicted by homology modelling using MODELLER9v11. The model with the lowest objective function was selected from hundred models generated using MODELLER9v11. Analysis of the structure revealed the longer loop at Fe2OX from G.antarctica that might be responsible for the flexibility of the structure, which contributes to its adaptation to low temperatures. Fe2OX hold a highly conserved Fe(II) binding HXD/E…H triad motif. The binding site for 2-oxoglutarate was found conserved for Arg280 among reported studies, however the Phe268 was found to be different in Fe2OX.

Genome sequence analysis of a flocculant-producing bacterium, Paenibacillus shenyangensis.

PubMed

Fu, Lili; Jiang, Binhui; Liu, Jinliang; Zhao, Xin; Liu, Qian; Hu, Xiaomin

2016-03-01

To explore the metabolic process of Paenibacillus shenyangensis that is an efficient bioflocculant-producing bacterium. The biosynthesis mechanism of bioflocculation was used to enrich the genome of Paenibacillus shenyangensis and provide a basis for molecular genetics and functional genomics analyses. According to the analysis of de novo assembly, a total of 5,501,467 bp clean reads were generated, and were assembled into 92 contigs. 4800 unigenes were predicted of which 4393 were annotated showing a specific gene function in the NCBI-Nr database. 3423 genes were found in the database of cluster of orthologous groups. Among the 168 Kyoto Encyclopedia of Genes and Genomes database, cell growth and metabolism were the main biological processes, and a potential metabolic pathway was predicted from glucose to exopolysaccharide within the starch and sucrose metabolism pathway. By using the high-throughput sequencing technology, we provide a genome analysis of Paenibacillus shenyangensis that predicts the main metabolic processes and a potential pathway of exopolysaccharide biosynthesis.

Low-coverage, whole-genome sequencing of Artocarpus camansi (Moraceae) for phylogenetic marker development and gene discovery1

PubMed Central

Gardner, Elliot M.; Johnson, Matthew G.; Ragone, Diane; Wickett, Norman J.; Zerega, Nyree J. C.

2016-01-01

Premise of the study: We used moderately low-coverage (17×) whole-genome sequencing of Artocarpus camansi (Moraceae) to develop genomic resources for Artocarpus and Moraceae. Methods and Results: A de novo assembly of Illumina short reads (251,378,536 pairs, 2 × 100 bp) accounted for 93% of the predicted genome size. Predicted coding regions were used in a three-way orthology search with published genomes of Morus notabilis and Cannabis sativa. Phylogenetic markers for Moraceae were developed from 333 inferred single-copy exons. Ninety-eight putative MADS-box genes were identified. Analysis of all predicted coding regions resulted in preliminary annotation of 49,089 genes. An analysis of synonymous substitutions for pairs of orthologs (Ks analysis) in M. notabilis and A. camansi strongly suggested a lineage-specific whole-genome duplication in Artocarpus. Conclusions: This study substantially increases the genomic resources available for Artocarpus and Moraceae and demonstrates the value of low-coverage de novo assemblies for nonmodel organisms with moderately large genomes. PMID:27437173

Genome signature analysis of thermal virus metagenomes reveals Archaea and thermophilic signatures

PubMed Central

Pride, David T; Schoenfeld, Thomas

2008-01-01

Background Metagenomic analysis provides a rich source of biological information for otherwise intractable viral communities. However, study of viral metagenomes has been hampered by its nearly complete reliance on BLAST algorithms for identification of DNA sequences. We sought to develop algorithms for examination of viral metagenomes to identify the origin of sequences independent of BLAST algorithms. We chose viral metagenomes obtained from two hot springs, Bear Paw and Octopus, in Yellowstone National Park, as they represent simple microbial populations where comparatively large contigs were obtained. Thermal spring metagenomes have high proportions of sequences without significant Genbank homology, which has hampered identification of viruses and their linkage with hosts. To analyze each metagenome, we developed a method to classify DNA fragments using genome signature-based phylogenetic classification (GSPC), where metagenomic fragments are compared to a database of oligonucleotide signatures for all previously sequenced Bacteria, Archaea, and viruses. Results From both Bear Paw and Octopus hot springs, each assembled contig had more similarity to other metagenome contigs than to any sequenced microbial genome based on GSPC analysis, suggesting a genome signature common to each of these extreme environments. While viral metagenomes from Bear Paw and Octopus share some similarity, the genome signatures from each locale are largely unique. GSPC using a microbial database predicts most of the Octopus metagenome has archaeal signatures, while bacterial signatures predominate in Bear Paw; a finding consistent with those of Genbank BLAST. When using a viral database, the majority of the Octopus metagenome is predicted to belong to archaeal virus Families Globuloviridae and Fuselloviridae, while none of the Bear Paw metagenome is predicted to belong to archaeal viruses. As expected, when microbial and viral databases are combined, each of the Octopus and Bear Paw metagenomic contigs are predicted to belong to viruses rather than to any Bacteria or Archaea, consistent with the apparent viral origin of both metagenomes. Conclusion That BLAST searches identify no significant homologs for most metagenome contigs, while GSPC suggests their origin as archaeal viruses or bacteriophages, indicates GSPC provides a complementary approach in viral metagenomic analysis. PMID:18798991

Genome signature analysis of thermal virus metagenomes reveals Archaea and thermophilic signatures.

PubMed

Pride, David T; Schoenfeld, Thomas

2008-09-17

Metagenomic analysis provides a rich source of biological information for otherwise intractable viral communities. However, study of viral metagenomes has been hampered by its nearly complete reliance on BLAST algorithms for identification of DNA sequences. We sought to develop algorithms for examination of viral metagenomes to identify the origin of sequences independent of BLAST algorithms. We chose viral metagenomes obtained from two hot springs, Bear Paw and Octopus, in Yellowstone National Park, as they represent simple microbial populations where comparatively large contigs were obtained. Thermal spring metagenomes have high proportions of sequences without significant Genbank homology, which has hampered identification of viruses and their linkage with hosts. To analyze each metagenome, we developed a method to classify DNA fragments using genome signature-based phylogenetic classification (GSPC), where metagenomic fragments are compared to a database of oligonucleotide signatures for all previously sequenced Bacteria, Archaea, and viruses. From both Bear Paw and Octopus hot springs, each assembled contig had more similarity to other metagenome contigs than to any sequenced microbial genome based on GSPC analysis, suggesting a genome signature common to each of these extreme environments. While viral metagenomes from Bear Paw and Octopus share some similarity, the genome signatures from each locale are largely unique. GSPC using a microbial database predicts most of the Octopus metagenome has archaeal signatures, while bacterial signatures predominate in Bear Paw; a finding consistent with those of Genbank BLAST. When using a viral database, the majority of the Octopus metagenome is predicted to belong to archaeal virus Families Globuloviridae and Fuselloviridae, while none of the Bear Paw metagenome is predicted to belong to archaeal viruses. As expected, when microbial and viral databases are combined, each of the Octopus and Bear Paw metagenomic contigs are predicted to belong to viruses rather than to any Bacteria or Archaea, consistent with the apparent viral origin of both metagenomes. That BLAST searches identify no significant homologs for most metagenome contigs, while GSPC suggests their origin as archaeal viruses or bacteriophages, indicates GSPC provides a complementary approach in viral metagenomic analysis.

AlignMe—a membrane protein sequence alignment web server

PubMed Central

Stamm, Marcus; Staritzbichler, René; Khafizov, Kamil; Forrest, Lucy R.

2014-01-01

We present a web server for pair-wise alignment of membrane protein sequences, using the program AlignMe. The server makes available two operational modes of AlignMe: (i) sequence to sequence alignment, taking two sequences in fasta format as input, combining information about each sequence from multiple sources and producing a pair-wise alignment (PW mode); and (ii) alignment of two multiple sequence alignments to create family-averaged hydropathy profile alignments (HP mode). For the PW sequence alignment mode, four different optimized parameter sets are provided, each suited to pairs of sequences with a specific similarity level. These settings utilize different types of inputs: (position-specific) substitution matrices, secondary structure predictions and transmembrane propensities from transmembrane predictions or hydrophobicity scales. In the second (HP) mode, each input multiple sequence alignment is converted into a hydrophobicity profile averaged over the provided set of sequence homologs; the two profiles are then aligned. The HP mode enables qualitative comparison of transmembrane topologies (and therefore potentially of 3D folds) of two membrane proteins, which can be useful if the proteins have low sequence similarity. In summary, the AlignMe web server provides user-friendly access to a set of tools for analysis and comparison of membrane protein sequences. Access is available at http://www.bioinfo.mpg.de/AlignMe PMID:24753425

Transcriptome Analysis of the Chrysanthemum Foliar Nematode, Aphelenchoides ritzemabosi (Aphelenchida: Aphelenchoididae)

PubMed Central

Li, Jun-Yi; Xie, Hui; Xu, Chun-Ling; Li, Yu

2016-01-01

The chrysanthemum foliar nematode (CFN), Aphelenchoides ritzemabosi, is a plant parasitic nematode that attacks many plants. In this study, a transcriptomes of mixed-stage population of CFN was sequenced on the Illumina HiSeq 2000 platform. 68.10 million Illumina high quality paired end reads were obtained which generated 26,817 transcripts with a mean length of 1,032 bp and an N50 of 1,672 bp, of which 16,467 transcripts were annotated against six databases. In total, 20,311 coding region sequences (CDS), 495 simple sequence repeats (SSRs) and 8,353 single-nucleotide polymorphisms (SNPs) were predicted, respectively. The CFN with the most shared sequences was B. xylophilus with 16,846 (62.82%) common transcripts and 10,543 (39.31%) CFN transcripts matched sequences of all of four plant parasitic nematodes compared. A total of 111 CFN transcripts were predicted as homologues of 7 types of carbohydrate-active enzymes (CAZymes) with plant/fungal cell wall-degrading activities, fewer transcripts were predicted as homologues of plant cell wall-degrading enzymes than fungal cell wall-degrading enzymes. The phylogenetic analysis of GH5, GH16, GH43 and GH45 proteins between CFN and other organisms showed CFN and other nematodes have a closer phylogenetic relationship. In the CFN transcriptome, sixteen types of genes orthologues with seven classes of protein families involved in the RNAi pathway in C. elegans were predicted. This research provides comprehensive gene expression information at the transcriptional level, which will facilitate the elucidation of the molecular mechanisms of CFN and the distribution of gene functions at the macro level, potentially revealing improved methods for controlling CFN. PMID:27875578

GenoMycDB: a database for comparative analysis of mycobacterial genes and genomes.

PubMed

Catanho, Marcos; Mascarenhas, Daniel; Degrave, Wim; Miranda, Antonio Basílio de

2006-03-31

Several databases and computational tools have been created with the aim of organizing, integrating and analyzing the wealth of information generated by large-scale sequencing projects of mycobacterial genomes and those of other organisms. However, with very few exceptions, these databases and tools do not allow for massive and/or dynamic comparison of these data. GenoMycDB (http://www.dbbm.fiocruz.br/GenoMycDB) is a relational database built for large-scale comparative analyses of completely sequenced mycobacterial genomes, based on their predicted protein content. Its central structure is composed of the results obtained after pair-wise sequence alignments among all the predicted proteins coded by the genomes of six mycobacteria: Mycobacterium tuberculosis (strains H37Rv and CDC1551), M. bovis AF2122/97, M. avium subsp. paratuberculosis K10, M. leprae TN, and M. smegmatis MC2 155. The database stores the computed similarity parameters of every aligned pair, providing for each protein sequence the predicted subcellular localization, the assigned cluster of orthologous groups, the features of the corresponding gene, and links to several important databases. Tables containing pairs or groups of potential homologs between selected species/strains can be produced dynamically by user-defined criteria, based on one or multiple sequence similarity parameters. In addition, searches can be restricted according to the predicted subcellular localization of the protein, the DNA strand of the corresponding gene and/or the description of the protein. Massive data search and/or retrieval are available, and different ways of exporting the result are offered. GenoMycDB provides an on-line resource for the functional classification of mycobacterial proteins as well as for the analysis of genome structure, organization, and evolution.

SNP Discovery in the Transcriptome of White Pacific Shrimp Litopenaeus vannamei by Next Generation Sequencing

PubMed Central

Yu, Yang; Wei, Jiankai; Zhang, Xiaojun; Liu, Jingwen; Liu, Chengzhang; Li, Fuhua; Xiang, Jianhai

2014-01-01

The application of next generation sequencing technology has greatly facilitated high throughput single nucleotide polymorphism (SNP) discovery and genotyping in genetic research. In the present study, SNPs were discovered based on two transcriptomes of Litopenaeus vannamei (L. vannamei) generated from Illumina sequencing platform HiSeq 2000. One transcriptome of L. vannamei was obtained through sequencing on the RNA from larvae at mysis stage and its reference sequence was de novo assembled. The data from another transcriptome were downloaded from NCBI and the reads of the two transcriptomes were mapped separately to the assembled reference by BWA. SNP calling was performed using SAMtools. A total of 58,717 and 36,277 SNPs with high quality were predicted from the two transcriptomes, respectively. SNP calling was also performed using the reads of two transcriptomes together, and a total of 96,040 SNPs with high quality were predicted. Among these 96,040 SNPs, 5,242 and 29,129 were predicted as non-synonymous and synonymous SNPs respectively. Characterization analysis of the predicted SNPs in L. vannamei showed that the estimated SNP frequency was 0.21% (one SNP per 476 bp) and the estimated ratio for transition to transversion was 2.0. Fifty SNPs were randomly selected for validation by Sanger sequencing after PCR amplification and 76% of SNPs were confirmed, which indicated that the SNPs predicted in this study were reliable. These SNPs will be very useful for genetic study in L. vannamei, especially for the high density linkage map construction and genome-wide association studies. PMID:24498047

Fuzzy cluster analysis of simple physicochemical properties of amino acids for recognizing secondary structure in proteins.

PubMed Central

Mocz, G.

1995-01-01

Fuzzy cluster analysis has been applied to the 20 amino acids by using 65 physicochemical properties as a basis for classification. The clustering products, the fuzzy sets (i.e., classical sets with associated membership functions), have provided a new measure of amino acid similarities for use in protein folding studies. This work demonstrates that fuzzy sets of simple molecular attributes, when assigned to amino acid residues in a protein's sequence, can predict the secondary structure of the sequence with reasonable accuracy. An approach is presented for discriminating standard folding states, using near-optimum information splitting in half-overlapping segments of the sequence of assigned membership functions. The method is applied to a nonredundant set of 252 proteins and yields approximately 73% matching for correctly predicted and correctly rejected residues with approximately 60% overall success rate for the correctly recognized ones in three folding states: alpha-helix, beta-strand, and coil. The most useful attributes for discriminating these states appear to be related to size, polarity, and thermodynamic factors. Van der Waals volume, apparent average thickness of surrounding molecular free volume, and a measure of dimensionless surface electron density can explain approximately 95% of prediction results. hydrogen bonding and hydrophobicity induces do not yet enable clear clustering and prediction. PMID:7549882

GAP Final Technical Report 12-14-04

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andrew J. Bordner, PhD, Senior Research Scientist

2004-12-14

The Genomics Annotation Platform (GAP) was designed to develop new tools for high throughput functional annotation and characterization of protein sequences and structures resulting from genomics and structural proteomics, benchmarking and application of those tools. Furthermore, this platform integrated the genomic scale sequence and structural analysis and prediction tools with the advanced structure prediction and bioinformatics environment of ICM. The development of GAP was primarily oriented towards the annotation of new biomolecular structures using both structural and sequence data. Even though the amount of protein X-ray crystal data is growing exponentially, the volume of sequence data is growing even moremore » rapidly. This trend was exploited by leveraging the wealth of sequence data to provide functional annotation for protein structures. The additional information provided by GAP is expected to assist the majority of the commercial users of ICM, who are involved in drug discovery, in identifying promising drug targets as well in devising strategies for the rational design of therapeutics directed at the protein of interest. The GAP also provided valuable tools for biochemistry education, and structural genomics centers. In addition, GAP incorporates many novel prediction and analysis methods not available in other molecular modeling packages. This development led to signing the first Molsoft agreement in the structural genomics annotation area with the University of oxford Structural Genomics Center. This commercial agreement validated the Molsoft efforts under the GAP project and provided the basis for further development of the large scale functional annotation platform.« less

Cloning and sequence analysis of Hemonchus contortus HC58cDNA.

PubMed

Muleke, Charles I; Ruofeng, Yan; Lixin, Xu; Xinwen, Bo; Xiangrui, Li

2007-06-01

The complete coding sequence of Hemonchus contortus HC58cDNA was generated by rapid amplification of cDNA ends and polymerase chain reaction using primers based on the 5' and 3' ends of the parasite mRNA, accession no. AF305964. The HC58cDNA gene was 851 bp long, with open reading frame of 717 bp, precursors to 239 amino acids coding for approximately 27 kDa protein. Analysis of amino acid sequence revealed conserved residues of cysteine, histidine, asparagine, occluding loop pattern, hemoglobinase motif and glutamine of the oxyanion hole characteristic of cathepsin B like proteases (CBL). Comparison of the predicted amino acid sequences showed the protein shared 33.5-58.7% identity to cathepsin B homologues in the papain clan CA family (family C1). Phylogenetic analysis revealed close evolutionary proximity of the protein sequence to counterpart sequences in the CBL, suggesting that HC58cDNA was a member of the papain family.

Clonal architecture of secondary acute myeloid leukemia defined by single-cell sequencing.

PubMed

Hughes, Andrew E O; Magrini, Vincent; Demeter, Ryan; Miller, Christopher A; Fulton, Robert; Fulton, Lucinda L; Eades, William C; Elliott, Kevin; Heath, Sharon; Westervelt, Peter; Ding, Li; Conrad, Donald F; White, Brian S; Shao, Jin; Link, Daniel C; DiPersio, John F; Mardis, Elaine R; Wilson, Richard K; Ley, Timothy J; Walter, Matthew J; Graubert, Timothy A

2014-07-01

Next-generation sequencing has been used to infer the clonality of heterogeneous tumor samples. These analyses yield specific predictions-the population frequency of individual clones, their genetic composition, and their evolutionary relationships-which we set out to test by sequencing individual cells from three subjects diagnosed with secondary acute myeloid leukemia, each of whom had been previously characterized by whole genome sequencing of unfractionated tumor samples. Single-cell mutation profiling strongly supported the clonal architecture implied by the analysis of bulk material. In addition, it resolved the clonal assignment of single nucleotide variants that had been initially ambiguous and identified areas of previously unappreciated complexity. Accordingly, we find that many of the key assumptions underlying the analysis of tumor clonality by deep sequencing of unfractionated material are valid. Furthermore, we illustrate a single-cell sequencing strategy for interrogating the clonal relationships among known variants that is cost-effective, scalable, and adaptable to the analysis of both hematopoietic and solid tumors, or any heterogeneous population of cells.

Validation of the GILLS score for tongue-lip adhesion in Robin sequence patients.

PubMed

Abramowicz, Shelly; Bacic, Janine D; Mulliken, John B; Rogers, Gary F

2012-03-01

The GILLS score consists of gastroesophageal reflux disease, preoperative intubation, late surgical intervention, low birth weight, and syndromic diagnosis. The purpose of this study was to test the validity of the GILLS score in predicting success of tongue-lip adhesion (TLA) in managing Robin sequence. Infants with Robin sequence were included in the study if they had a TLA for airway compromise subsequent to formulation of the GILLS scoring system, that is, they were not included in the original GILLS analysis. The patients were prospectively considered based on the presence of the 5 factors that constitute the GILLS score. A score of ≤ 2 predicts success of TLA. Twenty patients met the inclusion criteria. Tongue-lip adhesion managed the compromised airway in 18 (90%) of 20 patients. Overall, the GILLS score had a sensitivity of 83%, specificity of 50%, positive predictive value of 94%, and negative predictive value of 25%. The GILLS score accurately predicts a successful outcome for TLA in infants with Robin sequence. For infants with a score of 2 or less, TLA is the procedure of choice. Infants with a GILLS score of 3 or greater were 5 times more likely to fail TLA than those with a score of 2 or less. In these patients, other methods of managing the airway should be considered.

Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation.

PubMed

Yang, Jian-Yi; Peng, Zhen-Ling; Yu, Zu-Guo; Zhang, Rui-Jie; Anh, Vo; Wang, Desheng

2009-04-21

In this paper, we intend to predict protein structural classes (alpha, beta, alpha+beta, or alpha/beta) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher's linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes.

Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

PubMed Central

Feinauer, Christoph; Procaccini, Andrea; Zecchina, Riccardo; Weigt, Martin; Pagnani, Andrea

2014-01-01

In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation) have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids), exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i) the prediction of residue-residue contacts in proteins, and (ii) the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code. PMID:24663061

An exploration of the sequence of a 2.9-Mb region of the genome of Drosophila melanogaster: the Adh region.

PubMed Central

Ashburner, M; Misra, S; Roote, J; Lewis, S E; Blazej, R; Davis, T; Doyle, C; Galle, R; George, R; Harris, N; Hartzell, G; Harvey, D; Hong, L; Houston, K; Hoskins, R; Johnson, G; Martin, C; Moshrefi, A; Palazzolo, M; Reese, M G; Spradling, A; Tsang, G; Wan, K; Whitelaw, K; Celniker, S

1999-01-01

A contiguous sequence of nearly 3 Mb from the genome of Drosophila melanogaster has been sequenced from a series of overlapping P1 and BAC clones. This region covers 69 chromosome polytene bands on chromosome arm 2L, including the genetically well-characterized "Adh region." A computational analysis of the sequence predicts 218 protein-coding genes, 11 tRNAs, and 17 transposable element sequences. At least 38 of the protein-coding genes are arranged in clusters of from 2 to 6 closely related genes, suggesting extensive tandem duplication. The gene density is one protein-coding gene every 13 kb; the transposable element density is one element every 171 kb. Of 73 genes in this region identified by genetic analysis, 49 have been located on the sequence; P-element insertions have been mapped to 43 genes. Ninety-five (44%) of the known and predicted genes match a Drosophila EST, and 144 (66%) have clear similarities to proteins in other organisms. Genes known to have mutant phenotypes are more likely to be represented in cDNA libraries, and far more likely to have products similar to proteins of other organisms, than are genes with no known mutant phenotype. Over 650 chromosome aberration breakpoints map to this chromosome region, and their nonrandom distribution on the genetic map reflects variation in gene spacing on the DNA. This is the first large-scale analysis of the genome of D. melanogaster at the sequence level. In addition to the direct results obtained, this analysis has allowed us to develop and test methods that will be needed to interpret the complete sequence of the genome of this species.Before beginning a Hunt, it is wise to ask someone what you are looking for before you begin looking for it. Milne 1926 PMID:10471707

Integrating alternative splicing detection into gene prediction.

PubMed

Foissac, Sylvain; Schiex, Thomas

2005-02-10

Alternative splicing (AS) is now considered as a major actor in transcriptome/proteome diversity and it cannot be neglected in the annotation process of a new genome. Despite considerable progresses in term of accuracy in computational gene prediction, the ability to reliably predict AS variants when there is local experimental evidence of it remains an open challenge for gene finders. We have used a new integrative approach that allows to incorporate AS detection into ab initio gene prediction. This method relies on the analysis of genomically aligned transcript sequences (ESTs and/or cDNAs), and has been implemented in the dynamic programming algorithm of the graph-based gene finder EuGENE. Given a genomic sequence and a set of aligned transcripts, this new version identifies the set of transcripts carrying evidence of alternative splicing events, and provides, in addition to the classical optimal gene prediction, alternative optimal predictions (among those which are consistent with the AS events detected). This allows for multiple annotations of a single gene in a way such that each predicted variant is supported by a transcript evidence (but not necessarily with a full-length coverage). This automatic combination of experimental data analysis and ab initio gene finding offers an ideal integration of alternatively spliced gene prediction inside a single annotation pipeline.

Integrated databanks access and sequence/structure analysis services at the PBIL.

PubMed

Perrière, Guy; Combet, Christophe; Penel, Simon; Blanchet, Christophe; Thioulouse, Jean; Geourjon, Christophe; Grassot, Julien; Charavay, Céline; Gouy, Manolo; Duret, Laurent; Deléage, Gilbert

2003-07-01

The World Wide Web server of the PBIL (Pôle Bioinformatique Lyonnais) provides on-line access to sequence databanks and to many tools of nucleic acid and protein sequence analyses. This server allows to query nucleotide sequence banks in the EMBL and GenBank formats and protein sequence banks in the SWISS-PROT and PIR formats. The query engine on which our data bank access is based is the ACNUC system. It allows the possibility to build complex queries to access functional zones of biological interest and to retrieve large sequence sets. Of special interest are the unique features provided by this system to query the data banks of gene families developed at the PBIL. The server also provides access to a wide range of sequence analysis methods: similarity search programs, multiple alignments, protein structure prediction and multivariate statistics. An originality of this server is the integration of these two aspects: sequence retrieval and sequence analysis. Indeed, thanks to the introduction of re-usable lists, it is possible to perform treatments on large sets of data. The PBIL server can be reached at: http://pbil.univ-lyon1.fr.

Quantifying the relationship between sequence and three-dimensional structure conservation in RNA

PubMed Central

2010-01-01

Background In recent years, the number of available RNA structures has rapidly grown reflecting the increased interest on RNA biology. Similarly to the studies carried out two decades ago for proteins, which gave the fundamental grounds for developing comparative protein structure prediction methods, we are now able to quantify the relationship between sequence and structure conservation in RNA. Results Here we introduce an all-against-all sequence- and three-dimensional (3D) structure-based comparison of a representative set of RNA structures, which have allowed us to quantitatively confirm that: (i) there is a measurable relationship between sequence and structure conservation that weakens for alignments resulting in below 60% sequence identity, (ii) evolution tends to conserve more RNA structure than sequence, and (iii) there is a twilight zone for RNA homology detection. Discussion The computational analysis here presented quantitatively describes the relationship between sequence and structure for RNA molecules and defines a twilight zone region for detecting RNA homology. Our work could represent the theoretical basis and limitations for future developments in comparative RNA 3D structure prediction. PMID:20550657

Prediction of beta-turns from amino acid sequences using the residue-coupled model.

PubMed

Guruprasad, K; Shukla, S

2003-04-01

We evaluated the prediction of beta-turns from amino acid sequences using the residue-coupled model with an enlarged representative protein data set selected from the Protein Data Bank. Our results show that the probability values derived from a data set comprising 425 protein chains yielded an overall beta-turn prediction accuracy 68.74%, compared with 94.7% reported earlier on a data set of 30 proteins using the same method. However, we noted that the overall beta-turn prediction accuracy using probability values derived from the 30-protein data set reduces to 40.74% when tested on the data set comprising 425 protein chains. In contrast, using probability values derived from the 425 data set used in this analysis, the overall beta-turn prediction accuracy yielded consistent results when tested on either the 30-protein data set (64.62%) used earlier or a more recent representative data set comprising 619 protein chains (64.66%) or on a jackknife data set comprising 476 representative protein chains (63.38%). We therefore recommend the use of probability values derived from the 425 representative protein chains data set reported here, which gives more realistic and consistent predictions of beta-turns from amino acid sequences.

Uterus segmentation in dynamic MRI using LBP texture descriptors

NASA Astrophysics Data System (ADS)

Namias, R.; Bellemare, M.-E.; Rahim, M.; Pirró, N.

2014-03-01

Pelvic floor disorders cover pathologies of which physiopathology is not well understood. However cases get prevalent with an ageing population. Within the context of a project aiming at modelization of the dynamics of pelvic organs, we have developed an efficient segmentation process. It aims at alleviating the radiologist with a tedious one by one image analysis. From a first contour delineating the uterus-vagina set, the organ border is tracked along a dynamic mri sequence. The process combines movement prediction, local intensity and texture analysis and active contour geometry control. Movement prediction allows a contour intitialization for next image in the sequence. Intensity analysis provides image-based local contour detection enhanced by local binary pattern (lbp) texture descriptors. Geometry control prohibits self intersections and smoothes the contour. Results show the efficiency of the method with images produced in clinical routine.

Genome sequence analysis of predicted polyprenol reductase gene from mangrove plant kandelia obovata

NASA Astrophysics Data System (ADS)

Basyuni, M.; Sagami, H.; Baba, S.; Oku, H.

2018-03-01

It has been previously reported that dolichols but not polyprenols were predominated in mangrove leaves and roots. Therefore, the occurrence of larger amounts of dolichol in leaves of mangrove plants implies that polyprenol reductase is responsible for the conversion of polyprenol to dolichol may be active in mangrove leaves. Here we report the early assessment of probably polyprenol reductase gene from genome sequence of mangrove plant Kandelia obovata. The functional assignment of the gene was based on a homology search of the sequences against the non-redundant (nr) peptide database of NCBI using Blastx. The degree of sequence identity between DNA sequence and known polyprenol reductase was confirmed using the Blastx probability E-value, total score, and identity. The genome sequence data resulted in three partial sequences, termed c23157 (700 bp), c23901 (960 bp), and c24171 (531 bp). The c23157 gene showed the highest similarity (61%) to predicted polyprenol reductase 2- like from Gossypium raimondii with E-value 2e-100. The second gene was c23901 to exhibit high similarity (78%) to the steroid 5-alpha-reductase Det2 from J. curcas with E-value 2e-140. Furthermore, the c24171 gene depicted highest similarity (79%) to the polyprenol reductase 2 isoform X1 from Jatropha curcas with E- value 7e-21.The present study suggested that the c23157, c23901, and c24171, genes may encode predicted polyprenol reductase. The c23157, c23901, c24171 are therefore the new type of predicted polyprenol reductase from K. obovata.

Analysis of full-length sequences of two Citrus yellow mosaic badnavirus isolates infecting Citrus jambhiri (Rough Lemon) and Citrus sinensis L. Osbeck (Sweet Orange) from a nursery in India.

PubMed

Anthony Johnson, A M; Borah, B K; Sai Gopal, D V R; Dasgupta, I

2012-12-01

Citrus yellow mosaic badna virus (CMBV), a member of the Family Caulimoviridae, Genus Badnavirus is the causative agent of mosaic disease among Citrus species in southern India. Despite its reported prevalence in several citrus species, complete information on clear functional genomics or functional information of full-length genomes from all the CMBV isolates infecting citrus species are not available in publicly accessible databases. CMBV isolates from Rough Lemon and Sweet Orange collected from a nursery were cloned and sequenced. The analysis revealed high sequence homology of the two CMBV isolates with previously reported CMBV sequences implying that they represent new variants. Based on computational analysis of the predicted secondary structures, the possible functions of some CMBV proteins have been analyzed.

Investigation of Microbial Diversity in Geothermal Hot Springs in Unkeshwar, India, Based on 16S rRNA Amplicon Metagenome Sequencing

PubMed Central

Mehetre, Gajanan T.; Paranjpe, Aditi; Dastager, Syed G.

2016-01-01

Microbial diversity in geothermal waters of the Unkeshwar hot springs in Maharashtra, India, was studied using 16S rRNA amplicon metagenomic sequencing. Taxonomic analysis revealed the presence of Bacteroidetes, Proteobacteria, Cyanobacteria, Actinobacteria, Archeae, and OD1 phyla. Metabolic function prediction analysis indicated a battery of biological information systems indicating rich and novel microbial diversity, with potential biotechnological applications in this niche. PMID:26950332

Graph pyramids for protein function prediction

PubMed Central

2015-01-01

Background Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Methods Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Results Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data. PMID:26044522

Graph pyramids for protein function prediction.

PubMed

Sandhan, Tushar; Yoo, Youngjun; Choi, Jin; Kim, Sun

2015-01-01

Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data.

The Salmonella In Silico Typing Resource (SISTR): An Open Web-Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies.

PubMed

Yoshida, Catherine E; Kruczkiewicz, Peter; Laing, Chad R; Lingohr, Erika J; Gannon, Victor P J; Nash, John H E; Taboada, Eduardo N

2016-01-01

For nearly 100 years serotyping has been the gold standard for the identification of Salmonella serovars. Despite the increasing adoption of DNA-based subtyping approaches, serotype information remains a cornerstone in food safety and public health activities aimed at reducing the burden of salmonellosis. At the same time, recent advances in whole-genome sequencing (WGS) promise to revolutionize our ability to perform advanced pathogen characterization in support of improved source attribution and outbreak analysis. We present the Salmonella In Silico Typing Resource (SISTR), a bioinformatics platform for rapidly performing simultaneous in silico analyses for several leading subtyping methods on draft Salmonella genome assemblies. In addition to performing serovar prediction by genoserotyping, this resource integrates sequence-based typing analyses for: Multi-Locus Sequence Typing (MLST), ribosomal MLST (rMLST), and core genome MLST (cgMLST). We show how phylogenetic context from cgMLST analysis can supplement the genoserotyping analysis and increase the accuracy of in silico serovar prediction to over 94.6% on a dataset comprised of 4,188 finished genomes and WGS draft assemblies. In addition to allowing analysis of user-uploaded whole-genome assemblies, the SISTR platform incorporates a database comprising over 4,000 publicly available genomes, allowing users to place their isolates in a broader phylogenetic and epidemiological context. The resource incorporates several metadata driven visualizations to examine the phylogenetic, geospatial and temporal distribution of genome-sequenced isolates. As sequencing of Salmonella isolates at public health laboratories around the world becomes increasingly common, rapid in silico analysis of minimally processed draft genome assemblies provides a powerful approach for molecular epidemiology in support of public health investigations. Moreover, this type of integrated analysis using multiple sequence-based methods of sub-typing allows for continuity with historical serotyping data as we transition towards the increasing adoption of genomic analyses in epidemiology. The SISTR platform is freely available on the web at https://lfz.corefacility.ca/sistr-app/.

Bringing the fathead minnow into the genomic era | Science ...

EPA Pesticide Factsheets

The fathead minnow is a well-established ecotoxicological model organism that has been widely used for regulatory ecotoxicity testing and research for over a half century. While a large amount of molecular information has been gathered on the fathead minnow over the years, the lack of genomic sequence data has limited the utility of the fathead minnow for certain applications. To address this limitation, high-throughput Illumina sequencing technology was employed to sequence the fathead minnow genome. Approximately 100X coverage was achieved by sequencing several libraries of paired-end reads with differing genome insert sizes. Two draft genome assemblies were generated using the SOAPdenovo and String Graph Assembler (SGA) methods, respectively. When these were compared, the SOAPdenovo assembly had a higher scaffold N50 value of 60.4 kbp versus 15.4 kbp, and it also performed better in a Core Eukaryotic Genes Mapping Analysis (CEGMA), mapping 91% versus 67% of genes. As such, this assembly was selected for further development and annotation. The foundation for genome annotation was generated using AUGUSTUS, an ab initio method for gene prediction. A total of 43,345 potential coding sequences were predicted on the genome assembly. These predicted sequences were translated to peptides and queried in a BLAST search against all vertebrates, with 28,290 of these sequences corresponding to zebrafish peptides and 5,242 producing no significant alignments. Additional ty

Gross deletions in TCOF1 are a cause of Treacher–Collins–Franceschetti syndrome

PubMed Central

Bowman, Michael; Oldridge, Michael; Archer, Caroline; O'Rourke, Anthony; McParland, Joanna; Brekelmans, Roel; Seller, Anneke; Lester, Tracy

2012-01-01

Treacher–Collins–Franceschetti syndrome (TCS) is an autosomal dominant craniofacial disorder characterised by midface hypoplasia, micrognathia, downslanting palpebral fissures, eyelid colobomata, and ear deformities that often lead to conductive deafness. A total of 182 patients with signs consistent with a diagnosis of TCS were screened by DNA sequence and dosage analysis of the TCOF1 gene. In all, 92 cases were found to have a pathogenic mutation by sequencing and 5 to have a partial gene deletion. A further case had a novel in-frame deletion in the alternatively spliced exon 6A of uncertain pathogenicity. The majority of the pathogenic sequence changes were found to predict premature protein termination, however, four novel missense changes in the LIS1 homology motif at the 5′ end of the gene were identified. The partial gene deletions of different sizes represent ∼5.2% of all the pathogenic TCOF1 mutations identified, indicating that gene rearrangements account for a significant proportion of TCS cases. This is the first report of gene rearrangements resulting in TCS. These findings expand the TCOF1 mutation spectrum indicating that dosage analysis should be performed together with sequence analysis, a strategy that is predicted to have a sensitivity of 71% for patients in whom TCS is strongly suspected. PMID:22317976

Gross deletions in TCOF1 are a cause of Treacher-Collins-Franceschetti syndrome.

PubMed

Bowman, Michael; Oldridge, Michael; Archer, Caroline; O'Rourke, Anthony; McParland, Joanna; Brekelmans, Roel; Seller, Anneke; Lester, Tracy

2012-07-01

Treacher-Collins-Franceschetti syndrome (TCS) is an autosomal dominant craniofacial disorder characterised by midface hypoplasia, micrognathia, downslanting palpebral fissures, eyelid colobomata, and ear deformities that often lead to conductive deafness. A total of 182 patients with signs consistent with a diagnosis of TCS were screened by DNA sequence and dosage analysis of the TCOF1 gene. In all, 92 cases were found to have a pathogenic mutation by sequencing and 5 to have a partial gene deletion. A further case had a novel in-frame deletion in the alternatively spliced exon 6A of uncertain pathogenicity. The majority of the pathogenic sequence changes were found to predict premature protein termination, however, four novel missense changes in the LIS1 homology motif at the 5' end of the gene were identified. The partial gene deletions of different sizes represent ~5.2% of all the pathogenic TCOF1 mutations identified, indicating that gene rearrangements account for a significant proportion of TCS cases. This is the first report of gene rearrangements resulting in TCS. These findings expand the TCOF1 mutation spectrum indicating that dosage analysis should be performed together with sequence analysis, a strategy that is predicted to have a sensitivity of 71% for patients in whom TCS is strongly suspected.

Structure Prediction and Analysis of Neuraminidase Sequence Variants

ERIC Educational Resources Information Center

Thayer, Kelly M.

2016-01-01

Analyzing protein structure has become an integral aspect of understanding systems of biochemical import. The laboratory experiment endeavors to introduce protein folding to ascertain structures of proteins for which the structure is unavailable, as well as to critically evaluate the quality of the prediction obtained. The model system used is the…

Draft Sequencing of the Heterozygous Diploid Genome of Satsuma (Citrus unshiu Marc.) Using a Hybrid Assembly Approach

PubMed Central

Shimizu, Tokurou; Tanizawa, Yasuhiro; Mochizuki, Takako; Nagasaki, Hideki; Yoshioka, Terutaka; Toyoda, Atsushi; Fujiyama, Asao; Kaminuma, Eli; Nakamura, Yasukazu

2017-01-01

Satsuma (Citrus unshiu Marc.) is one of the most abundantly produced mandarin varieties of citrus, known for its seedless fruit production and as a breeding parent of citrus. De novo assembly of the heterozygous diploid genome of Satsuma (“Miyagawa Wase”) was conducted by a hybrid assembly approach using short-read sequences, three mate-pair libraries, and a long-read sequence of PacBio by the PLATANUS assembler. The assembled sequence, with a total size of 359.7 Mb at the N50 length of 386,404 bp, consisted of 20,876 scaffolds. Pseudomolecules of Satsuma constructed by aligning the scaffolds to three genetic maps showed genome-wide synteny to the genomes of Clementine, pummelo, and sweet orange. Gene prediction by modeling with MAKER-P proposed 29,024 genes and 37,970 mRNA; additionally, gene prediction analysis found candidates for novel genes in several biosynthesis pathways for gibberellin and violaxanthin catabolism. BUSCO scores for the assembled scaffold and predicted transcripts, and another analysis by BAC end sequence mapping indicated the assembled genome consistency was close to those of the haploid Clementine, pummel, and sweet orange genomes. The number of repeat elements and long terminal repeat retrotransposon were comparable to those of the seven citrus genomes; this suggested no significant failure in the assembly at the repeat region. A resequencing application using the assembled sequence confirmed that both kunenbo-A and Satsuma are offsprings of Kishu, and Satsuma is a back-crossed offspring of Kishu. These results illustrated the performance of the hybrid assembly approach and its ability to construct an accurate heterozygous diploid genome. PMID:29259619

Criteria for confirming sequence periodicity identified by Fourier transform analysis: application to GCR2, a candidate plant GPCR?

PubMed

Illingworth, Christopher J R; Parkes, Kevin E; Snell, Christopher R; Mullineaux, Philip M; Reynolds, Christopher A

2008-03-01

Methods to determine periodicity in protein sequences are useful for inferring function. Fourier transformation is one approach but care is required to ensure the periodicity is genuine. Here we have shown that empirically-derived statistical tables can be used as a measure of significance. Genuine protein sequences data rather than randomly generated sequences were used as the statistical backdrop. The method has been applied to G-protein coupled receptor (GPCR) sequences, by Fourier transformation of hydrophobicity values, codon frequencies and the extent of over-representation of codon pairs; the latter being related to translational step times. Genuine periodicity was observed in the hydrophobicity whereas the apparent periodicity (as inferred from previously reported measures) in the translation step times was not validated statistically. GCR2 has recently been proposed as the plant GPCR receptor for the hormone abscisic acid. It has homology to the Lanthionine synthetase C-like family of proteins, an observation confirmed by fold recognition. Application of the Fourier transform algorithm to the GCR2 family revealed strongly predicted seven fold periodicity in hydrophobicity, suggesting why GCR2 has been reported to be a GPCR, despite negative indications in most transmembrane prediction algorithms. The underlying multiple sequence alignment, also required for the Fourier transform analysis of periodicity, indicated that the hydrophobic regions around the 7 GXXG motifs commence near the C-terminal end of each of the 7 inner helices of the alpha-toroid and continue to the N-terminal region of the helix. The results clearly explain why GCR2 has been understandably but erroneously predicted to be a GPCR.

Draft Sequencing of the Heterozygous Diploid Genome of Satsuma (Citrus unshiu Marc.) Using a Hybrid Assembly Approach.

PubMed

Shimizu, Tokurou; Tanizawa, Yasuhiro; Mochizuki, Takako; Nagasaki, Hideki; Yoshioka, Terutaka; Toyoda, Atsushi; Fujiyama, Asao; Kaminuma, Eli; Nakamura, Yasukazu

2017-01-01

Satsuma ( Citrus unshiu Marc.) is one of the most abundantly produced mandarin varieties of citrus, known for its seedless fruit production and as a breeding parent of citrus. De novo assembly of the heterozygous diploid genome of Satsuma ("Miyagawa Wase") was conducted by a hybrid assembly approach using short-read sequences, three mate-pair libraries, and a long-read sequence of PacBio by the PLATANUS assembler. The assembled sequence, with a total size of 359.7 Mb at the N 50 length of 386,404 bp, consisted of 20,876 scaffolds. Pseudomolecules of Satsuma constructed by aligning the scaffolds to three genetic maps showed genome-wide synteny to the genomes of Clementine, pummelo, and sweet orange. Gene prediction by modeling with MAKER-P proposed 29,024 genes and 37,970 mRNA; additionally, gene prediction analysis found candidates for novel genes in several biosynthesis pathways for gibberellin and violaxanthin catabolism. BUSCO scores for the assembled scaffold and predicted transcripts, and another analysis by BAC end sequence mapping indicated the assembled genome consistency was close to those of the haploid Clementine, pummel, and sweet orange genomes. The number of repeat elements and long terminal repeat retrotransposon were comparable to those of the seven citrus genomes; this suggested no significant failure in the assembly at the repeat region. A resequencing application using the assembled sequence confirmed that both kunenbo-A and Satsuma are offsprings of Kishu, and Satsuma is a back-crossed offspring of Kishu. These results illustrated the performance of the hybrid assembly approach and its ability to construct an accurate heterozygous diploid genome.

Computational prediction of over-annotated protein-coding genes in the genome of Agrobacterium tumefaciens strain C58

NASA Astrophysics Data System (ADS)

Yu, Jia-Feng; Sui, Tian-Xiang; Wang, Hong-Mei; Wang, Chun-Ling; Jing, Li; Wang, Ji-Hua

2015-12-01

Agrobacterium tumefaciens strain C58 is a type of pathogen that can cause tumors in some dicotyledonous plants. Ever since the genome of A. tumefaciens strain C58 was sequenced, the quality of annotation of its protein-coding genes has been queried continually, because the annotation varies greatly among different databases. In this paper, the questionable hypothetical genes were re-predicted by integrating the TN curve and Z curve methods. As a result, 30 genes originally annotated as “hypothetical” were discriminated as being non-coding sequences. By testing the re-prediction program 10 times on data sets composed of the function-known genes, the mean accuracy of 99.99% and mean Matthews correlation coefficient value of 0.9999 were obtained. Further sequence analysis and COG analysis showed that the re-annotation results were very reliable. This work can provide an efficient tool and data resources for future studies of A. tumefaciens strain C58. Project supported by the National Natural Science Foundation of China (Grant Nos. 61302186 and 61271378) and the Funding from the State Key Laboratory of Bioelectronics of Southeast University.

Sequence stratigraphy and sedimentary study on Mishrif formation of Fauqi Oilfield of Missan in south east Iraq

NASA Astrophysics Data System (ADS)

Sang, Hua; Lin, Changsong; Jiang, Yiming

2017-05-01

The reservoir of Mishrif formation has a large scale distribution of marine facies carbonate sediments in great thickness in central and south east Iraq. Rudist reef and shoal facies limestones of the Mishrif Formation (Late Cenomanian - Middle Turonian) form a great potential reservoir rocks at oilfields and structures of Iraq. Facies modelling was applied to predict the relationship between facies distribution and reservoir characteristics to construct a predictive geologic model which will assist future exploration and development in south east Iraq. Microfacies analysis and electrofacies identification and correlations indicate that the limestone of the Mishrif Formation were mainly deposited in open platform setting. Sequence stratigraphic analyses of the Mishrif Formation indicate 3 third order depositional sequences.

Characterization and prediction of residues determining protein functional specificity.

PubMed

Capra, John A; Singh, Mona

2008-07-01

Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular functional specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolutionary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/. Supplementary data are available at Bioinformatics online.

Identifying Novel Helix-Loop-Helix Genes in "Caenorhabditis elegans" through a Classroom Demonstration of Functional Genomics

ERIC Educational Resources Information Center

Griffin, Vernetta; McMiller, Tracee; Jones, Erika; Johnson, Casonya M.

2003-01-01

A 14-week, undergraduate-level Genetics and Population Biology course at Morgan State University was modified to include a demonstration of functional genomics in the research laboratory. Students performed a rudimentary sequence analysis of the "Caenorhabditis elegans" genome and further characterized three sequences that were predicted to encode…

Simulation for Prediction of Entry Article Demise (SPEAD): An Analysis Tool for Spacecraft Safety Analysis and Ascent/Reentry Risk Assessment

NASA Technical Reports Server (NTRS)

Ling, Lisa

2014-01-01

For the purpose of performing safety analysis and risk assessment for a potential off-nominal atmospheric reentry resulting in vehicle breakup, a synthesis of trajectory propagation coupled with thermal analysis and the evaluation of node failure is required to predict the sequence of events, the timeline, and the progressive demise of spacecraft components. To provide this capability, the Simulation for Prediction of Entry Article Demise (SPEAD) analysis tool was developed. The software and methodology have been validated against actual flights, telemetry data, and validated software, and safety/risk analyses were performed for various programs using SPEAD. This report discusses the capabilities, modeling, validation, and application of the SPEAD analysis tool.

Mediators of exposure therapy for youth obsessive-compulsive disorder: specificity and temporal sequence of client and treatment factors.

PubMed

Chu, Brian C; Colognori, Daniela B; Yang, Guang; Xie, Min-ge; Lindsey Bergman, R; Piacentini, John

2015-05-01

Behavioral engagement and cognitive coping have been hypothesized to mediate effectiveness of exposure-based therapies. Identifying which specific child factors mediate successful therapy and which therapist factors facilitate change can help make our evidence-based treatments more efficient and robust. The current study examines the specificity and temporal sequence of relations among hypothesized client and therapist mediators in exposure therapy for pediatric Obsessive Compulsive Disorder (OCD). Youth coping (cognitive, behavioral), youth safety behaviors (avoidance, escape, compulsive behaviors), therapist interventions (cognitive, exposure extensiveness), and youth anxiety were rated via observational ratings of therapy sessions of OCD youth (N=43; ages=8 - 17; 62.8% male) who had received Exposure and Response Prevention (ERP). Regression analysis using Generalized Estimation Equations and cross-lagged panel analysis (CLPA) were conducted to model anxiety change within and across sessions, to determine formal mediators of anxiety change, and to establish sequence of effects. Anxiety ratings decreased linearly across exposures within sessions. Youth coping and therapist interventions significantly mediated anxiety change across exposures, and youth-interfering behavior mediated anxiety change at the trend level. In CLPA, youth-interfering behaviors predicted, and were predicted by, changes in anxiety. Youth coping was predicted by prior anxiety change. The study provides a preliminary examination of specificity and temporal sequence among child and therapist behaviors in predicting youth anxiety. Results suggest that therapists should educate clients in the natural rebound effects of anxiety between sessions and should be aware of the negatively reinforcing properties of avoidance during exposure. Copyright © 2015. Published by Elsevier Ltd.

Introduction on Using the FastPCR Software and the Related Java Web Tools for PCR and Oligonucleotide Assembly and Analysis.

PubMed

Kalendar, Ruslan; Tselykh, Timofey V; Khassenov, Bekbolat; Ramanculov, Erlan M

2017-01-01

This chapter introduces the FastPCR software as an integrated tool environment for PCR primer and probe design, which predicts properties of oligonucleotides based on experimental studies of the PCR efficiency. The software provides comprehensive facilities for designing primers for most PCR applications and their combinations. These include the standard PCR as well as the multiplex, long-distance, inverse, real-time, group-specific, unique, overlap extension PCR for multi-fragments assembling cloning and loop-mediated isothermal amplification (LAMP). It also contains a built-in program to design oligonucleotide sets both for long sequence assembly by ligase chain reaction and for design of amplicons that tile across a region(s) of interest. The software calculates the melting temperature for the standard and degenerate oligonucleotides including locked nucleic acid (LNA) and other modifications. It also provides analyses for a set of primers with the prediction of oligonucleotide properties, dimer and G/C-quadruplex detection, linguistic complexity as well as a primer dilution and resuspension calculator. The program consists of various bioinformatical tools for analysis of sequences with the GC or AT skew, CG% and GA% content, and the purine-pyrimidine skew. It also analyzes the linguistic sequence complexity and performs generation of random DNA sequence as well as restriction endonucleases analysis. The program allows to find or create restriction enzyme recognition sites for coding sequences and supports the clustering of sequences. It performs efficient and complete detection of various repeat types with visual display. The FastPCR software allows the sequence file batch processing that is essential for automation. The program is available for download at http://primerdigital.com/fastpcr.html , and its online version is located at http://primerdigital.com/tools/pcr.html .

Rapid diagnosis of pyrazinamide-resistant multidrug-resistant tuberculosis using a molecular-based diagnostic algorithm.

PubMed

Simons, S O; van der Laan, T; Mulder, A; van Ingen, J; Rigouts, L; Dekhuijzen, P N R; Boeree, M J; van Soolingen, D

2014-10-01

There is an urgent need for rapid and accurate diagnosis of pyrazinamide-resistant multidrug-resistant tuberculosis (MDR-TB). No diagnostic algorithm has been validated in this population. We hypothesized that pncA sequencing added to rpoB mutation analysis can accurately identify patients with pyrazinamide-resistant MDR-TB. We identified from the Dutch national database (2007-11) patients with a positive Mycobacterium tuberculosis culture containing a mutation in the rpoB gene. In these cases, we prospectively sequenced the pncA gene. Results from the rpoB and pncA mutation analysis (pncA added to rpoB) were compared with phenotypic susceptibility testing results to rifampicin, isoniazid and pyrazinamide (reference standard) using the Mycobacterial Growth Indicator Tube 960 system. We included 83 clinical M. tuberculosis isolates containing rpoB mutations in the primary analysis. Rifampicin resistance was seen in 72 isolates (87%), isoniazid resistance in 73 isolates (88%) and MDR-TB in 65 isolates (78%). Phenotypic reference testing identified pyrazinamide-resistant MDR-TB in 31 isolates (48%). Sensitivity of pncA sequencing added to rpoB mutation analysis for detecting pyrazinamide-resistant MDR-TB was 96.8%, the specificity was 94.2%, the positive predictive value was 90.9%, the negative predictive value was 98.0%, the positive likelihood was 16.8 and the negative likelihood was 0.03. In conclusion, pyrazinamide-resistant MDR-TB can be accurately detected using pncA sequencing added to rpoB mutation analysis. We propose to include pncA sequencing in every isolate with an rpoB mutation, allowing for stratification of MDR-TB treatment according to pyrazinamide susceptibility. © 2014 The Authors Clinical Microbiology and Infection © 2014 European Society of Clinical Microbiology and Infectious Diseases.

Identification, cloning and sequencing of Escherichia coli strain chi1378 (O78:K80) iss gene isolated from poultry colibacillosis in Iran.

PubMed

Derakhshandeh, A; Zahraei Salehi, T; Tadjbakhsh, H; Karimi, V

2009-09-01

To identify, clone and sequence the iss (increased serum survival) gene from E. coli strain chi1378 isolated from Iranian poultry and to predict its protein product, Iss. The iss gene from E. coli strain chi1378 was amplified and cloned into the pTZ57R/T vector and sequenced. From the DNA sequence, the Iss predictive protein was evaluated using bioinformatics. Iss from strain chi1378 had 100% identity with other E. coli serotypes and isolates from different origins and also 98% identity with E. coli O157:H7 Iss protein. Phylogenetic analysis showed no significant different phylogenic groups among E. coli strains. The strong association of predicted Iss protein among different E. coli strains suggests that it could be a good antigen to control and detect avian pathogenic E. coli (APEC). Because the exact pathogenesis and the role of virulence factors are unknown, the Iss protein could be used as a target for vaccination in the future, but further research is required.

Information theory applications for biological sequence analysis.

PubMed

Vinga, Susana

2014-05-01

Information theory (IT) addresses the analysis of communication systems and has been widely applied in molecular biology. In particular, alignment-free sequence analysis and comparison greatly benefited from concepts derived from IT, such as entropy and mutual information. This review covers several aspects of IT applications, ranging from genome global analysis and comparison, including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis, comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles. IT has also been applied to high-level correlations that combine DNA, RNA or protein features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes. While not exhaustive, this review attempts to categorize existing methods and to indicate their relation with broader transversal topics such as genomic signatures, data compression and complexity, time series analysis and phylogenetic classification, providing a resource for future developments in this promising area.

Identification of human chromosome 22 transcribed sequences with ORF expressed sequence tags

PubMed Central

de Souza, Sandro J.; Camargo, Anamaria A.; Briones, Marcelo R. S.; Costa, Fernando F.; Nagai, Maria Aparecida; Verjovski-Almeida, Sergio; Zago, Marco A.; Andrade, Luis Eduardo C.; Carrer, Helaine; El-Dorry, Hamza F. A.; Espreafico, Enilza M.; Habr-Gama, Angelita; Giannella-Neto, Daniel; Goldman, Gustavo H.; Gruber, Arthur; Hackel, Christine; Kimura, Edna T.; Maciel, Rui M. B.; Marie, Suely K. N.; Martins, Elizabeth A. L.; Nóbrega, Marina P.; Paçó-Larson, Maria Luisa; Pardini, Maria Inês M. C.; Pereira, Gonçalo G.; Pesquero, João Bosco; Rodrigues, Vanderlei; Rogatto, Silvia R.; da Silva, Ismael D. C. G.; Sogayar, Mari C.; de Fátima Sonati, Maria; Tajara, Eloiza H.; Valentini, Sandro R.; Acencio, Marcio; Alberto, Fernando L.; Amaral, Maria Elisabete J.; Aneas, Ivy; Bengtson, Mário Henrique; Carraro, Dirce M.; Carvalho, Alex F.; Carvalho, Lúcia Helena; Cerutti, Janete M.; Corrêa, Maria Lucia C.; Costa, Maria Cristina R.; Curcio, Cyntia; Gushiken, Tsieko; Ho, Paulo L.; Kimura, Elza; Leite, Luciana C. C.; Maia, Gustavo; Majumder, Paromita; Marins, Mozart; Matsukuma, Adriana; Melo, Analy S. A.; Mestriner, Carlos Alberto; Miracca, Elisabete C.; Miranda, Daniela C.; Nascimento, Ana Lucia T. O.; Nóbrega, Francisco G.; Ojopi, Élida P. B.; Pandolfi, José Rodrigo C.; Pessoa, Luciana Gilbert; Rahal, Paula; Rainho, Claudia A.; da Ro's, Nancy; de Sá, Renata G.; Sales, Magaly M.; da Silva, Neusa P.; Silva, Tereza C.; da Silva, Wilson; Simão, Daniel F.; Sousa, Josane F.; Stecconi, Daniella; Tsukumo, Fernando; Valente, Valéria; Zalcberg, Heloisa; Brentani, Ricardo R.; Reis, Luis F. L.; Dias-Neto, Emmanuel; Simpson, Andrew J. G.

2000-01-01

Transcribed sequences in the human genome can be identified with confidence only by alignment with sequences derived from cDNAs synthesized from naturally occurring mRNAs. We constructed a set of 250,000 cDNAs that represent partial expressed gene sequences and that are biased toward the central coding regions of the resulting transcripts. They are termed ORF expressed sequence tags (ORESTES). The 250,000 ORESTES were assembled into 81,429 contigs. Of these, 1,181 (1.45%) were found to match sequences in chromosome 22 with at least one ORESTES contig for 162 (65.6%) of the 247 known genes, for 67 (44.6%) of the 150 related genes, and for 45 of the 148 (30.4%) EST-predicted genes on this chromosome. Using a set of stringent criteria to validate our sequences, we identified a further 219 previously unannotated transcribed sequences on chromosome 22. Of these, 171 were in fact also defined by EST or full length cDNA sequences available in GenBank but not utilized in the initial annotation of the first human chromosome sequence. Thus despite representing less than 15% of all expressed human sequences in the public databases at the time of the present analysis, ORESTES sequences defined 48 transcribed sequences on chromosome 22 not defined by other sequences. All of the transcribed sequences defined by ORESTES coincided with DNA regions predicted as encoding exons by genscan. (http://genes.mit.edu/GENSCAN.html). PMID:11070084

Nucleotide sequence and regulatory studies of VGF, a nervous system-specific mRNA that is rapidly and relatively selectively induced by nerve growth factor.

PubMed

Salton, S R

1991-09-01

A nervous system-specific mRNA that is rapidly induced in PC12 cells to a greater extent by nerve growth factor (NGF) than by epidermal growth factor treatment has been cloned. The polypeptide deduced from the nucleic acid sequence of the NGF33.1 cDNA clone contains regions of amino acid sequence identity with that predicted by the cDNA clone VGF, and further analysis suggests that both NGF33.1 and VGF cDNA clones very likely correspond to the same mRNA (VGF). In this report both the nucleic acid sequence that corresponds to VGF mRNA and the polypeptide predicted by the NGF33.1 cDNA clone are presented. Genomic Southern analysis and database comparison did not detect additional sequences with high homology to the VGF gene. Induction of VGF mRNA by depolarization and phorbol 12-myristate 13-acetate treatment was greater than by serum stimulation or protein kinase A pathway activation. These studies suggest that VGF mRNA is induced to the greatest extent by NGF treatment and that VGF is one of the most rapidly regulated neuronal mRNAs identified in PC12 cells.

Integrating protein structural dynamics and evolutionary analysis with Bio3D.

PubMed

Skjærven, Lars; Yao, Xin-Qiu; Scarabelli, Guido; Grant, Barry J

2014-12-10

Popular bioinformatics approaches for studying protein functional dynamics include comparisons of crystallographic structures, molecular dynamics simulations and normal mode analysis. However, determining how observed displacements and predicted motions from these traditionally separate analyses relate to each other, as well as to the evolution of sequence, structure and function within large protein families, remains a considerable challenge. This is in part due to the general lack of tools that integrate information of molecular structure, dynamics and evolution. Here, we describe the integration of new methodologies for evolutionary sequence, structure and simulation analysis into the Bio3D package. This major update includes unique high-throughput normal mode analysis for examining and contrasting the dynamics of related proteins with non-identical sequences and structures, as well as new methods for quantifying dynamical couplings and their residue-wise dissection from correlation network analysis. These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis. New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included. We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case. The integration of structural dynamics and evolutionary analysis in Bio3D enables researchers to go beyond a prediction of single protein dynamics to investigate dynamical features across large protein families. The Bio3D package is distributed with full source code and extensive documentation as a platform independent R package under a GPL2 license from http://thegrantlab.org/bio3d/ .

On Burst Detection and Prediction in Retweeting Sequence

DTIC Science & Technology

2015-05-22

We conduct a comprehensive empirical analysis of a large microblogging dataset collected from the Sina Weibo and report our observations of burst...whether and how accurate we can predict bursts using classifiers based on the extracted features. Our empirical study of the Sina Weibo data shows the...feasibility of burst prediction using appropriately extracted features and classic classifiers. 1 Introduction Microblogging, such as Twitter and Sina

Investigation of Microbial Diversity in Geothermal Hot Springs in Unkeshwar, India, Based on 16S rRNA Amplicon Metagenome Sequencing.

PubMed

Mehetre, Gajanan T; Paranjpe, Aditi; Dastager, Syed G; Dharne, Mahesh S

2016-02-25

Microbial diversity in geothermal waters of the Unkeshwar hot springs in Maharashtra, India, was studied using 16S rRNA amplicon metagenomic sequencing. Taxonomic analysis revealed the presence of Bacteroidetes, Proteobacteria, Cyanobacteria, Actinobacteria, Archeae, and OD1 phyla. Metabolic function prediction analysis indicated a battery of biological information systems indicating rich and novel microbial diversity, with potential biotechnological applications in this niche. Copyright © 2016 Mehetre et al.

Using RSAT to scan genome sequences for transcription factor binding sites and cis-regulatory modules.

PubMed

Turatsinze, Jean-Valery; Thomas-Chollier, Morgane; Defrance, Matthieu; van Helden, Jacques

2008-01-01

This protocol shows how to detect putative cis-regulatory elements and regions enriched in such elements with the regulatory sequence analysis tools (RSAT) web server (http://rsat.ulb.ac.be/rsat/). The approach applies to known transcription factors, whose binding specificity is represented by position-specific scoring matrices, using the program matrix-scan. The detection of individual binding sites is known to return many false predictions. However, results can be strongly improved by estimating P value, and by searching for combinations of sites (homotypic and heterotypic models). We illustrate the detection of sites and enriched regions with a study case, the upstream sequence of the Drosophila melanogaster gene even-skipped. This protocol is also tested on random control sequences to evaluate the reliability of the predictions. Each task requires a few minutes of computation time on the server. The complete protocol can be executed in about one hour.

Using RSAT oligo-analysis and dyad-analysis tools to discover regulatory signals in nucleic sequences.

PubMed

Defrance, Matthieu; Janky, Rekin's; Sand, Olivier; van Helden, Jacques

2008-01-01

This protocol explains how to discover functional signals in genomic sequences by detecting over- or under-represented oligonucleotides (words) or spaced pairs thereof (dyads) with the Regulatory Sequence Analysis Tools (http://rsat.ulb.ac.be/rsat/). Two typical applications are presented: (i) predicting transcription factor-binding motifs in promoters of coregulated genes and (ii) discovering phylogenetic footprints in promoters of orthologous genes. The steps of this protocol include purging genomic sequences to discard redundant fragments, discovering over-represented patterns and assembling them to obtain degenerate motifs, scanning sequences and drawing feature maps. The main strength of the method is its statistical ground: the binomial significance provides an efficient control on the rate of false positives. In contrast with optimization-based pattern discovery algorithms, the method supports the detection of under- as well as over-represented motifs. Computation times vary from seconds (gene clusters) to minutes (whole genomes). The execution of the whole protocol should take approximately 1 h.

Structural protein descriptors in 1-dimension and their sequence-based predictions.

PubMed

Kurgan, Lukasz; Disfani, Fatemeh Miri

2011-09-01

The last few decades observed an increasing interest in development and application of 1-dimensional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods.

Protein Sectors: Statistical Coupling Analysis versus Conservation

PubMed Central

Teşileanu, Tiberiu; Colwell, Lucy J.; Leibler, Stanislas

2015-01-01

Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed “sectors”. The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation. PMID:25723535

Identifying functionally informative evolutionary sequence profiles.

PubMed

Gil, Nelson; Fiser, Andras

2018-04-15

Multiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually. We present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein-ligand (peptides, nucleic acids and small substrates) and protein-protein interaction databases. Availability and implementation: A freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git. andras.fiser@einstein.yu.edu. Supplementary data are available at Bioinformatics online.

Whole-Genome Thermodynamic Analysis Reduces siRNA Off-Target Effects

PubMed Central

Chen, Xi; Liu, Peng; Chou, Hui-Hsien

2013-01-01

Small interfering RNAs (siRNAs) are important tools for knocking down targeted genes, and have been widely applied to biological and biomedical research. To design siRNAs, two important aspects must be considered: the potency in knocking down target genes and the off-target effect on any nontarget genes. Although many studies have produced useful tools to design potent siRNAs, off-target prevention has mostly been delegated to sequence-level alignment tools such as BLAST. We hypothesize that whole-genome thermodynamic analysis can identify potential off-targets with higher precision and help us avoid siRNAs that may have strong off-target effects. To validate this hypothesis, two siRNA sets were designed to target three human genes IDH1, ITPR2 and TRIM28. They were selected from the output of two popular siRNA design tools, siDirect and siDesign. Both siRNA design tools have incorporated sequence-level screening to avoid off-targets, thus their output is believed to be optimal. However, one of the sets we tested has off-target genes predicted by Picky, a whole-genome thermodynamic analysis tool. Picky can identify off-target genes that may hybridize to a siRNA within a user-specified melting temperature range. Our experiments validated that some off-target genes predicted by Picky can indeed be inhibited by siRNAs. Similar experiments were performed using commercially available siRNAs and a few off-target genes were also found to be inhibited as predicted by Picky. In summary, we demonstrate that whole-genome thermodynamic analysis can identify off-target genes that are missed in sequence-level screening. Because Picky prediction is deterministic according to thermodynamics, if a siRNA candidate has no Picky predicted off-targets, it is unlikely to cause off-target effects. Therefore, we recommend including Picky as an additional screening step in siRNA design. PMID:23484018

PEA: an integrated R toolkit for plant epitranscriptome analysis.

PubMed

Zhai, Jingjing; Song, Jie; Cheng, Qian; Tang, Yunjia; Ma, Chuang

2018-05-29

The epitranscriptome, also known as chemical modifications of RNA (CMRs), is a newly discovered layer of gene regulation, the biological importance of which emerged through analysis of only a small fraction of CMRs detected by high-throughput sequencing technologies. Understanding of the epitranscriptome is hampered by the absence of computational tools for the systematic analysis of epitranscriptome sequencing data. In addition, no tools have yet been designed for accurate prediction of CMRs in plants, or to extend epitranscriptome analysis from a fraction of the transcriptome to its entirety. Here, we introduce PEA, an integrated R toolkit to facilitate the analysis of plant epitranscriptome data. The PEA toolkit contains a comprehensive collection of functions required for read mapping, CMR calling, motif scanning and discovery, and gene functional enrichment analysis. PEA also takes advantage of machine learning technologies for transcriptome-scale CMR prediction, with high prediction accuracy, using the Positive Samples Only Learning algorithm, which addresses the two-class classification problem by using only positive samples (CMRs), in the absence of negative samples (non-CMRs). Hence PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction, and annotation, and we describe its application to predict N6-methyladenosine (m6A) modifications in Arabidopsis thaliana. Experimental results demonstrate that the toolkit achieved 71.6% sensitivity and 73.7% specificity, which is superior to existing m6A predictors. PEA is potentially broadly applicable to the in-depth study of epitranscriptomics. PEA Docker image is available at https://hub.docker.com/r/malab/pea, source codes and user manual are available at https://github.com/cma2015/PEA. chuangma2006@gmail.com. Supplementary data are available at Bioinformatics online.

Predicting residue-wise contact orders in proteins by support vector regression.

PubMed

Song, Jiangning; Burrage, Kevin

2006-10-03

The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Predictive models of safety based on audit findings: Part 2: Measurement of model validity.

PubMed

Hsiao, Yu-Lin; Drury, Colin; Wu, Changxu; Paquet, Victor

2013-07-01

Part 1 of this study sequence developed a human factors/ergonomics (HF/E) based classification system (termed HFACS-MA) for safety audit findings and proved its measurement reliability. In Part 2, we used the human error categories of HFACS-MA as predictors of future safety performance. Audit records and monthly safety incident reports from two airlines submitted to their regulatory authority were available for analysis, covering over 6.5 years. Two participants derived consensus results of HF/E errors from the audit reports using HFACS-MA. We adopted Neural Network and Poisson regression methods to establish nonlinear and linear prediction models respectively. These models were tested for the validity of prediction of the safety data, and only Neural Network method resulted in substantially significant predictive ability for each airline. Alternative predictions from counting of audit findings and from time sequence of safety data produced some significant results, but of much smaller magnitude than HFACS-MA. The use of HF/E analysis of audit findings provided proactive predictors of future safety performance in the aviation maintenance field. Copyright © 2013 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Computational allergenicity prediction of transgenic proteins expressed in genetically modified crops.

PubMed

Verma, Alok Kumar; Misra, Amita; Subash, Swarna; Das, Mukul; Dwivedi, Premendra D

2011-09-01

Development of genetically modified (GM) crops is on increase to improve food quality, increase harvest yields, and reduce the dependency on chemical pesticides. Before their release in marketplace, they should be scrutinized for their safety. Several guidelines of different regulatory agencies like ILSI, WHO Codex, OECD, and so on for allergenicity evaluation of transgenics are available and sequence homology analysis is the first test to determine the allergenic potential of inserted proteins. Therefore, to test and validate, 312 allergenic, 100 non-allergenic, and 48 inserted proteins were assessed for sequence similarity using 8-mer, 80-mer, and full FASTA search. On performing sequence homology studies, ~94% the allergenic proteins gave exact matches for 8-mer and 80-mer homology. However, 20 allergenic proteins showed non-allergenic behavior. Out of 100 non-allergenic proteins, seven qualified as allergens. None of the inserted proteins demonstrated allergenic behavior. In order to improve the predictability, proteins showing anomalous behavior were tested by Algpred and ADFS separately. Use of Algpred and ADFS softwares reduced the tendency of false prediction to a great extent (74-78%). In conclusion, routine sequence homology needs to be coupled with some other bioinformatic method like ADFS/Algpred to reduce false allergenicity prediction of novel proteins.

Sequence and analysis of the genome of a baculovirus pathogenic for Lymantria dispar

Treesearch

John Kuzio; Margot N. Pearson; Steve H. Harwood; C. Joel Funk; Jay T. Evans; James M. Slavicek; George F. Rohrmann

1999-01-01

The genome of the Lymantria dispar multinucleocapsid nucleopolyhedrovirus (LdMNPV) was sequenced and analyzed. It is composed of 161,046 bases with a G + C content of 57.5% and contains 163 putative open reading frames (ORFs) of ≥150 nucleotides. Homologs were found to 95 of the 155 genes predicted for the Autographa californica...

Special Focus

PubMed Central

Nawrocki, Eric P.; Burge, Sarah W.

2013-01-01

The development of RNA bioinformatic tools began more than 30 y ago with the description of the Nussinov and Zuker dynamic programming algorithms for single sequence RNA secondary structure prediction. Since then, many tools have been developed for various RNA sequence analysis problems such as homology search, multiple sequence alignment, de novo RNA discovery, read-mapping, and many more. In this issue, we have collected a sampling of reviews and original research that demonstrate some of the many ways bioinformatics is integrated with current RNA biology research. PMID:23948768

Empirical analysis of RNA robustness and evolution using high-throughput sequencing of ribozyme reactions.

PubMed

Hayden, Eric J

2016-08-15

RNA molecules provide a realistic but tractable model of a genotype to phenotype relationship. This relationship has been extensively investigated computationally using secondary structure prediction algorithms. Enzymatic RNA molecules, or ribozymes, offer access to genotypic and phenotypic information in the laboratory. Advancements in high-throughput sequencing technologies have enabled the analysis of sequences in the lab that now rivals what can be accomplished computationally. This has motivated a resurgence of in vitro selection experiments and opened new doors for the analysis of the distribution of RNA functions in genotype space. A body of computational experiments has investigated the persistence of specific RNA structures despite changes in the primary sequence, and how this mutational robustness can promote adaptations. This article summarizes recent approaches that were designed to investigate the role of mutational robustness during the evolution of RNA molecules in the laboratory, and presents theoretical motivations, experimental methods and approaches to data analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

SIMAP--a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters.

PubMed

Rattei, Thomas; Tischler, Patrick; Götz, Stefan; Jehl, Marc-André; Hoser, Jonathan; Arnold, Roland; Conesa, Ana; Mewes, Hans-Werner

2010-01-01

The prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the similarity matrix, the computation of the Similarity Matrix of Proteins (SIMAP) becomes a computational intensive task. The SIMAP database provides a comprehensive and up-to-date pre-calculation of the protein sequence similarity matrix, sequence-based features and sequence clusters. As of September 2009, SIMAP covers 48 million proteins and more than 23 million non-redundant sequences. Novel features of SIMAP include the expansion of the sequence space by including databases such as ENSEMBL as well as the integration of metagenomes based on their consistent processing and annotation. Furthermore, protein function predictions by Blast2GO are pre-calculated for all sequences in SIMAP and the data access and query functions have been improved. SIMAP assists biologists to query the up-to-date sequence space systematically and facilitates large-scale downstream projects in computational biology. Access to SIMAP is freely provided through the web portal for individuals (http://mips.gsf.de/simap/) and for programmatic access through DAS (http://webclu.bio.wzw.tum.de/das/) and Web-Service (http://mips.gsf.de/webservices/services/SimapService2.0?wsdl).

BiPPred: Combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP.

PubMed

Schneider, Markus; Rosam, Mathias; Glaser, Manuel; Patronov, Atanas; Shah, Harpreet; Back, Katrin Christiane; Daake, Marina Angelika; Buchner, Johannes; Antes, Iris

2016-10-01

Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi-scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence-based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence-based prediction models were fitted using this and other peptide binding data. A structure-based position-specific scoring matrix (SB-PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB-PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA-based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi-scale pipeline can readily be applied to other protein-peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence-based prediction models is not available. Proteins 2016; 84:1390-1407. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Re-Assembly and Analysis of an Ancient Variola Virus Genome.

PubMed

Smithson, Chad; Imbery, Jacob; Upton, Chris

2017-09-08

We report a major improvement to the assembly of published short read sequencing data from an ancient variola virus (VARV) genome by the removal of contig-capping sequencing tags and manual searches for gap-spanning reads. The new assembly, together with camelpox and taterapox genomes, permitted new dates to be calculated for the last common ancestor of all VARV genomes. The analysis of recently sequenced VARV-like cowpox virus genomes showed that single nucleotide polymorphisms (SNPs) and amino acid changes in the vaccinia virus (VACV)-Cop-O1L ortholog, predicted to be associated with VARV host specificity and virulence, were introduced into the lineage before the divergence of these viruses. A comparison of the ancient and modern VARV genome sequences also revealed a measurable drift towards adenine + thymine (A + T) richness.

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

PubMed

Li, Ying Hong; Xu, Jing Yu; Tao, Lin; Li, Xiao Feng; Li, Shuang; Zeng, Xian; Chen, Shang Ying; Zhang, Peng; Qin, Chu; Zhang, Cheng; Chen, Zhe; Zhu, Feng; Chen, Yu Zong

2016-01-01

Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

[Complete genome sequencing of polymalic acid-producing strain Aureobasidium pullulans CCTCC M2012223].

PubMed

Wang, Yongkang; Song, Xiaodan; Li, Xiaorong; Yang, Sang-tian; Zou, Xiang

2017-01-04

To explore the genome sequence of Aureobasidium pullulans CCTCC M2012223, analyze the key genes related to the biosynthesis of important metabolites, and provide genetic background for metabolic engineering. Complete genome of A. pullulans CCTCC M2012223 was sequenced by Illumina HiSeq high throughput sequencing platform. Then, fragment assembly, gene prediction, functional annotation, and GO/COG cluster were analyzed in comparison with those of other five A. pullulans varieties. The complete genome sequence of A. pullulans CCTCC M2012223 was 30756831 bp with an average GC content of 47.49%, and 9452 genes were successfully predicted. Genome-wide analysis showed that A. pullulans CCTCC M2012223 had the biggest genome assembly size. Protein sequences involved in the pullulan and polymalic acid pathway were highly conservative in all of six A. pullulans varieties. Although both A. pullulans CCTCC M2012223 and A. pullulans var. melanogenum have a close affinity, some point mutation and inserts were occurred in protein sequences involved in melanin biosynthesis. Genome information of A. pullulans CCTCC M2012223 was annotated and genes involved in melanin, pullulan and polymalic acid pathway were compared, which would provide a theoretical basis for genetic modification of metabolic pathway in A. pullulans.

Variability and transmission by Aphis glycines of North American and Asian Soybean mosaic virus isolates.

PubMed

Domier, L L; Latorre, I J; Steinlage, T A; McCoppin, N; Hartman, G L

2003-10-01

The variability of North American and Asian strains and isolates of Soybean mosaic virus was investigated. First, polymerase chain reaction (PCR) products representing the coat protein (CP)-coding regions of 38 SMVs were analyzed for restriction fragment length polymorphisms (RFLP). Second, the nucleotide and predicted amino acid sequence variability of the P1-coding region of 18 SMVs and the helper component/protease (HC/Pro) and CP-coding regions of 25 SMVs were assessed. The CP nucleotide and predicted amino acid sequences were the most similar and predicted phylogenetic relationships similar to those obtained from RFLP analysis. Neither RFLP nor sequence analyses of the CP-coding regions grouped the SMVs by geographical origin. The P1 and HC/Pro sequences were more variable and separated the North American and Asian SMV isolates into two groups similar to previously reported differences in pathogenic diversity of the two sets of SMV isolates. The P1 region was the most informative of the three regions analyzed. To assess the biological relevance of the sequence differences in the HC/Pro and CP coding regions, the transmissibility of 14 SMV isolates by Aphis glycines was tested. All field isolates of SMV were transmitted efficiently by A. glycines, but the laboratory isolates analyzed were transmitted poorly. The amino acid sequences from most, but not all, of the poorly transmitted isolates contained mutations in the aphid transmission-associated DAG and/or KLSC amino acid sequence motifs of CP and HC/Pro, respectively.

Hydrophobic cluster analysis of G protein-coupled receptors: a powerful tool to derive structural and functional information from 2D-representation of protein sequences.

PubMed

Lentes, K U; Mathieu, E; Bischoff, R; Rasmussen, U B; Pavirani, A

1993-01-01

Current methods for comparative analyses of protein sequences are 1D-alignments of amino acid sequences based on the maximization of amino acid identity (homology) and the prediction of secondary structure elements. This method has a major drawback once the amino acid identity drops below 20-25%, since maximization of a homology score does not take into account any structural information. A new technique called Hydrophobic Cluster Analysis (HCA) has been developed by Lemesle-Varloot et al. (Biochimie 72, 555-574), 1990). This consists of comparing several sequences simultaneously and combining homology detection with secondary structure analysis. HCA is primarily based on the detection and comparison of structural segments constituting the hydrophobic core of globular protein domains, with or without transmembrane domains. We have applied HCA to the analysis of different families of G-protein coupled receptors, such as catecholamine receptors as well as peptide hormone receptors. Utilizing HCA the thrombin receptor, a new and as yet unique member of the family of G-protein coupled receptors, can be clearly classified as being closely related to the family of neuropeptide receptors rather than to the catecholamine receptors for which the shape of the hydrophobic clusters and the length of their third cytoplasmic loop are very different. Furthermore, the potential of HCA to predict relationships between new putative and already characterized members of this family of receptors will be presented.

Identification of tissue-specific targeting peptide

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Lee, Nam Kyung; Kang, Sang-Kee; Choi, Seung-Hoon; Kim, Daejin; Park, Kisoo; Choi, Kihang; Choi, Yun-Jaie; Jung, Dong Hyun

2012-11-01

Using phage display technique, we identified tissue-targeting peptide sets that recognize specific tissues (bone-marrow dendritic cell, kidney, liver, lung, spleen and visceral adipose tissue). In order to rapidly evaluate tissue-specific targeting peptides, we performed machine learning studies for predicting the tissue-specific targeting activity of peptides on the basis of peptide sequence information using four machine learning models and isolated the groups of peptides capable of mediating selective targeting to specific tissues. As a representative liver-specific targeting sequence, the peptide "DKNLQLH" was selected by the sequence similarity analysis. This peptide has a high degree of homology with protein ligands which can interact with corresponding membrane counterparts. We anticipate that our models will be applicable to the prediction of tissue-specific targeting peptides which can recognize the endothelial markers of target tissues.

In silico prediction of splice-altering single nucleotide variants in the human genome.

PubMed

Jian, Xueqiu; Boerwinkle, Eric; Liu, Xiaoming

2014-12-16

In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.

A novel method based on new adaptive LVQ neural network for predicting protein-protein interactions from protein sequences.

PubMed

Yousef, Abdulaziz; Moghadam Charkari, Nasrollah

2013-11-07

Protein-Protein interaction (PPI) is one of the most important data in understanding the cellular processes. Many interesting methods have been proposed in order to predict PPIs. However, the methods which are based on the sequence of proteins as a prior knowledge are more universal. In this paper, a sequence-based, fast, and adaptive PPI prediction method is introduced to assign two proteins to an interaction class (yes, no). First, in order to improve the presentation of the sequences, twelve physicochemical properties of amino acid have been used by different representation methods to transform the sequence of protein pairs into different feature vectors. Then, for speeding up the learning process and reducing the effect of noise PPI data, principal component analysis (PCA) is carried out as a proper feature extraction algorithm. Finally, a new and adaptive Learning Vector Quantization (LVQ) predictor is designed to deal with different models of datasets that are classified into balanced and imbalanced datasets. The accuracy of 93.88%, 90.03%, and 89.72% has been found on S. cerevisiae, H. pylori, and independent datasets, respectively. The results of various experiments indicate the efficiency and validity of the method. © 2013 Published by Elsevier Ltd.

Amino acid sequence analysis of the annexin super-gene family of proteins.

PubMed

Barton, G J; Newman, R H; Freemont, P S; Crumpton, M J

1991-06-15

The annexins are a widespread family of calcium-dependent membrane-binding proteins. No common function has been identified for the family and, until recently, no crystallographic data existed for an annexin. In this paper we draw together 22 available annexin sequences consisting of 88 similar repeat units, and apply the techniques of multiple sequence alignment, pattern matching, secondary structure prediction and conservation analysis to the characterisation of the molecules. The analysis clearly shows that the repeats cluster into four distinct families and that greatest variation occurs within the repeat 3 units. Multiple alignment of the 88 repeats shows amino acids with conserved physicochemical properties at 22 positions, with only Gly at position 23 being absolutely conserved in all repeats. Secondary structure prediction techniques identify five conserved helices in each repeat unit and patterns of conserved hydrophobic amino acids are consistent with one face of a helix packing against the protein core in predicted helices a, c, d, e. Helix b is generally hydrophobic in all repeats, but contains a striking pattern of repeat-specific residue conservation at position 31, with Arg in repeats 4 and Glu in repeats 2, but unconserved amino acids in repeats 1 and 3. This suggests repeats 2 and 4 may interact via a buried saltbridge. The loop between predicted helices a and b of repeat 3 shows features distinct from the equivalent loop in repeats 1, 2 and 4, suggesting an important structural and/or functional role for this region. No compelling evidence emerges from this study for uteroglobin and the annexins sharing similar tertiary structures, or for uteroglobin representing a derivative of a primordial one-repeat structure that underwent duplication to give the present day annexins. The analyses performed in this paper are re-evaluated in the Appendix, in the light of the recently published X-ray structure for human annexin V. The structure confirms most of the predictions and shows the power of techniques for the determination of tertiary structural information from the amino acid sequences of an aligned protein family.

SEAN: SNP prediction and display program utilizing EST sequence clusters.

PubMed

Huntley, Derek; Baldo, Angela; Johri, Saurabh; Sergot, Marek

2006-02-15

SEAN is an application that predicts single nucleotide polymorphisms (SNPs) using multiple sequence alignments produced from expressed sequence tag (EST) clusters. The algorithm uses rules of sequence identity and SNP abundance to determine the quality of the prediction. A Java viewer is provided to display the EST alignments and predicted SNPs.

SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

PubMed

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-05-01

Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

PubMed Central

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-01-01

Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods. PMID:18452616

Transcriptomic Analysis of the Salivary Glands of an Invasive Whitefly

PubMed Central

Su, Yun-Lin; Li, Jun-Min; Li, Meng; Luan, Jun-Bo; Ye, Xiao-Dong; Wang, Xiao-Wei; Liu, Shu-Sheng

2012-01-01

Background Some species of the whitefly Bemisia tabaci complex cause tremendous losses to crops worldwide through feeding directly and virus transmission indirectly. The primary salivary glands of whiteflies are critical for their feeding and virus transmission. However, partly due to their tiny size, research on whitefly salivary glands is limited and our knowledge on these glands is scarce. Methodology/Principal Findings We sequenced the transcriptome of the primary salivary glands of the Mediterranean species of B. tabaci complex using an effective cDNA amplification method in combination with short read sequencing (Illumina). In a single run, we obtained 13,615 unigenes. The quantity of the unigenes obtained from the salivary glands of the whitefly is at least four folds of the salivary gland genes from other plant-sucking insects. To reveal the functions of the primary glands, sequence similarity search and comparisons with the whole transcriptome of the whitefly were performed. The results demonstrated that the genes related to metabolism and transport were significantly enriched in the primary salivary glands. Furthermore, we found that a number of highly expressed genes in the salivary glands might be involved in secretory protein processing, secretion and virus transmission. To identify potential proteins of whitefly saliva, the translated unigenes were put into secretory protein prediction. Finally, 295 genes were predicted to encode secretory proteins and some of them might play important roles in whitefly feeding. Conclusions/Significance: The combined method of cDNA amplification, Illumina sequencing and de novo assembly is suitable for transcriptomic analysis of tiny organs in insects. Through analysis of the transcriptome, genomic features of the primary salivary glands were dissected and biologically important proteins, especially secreted proteins, were predicted. Our findings provide substantial sequence information for the primary salivary glands of whiteflies and will be the basis for future studies on whitefly-plant interactions and virus transmission. PMID:22745728

Insilico profiling of microRNAs in Korean ginseng (Panax ginseng Meyer)

PubMed Central

Mathiyalagan, Ramya; Subramaniyam, Sathiyamoorthy; Natarajan, Sathishkumar; Kim, Yeon Ju; Sun, Myung Suk; Kim, Se Young; Kim, Yu-Jin; Yang, Deok Chun

2013-01-01

MicroRNAs (miRNAs) are a class of recently discovered non-coding small RNA molecules, on average approximately 21 nucleotides in length, which underlie numerous important biological roles in gene regulation in various organisms. The miRNA database (release 18) has 18,226 miRNAs, which have been deposited from different species. Although miRNAs have been identified and validated in many plant species, no studies have been reported on discovering miRNAs in Panax ginseng Meyer, which is a traditionally known medicinal plant in oriental medicine, also known as Korean ginseng. It has triterpene ginseng saponins called ginsenosides, which are responsible for its various pharmacological activities. Predicting conserved miRNAs by homology-based analysis with available expressed sequence tag (EST) sequences can be powerful, if the species lacks whole genome sequence information. In this study by using the EST based computational approach, 69 conserved miRNAs belonging to 44 miRNA families were identified in Korean ginseng. The digital gene expression patterns of predicted conserved miRNAs were analyzed by deep sequencing using small RNA sequences of flower buds, leaves, and lateral roots. We have found that many of the identified miRNAs showed tissue specific expressions. Using the insilico method, 346 potential targets were identified for the predicted 69 conserved miRNAs by searching the ginseng EST database, and the predicted targets were mainly involved in secondary metabolic processes, responses to biotic and abiotic stress, and transcription regulator activities, as well as a variety of other metabolic processes. PMID:23717176

First complete genome sequence of infectious laryngotracheitis virus

PubMed Central

2011-01-01

Background Infectious laryngotracheitis virus (ILTV) is an alphaherpesvirus that causes acute respiratory disease in chickens worldwide. To date, only one complete genomic sequence of ILTV has been reported. This sequence was generated by concatenating partial sequences from six different ILTV strains. Thus, the full genomic sequence of a single (individual) strain of ILTV has not been determined previously. This study aimed to use high throughput sequencing technology to determine the complete genomic sequence of a live attenuated vaccine strain of ILTV. Results The complete genomic sequence of the Serva vaccine strain of ILTV was determined, annotated and compared to the concatenated ILTV reference sequence. The genome size of the Serva strain was 152,628 bp, with a G + C content of 48%. A total of 80 predicted open reading frames were identified. The Serva strain had 96.5% DNA sequence identity with the concatenated ILTV sequence. Notably, the concatenated ILTV sequence was found to lack four large regions of sequence, including 528 bp and 594 bp of sequence in the UL29 and UL36 genes, respectively, and two copies of a 1,563 bp sequence in the repeat regions. Considerable differences in the size of the predicted translation products of 4 other genes (UL54, UL30, UL37 and UL38) were also identified. More than 530 single-nucleotide polymorphisms (SNPs) were identified. Most SNPs were located within three genomic regions, corresponding to sequence from the SA-2 ILTV vaccine strain in the concatenated ILTV sequence. Conclusions This is the first complete genomic sequence of an individual ILTV strain. This sequence will facilitate future comparative genomic studies of ILTV by providing an appropriate reference sequence for the sequence analysis of other ILTV strains. PMID:21501528

Rose spring dwarf-associated virus has RNA structural and gene-expression features like those of Barley yellow dwarf virus

PubMed Central

Salem, Nida’ M.; Miller, W. Allen; Rowhani, Adib; Golino, Deborah A.; Moyne, Anne-Laure; Falk, Bryce W.

2015-01-01

We determined the complete nucleotide sequence of the Rose spring dwarf-associated virus (RSDaV) genomic RNA (GenBank accession no. EU024678) and compared its predicted RNA structural characteristics affecting gene expression. A cDNA library was derived from RSDaV double-stranded RNAs (dsRNAs) purified from infected tissue. Nucleotide sequence analysis of the cloned cDNAs, plus for clones generated by 5′- and 3′-RACE showed the RSDaV genomic RNA to be 5,808 nucleotides. The genomic RNA contains five major open reading frames (ORFs), and three small ORFs in the 3′-terminal 800 nucleotides, typical for viruses of genus Luteovirus in the family Luteoviridae. Northern blot hybridization analysis revealed the genomic RNA and two prominent subgenomic RNAs of approximately 3 kb and 1 kb. Putative 5′ ends of the sgRNAs were predicted by identification of conserved sequences and secondary structures which resembled the Barley yellow dwarf virus (BYDV) genomic RNA 5′ end and subgenomic RNA promoter sequences. Secondary structures of the BYDV-like ribosomal frameshift elements and cap-independent translation elements, including long-distance base pairing spanning four kb were identified. These contain similarities but also informative differences with the BYDV structures, including a strikingly different structure predicted for the 3′ cap-independent translation element. These analyses of the RSDaV genomic RNA show more complexity for the RNA structural elements for members of the Luteoviridae. PMID:18329064

Rose spring dwarf-associated virus has RNA structural and gene-expression features like those of Barley yellow dwarf virus.

PubMed

Salem, Nida' M; Miller, W Allen; Rowhani, Adib; Golino, Deborah A; Moyne, Anne-Laure; Falk, Bryce W

2008-06-05

We determined the complete nucleotide sequence of the Rose spring dwarf-associated virus (RSDaV) genomic RNA (GenBank accession no. EU024678) and compared its predicted RNA structural characteristics affecting gene expression. A cDNA library was derived from RSDaV double-stranded RNAs (dsRNAs) purified from infected tissue. Nucleotide sequence analysis of the cloned cDNAs, plus for clones generated by 5'- and 3'-RACE showed the RSDaV genomic RNA to be 5808 nucleotides. The genomic RNA contains five major open reading frames (ORFs), and three small ORFs in the 3'-terminal 800 nucleotides, typical for viruses of genus Luteovirus in the family Luteoviridae. Northern blot hybridization analysis revealed the genomic RNA and two prominent subgenomic RNAs of approximately 3 kb and 1 kb. Putative 5' ends of the sgRNAs were predicted by identification of conserved sequences and secondary structures which resembled the Barley yellow dwarf virus (BYDV) genomic RNA 5' end and subgenomic RNA promoter sequences. Secondary structures of the BYDV-like ribosomal frameshift elements and cap-independent translation elements, including long-distance base pairing spanning four kb were identified. These contain similarities but also informative differences with the BYDV structures, including a strikingly different structure predicted for the 3' cap-independent translation element. These analyses of the RSDaV genomic RNA show more complexity for the RNA structural elements for members of the Luteoviridae.

In Silico Detection of Sequence Variations Modifying Transcriptional Regulation

PubMed Central

Andersen, Malin C; Engström, Pär G; Lithwick, Stuart; Arenillas, David; Eriksson, Per; Lenhard, Boris; Wasserman, Wyeth W; Odeberg, Jacob

2008-01-01

Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers). The RAVEN system is available at http://www.cisreg.ca for all researchers interested in the detection and characterization of regulatory sequence variation. PMID:18208319

Building a genome analysis pipeline to predict disease risk and prevent disease.

PubMed

Bromberg, Y

2013-11-01

Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice. © 2013. Published by Elsevier Ltd. All rights reserved.

Phylogenetic analysis of a spontaneous cocoa bean fermentation metagenome reveals new insights into its bacterial and fungal community diversity.

PubMed

Illeghems, Koen; De Vuyst, Luc; Papalexandratou, Zoi; Weckx, Stefan

2012-01-01

This is the first report on the phylogenetic analysis of the community diversity of a single spontaneous cocoa bean box fermentation sample through a metagenomic approach involving 454 pyrosequencing. Several sequence-based and composition-based taxonomic profiling tools were used and evaluated to avoid software-dependent results and their outcome was validated by comparison with previously obtained culture-dependent and culture-independent data. Overall, this approach revealed a wider bacterial (mainly γ-Proteobacteria) and fungal diversity than previously found. Further, the use of a combination of different classification methods, in a software-independent way, helped to understand the actual composition of the microbial ecosystem under study. In addition, bacteriophage-related sequences were found. The bacterial diversity depended partially on the methods used, as composition-based methods predicted a wider diversity than sequence-based methods, and as classification methods based solely on phylogenetic marker genes predicted a more restricted diversity compared with methods that took all reads into account. The metagenomic sequencing analysis identified Hanseniaspora uvarum, Hanseniaspora opuntiae, Saccharomyces cerevisiae, Lactobacillus fermentum, and Acetobacter pasteurianus as the prevailing species. Also, the presence of occasional members of the cocoa bean fermentation process was revealed (such as Erwinia tasmaniensis, Lactobacillus brevis, Lactobacillus casei, Lactobacillus rhamnosus, Lactococcus lactis, Leuconostoc mesenteroides, and Oenococcus oeni). Furthermore, the sequence reads associated with viral communities were of a restricted diversity, dominated by Myoviridae and Siphoviridae, and reflecting Lactobacillus as the dominant host. To conclude, an accurate overview of all members of a cocoa bean fermentation process sample was revealed, indicating the superiority of metagenomic sequencing over previously used techniques.

Tissue-specific Proteogenomic Analysis of Plutella xylostella Larval Midgut Using a Multialgorithm Pipeline*

PubMed Central

Zhu, Xun; Xie, Shangbo; Armengaud, Jean; Xie, Wen; Guo, Zhaojiang; Kang, Shi; Wu, Qingjun; Wang, Shaoli; Xia, Jixing; He, Rongjun; Zhang, Youjun

2016-01-01

The diamondback moth, Plutella xylostella (L.), is the major cosmopolitan pest of brassica and other cruciferous crops. Its larval midgut is a dynamic tissue that interfaces with a wide variety of toxicological and physiological processes. The draft sequence of the P. xylostella genome was recently released, but its annotation remains challenging because of the low sequence coverage of this branch of life and the poor description of exon/intron splicing rules for these insects. Peptide sequencing by computational assignment of tandem mass spectra to genome sequence information provides an experimental independent approach for confirming or refuting protein predictions, a concept that has been termed proteogenomics. In this study, we carried out an in-depth proteogenomic analysis to complement genome annotation of P. xylostella larval midgut based on shotgun HPLC-ESI-MS/MS data by means of a multialgorithm pipeline. A total of 876,341 tandem mass spectra were searched against the predicted P. xylostella protein sequences and a whole-genome six-frame translation database. Based on a data set comprising 2694 novel genome search specific peptides, we discovered 439 novel protein-coding genes and corrected 128 existing gene models. To get the most accurate data to seed further insect genome annotation, more than half of the novel protein-coding genes, i.e. 235 over 439, were further validated after RT-PCR amplification and sequencing of the corresponding transcripts. Furthermore, we validated 53 novel alternative splicings. Finally, a total of 6764 proteins were identified, resulting in one of the most comprehensive proteogenomic study of a nonmodel animal. As the first tissue-specific proteogenomics analysis of P. xylostella, this study provides the fundamental basis for high-throughput proteomics and functional genomics approaches aimed at deciphering the molecular mechanisms of resistance and controlling this pest. PMID:26902207

RNA 3D Modules in Genome-Wide Predictions of RNA 2D Structure

PubMed Central

Theis, Corinna; Zirbel, Craig L.; zu Siederdissen, Christian Höner; Anthon, Christian; Hofacker, Ivo L.; Nielsen, Henrik; Gorodkin, Jan

2015-01-01

Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D module prediction tools and apply them on a 13-way vertebrate sequence-based alignment. We find that RNA 3D modules predicted by metaRNAmodules and JAR3D are significantly enriched in the screened windows compared to their shuffled counterparts. The initially estimated FDR of 47.0% is lowered to below 25% when certain 3D module predictions are present in the window of the 2D prediction. We discuss the implications and prospects for further development of computational strategies for detection of RNA 2D structure in genomic sequence. PMID:26509713

Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis.

PubMed

You, Zhu-Hong; Lei, Ying-Ke; Zhu, Lin; Xia, Junfeng; Wang, Bing

2013-01-01

Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs. We present here a novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences. In the proposed method, 11188 protein pairs retrieved from the DIP database were encoded into feature vectors by using four kinds of protein sequences information. Focusing on dimension reduction, an effective feature extraction method PCA was then employed to construct the most discriminative new feature set. Finally, multiple extreme learning machines were trained and then aggregated into a consensus classifier by majority voting. The ensembling of extreme learning machine removes the dependence of results on initial random weights and improves the prediction performance. When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 87.00% prediction accuracy with 86.15% sensitivity at the precision of 87.59%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Experimental results demonstrate that proposed PCA-EELM outperforms the SVM method by 5-fold cross-validation. Besides, PCA-EELM performs faster than PCA-SVM based method. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time.

Protein Structure Prediction with Evolutionary Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, W.E.; Krasnogor, N.; Pelta, D.A.

1999-02-08

Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems. In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem. Our analysis considers the impact of several algorithmic factors for this problem: the confirmational representation, the energy formulation and the way in which infeasible conformations are penalized, Further we empirically evaluated the impact of these factors on a small set of polymer sequences. Our analysis leads to specific recommendations for both GAs as well as other heuristic methods for solving PSP on the HP model.

MEGANTE: A Web-Based System for Integrated Plant Genome Annotation

PubMed Central

Numa, Hisataka; Itoh, Takeshi

2014-01-01

The recent advancement of high-throughput genome sequencing technologies has resulted in a considerable increase in demands for large-scale genome annotation. While annotation is a crucial step for downstream data analyses and experimental studies, this process requires substantial expertise and knowledge of bioinformatics. Here we present MEGANTE, a web-based annotation system that makes plant genome annotation easy for researchers unfamiliar with bioinformatics. Without any complicated configuration, users can perform genomic sequence annotations simply by uploading a sequence and selecting the species to query. MEGANTE automatically runs several analysis programs and integrates the results to select the appropriate consensus exon–intron structures and to predict open reading frames (ORFs) at each locus. Functional annotation, including a similarity search against known proteins and a functional domain search, are also performed for the predicted ORFs. The resultant annotation information is visualized with a widely used genome browser, GBrowse. For ease of analysis, the results can be downloaded in Microsoft Excel format. All of the query sequences and annotation results are stored on the server side so that users can access their own data from virtually anywhere on the web. The current release of MEGANTE targets 24 plant species from the Brassicaceae, Fabaceae, Musaceae, Poaceae, Salicaceae, Solanaceae, Rosaceae and Vitaceae families, and it allows users to submit a sequence up to 10 Mb in length and to save up to 100 sequences with the annotation information on the server. The MEGANTE web service is available at https://megante.dna.affrc.go.jp/. PMID:24253915

Advanced colorectal adenoma related gene expression signature may predict prognostic for colorectal cancer patients with adenoma-carcinoma sequence.

PubMed

Li, Bing; Shi, Xiao-Yu; Liao, Dai-Xiang; Cao, Bang-Rong; Luo, Cheng-Hua; Cheng, Shu-Jun

2015-01-01

There are still no absolute parameters predicting progression of adenoma into cancer. The present study aimed to characterize functional differences on the multistep carcinogenetic process from the adenoma-carcinoma sequence. All samples were collected and mRNA expression profiling was performed by using Agilent Microarray high-throughput gene-chip technology. Then, the characteristics of mRNA expression profiles of adenoma-carcinoma sequence were described with bioinformatics software, and we analyzed the relationship between gene expression profiles of adenoma-adenocarcinoma sequence and clinical prognosis of colorectal cancer. The mRNA expressions of adenoma-carcinoma sequence were significantly different between high-grade intraepithelial neoplasia group and adenocarcinoma group. The biological process of gene ontology function enrichment analysis on differentially expressed genes between high-grade intraepithelial neoplasia group and adenocarcinoma group showed that genes enriched in the extracellular structure organization, skeletal system development, biological adhesion and itself regulated growth regulation, with the P value after FDR correction of less than 0.05. In addition, IPR-related protein mainly focused on the insulin-like growth factor binding proteins. The variable trends of gene expression profiles for adenoma-carcinoma sequence were mainly concentrated in high-grade intraepithelial neoplasia and adenocarcinoma. The differentially expressed genes are significantly correlated between high-grade intraepithelial neoplasia group and adenocarcinoma group. Bioinformatics analysis is an effective way to study the gene expression profiles in the adenoma-carcinoma sequence, and may provide an effective tool to involve colorectal cancer research strategy into colorectal adenoma or advanced adenoma.

Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer.

PubMed

Xia, Shu; Kohli, Manish; Du, Meijun; Dittmar, Rachel L; Lee, Adam; Nandy, Debashis; Yuan, Tiezheng; Guo, Yongchen; Wang, Yuan; Tschannen, Michael R; Worthey, Elizabeth; Jacob, Howard; See, William; Kilari, Deepak; Wang, Xuexia; Hovey, Raymond L; Huang, Chiang-Ching; Wang, Liang

2015-06-30

Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer.

Revealing the transcriptomic complexity of switchgrass by PacBio long-read sequencing.

PubMed

Zuo, Chunman; Blow, Matthew; Sreedasyam, Avinash; Kuo, Rita C; Ramamoorthy, Govindarajan Kunde; Torres-Jerez, Ivone; Li, Guifen; Wang, Mei; Dilworth, David; Barry, Kerrie; Udvardi, Michael; Schmutz, Jeremy; Tang, Yuhong; Xu, Ying

2018-01-01

Switchgrass ( Panicum virgatum L.) is an important bioenergy crop widely used for lignocellulosic research. While extensive transcriptomic analyses have been conducted on this species using short read-based sequencing techniques, very little has been reliably derived regarding alternatively spliced (AS) transcripts. We present an analysis of transcriptomes of six switchgrass tissue types pooled together, sequenced using Pacific Biosciences (PacBio) single-molecular long-read technology. Our analysis identified 105,419 unique transcripts covering 43,570 known genes and 8795 previously unknown genes. 45,168 are novel transcripts of known genes. A total of 60,096 AS transcripts are identified, 45,628 being novel. We have also predicted 1549 transcripts of genes involved in cell wall construction and remodeling, 639 being novel transcripts of known cell wall genes. Most of the predicted transcripts are validated against Illumina-based short reads. Specifically, 96% of the splice junction sites in all the unique transcripts are validated by at least five Illumina reads. Comparisons between genes derived from our identified transcripts and the current genome annotation revealed that among the gene set predicted by both analyses, 16,640 have different exon-intron structures. Overall, substantial amount of new information is derived from the PacBio RNA data regarding both the transcriptome and the genome of switchgrass.

KONAGAbase: a genomic and transcriptomic database for the diamondback moth, Plutella xylostella.

PubMed

Jouraku, Akiya; Yamamoto, Kimiko; Kuwazaki, Seigo; Urio, Masahiro; Suetsugu, Yoshitaka; Narukawa, Junko; Miyamoto, Kazuhisa; Kurita, Kanako; Kanamori, Hiroyuki; Katayose, Yuichi; Matsumoto, Takashi; Noda, Hiroaki

2013-07-09

The diamondback moth (DBM), Plutella xylostella, is one of the most harmful insect pests for crucifer crops worldwide. DBM has rapidly evolved high resistance to most conventional insecticides such as pyrethroids, organophosphates, fipronil, spinosad, Bacillus thuringiensis, and diamides. Therefore, it is important to develop genomic and transcriptomic DBM resources for analysis of genes related to insecticide resistance, both to clarify the mechanism of resistance of DBM and to facilitate the development of insecticides with a novel mode of action for more effective and environmentally less harmful insecticide rotation. To contribute to this goal, we developed KONAGAbase, a genomic and transcriptomic database for DBM (KONAGA is the Japanese word for DBM). KONAGAbase provides (1) transcriptomic sequences of 37,340 ESTs/mRNAs and 147,370 RNA-seq contigs which were clustered and assembled into 84,570 unigenes (30,695 contigs, 50,548 pseudo singletons, and 3,327 singletons); and (2) genomic sequences of 88,530 WGS contigs with 246,244 degenerate contigs and 106,455 singletons from which 6,310 de novo identified repeat sequences and 34,890 predicted gene-coding sequences were extracted. The unigenes and predicted gene-coding sequences were clustered and 32,800 representative sequences were extracted as a comprehensive putative gene set. These sequences were annotated with BLAST descriptions, Gene Ontology (GO) terms, and Pfam descriptions, respectively. KONAGAbase contains rich graphical user interface (GUI)-based web interfaces for easy and efficient searching, browsing, and downloading sequences and annotation data. Five useful search interfaces consisting of BLAST search, keyword search, BLAST result-based search, GO tree-based search, and genome browser are provided. KONAGAbase is publicly available from our website (http://dbm.dna.affrc.go.jp/px/) through standard web browsers. KONAGAbase provides DBM comprehensive transcriptomic and draft genomic sequences with useful annotation information with easy-to-use web interfaces, which helps researchers to efficiently search for target sequences such as insect resistance-related genes. KONAGAbase will be continuously updated and additional genomic/transcriptomic resources and analysis tools will be provided for further efficient analysis of the mechanism of insecticide resistance and the development of effective insecticides with a novel mode of action for DBM.

FrameD: A flexible program for quality check and gene prediction in prokaryotic genomes and noisy matured eukaryotic sequences.

PubMed

Schiex, Thomas; Gouzy, Jérôme; Moisan, Annick; de Oliveira, Yannick

2003-07-01

We describe FrameD, a program that predicts coding regions in prokaryotic and matured eukaryotic sequences. Initially targeted at gene prediction in bacterial GC rich genomes, the gene model used in FrameD also allows to predict genes in the presence of frameshifts and partially undetermined sequences which makes it also very suitable for gene prediction and frameshift correction in unfinished sequences such as EST and EST cluster sequences. Like recent eukaryotic gene prediction programs, FrameD also includes the ability to take into account protein similarity information both in its prediction and its graphical output. Its performances are evaluated on different bacterial genomes. The web site (http://genopole.toulouse.inra.fr/bioinfo/FrameD/FD) allows direct prediction, sequence correction and translation and the ability to learn new models for new organisms.

Parallel analysis of RNA ends enhances global investigation of microRNAs and target RNAs of Brachypodium distachyon

PubMed Central

2013-01-01

Background The wild grass Brachypodium distachyon has emerged as a model system for temperate grasses and biofuel plants. However, the global analysis of miRNAs, molecules known to be key for eukaryotic gene regulation, has been limited in B. distachyon to studies examining a few samples or that rely on computational predictions. Similarly an in-depth global analysis of miRNA-mediated target cleavage using parallel analysis of RNA ends (PARE) data is lacking in B. distachyon. Results B. distachyon small RNAs were cloned and deeply sequenced from 17 libraries that represent different tissues and stresses. Using a computational pipeline, we identified 116 miRNAs including not only conserved miRNAs that have not been reported in B. distachyon, but also non-conserved miRNAs that were not found in other plants. To investigate miRNA-mediated cleavage function, four PARE libraries were constructed from key tissues and sequenced to a total depth of approximately 70 million sequences. The roughly 5 million distinct genome-matched sequences that resulted represent an extensive dataset for analyzing small RNA-guided cleavage events. Analysis of the PARE and miRNA data provided experimental evidence for miRNA-mediated cleavage of 264 sites in predicted miRNA targets. In addition, PARE analysis revealed that differentially expressed miRNAs in the same family guide specific target RNA cleavage in a correspondingly tissue-preferential manner. Conclusions B. distachyon miRNAs and target RNAs were experimentally identified and analyzed. Knowledge gained from this study should provide insights into the roles of miRNAs and the regulation of their targets in B. distachyon and related plants. PMID:24367943

Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification

PubMed Central

Kamps, Rick; Brandão, Rita D.; van den Bosch, Bianca J.; Paulussen, Aimee D. C.; Xanthoulea, Sofia; Blok, Marinus J.; Romano, Andrea

2017-01-01

Next-generation sequencing (NGS) technology has expanded in the last decades with significant improvements in the reliability, sequencing chemistry, pipeline analyses, data interpretation and costs. Such advances make the use of NGS feasible in clinical practice today. This review describes the recent technological developments in NGS applied to the field of oncology. A number of clinical applications are reviewed, i.e., mutation detection in inherited cancer syndromes based on DNA-sequencing, detection of spliceogenic variants based on RNA-sequencing, DNA-sequencing to identify risk modifiers and application for pre-implantation genetic diagnosis, cancer somatic mutation analysis, pharmacogenetics and liquid biopsy. Conclusive remarks, clinical limitations, implications and ethical considerations that relate to the different applications are provided. PMID:28146134

Unified Sequence-Based Association Tests Allowing for Multiple Functional Annotations and Meta-analysis of Noncoding Variation in Metabochip Data.

PubMed

He, Zihuai; Xu, Bin; Lee, Seunggeun; Ionita-Laza, Iuliana

2017-09-07

Substantial progress has been made in the functional annotation of genetic variation in the human genome. Integrative analysis that incorporates such functional annotations into sequencing studies can aid the discovery of disease-associated genetic variants, especially those with unknown function and located outside protein-coding regions. Direct incorporation of one functional annotation as weight in existing dispersion and burden tests can suffer substantial loss of power when the functional annotation is not predictive of the risk status of a variant. Here, we have developed unified tests that can utilize multiple functional annotations simultaneously for integrative association analysis with efficient computational techniques. We show that the proposed tests significantly improve power when variant risk status can be predicted by functional annotations. Importantly, when functional annotations are not predictive of risk status, the proposed tests incur only minimal loss of power in relation to existing dispersion and burden tests, and under certain circumstances they can even have improved power by learning a weight that better approximates the underlying disease model in a data-adaptive manner. The tests can be constructed with summary statistics of existing dispersion and burden tests for sequencing data, therefore allowing meta-analysis of multiple studies without sharing individual-level data. We applied the proposed tests to a meta-analysis of noncoding rare variants in Metabochip data on 12,281 individuals from eight studies for lipid traits. By incorporating the Eigen functional score, we detected significant associations between noncoding rare variants in SLC22A3 and low-density lipoprotein and total cholesterol, associations that are missed by standard dispersion and burden tests. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

cyclostratigraphy, sequence stratigraphy and organic matter accumulation mechanism

NASA Astrophysics Data System (ADS)

Cong, F.; Li, J.

2016-12-01

The first member of Maokou Formation of Sichuan basin is composed of well preserved carbonate ramp couplets of limestone and marlstone/shale. It acts as one of the potential shale gas source rock, and is suitable for time-series analysis. We conducted time-series analysis to identify high-frequency sequences, reconstruct high-resolution sedimentation rate, estimate detailed primary productivity for the first time in the study intervals and discuss organic matter accumulation mechanism of source rock under sequence stratigraphic framework.Using the theory of cyclostratigraphy and sequence stratigraphy, the high-frequency sequences of one outcrop profile and one drilling well are identified. Two third-order sequences and eight fourth-order sequences are distinguished on outcrop profile based on the cycle stacking patterns. For drilling well, sequence boundary and four system tracts is distinguished by "integrated prediction error filter analysis" (INPEFA) of Gamma-ray logging data, and eight fourth-order sequences is identified by 405ka long eccentricity curve in depth domain which is quantified and filtered by integrated analysis of MTM spectral analysis, evolutive harmonic analysis (EHA), evolutive average spectral misfit (eASM) and band-pass filtering. It suggests that high-frequency sequences correlate well with Milankovitch orbital signals recorded in sediments, and it is applicable to use cyclostratigraphy theory in dividing high-frequency(4-6 orders) sequence stratigraphy.High-resolution sedimentation rate is reconstructed through the study interval by tracking the highly statistically significant short eccentricity component (123ka) revealed by EHA. Based on sedimentation rate, measured TOC and density data, the burial flux, delivery flux and primary productivity of organic carbon was estimated. By integrating redox proxies, we can discuss the controls on organic matter accumulation by primary production and preservation under the high-resolution sequence stratigraphic framework. Results show that high average organic carbon contents in the study interval are mainly attributed to high primary production. The results also show a good correlation between high organic carbon accumulation and intervals of transgression.

ReadXplorer—visualization and analysis of mapped sequences

PubMed Central

Hilker, Rolf; Stadermann, Kai Bernd; Doppmeier, Daniel; Kalinowski, Jörn; Stoye, Jens; Straube, Jasmin; Winnebald, Jörn; Goesmann, Alexander

2014-01-01

Motivation: Fast algorithms and well-arranged visualizations are required for the comprehensive analysis of the ever-growing size of genomic and transcriptomic next-generation sequencing data. Results: ReadXplorer is a software offering straightforward visualization and extensive analysis functions for genomic and transcriptomic DNA sequences mapped on a reference. A unique specialty of ReadXplorer is the quality classification of the read mappings. It is incorporated in all analysis functions and displayed in ReadXplorer's various synchronized data viewers for (i) the reference sequence, its base coverage as (ii) normalizable plot and (iii) histogram, (iv) read alignments and (v) read pairs. ReadXplorer's analysis capability covers RNA secondary structure prediction, single nucleotide polymorphism and deletion–insertion polymorphism detection, genomic feature and general coverage analysis. Especially for RNA-Seq data, it offers differential gene expression analysis, transcription start site and operon detection as well as RPKM value and read count calculations. Furthermore, ReadXplorer can combine or superimpose coverage of different datasets. Availability and implementation: ReadXplorer is available as open-source software at http://www.readxplorer.org along with a detailed manual. Contact: rhilker@mikrobio.med.uni-giessen.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24790157

Cost analysis of whole genome sequencing in German clinical practice.

PubMed

Plöthner, Marika; Frank, Martin; von der Schulenburg, J-Matthias Graf

2017-06-01

Whole genome sequencing (WGS) is an emerging tool in clinical diagnostics. However, little has been said about its procedure costs, owing to a dearth of related cost studies. This study helps fill this research gap by analyzing the execution costs of WGS within the setting of German clinical practice. First, to estimate costs, a sequencing process related to clinical practice was undertaken. Once relevant resources were identified, a quantification and monetary evaluation was conducted using data and information from expert interviews with clinical geneticists, and personnel at private enterprises and hospitals. This study focuses on identifying the costs associated with the standard sequencing process, and the procedure costs for a single WGS were analyzed on the basis of two sequencing platforms-namely, HiSeq 2500 and HiSeq Xten, both by Illumina, Inc. In addition, sensitivity analyses were performed to assess the influence of various uses of sequencing platforms and various coverage values on a fixed-cost degression. In the base case scenario-which features 80 % utilization and 30-times coverage-the cost of a single WGS analysis with the HiSeq 2500 was estimated at €3858.06. The cost of sequencing materials was estimated at €2848.08; related personnel costs of €396.94 and acquisition/maintenance costs (€607.39) were also found. In comparison, the cost of sequencing that uses the latest technology (i.e., HiSeq Xten) was approximately 63 % cheaper, at €1411.20. The estimated costs of WGS currently exceed the prediction of a 'US$1000 per genome', by more than a factor of 3.8. In particular, the material costs in themselves exceed this predicted cost.

An FPGA Implementation to Detect Selective Cationic Antibacterial Peptides

PubMed Central

Polanco González, Carlos; Nuño Maganda, Marco Aurelio; Arias-Estrada, Miguel; del Rio, Gabriel

2011-01-01

Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides. PMID:21738652

Molecular homogeneity of heat-stable enterotoxins produced by bovine enterotoxigenic Escherichia coli.

PubMed Central

Saeed, A M; Magnuson, N S; Sriranganathan, N; Burger, D; Cosand, W

1984-01-01

Heat-stable enterotoxins (STs) from four strains of bovine enterotoxigenic Escherichia coli representing four serogroups were purified to homogeneity by utilizing previously published purification schemata. Biochemical characterization of the purified STs showed that they met the basic criteria for the heat-stable enterotoxins of E. coli. Amino acid analysis of the purified STs revealed that they were peptides of identical amino acid composition. This composition consisted of 18 residues of 10 different amino acids, 6 of which were cysteine. The amino acid composition of the four ST peptides was identical to that reported for the STs of human and porcine E. coli. In addition, complete sequence analysis of two of the ST peptides and partial sequencing of several others revealed strong homology to the sequences of STs from human and porcine E. coli and to the sequence predicted from the last 18 codons of the transposon Tn1681. There was also substantial homology to the sequence predicted from the ST-coding genetic element of human E. coli, which may indicate the existence of identical bioactive configuration among ST peptides of E. coli strains of various host origins. These data support the hypothesis that STs produced by human, bovine, and porcine E. coli are coded by a closely related genetic element which may have originated from a single, widely disseminated transposon. Images PMID:6376355

ASPIC: a novel method to predict the exon-intron structure of a gene that is optimally compatible to a set of transcript sequences.

PubMed

Bonizzoni, Paola; Rizzi, Raffaella; Pesole, Graziano

2005-10-05

Currently available methods to predict splice sites are mainly based on the independent and progressive alignment of transcript data (mostly ESTs) to the genomic sequence. Apart from often being computationally expensive, this approach is vulnerable to several problems--hence the need to develop novel strategies. We propose a method, based on a novel multiple genome-EST alignment algorithm, for the detection of splice sites. To avoid limitations of splice sites prediction (mainly, over-predictions) due to independent single EST alignments to the genomic sequence our approach performs a multiple alignment of transcript data to the genomic sequence based on the combined analysis of all available data. We recast the problem of predicting constitutive and alternative splicing as an optimization problem, where the optimal multiple transcript alignment minimizes the number of exons and hence of splice site observations. We have implemented a splice site predictor based on this algorithm in the software tool ASPIC (Alternative Splicing PredICtion). It is distinguished from other methods based on BLAST-like tools by the incorporation of entirely new ad hoc procedures for accurate and computationally efficient transcript alignment and adopts dynamic programming for the refinement of intron boundaries. ASPIC also provides the minimal set of non-mergeable transcript isoforms compatible with the detected splicing events. The ASPIC web resource is dynamically interconnected with the Ensembl and Unigene databases and also implements an upload facility. Extensive bench marking shows that ASPIC outperforms other existing methods in the detection of novel splicing isoforms and in the minimization of over-predictions. ASPIC also requires a lower computation time for processing a single gene and an EST cluster. The ASPIC web resource is available at http://aspic.algo.disco.unimib.it/aspic-devel/.

Strain-specific and pooled genome sequences for populations of Drosophila melanogaster from three continents.

PubMed Central

Bergman, Casey M.; Haddrill, Penelope R.

2015-01-01

To contribute to our general understanding of the evolutionary forces that shape variation in genome sequences in nature, we have sequenced genomes from 50 isofemale lines and six pooled samples from populations of Drosophila melanogaster on three continents. Analysis of raw and reference-mapped reads indicates the quality of these genomic sequence data is very high. Comparison of the predicted and experimentally-determined Wolbachia infection status of these samples suggests that strain or sample swaps are unlikely to have occurred in the generation of these data. Genome sequences are freely available in the European Nucleotide Archive under accession ERP009059. Isofemale lines can be obtained from the Drosophila Species Stock Center. PMID:25717372

Strain-specific and pooled genome sequences for populations of Drosophila melanogaster from three continents.

PubMed

Bergman, Casey M; Haddrill, Penelope R

2015-01-01

To contribute to our general understanding of the evolutionary forces that shape variation in genome sequences in nature, we have sequenced genomes from 50 isofemale lines and six pooled samples from populations of Drosophila melanogaster on three continents. Analysis of raw and reference-mapped reads indicates the quality of these genomic sequence data is very high. Comparison of the predicted and experimentally-determined Wolbachia infection status of these samples suggests that strain or sample swaps are unlikely to have occurred in the generation of these data. Genome sequences are freely available in the European Nucleotide Archive under accession ERP009059. Isofemale lines can be obtained from the Drosophila Species Stock Center.

Molecular beacon sequence design algorithm.

PubMed

Monroe, W Todd; Haselton, Frederick R

2003-01-01

A method based on Web-based tools is presented to design optimally functioning molecular beacons. Molecular beacons, fluorogenic hybridization probes, are a powerful tool for the rapid and specific detection of a particular nucleic acid sequence. However, their synthesis costs can be considerable. Since molecular beacon performance is based on its sequence, it is imperative to rationally design an optimal sequence before synthesis. The algorithm presented here uses simple Microsoft Excel formulas and macros to rank candidate sequences. This analysis is carried out using mfold structural predictions along with other free Web-based tools. For smaller laboratories where molecular beacons are not the focus of research, the public domain algorithm described here may be usefully employed to aid in molecular beacon design.

GASP: Gapped Ancestral Sequence Prediction for proteins

PubMed Central

Edwards, Richard J; Shields, Denis C

2004-01-01

Background The prediction of ancestral protein sequences from multiple sequence alignments is useful for many bioinformatics analyses. Predicting ancestral sequences is not a simple procedure and relies on accurate alignments and phylogenies. Several algorithms exist based on Maximum Parsimony or Maximum Likelihood methods but many current implementations are unable to process residues with gaps, which may represent insertion/deletion (indel) events or sequence fragments. Results Here we present a new algorithm, GASP (Gapped Ancestral Sequence Prediction), for predicting ancestral sequences from phylogenetic trees and the corresponding multiple sequence alignments. Alignments may be of any size and contain gaps. GASP first assigns the positions of gaps in the phylogeny before using a likelihood-based approach centred on amino acid substitution matrices to assign ancestral amino acids. Important outgroup information is used by first working down from the tips of the tree to the root, using descendant data only to assign probabilities, and then working back up from the root to the tips using descendant and outgroup data to make predictions. GASP was tested on a number of simulated datasets based on real phylogenies. Prediction accuracy for ungapped data was similar to three alternative algorithms tested, with GASP performing better in some cases and worse in others. Adding simple insertions and deletions to the simulated data did not have a detrimental effect on GASP accuracy. Conclusions GASP (Gapped Ancestral Sequence Prediction) will predict ancestral sequences from multiple protein alignments of any size. Although not as accurate in all cases as some of the more sophisticated maximum likelihood approaches, it can process a wide range of input phylogenies and will predict ancestral sequences for gapped and ungapped residues alike. PMID:15350199

Systematic elucidation and in vivo validation of sequences enriched in hindbrain transcriptional control

PubMed Central

Burzynski, Grzegorz M.; Reed, Xylena; Taher, Leila; Stine, Zachary E.; Matsui, Takeshi; Ovcharenko, Ivan; McCallion, Andrew S.

2012-01-01

Illuminating the primary sequence encryption of enhancers is central to understanding the regulatory architecture of genomes. We have developed a machine learning approach to decipher motif patterns of hindbrain enhancers and identify 40,000 sequences in the human genome that we predict display regulatory control that includes the hindbrain. Consistent with their roles in hindbrain patterning, MEIS1, NKX6-1, as well as HOX and POU family binding motifs contributed strongly to this enhancer model. Predicted hindbrain enhancers are overrepresented at genes expressed in hindbrain and associated with nervous system development, and primarily reside in the areas of open chromatin. In addition, 77 (0.2%) of these predictions are identified as hindbrain enhancers on the VISTA Enhancer Browser, and 26,000 (60%) overlap enhancer marks (H3K4me1 or H3K27ac). To validate these putative hindbrain enhancers, we selected 55 elements distributed throughout our predictions and six low scoring controls for evaluation in a zebrafish transgenic assay. When assayed in mosaic transgenic embryos, 51/55 elements directed expression in the central nervous system. Furthermore, 30/34 (88%) predicted enhancers analyzed in stable zebrafish transgenic lines directed expression in the larval zebrafish hindbrain. Subsequent analysis of sequence fragments selected based upon motif clustering further confirmed the critical role of the motifs contributing to the classifier. Our results demonstrate the existence of a primary sequence code characteristic to hindbrain enhancers. This code can be accurately extracted using machine-learning approaches and applied successfully for de novo identification of hindbrain enhancers. This study represents a critical step toward the dissection of regulatory control in specific neuronal subtypes. PMID:22759862

Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm.

PubMed

Wang, ShaoPeng; Zhang, Yu-Hang; Huang, GuoHua; Chen, Lei; Cai, Yu-Dong

2017-01-01

Myristoylation is an important hydrophobic post-translational modification that is covalently bound to the amino group of Gly residues on the N-terminus of proteins. The many diverse functions of myristoylation on proteins, such as membrane targeting, signal pathway regulation and apoptosis, are largely due to the lipid modification, whereas abnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. To better understand the function of myristoylated sites and to correctly identify them in protein sequences, this study conducted a novel computational investigation on identifying myristoylation sites in protein sequences. A training dataset with 196 positive and 84 negative peptide segments were obtained. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylatedand non-myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy (mRMR), incremental feature selection (IFS), and a machine learning algorithm (extreme learning machine method) were adopted to extract optimal features for the algorithm to identify myristoylation sites in protein sequences, thereby building an optimal prediction model. As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also performed on the extracted 41 features to gain insight into the mechanism of myristoylation modification. This study provided a new computational method for identifying myristoylation sites in protein sequences. We believe that it can be a useful tool to predict myristoylation sites from protein sequences. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

PubMed

Hayat, Maqsood; Khan, Asifullah

2011-02-21

Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://111.68.99.218/Mem-Predictor. Copyright © 2010 Elsevier Ltd. All rights reserved.

Analysis of predicted B and T-cell epitopes in Der p 23, allergen from Dermatophagoides pteronyssinus.

PubMed

Fanuel, Songwe; Tabesh, Saeideh; Sadroddiny, Esmaeil; Kardar, Gholam Ali

2017-01-01

House dust mite (HDM) allergy is the leading cause of IgE-mediated hypersensitivity. Therefore identifying potential epitopes in the Dermatophagoide pteronyssinus 23 (Der p 23), a major house dust mite allergen will aid in the development of therapeutic vaccines and diagnostic kits for HDM allergy. Experimental methods of epitope discovery have been widely exploited for the mapping of potential allergens. This study sought to use immunoinformatic methods to analyze the structure of Der p 23 for potential immunoreactive B and T-cell epitopes that could be useful for AIT and allergy diagnosis. We retrieved a Der p 23 allergen sequence from Genbank database and then analyzed it using a combination of web-based sequence analysis tools including the Immune Epitope Database (IEDB), Protparam, BCPREDS, ABCpred, BepiPred, Bcepred among others to predict the physiochemical properties and epitope spectra of the Der p 23 allergen. We then built 3D models of the predicted B-cell epitopes, T cell epitopes and Der p 23 for sequence structure homology analysis. Our results identified peptides 'TRWNEDE', 'TVHPTTTEQPDDK', and 'NDDDPTT' as immunogenic linear B-cell epitopes while 'CPSRFGYFADPKDPH' and 'CPGNTRWNEDEETCT' were found to be the most suitable T-cell epitopes that interacted well with a large number of MHC II alleles. Both epitopes had high population coverage as well as showing a 100% conservancy. These five Der p 23 epitopes are useful for AIT vaccines and HDM allergy diagnosis development.

Discrete sequence prediction and its applications

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

Learning from experience to predict sequences of discrete symbols is a fundamental problem in machine learning with many applications. We apply sequence prediction using a simple and practical sequence-prediction algorithm, called TDAG. The TDAG algorithm is first tested by comparing its performance with some common data compression algorithms. Then it is adapted to the detailed requirements of dynamic program optimization, with excellent results.

The Ensembl genome database project.

PubMed

Hubbard, T; Barker, D; Birney, E; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Huminiecki, L; Kasprzyk, A; Lehvaslaiho, H; Lijnzaad, P; Melsopp, C; Mongin, E; Pettett, R; Pocock, M; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Clamp, M

2002-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements from sequence analysis to data storage and visualisation. The Ensembl site is one of the leading sources of human genome sequence annotation and provided much of the analysis for publication by the international human genome project of the draft genome. The Ensembl system is being installed around the world in both companies and academic sites on machines ranging from supercomputers to laptops.

Impact of the number of mutations in survival and response outcomes to hypomethylating agents in patients with myelodysplastic syndromes or myelodysplastic/myeloproliferative neoplasms

PubMed Central

Montalban-Bravo, Guillermo; Takahashi, Koichi; Patel, Keyur; Wang, Feng; Xingzhi, Song; Nogueras, Graciela M.; Huang, Xuelin; Pierola, Ana Alfonso; Jabbour, Elias; Colla, Simona; Gañan-Gomez, Irene; Borthakur, Gautham; Daver, Naval; Estrov, Zeev; Kadia, Tapan; Pemmaraju, Naveen; Ravandi, Farhad; Bueso-Ramos, Carlos; Chamseddine, Ali; Konopleva, Marina; Zhang, Jianhua; Kantarjian, Hagop; Futreal, Andrew; Garcia-Manero, Guillermo

2018-01-01

The prognostic and predictive value of sequencing analysis in myelodysplastic syndromes (MDS) has not been fully integrated into clinical practice. We performed whole exome sequencing (WES) of bone marrow samples from 83 patients with MDS and 31 with MDS/MPN identifying 218 driver mutations in 31 genes in 98 (86%) patients. A total of 65 (57%) patients received therapy with hypomethylating agents. By univariate analysis, mutations in BCOR, STAG2, TP53 and SF3B1 significantly influenced survival. Increased number of mutations (≥ 3), but not clonal heterogeneity, predicted for shorter survival and LFS. Presence of 3 or more mutations also predicted for lower likelihood of response (26 vs 50%, p = 0.055), and shorter response duration (3.6 vs 26.5 months, p = 0.022). By multivariate analysis, TP53 mutations (HR 3.1, CI 1.3–7.5, p = 0.011) and number of mutations (≥ 3) (HR 2.5, CI 1.3–4.8, p = 0.005) predicted for shorter survival. A novel prognostic model integrating this mutation data with IPSS-R separated patients into three categories with median survival of not reached, 29 months and 12 months respectively (p < 0.001) and increased stratification potential, compared to IPSS-R, in patients with high/very-high IPSS-R. This model was validated in a separate cohort of 413 patients with untreated MDS. Although the use of WES did not provide significant more information than that obtained with targeted sequencing, our findings indicate that increased number of mutations is an independent prognostic factor in MDS and that mutation data can add value to clinical prognostic models. PMID:29515765

Impact of the number of mutations in survival and response outcomes to hypomethylating agents in patients with myelodysplastic syndromes or myelodysplastic/myeloproliferative neoplasms.

PubMed

Montalban-Bravo, Guillermo; Takahashi, Koichi; Patel, Keyur; Wang, Feng; Xingzhi, Song; Nogueras, Graciela M; Huang, Xuelin; Pierola, Ana Alfonso; Jabbour, Elias; Colla, Simona; Gañan-Gomez, Irene; Borthakur, Gautham; Daver, Naval; Estrov, Zeev; Kadia, Tapan; Pemmaraju, Naveen; Ravandi, Farhad; Bueso-Ramos, Carlos; Chamseddine, Ali; Konopleva, Marina; Zhang, Jianhua; Kantarjian, Hagop; Futreal, Andrew; Garcia-Manero, Guillermo

2018-02-09

The prognostic and predictive value of sequencing analysis in myelodysplastic syndromes (MDS) has not been fully integrated into clinical practice. We performed whole exome sequencing (WES) of bone marrow samples from 83 patients with MDS and 31 with MDS/MPN identifying 218 driver mutations in 31 genes in 98 (86%) patients. A total of 65 (57%) patients received therapy with hypomethylating agents. By univariate analysis, mutations in BCOR, STAG2, TP53 and SF3B1 significantly influenced survival. Increased number of mutations (≥ 3), but not clonal heterogeneity, predicted for shorter survival and LFS. Presence of 3 or more mutations also predicted for lower likelihood of response (26 vs 50%, p = 0.055), and shorter response duration (3.6 vs 26.5 months, p = 0.022). By multivariate analysis, TP53 mutations (HR 3.1, CI 1.3-7.5, p = 0.011) and number of mutations (≥ 3) (HR 2.5, CI 1.3-4.8, p = 0.005) predicted for shorter survival. A novel prognostic model integrating this mutation data with IPSS-R separated patients into three categories with median survival of not reached, 29 months and 12 months respectively ( p < 0.001) and increased stratification potential, compared to IPSS-R, in patients with high/very-high IPSS-R. This model was validated in a separate cohort of 413 patients with untreated MDS. Although the use of WES did not provide significant more information than that obtained with targeted sequencing, our findings indicate that increased number of mutations is an independent prognostic factor in MDS and that mutation data can add value to clinical prognostic models.

Transcriptome-wide identification of Rauvolfia serpentina microRNAs and prediction of their potential targets.

PubMed

Prakash, Pravin; Rajakani, Raja; Gupta, Vikrant

2016-04-01

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 19-24 nucleotides (nt) in length and considered as potent regulators of gene expression at transcriptional and post-transcriptional levels. Here we report the identification and characterization of 15 conserved miRNAs belonging to 13 families from Rauvolfia serpentina through in silico analysis of available nucleotide dataset. The identified mature R. serpentina miRNAs (rse-miRNAs) ranged between 20 and 22nt in length, and the average minimal folding free energy index (MFEI) value of rse-miRNA precursor sequences was found to be -0.815 kcal/mol. Using the identified rse-miRNAs as query, their potential targets were predicted in R. serpentina and other plant species. Gene Ontology (GO) annotation showed that predicted targets of rse-miRNAs include transcription factors as well as genes involved in diverse biological processes such as primary and secondary metabolism, stress response, disease resistance, growth, and development. Few rse-miRNAs were predicted to target genes of pharmaceutically important secondary metabolic pathways such as alkaloids and anthocyanin biosynthesis. Phylogenetic analysis showed the evolutionary relationship of rse-miRNAs and their precursor sequences to homologous pre-miRNA sequences from other plant species. The findings under present study besides giving first hand information about R. serpentina miRNAs and their targets, also contributes towards the better understanding of miRNA-mediated gene regulatory processes in plants. Copyright © 2015 Elsevier Ltd. All rights reserved.

StructRNAfinder: an automated pipeline and web server for RNA families prediction.

PubMed

Arias-Carrasco, Raúl; Vásquez-Morán, Yessenia; Nakaya, Helder I; Maracaja-Coutinho, Vinicius

2018-02-17

The function of many noncoding RNAs (ncRNAs) depend upon their secondary structures. Over the last decades, several methodologies have been developed to predict such structures or to use them to functionally annotate RNAs into RNA families. However, to fully perform this analysis, researchers should utilize multiple tools, which require the constant parsing and processing of several intermediate files. This makes the large-scale prediction and annotation of RNAs a daunting task even to researchers with good computational or bioinformatics skills. We present an automated pipeline named StructRNAfinder that predicts and annotates RNA families in transcript or genome sequences. This single tool not only displays the sequence/structural consensus alignments for each RNA family, according to Rfam database but also provides a taxonomic overview for each assigned functional RNA. Moreover, we implemented a user-friendly web service that allows researchers to upload their own nucleotide sequences in order to perform the whole analysis. Finally, we provided a stand-alone version of StructRNAfinder to be used in large-scale projects. The tool was developed under GNU General Public License (GPLv3) and is freely available at http://structrnafinder.integrativebioinformatics.me . The main advantage of StructRNAfinder relies on the large-scale processing and integrating the data obtained by each tool and database employed along the workflow, of which several files are generated and displayed in user-friendly reports, useful for downstream analyses and data exploration.

Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time.

PubMed

Hake, Anna; Pfeifer, Nico

2017-10-01

Treatment with broadly neutralizing antibodies (bNAbs) has proven effective against HIV-1 infections in humanized mice, non-human primates, and humans. Due to the high mutation rate of HIV-1, resistance testing of the patient's viral strains to the bNAbs is still inevitable. So far, bNAb resistance can only be tested in expensive and time-consuming neutralization experiments. Here, we introduce well-performing computational models that predict the neutralization response of HIV-1 to bNAbs given only the envelope sequence of the virus. Using non-linear support vector machines based on a string kernel, the models learnt even the important binding sites of bNAbs with more complex epitopes, i.e., the CD4 binding site targeting bNAbs, proving thereby the biological relevance of the models. To increase the interpretability of the models, we additionally provide a new kind of motif logo for each query sequence, visualizing those residues of the test sequence that influenced the prediction outcome the most. Moreover, we predicted the neutralization sensitivity of around 34,000 HIV-1 samples from different time points to a broad range of bNAbs, enabling the first analysis of HIV resistance to bNAbs on a global scale. The analysis showed for many of the bNAbs a trend towards antibody resistance over time, which had previously only been discovered for a small non-representative subset of the global HIV-1 population.

Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time

PubMed Central

2017-01-01

Treatment with broadly neutralizing antibodies (bNAbs) has proven effective against HIV-1 infections in humanized mice, non-human primates, and humans. Due to the high mutation rate of HIV-1, resistance testing of the patient’s viral strains to the bNAbs is still inevitable. So far, bNAb resistance can only be tested in expensive and time-consuming neutralization experiments. Here, we introduce well-performing computational models that predict the neutralization response of HIV-1 to bNAbs given only the envelope sequence of the virus. Using non-linear support vector machines based on a string kernel, the models learnt even the important binding sites of bNAbs with more complex epitopes, i.e., the CD4 binding site targeting bNAbs, proving thereby the biological relevance of the models. To increase the interpretability of the models, we additionally provide a new kind of motif logo for each query sequence, visualizing those residues of the test sequence that influenced the prediction outcome the most. Moreover, we predicted the neutralization sensitivity of around 34,000 HIV-1 samples from different time points to a broad range of bNAbs, enabling the first analysis of HIV resistance to bNAbs on a global scale. The analysis showed for many of the bNAbs a trend towards antibody resistance over time, which had previously only been discovered for a small non-representative subset of the global HIV-1 population. PMID:29065122

Transcription Factor Map Alignment of Promoter Regions

PubMed Central

Blanco, Enrique; Messeguer, Xavier; Smith, Temple F; Guigó, Roderic

2006-01-01

We address the problem of comparing and characterizing the promoter regions of genes with similar expression patterns. This remains a challenging problem in sequence analysis, because often the promoter regions of co-expressed genes do not show discernible sequence conservation. In our approach, thus, we have not directly compared the nucleotide sequence of promoters. Instead, we have obtained predictions of transcription factor binding sites, annotated the predicted sites with the labels of the corresponding binding factors, and aligned the resulting sequences of labels—to which we refer here as transcription factor maps (TF-maps). To obtain the global pairwise alignment of two TF-maps, we have adapted an algorithm initially developed to align restriction enzyme maps. We have optimized the parameters of the algorithm in a small, but well-curated, collection of human–mouse orthologous gene pairs. Results in this dataset, as well as in an independent much larger dataset from the CISRED database, indicate that TF-map alignments are able to uncover conserved regulatory elements, which cannot be detected by the typical sequence alignments. PMID:16733547

Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting

NASA Astrophysics Data System (ADS)

Chakrabarti, Prasun; Basu, Jayanta Kumar; Kim, Tai-Hoon

In this paper we have pointed out gain sensing on forecast based techniques.We have cited an idea of neural based gain forecasting. Testing of sequence of gain pattern is also verifies using statsistical analysis of fuzzy value assignment. The paper also suggests realization of stable gain condition using K-Means clustering of data mining. A new concept of 3D based gain sensing has been pointed out. The paper also reveals what type of trend analysis can be observed for probabilistic gain prediction.

RSAT: regulatory sequence analysis tools.

PubMed

Thomas-Chollier, Morgane; Sand, Olivier; Turatsinze, Jean-Valéry; Janky, Rekin's; Defrance, Matthieu; Vervisch, Eric; Brohée, Sylvain; van Helden, Jacques

2008-07-01

The regulatory sequence analysis tools (RSAT, http://rsat.ulb.ac.be/rsat/) is a software suite that integrates a wide collection of modular tools for the detection of cis-regulatory elements in genome sequences. The suite includes programs for sequence retrieval, pattern discovery, phylogenetic footprint detection, pattern matching, genome scanning and feature map drawing. Random controls can be performed with random gene selections or by generating random sequences according to a variety of background models (Bernoulli, Markov). Beyond the original word-based pattern-discovery tools (oligo-analysis and dyad-analysis), we recently added a battery of tools for matrix-based detection of cis-acting elements, with some original features (adaptive background models, Markov-chain estimation of P-values) that do not exist in other matrix-based scanning tools. The web server offers an intuitive interface, where each program can be accessed either separately or connected to the other tools. In addition, the tools are now available as web services, enabling their integration in programmatic workflows. Genomes are regularly updated from various genome repositories (NCBI and EnsEMBL) and 682 organisms are currently supported. Since 1998, the tools have been used by several hundreds of researchers from all over the world. Several predictions made with RSAT were validated experimentally and published.

Transcriptome sequencing analysis of novel sRNAs of Kineococcus radiotolerans in response to ionizing radiation.

PubMed

Chen, Zhouwei; Li, Lufeng; Shan, Zhan; Huang, Hannian; Chen, Huan; Ding, Xianfeng; Guo, Jiangfeng; Liu, Lili

2016-11-01

Kineococcus radiotolerans is a Gram-positive, radio-resistant bacterium isolated from a radioactive environment. The small noncoding RNAs (sRNAs) in bacteria are reported to play roles in the immediate response to stress and/or the recovery from stress. The analysis of K. radiotolerans transcriptome sequencing results can identify these sRNAs in a genome-wide detection, using RNA sequencing (RNA-seq) by the deep sequencing technique. In this study, the raw data of radiation-exposed samples (RS) and control samples (CS) were acquired separately from the sequencing platform. There were 217 common sRNA candidates in the two samples screened in the genome-wide scale by bioinformatics analysis. There were 43 differentially expressed sRNA candidates, including 28 up-regulated and 15 down-regulated ones. The down-regulated sRNAs were selected for the sRNA target prediction, of which 12 sRNAs that may modulate the genes related to the transcription regulation and DNA repair were considered as the candidates involved in the radio-resistance regulation system. Copyright © 2016 Elsevier GmbH. All rights reserved.

Correlations of nucleotide substitution rates and base composition of mammalian coding sequences with protein structure.

PubMed

Chiusano, M L; D'Onofrio, G; Alvarez-Valin, F; Jabbari, K; Colonna, G; Bernardi, G

1999-09-30

We investigated the relationships between the nucleotide substitution rates and the predicted secondary structures in the three states representation (alpha-helix, beta-sheet, and coil). The analysis was carried out on 34 alignments, each of which comprised sequences belonging to at least four different mammalian orders. The rates of synonymous substitution were found to be significantly different in regions predicted to be alpha-helix, beta-sheet, or coil. Likewise, the nonsynonymous rates also differ, although expectedly at a lower extent, in the three types of secondary structure, suggesting that different selective constraints associated with the different structures are affecting in a similar way the synonymous and nonsynonymous rates. Moreover, the base composition of the third codon positions is different in coding sequence regions corresponding to different secondary structures of proteins.

Genome analysis and identification of gelatinase encoded gene in Enterobacter aerogenes

NASA Astrophysics Data System (ADS)

Shahimi, Safiyyah; Mutalib, Sahilah Abdul; Khalid, Rozida Abdul; Repin, Rul Aisyah Mat; Lamri, Mohd Fadly; Bakar, Mohd Faizal Abu; Isa, Mohd Noor Mat

2016-11-01

In this study, bioinformatic analysis towards genome sequence of E. aerogenes was done to determine gene encoded for gelatinase. Enterobacter aerogenes was isolated from hot spring water and gelatinase species-specific bacterium to porcine and fish gelatin. This bacterium offers the possibility of enzymes production which is specific to both species gelatine, respectively. Enterobacter aerogenes was partially genome sequenced resulting in 5.0 mega basepair (Mbp) total size of sequence. From pre-process pipeline, 87.6 Mbp of total reads, 68.8 Mbp of total high quality reads and 78.58 percent of high quality percentage was determined. Genome assembly produced 120 contigs with 67.5% of contigs over 1 kilo base pair (kbp), 124856 bp of N50 contig length and 55.17 % of GC base content percentage. About 4705 protein gene was identified from protein prediction analysis. Two candidate genes selected have highest similarity identity percentage against gelatinase enzyme available in Swiss-Prot and NCBI online database. They were NODE_9_length_26866_cov_148.013245_12 containing 1029 base pair (bp) sequence with 342 amino acid sequence and NODE_24_length_155103_cov_177.082458_62 which containing 717 bp sequence with 238 amino acid sequence, respectively. Thus, two paired of primers (forward and reverse) were designed, based on the open reading frame (ORF) of selected genes. Genome analysis of E. aerogenes resulting genes encoded gelatinase were identified.

Analysis of conserved noncoding DNA in Drosophila reveals similar constraints in intergenic and intronic sequences.

PubMed

Bergman, C M; Kreitman, M

2001-08-01

Comparative genomic approaches to gene and cis-regulatory prediction are based on the principle that differential DNA sequence conservation reflects variation in functional constraint. Using this principle, we analyze noncoding sequence conservation in Drosophila for 40 loci with known or suspected cis-regulatory function encompassing >100 kb of DNA. We estimate the fraction of noncoding DNA conserved in both intergenic and intronic regions and describe the length distribution of ungapped conserved noncoding blocks. On average, 22%-26% of noncoding sequences surveyed are conserved in Drosophila, with median block length approximately 19 bp. We show that point substitution in conserved noncoding blocks exhibits transition bias as well as lineage effects in base composition, and occurs more than an order of magnitude more frequently than insertion/deletion (indel) substitution. Overall, patterns of noncoding DNA structure and evolution differ remarkably little between intergenic and intronic conserved blocks, suggesting that the effects of transcription per se contribute minimally to the constraints operating on these sequences. The results of this study have implications for the development of alignment and prediction algorithms specific to noncoding DNA, as well as for models of cis-regulatory DNA sequence evolution.

Characterization of the N-terminal segment used by the barley yellow dwarf virus movement protein to promote interaction with the nuclear membrane of host plant cells.

PubMed

Dennison, Sarah Rachel; Harris, Frederick; Brandenburg, Klaus; Phoenix, David Andrew

2007-11-01

The barley yellow dwarf virus movement protein (BYDV-MP) requires its N-terminal sequence to promote the transport of viral RNA into the nuclear compartment of host plant cells. Here, graphical analysis predicts that this sequence would form a membrane interactive amphiphilic alpha-helix. Confirming this prediction, NT1, a peptide homologue of the BYDV-MP N-terminal sequence, was found to be alpha-helical (65%) in the presence of vesicles mimics of the nuclear membrane. The peptide increased the fluidity of these nuclear membrane mimics (rise in wavenumber of circa 0.5-1.0 cm(-1)) and induced surface pressure changes of 2 mN m(-1) in lipid monolayers with corresponding compositions. Taken with isotherm analysis these results suggest that BYDV-MP forms an N-terminal amphiphilic alpha-helix, which partitions into the nuclear membrane primarily through thermodynamically stable associations with the membrane lipid headgroup region. We speculate that these associations may play a role in targeting of the nuclear membrane by BYDM-MP.

miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments

PubMed Central

Hackenberg, Michael; Rodríguez-Ezpeleta, Naiara; Aransay, Ana M.

2011-01-01

We present a new version of miRanalyzer, a web server and stand-alone tool for the detection of known and prediction of new microRNAs in high-throughput sequencing experiments. The new version has been notably improved regarding speed, scope and available features. Alignments are now based on the ultrafast short-read aligner Bowtie (granting also colour space support, allowing mismatches and improving speed) and 31 genomes, including 6 plant genomes, can now be analysed (previous version contained only 7). Differences between plant and animal microRNAs have been taken into account for the prediction models and differential expression of both, known and predicted microRNAs, between two conditions can be calculated. Additionally, consensus sequences of predicted mature and precursor microRNAs can be obtained from multiple samples, which increases the reliability of the predicted microRNAs. Finally, a stand-alone version of the miRanalyzer that is based on a local and easily customized database is also available; this allows the user to have more control on certain parameters as well as to use specific data such as unpublished assemblies or other libraries that are not available in the web server. miRanalyzer is available at http://bioinfo2.ugr.es/miRanalyzer/miRanalyzer.php. PMID:21515631

Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions

PubMed Central

Laine, Elodie; Carbone, Alessandra

2015-01-01

Protein-protein interactions (PPIs) are essential to all biological processes and they represent increasingly important therapeutic targets. Here, we present a new method for accurately predicting protein-protein interfaces, understanding their properties, origins and binding to multiple partners. Contrary to machine learning approaches, our method combines in a rational and very straightforward way three sequence- and structure-based descriptors of protein residues: evolutionary conservation, physico-chemical properties and local geometry. The implemented strategy yields very precise predictions for a wide range of protein-protein interfaces and discriminates them from small-molecule binding sites. Beyond its predictive power, the approach permits to dissect interaction surfaces and unravel their complexity. We show how the analysis of the predicted patches can foster new strategies for PPIs modulation and interaction surface redesign. The approach is implemented in JET2, an automated tool based on the Joint Evolutionary Trees (JET) method for sequence-based protein interface prediction. JET2 is freely available at www.lcqb.upmc.fr/JET2. PMID:26690684

Use of simulated data sets to evaluate the fidelity of metagenomic processing methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mavromatis, K; Ivanova, N; Barry, Kerrie

2007-01-01

Metagenomics is a rapidly emerging field of research for studying microbial communities. To evaluate methods presently used to process metagenomic sequences, we constructed three simulated data sets of varying complexity by combining sequencing reads randomly selected from 113 isolate genomes. These data sets were designed to model real metagenomes in terms of complexity and phylogenetic composition. We assembled sampled reads using three commonly used genome assemblers (Phrap, Arachne and JAZZ), and predicted genes using two popular gene-finding pipelines (fgenesb and CRITICA/GLIMMER). The phylogenetic origins of the assembled contigs were predicted using one sequence similarity-based ( blast hit distribution) and twomore » sequence composition-based (PhyloPythia, oligonucleotide frequencies) binning methods. We explored the effects of the simulated community structure and method combinations on the fidelity of each processing step by comparison to the corresponding isolate genomes. The simulated data sets are available online to facilitate standardized benchmarking of tools for metagenomic analysis.« less

Use of simulated data sets to evaluate the fidelity of Metagenomicprocessing methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mavromatis, Konstantinos; Ivanova, Natalia; Barry, Kerri

2006-12-01

Metagenomics is a rapidly emerging field of research for studying microbial communities. To evaluate methods presently used to process metagenomic sequences, we constructed three simulated data sets of varying complexity by combining sequencing reads randomly selected from 113 isolate genomes. These data sets were designed to model real metagenomes in terms of complexity and phylogenetic composition. We assembled sampled reads using three commonly used genome assemblers (Phrap, Arachne and JAZZ), and predicted genes using two popular gene finding pipelines (fgenesb and CRITICA/GLIMMER). The phylogenetic origins of the assembled contigs were predicted using one sequence similarity--based (blast hit distribution) and twomore » sequence composition--based (PhyloPythia, oligonucleotide frequencies) binning methods. We explored the effects of the simulated community structure and method combinations on the fidelity of each processing step by comparison to the corresponding isolate genomes. The simulated data sets are available online to facilitate standardized benchmarking of tools for metagenomic analysis.« less

A chemogenomic analysis of the human proteome: application to enzyme families.

PubMed

Bernasconi, Paul; Chen, Min; Galasinski, Scott; Popa-Burke, Ioana; Bobasheva, Anna; Coudurier, Louis; Birkos, Steve; Hallam, Rhonda; Janzen, William P

2007-10-01

Sequence-based phylogenies (SBP) are well-established tools for describing relationships between proteins. They have been used extensively to predict the behavior and sensitivity toward inhibitors of enzymes within a family. The utility of this approach diminishes when comparing proteins with little sequence homology. Even within an enzyme family, SBPs must be complemented by an orthogonal method that is independent of sequence to better predict enzymatic behavior. A chemogenomic approach is demonstrated here that uses the inhibition profile of a 130,000 diverse molecule library to uncover relationships within a set of enzymes. The profile is used to construct a semimetric additive distance matrix. This matrix, in turn, defines a sequence-independent phylogeny (SIP). The method was applied to 97 enzymes (kinases, proteases, and phosphatases). SIP does not use structural information from the molecules used for establishing the profile, thus providing a more heuristic method than the current approaches, which require knowledge of the specific inhibitor's structure. Within enzyme families, SIP shows a good overall correlation with SBP. More interestingly, SIP uncovers distances within families that are not recognizable by sequence-based methods. In addition, SIP allows the determination of distance between enzymes with no sequence homology, thus uncovering novel relationships not predicted by SBP. This chemogenomic approach, used in conjunction with SBP, should prove to be a powerful tool for choosing target combinations for drug discovery programs as well as for guiding the selection of profiling and liability targets.

High-throughput amplification of mature microRNAs in uncharacterized animal models using polyadenylated RNA and stem-loop reverse transcription polymerase chain reaction.

PubMed

Biggar, Kyle K; Wu, Cheng-Wei; Storey, Kenneth B

2014-10-01

This study makes a significant advancement on a microRNA amplification technique previously used for expression analysis and sequencing in animal models without annotated mature microRNA sequences. As research progresses into the post-genomic era of microRNA prediction and analysis, the need for a rapid and cost-effective method for microRNA amplification is critical to facilitate wide-scale analysis of microRNA expression. To facilitate this requirement, we have reoptimized the design of amplification primers and introduced a polyadenylation step to allow amplification of all mature microRNAs from a single RNA sample. Importantly, this method retains the ability to sequence reverse transcription polymerase chain reaction (RT-PCR) products, validating microRNA-specific amplification. Copyright © 2014 Elsevier Inc. All rights reserved.

Sequence-controlled RNA self-processing: computational design, biochemical analysis, and visualization by AFM

PubMed Central

Petkovic, Sonja; Badelt, Stefan; Flamm, Christoph; Delcea, Mihaela

2015-01-01

Reversible chemistry allowing for assembly and disassembly of molecular entities is important for biological self-organization. Thus, ribozymes that support both cleavage and formation of phosphodiester bonds may have contributed to the emergence of functional diversity and increasing complexity of regulatory RNAs in early life. We have previously engineered a variant of the hairpin ribozyme that shows how ribozymes may have circularized or extended their own length by forming concatemers. Using the Vienna RNA package, we now optimized this hairpin ribozyme variant and selected four different RNA sequences that were expected to circularize more efficiently or form longer concatemers upon transcription. (Two-dimensional) PAGE analysis confirms that (i) all four selected ribozymes are catalytically active and (ii) high yields of cyclic species are obtained. AFM imaging in combination with RNA structure prediction enabled us to calculate the distributions of monomers and self-concatenated dimers and trimers. Our results show that computationally optimized molecules do form reasonable amounts of trimers, which has not been observed for the original system so far, and we demonstrate that the combination of theoretical prediction, biochemical and physical analysis is a promising approach toward accurate prediction of ribozyme behavior and design of ribozymes with predefined functions. PMID:25999318

Phylogenetic and Structural Analysis of the Pluripotency Factor Sex-Determining Region Y box2 Gene of Camelus dromedarius (cSox2).

PubMed

Alawad, Abdullah; Alharbi, Sultan; Alhazzaa, Othman; Alagrafi, Faisal; Alkhrayef, Mohammed; Alhamdan, Ziyad; Alenazi, Abdullah; Al-Johi, Hasan; Alanazi, Ibrahim O; Hammad, Mohamed

2016-01-01

Although the sequencing information of Sox2 cDNA for many mammalian is available, the Sox2 cDNA of Camelus dromedaries has not yet been characterized. The objective of this study was to sequence and characterize Sox2 cDNA from the brain of C. dromedarius (also known as Arabian camel). A full coding sequence of the Sox2 gene from the brain of C. dromedarius was amplified by reverse transcription PCRjmc and then sequenced using the 3730XL series platform Sequencer (Applied Biosystem) for the first time. The cDNA sequence displayed an open reading frame of 822 nucleotides, encoding a protein of 273 amino acids. The molecular weight and the isoelectric point of the translated protein were calculated as 29.825 kDa and 10.11, respectively, using bioinformatics analysis. The predicted cSox2 protein sequence exhibited high identity: 99% for Homo sapiens, Mus musculus, Bos taurus, and Vicugna pacos; 98% for Sus scrofa and 93% for Camelus ferus. A 3D structure was built based on the available crystal structure of the HMG-box domain of human stem cell transcription factor Sox2 (PDB: 2 LE4) with 81 residues and predicting bioinformatics software for 273 amino acid residues. The comparison confirms the presence of the HMG-box domain in the cSox2 protein. The orthologous phylogenetic analysis showed that the Sox2 isoform from C. dromedarius was grouped with humans, alpacas, cattle, and pigs. We believe that this genetic and structural information will be a helpful source for the annotation. Furthermore, Sox2 is one of the transcription factors that contributes to the generation-induced pluripotent stem cells (iPSCs), which in turn will probably help generate camel induced pluripotent stem cells (CiPSCs).

Predicting the tolerated sequences for proteins and protein interfaces using RosettaBackrub flexible backbone design.

PubMed

Smith, Colin A; Kortemme, Tanja

2011-01-01

Predicting the set of sequences that are tolerated by a protein or protein interface, while maintaining a desired function, is useful for characterizing protein interaction specificity and for computationally designing sequence libraries to engineer proteins with new functions. Here we provide a general method, a detailed set of protocols, and several benchmarks and analyses for estimating tolerated sequences using flexible backbone protein design implemented in the Rosetta molecular modeling software suite. The input to the method is at least one experimentally determined three-dimensional protein structure or high-quality model. The starting structure(s) are expanded or refined into a conformational ensemble using Monte Carlo simulations consisting of backrub backbone and side chain moves in Rosetta. The method then uses a combination of simulated annealing and genetic algorithm optimization methods to enrich for low-energy sequences for the individual members of the ensemble. To emphasize certain functional requirements (e.g. forming a binding interface), interactions between and within parts of the structure (e.g. domains) can be reweighted in the scoring function. Results from each backbone structure are merged together to create a single estimate for the tolerated sequence space. We provide an extensive description of the protocol and its parameters, all source code, example analysis scripts and three tests applying this method to finding sequences predicted to stabilize proteins or protein interfaces. The generality of this method makes many other applications possible, for example stabilizing interactions with small molecules, DNA, or RNA. Through the use of within-domain reweighting and/or multistate design, it may also be possible to use this method to find sequences that stabilize particular protein conformations or binding interactions over others.

Genome sequence and analysis of a stress-tolerant, wild-derived strain of Saccharomyces cerevisiae used in biofuels research

DOE Office of Scientific and Technical Information (OSTI.GOV)

McIlwain, Sean J.; Peris, Davis; Sardi, Maria

The genome sequences of more than 100 strains of the yeast Saccharomyces cerevisiae have been published. Unfortunately, most of these genome assemblies contain dozens to hundreds of gaps at repetitive sequences, including transposable elements, tRNAs, and subtelomeric regions, which is where novel genes generally reside. Relatively few strains have been chosen for genome sequencing based on their biofuel production potential, leaving an additional knowledge gap. Here, we describe the nearly complete genome sequence of GLBRCY22-3 (Y22-3), a strain of S. cerevisiae derived from the stress-tolerant wild strain NRRL YB-210 and subsequently engineered for xylose metabolism. After benchmarking several genome assemblymore » approaches, we developed a pipeline to integrate Pacific Biosciences (PacBio) and Illumina sequencing data and achieved one of the highest quality genome assemblies for any S. cerevisiae strain. Specifically, the contig N50 is 693 kbp, and the sequences of most chromosomes, the mitochondrial genome, and the 2-micron plasmid are complete. Our annotation predicts 92 genes that are not present in the reference genome of the laboratory strain S288c, over 70% of which were expressed. We predicted functions for 43 of these genes, 28 of which were previously uncharacterized and unnamed. Remarkably, many of these genes are predicted to be involved in stress tolerance and carbon metabolism and are shared with a Brazilian bioethanol production strain, even though the strains differ dramatically at most genetic loci. Lastly, the Y22-3 genome sequence provides an exceptionally high-quality resource for basic and applied research in bioenergy and genetics.« less

Genome sequence and analysis of a stress-tolerant, wild-derived strain of Saccharomyces cerevisiae used in biofuels research

DOE PAGES

McIlwain, Sean J.; Peris, Davis; Sardi, Maria; ...

2016-04-20

The genome sequences of more than 100 strains of the yeast Saccharomyces cerevisiae have been published. Unfortunately, most of these genome assemblies contain dozens to hundreds of gaps at repetitive sequences, including transposable elements, tRNAs, and subtelomeric regions, which is where novel genes generally reside. Relatively few strains have been chosen for genome sequencing based on their biofuel production potential, leaving an additional knowledge gap. Here, we describe the nearly complete genome sequence of GLBRCY22-3 (Y22-3), a strain of S. cerevisiae derived from the stress-tolerant wild strain NRRL YB-210 and subsequently engineered for xylose metabolism. After benchmarking several genome assemblymore » approaches, we developed a pipeline to integrate Pacific Biosciences (PacBio) and Illumina sequencing data and achieved one of the highest quality genome assemblies for any S. cerevisiae strain. Specifically, the contig N50 is 693 kbp, and the sequences of most chromosomes, the mitochondrial genome, and the 2-micron plasmid are complete. Our annotation predicts 92 genes that are not present in the reference genome of the laboratory strain S288c, over 70% of which were expressed. We predicted functions for 43 of these genes, 28 of which were previously uncharacterized and unnamed. Remarkably, many of these genes are predicted to be involved in stress tolerance and carbon metabolism and are shared with a Brazilian bioethanol production strain, even though the strains differ dramatically at most genetic loci. Lastly, the Y22-3 genome sequence provides an exceptionally high-quality resource for basic and applied research in bioenergy and genetics.« less

Fine tangled pili expressed by Haemophilus ducreyi are a novel class of pili.

PubMed Central

Brentjens, R J; Ketterer, M; Apicella, M A; Spinola, S M

1996-01-01

Haemophilus ducreyi synthesizes fine, tangled pili composed predominantly of a protein whose apparent molecular weight is 24,000 (24K). A hybridoma, 2D8, produced a monoclonal antibody (MAb) that bound to a 24K protein in H. ducreyi strains isolated from diverse geographic locations. A lambda gt11 H. ducreyi library was screened with MAb 2D8. A 3.5-kb chromosomal insert from one reactive plaque was amplified and ligated into the pCRII vector. The recombinant plasmid, designated pHD24, expressed a 24K protein in Escherichia coli INV alpha F that bound MAb 2D8. The coding sequence of the 24K gene was localized by exonuclease III digestion. The insert contained a 570-bp open reading frame, designated ftpA (fine, tangled pili). Translation of ftpA predicted a polypeptide with a molecular weight of 21.1K. The predicted N-terminal amino acid sequence of the polypeptide encoded by ftpA was identical to the N-terminal amino acid sequence of purified pilin and lacked a cleavable signal sequence. Primer extension analysis of ftpA confirmed the lack of a leader peptide. The predicted amino acid sequence lacked homology to known pilin sequences but shared homology with the sequences of E. coli Dps and Treponema pallidum antigen TpF1 or 4D, proteins which associate to form ordered rings. An isogenic pilin mutant, H. ducreyi 35000ftpA::mTn3(Cm), was constructed by shuttle mutagenesis and did not contain pili when examined by electron microscopy. We conclude that H. ducreyi synthesizes fine, tangled pili that are composed of a unique major subunit, which may be exported by a signal sequence independent mechanism. PMID:8550517

The genome of flax (Linum usitatissimum) assembled de novo from short shotgun sequence reads.

PubMed

Wang, Zhiwen; Hobson, Neil; Galindo, Leonardo; Zhu, Shilin; Shi, Daihu; McDill, Joshua; Yang, Linfeng; Hawkins, Simon; Neutelings, Godfrey; Datla, Raju; Lambert, Georgina; Galbraith, David W; Grassa, Christopher J; Geraldes, Armando; Cronk, Quentin C; Cullis, Christopher; Dash, Prasanta K; Kumar, Polumetla A; Cloutier, Sylvie; Sharpe, Andrew G; Wong, Gane K-S; Wang, Jun; Deyholos, Michael K

2012-11-01

Flax (Linum usitatissimum) is an ancient crop that is widely cultivated as a source of fiber, oil and medicinally relevant compounds. To accelerate crop improvement, we performed whole-genome shotgun sequencing of the nuclear genome of flax. Seven paired-end libraries ranging in size from 300 bp to 10 kb were sequenced using an Illumina genome analyzer. A de novo assembly, comprised exclusively of deep-coverage (approximately 94× raw, approximately 69× filtered) short-sequence reads (44-100 bp), produced a set of scaffolds with N(50) =694 kb, including contigs with N(50)=20.1 kb. The contig assembly contained 302 Mb of non-redundant sequence representing an estimated 81% genome coverage. Up to 96% of published flax ESTs aligned to the whole-genome shotgun scaffolds. However, comparisons with independently sequenced BACs and fosmids showed some mis-assembly of regions at the genome scale. A total of 43384 protein-coding genes were predicted in the whole-genome shotgun assembly, and up to 93% of published flax ESTs, and 86% of A. thaliana genes aligned to these predicted genes, indicating excellent coverage and accuracy at the gene level. Analysis of the synonymous substitution rates (K(s) ) observed within duplicate gene pairs was consistent with a recent (5-9 MYA) whole-genome duplication in flax. Within the predicted proteome, we observed enrichment of many conserved domains (Pfam-A) that may contribute to the unique properties of this crop, including agglutinin proteins. Together these results show that de novo assembly, based solely on whole-genome shotgun short-sequence reads, is an efficient means of obtaining nearly complete genome sequence information for some plant species. © 2012 The Authors. The Plant Journal © 2012 Blackwell Publishing Ltd.

Complete genome sequence analysis of the fish pathogen Flavobacterium columnare provides insights into antibiotic resistance and pathogenicity related genes.

PubMed

Zhang, Yulei; Zhao, Lijuan; Chen, Wenjie; Huang, Yunmao; Yang, Ling; Sarathbabu, V; Wu, Zaohe; Li, Jun; Nie, Pin; Lin, Li

2017-10-01

We analyzed here the complete genome sequences of a highly virulent Flavobacterium columnare Pf1 strain isolated in our laboratory. The complete genome consists of a 3,171,081 bp circular DNA with 2784 predicted protein-coding genes. Among these, 286 genes were predicted as antibiotic resistance genes, including 32 RND-type efflux pump related genes which were associated with the export of aminoglycosides, indicating inducible aminoglycosides resistances in F. columnare. On the other hand, 328 genes were predicted as pathogenicity related genes which could be classified as virulence factors, gliding motility proteins, adhesins, and many putative secreted proteases. These genes were probably involved in the colonization, invasion and destruction of fish tissues during the infection of F. columnare. Apparently, our obtained complete genome sequences provide the basis for the explanation of the interactions between the F. columnare and the infected fish. The predicted antibiotic resistance and pathogenicity related genes will shed a new light on the development of more efficient preventional strategies against the infection of F. columnare, which is a major worldwide fish pathogen. Copyright © 2017 Elsevier Ltd. All rights reserved.

Efficient pairwise RNA structure prediction using probabilistic alignment constraints in Dynalign

PubMed Central

2007-01-01

Background Joint alignment and secondary structure prediction of two RNA sequences can significantly improve the accuracy of the structural predictions. Methods addressing this problem, however, are forced to employ constraints that reduce computation by restricting the alignments and/or structures (i.e. folds) that are permissible. In this paper, a new methodology is presented for the purpose of establishing alignment constraints based on nucleotide alignment and insertion posterior probabilities. Using a hidden Markov model, posterior probabilities of alignment and insertion are computed for all possible pairings of nucleotide positions from the two sequences. These alignment and insertion posterior probabilities are additively combined to obtain probabilities of co-incidence for nucleotide position pairs. A suitable alignment constraint is obtained by thresholding the co-incidence probabilities. The constraint is integrated with Dynalign, a free energy minimization algorithm for joint alignment and secondary structure prediction. The resulting method is benchmarked against the previous version of Dynalign and against other programs for pairwise RNA structure prediction. Results The proposed technique eliminates manual parameter selection in Dynalign and provides significant computational time savings in comparison to prior constraints in Dynalign while simultaneously providing a small improvement in the structural prediction accuracy. Savings are also realized in memory. In experiments over a 5S RNA dataset with average sequence length of approximately 120 nucleotides, the method reduces computation by a factor of 2. The method performs favorably in comparison to other programs for pairwise RNA structure prediction: yielding better accuracy, on average, and requiring significantly lesser computational resources. Conclusion Probabilistic analysis can be utilized in order to automate the determination of alignment constraints for pairwise RNA structure prediction methods in a principled fashion. These constraints can reduce the computational and memory requirements of these methods while maintaining or improving their accuracy of structural prediction. This extends the practical reach of these methods to longer length sequences. The revised Dynalign code is freely available for download. PMID:17445273

GAMES identifies and annotates mutations in next-generation sequencing projects.

PubMed

Sana, Maria Elena; Iascone, Maria; Marchetti, Daniela; Palatini, Jeff; Galasso, Marco; Volinia, Stefano

2011-01-01

Next-generation sequencing (NGS) methods have the potential for changing the landscape of biomedical science, but at the same time pose several problems in analysis and interpretation. Currently, there are many commercial and public software packages that analyze NGS data. However, the limitations of these applications include output which is insufficiently annotated and of difficult functional comprehension to end users. We developed GAMES (Genomic Analysis of Mutations Extracted by Sequencing), a pipeline aiming to serve as an efficient middleman between data deluge and investigators. GAMES attains multiple levels of filtering and annotation, such as aligning the reads to a reference genome, performing quality control and mutational analysis, integrating results with genome annotations and sorting each mismatch/deletion according to a range of parameters. Variations are matched to known polymorphisms. The prediction of functional mutations is achieved by using different approaches. Overall GAMES enables an effective complexity reduction in large-scale DNA-sequencing projects. GAMES is available free of charge to academic users and may be obtained from http://aqua.unife.it/GAMES.

Methylation analysis of plasma cell-free DNA for breast cancer early detection using bisulfite next-generation sequencing.

PubMed

Li, Zibo; Guo, Xinwu; Tang, Lili; Peng, Limin; Chen, Ming; Luo, Xipeng; Wang, Shouman; Xiao, Zhi; Deng, Zhongping; Dai, Lizhong; Xia, Kun; Wang, Jun

2016-10-01

Circulating cell-free DNA (cfDNA) has been considered as a potential biomarker for non-invasive cancer detection. To evaluate the methylation levels of six candidate genes (EGFR, GREM1, PDGFRB, PPM1E, SOX17, and WRN) in plasma cfDNA as biomarkers for breast cancer early detection, quantitative analysis of the promoter methylation of these genes from 86 breast cancer patients and 67 healthy controls was performed by using microfluidic-PCR-based target enrichment and next-generation bisulfite sequencing technology. The predictive performance of different logistic models based on methylation status of candidate genes was investigated by means of the area under the ROC curve (AUC) and odds ratio (OR) analysis. Results revealed that EGFR, PPM1E, and 8 gene-specific CpG sites showed significantly hypermethylation in cancer patients' plasma and significantly associated with breast cancer (OR ranging from 2.51 to 9.88). The AUC values for these biomarkers were ranging from 0.66 to 0.75. Combinations of multiple hypermethylated genes or CpG sites substantially improved the predictive performance for breast cancer detection. Our study demonstrated the feasibility of quantitative measurement of candidate gene methylation in cfDNA by using microfluidic-PCR-based target enrichment and bisulfite next-generation sequencing, which is worthy of further validation and potentially benefits a broad range of applications in clinical oncology practice. Quantitative analysis of methylation pattern of plasma cfDNA by next-generation sequencing might be a valuable non-invasive tool for early detection of breast cancer.

Spatial Data Structures for Robotic Vehicle Route Planning

DTIC Science & Technology

1988-12-01

goal will be realized in an intelligent Spatial Data Structure Development System (SDSDS) intended for use by Terrain Analysis applications...from the user the details of representation and to permit the infrastructure itself to decide which representations will be most efficient or effective ...to intelligently predict performance of algorithmic sequences and thereby optimize the application (within the accuracy of the prediction models). The

Sequence analysis, structure prediction and functional validation of phaC1/phaC2 genes of Pseudomonas sp. LDC-25 and its importance in polyhydroxyalkanoate accumulation

USDA-ARS?s Scientific Manuscript database

Polyhydroxyalkanoates (PHAs) are attractive biomaterials in both conventional medical devices and tissue engineering. PHA synthase is responsible for catalyzing the formation of polyhydroxyalkanoates (PHA), but its structural information is limited. Hence, the focus of this study is to predict 3D mo...

Sarment: Python modules for HMM analysis and partitioning of sequences.

PubMed

Guéguen, Laurent

2005-08-15

Sarment is a package of Python modules for easy building and manipulation of sequence segmentations. It provides efficient implementation of usual algorithms for hidden Markov Model computation, as well as for maximal predictive partitioning. Owing to its very large variety of criteria for computing segmentations, Sarment can handle many kinds of models. Because of object-oriented programming, the results of the segmentation are very easy tomanipulate.

Efficient use of unlabeled data for protein sequence classification: a comparative study.

PubMed

Kuksa, Pavel; Huang, Pai-Hsi; Pavlovic, Vladimir

2009-04-29

Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags-the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers. Combined with state-of-the-art string kernels, our proposed computational framework achieves very accurate semi-supervised protein remote fold and homology detection on three large unlabeled databases. It outperforms current state-of-the-art methods and exhibits significant reduction in running time. The unlabeled sequences used under the semi-supervised setting resemble the unpolished gemstones; when used as-is, they may carry unnecessary features and hence compromise the classification accuracy but once cut and polished, they improve the accuracy of the classifiers considerably.

[Transcriptome analysis of Dunaliella viridis].

PubMed

Zhu, Shuai-qi; Gong, Yi-fu; Hang, Yu-qing; Liu, Hao; Wang, He-yu

2015-08-01

In order to understand the gene information, function, haloduric pathway (glycerolipid metabolism) and related key genes for Dunaliella viridis, we used Illumina HiSeqTM 2000 high-throughput sequencing technology to sequence its transcriptome. Trinity soft was used to assemble the data to form transcripts. Based on the Clusters of Orthologous Groups (COG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG ) databases, we carried out functional annotation and classification, pathway annotation, and the opening reading fragment (ORF) sequence prediction of transcripts. The key genes in the glycerolipid metabolism were analyzed. The results suggested that 81,593 transcripts were found, and 77,117 ORF sequences were predicted, accounting for 94.50% of all transcripts. COG classification results showed that 16,569 transcripts were assigned to 24 categories. GO classification annotated 76,436 transcripts. The number of transcripts for biologcial processes was 30,678, accounting for 40.14% of all transcripts. KEGG pathway analysis showed that 26,428 transcripts were annotated to 317 pathways, and 131 pathways were related to metabolism, accounting for 41.32% of all annotated pathways. Only one transcript was annotated as coding the key enzyme dihydroxyacetone kinase involved in the glycerolipid pathway. This enzyme could be related to glycerol biosynthesis under salt stress. This study further improved the gene information and laid the foundation of metabolic pathway research for Dunaliella viridis.

1,4-Benzoquinone reductase from Phanerochaete chrysosporium: cDNA cloning and regulation of expression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akileswaran, L.; Brock, B.J.; Cereghino, J.L.

1999-02-01

A cDNA clone encoding a quinone reductase (QR) from the white rot basidiomycete Phanerochaete chrysosporium was isolated and sequenced. The cDNA consisted of 1,007 nucleotides and a poly(A) tail and encoded a deduced protein containing 271 amino acids. The experimentally determined eight-amino-acid N-germinal sequence of the purified QR protein from P. chrysosporium matched amino acids 72 to 79 of the predicted translation product of the cDNA. The M{sub r} of the predicted translation product, beginning with Pro-72, was essentially identical to the experimentally determined M{sub r} of one monomer of the QR dimer, and this finding suggested that QR ismore » synthesized as a proenzyme. The results of in vitro transcription-translation experiments suggested that QR is synthesized as a proenzyme with a 71-amino-acid leader sequence. This leader sequence contains two potential KEX2 cleavage sites and numerous potential cleavage sites for dipeptidyl aminopeptidase. The QR activity in cultures of P. chrysosporium increased following the addition of 2-dimethoxybenzoquinone, vanillic acid, or several other aromatic compounds. An immunoblot analysis indicated that induction resulted in an increase in the amount of QR protein, and a Northern blot analysis indicated that this regulation occurs at the level of the qr mRNA.« less

G-quadruplex prediction in E. coli genome reveals a conserved putative G-quadruplex-Hairpin-Duplex switch.

PubMed

Kaplan, Oktay I; Berber, Burak; Hekim, Nezih; Doluca, Osman

2016-11-02

Many studies show that short non-coding sequences are widely conserved among regulatory elements. More and more conserved sequences are being discovered since the development of next generation sequencing technology. A common approach to identify conserved sequences with regulatory roles relies on topological changes such as hairpin formation at the DNA or RNA level. G-quadruplexes, non-canonical nucleic acid topologies with little established biological roles, are increasingly considered for conserved regulatory element discovery. Since the tertiary structure of G-quadruplexes is strongly dependent on the loop sequence which is disregarded by the generally accepted algorithm, we hypothesized that G-quadruplexes with similar topology and, indirectly, similar interaction patterns, can be determined using phylogenetic clustering based on differences in the loop sequences. Phylogenetic analysis of 52 G-quadruplex forming sequences in the Escherichia coli genome revealed two conserved G-quadruplex motifs with a potential regulatory role. Further analysis revealed that both motifs tend to form hairpins and G quadruplexes, as supported by circular dichroism studies. The phylogenetic analysis as described in this work can greatly improve the discovery of functional G-quadruplex structures and may explain unknown regulatory patterns. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Computational analysis of histidine mutations on the structural stability of human tyrosinases leading to albinism insurgence.

PubMed

Hassan, Mubashir; Abbas, Qamar; Raza, Hussain; Moustafa, Ahmed A; Seo, Sung-Yum

2017-07-25

Misfolding and structural alteration in proteins lead to serious malfunctions and cause various diseases in humans. Mutations at the active binding site in tyrosinase impair structural stability and cause lethal albinism by abolishing copper binding. To evaluate the histidine mutational effect, all mutated structures were built using homology modelling. The protein sequence was retrieved from the UniProt database, and 3D models of original and mutated human tyrosinase sequences were predicted by changing the residual positions within the target sequence separately. Structural and mutational analyses were performed to interpret the significance of mutated residues (N 180 , R 202 , Q 202 , R 211 , Y 363 , R 367 , Y 367 and D 390 ) at the active binding site of tyrosinases. CSpritz analysis depicted that 23.25% residues actively participate in the instability of tyrosinase. The accuracy of predicted models was confirmed through online servers ProSA-web, ERRAT score and VERIFY 3D values. The theoretical pI and GRAVY generated results also showed the accuracy of the predicted models. The CCA negative correlation results depicted that the replacement of mutated residues at His within the active binding site disturbs the structural stability of tyrosinases. The predicted CCA scores of Tyr 367 (-0.079) and Q/R 202 (0.032) revealed that both mutations have more potential to disturb the structural stability. MD simulation analyses of all predicted models justified that Gln 202 , Arg 202 , Tyr 367 and D 390 replacement made the protein structures more susceptible to destabilization. Mutational results showed that the replacement of His with Q/R 202 and Y/R 363 has a lethal effect and may cause melanin associated diseases such as OCA1. Taken together, our computational analysis depicts that the mutated residues such as Q/R 202 and Y/R 363 actively participate in instability and misfolding of tyrosinases, which may govern OCA1 through disturbing the melanin biosynthetic pathway.

Order within disorder: Aggrecan chondroitin sulphate-attachment region provides new structural insights into protein sequences classified as disordered

PubMed Central

Jowitt, Thomas A; Murdoch, Alan D; Baldock, Clair; Berry, Richard; Day, Joanna M; Hardingham, Timothy E

2010-01-01

Structural investigation of proteins containing large stretches of sequences without predicted secondary structure is the focus of much increased attention. Here, we have produced an unglycosylated 30 kDa peptide from the chondroitin sulphate (CS)-attachment region of human aggrecan (CS-peptide), which was predicted to be intrinsically disordered and compared its structure with the adjacent aggrecan G3 domain. Biophysical analyses, including analytical ultracentrifugation, light scattering, and circular dichroism showed that the CS-peptide had an elongated and stiffened conformation in contrast to the globular G3 domain. The results suggested that it contained significant secondary structure, which was sensitive to urea, and we propose that the CS-peptide forms an elongated wormlike molecule based on a dynamic range of energetically equivalent secondary structures stabilized by hydrogen bonds. The dimensions of the structure predicted from small-angle X-ray scattering analysis were compatible with EM images of fully glycosylated aggrecan and a partly glycosylated aggrecan CS2-G3 construct. The semiordered structure identified in CS-peptide was not predicted by common structural algorithms and identified a potentially distinct class of semiordered structure within sequences currently identified as disordered. Sequence comparisons suggested some evidence for comparable structures in proteins encoded by other genes (PRG4, MUC5B, and CBP). The function of these semiordered sequences may serve to spatially position attached folded modules and/or to present polypeptides for modification, such as glycosylation, and to provide templates for the multiple pleiotropic interactions proposed for disordered proteins. Proteins 2010. © 2010 Wiley-Liss, Inc. PMID:20806220

Experimental and statistical post-validation of positive example EST sequences carrying peroxisome targeting signals type 1 (PTS1)

PubMed Central

Lingner, Thomas; Kataya, Amr R. A.; Reumann, Sigrun

2012-01-01

We recently developed the first algorithms specifically for plants to predict proteins carrying peroxisome targeting signals type 1 (PTS1) from genome sequences.1 As validated experimentally, the prediction methods are able to correctly predict unknown peroxisomal Arabidopsis proteins and to infer novel PTS1 tripeptides. The high prediction performance is primarily determined by the large number and sequence diversity of the underlying positive example sequences, which mainly derived from EST databases. However, a few constructs remained cytosolic in experimental validation studies, indicating sequencing errors in some ESTs. To identify erroneous sequences, we validated subcellular targeting of additional positive example sequences in the present study. Moreover, we analyzed the distribution of prediction scores separately for each orthologous group of PTS1 proteins, which generally resembled normal distributions with group-specific mean values. The cytosolic sequences commonly represented outliers of low prediction scores and were located at the very tail of a fitted normal distribution. Three statistical methods for identifying outliers were compared in terms of sensitivity and specificity.” Their combined application allows elimination of erroneous ESTs from positive example data sets. This new post-validation method will further improve the prediction accuracy of both PTS1 and PTS2 protein prediction models for plants, fungi, and mammals. PMID:22415050

Experimental and statistical post-validation of positive example EST sequences carrying peroxisome targeting signals type 1 (PTS1).

PubMed

Lingner, Thomas; Kataya, Amr R A; Reumann, Sigrun

2012-02-01

We recently developed the first algorithms specifically for plants to predict proteins carrying peroxisome targeting signals type 1 (PTS1) from genome sequences. As validated experimentally, the prediction methods are able to correctly predict unknown peroxisomal Arabidopsis proteins and to infer novel PTS1 tripeptides. The high prediction performance is primarily determined by the large number and sequence diversity of the underlying positive example sequences, which mainly derived from EST databases. However, a few constructs remained cytosolic in experimental validation studies, indicating sequencing errors in some ESTs. To identify erroneous sequences, we validated subcellular targeting of additional positive example sequences in the present study. Moreover, we analyzed the distribution of prediction scores separately for each orthologous group of PTS1 proteins, which generally resembled normal distributions with group-specific mean values. The cytosolic sequences commonly represented outliers of low prediction scores and were located at the very tail of a fitted normal distribution. Three statistical methods for identifying outliers were compared in terms of sensitivity and specificity." Their combined application allows elimination of erroneous ESTs from positive example data sets. This new post-validation method will further improve the prediction accuracy of both PTS1 and PTS2 protein prediction models for plants, fungi, and mammals.

In Silico Prediction Analysis of Idiotope-Driven T–B Cell Collaboration in Multiple Sclerosis

PubMed Central

Høglund, Rune A.; Lossius, Andreas; Johansen, Jorunn N.; Homan, Jane; Benth, Jūratė Šaltytė; Robins, Harlan; Bogen, Bjarne; Bremel, Robert D.; Holmøy, Trygve

2017-01-01

Memory B cells acting as antigen-presenting cells are believed to be important in multiple sclerosis (MS), but the antigen they present remains unknown. We hypothesized that B cells may activate CD4+ T cells in the central nervous system of MS patients by presenting idiotopes from their own immunoglobulin variable regions on human leukocyte antigen (HLA) class II molecules. Here, we use bioinformatics prediction analysis of B cell immunoglobulin variable regions from 11 MS patients and 6 controls with other inflammatory neurological disorders (OINDs), to assess whether the prerequisites for such idiotope-driven T–B cell collaboration are present. Our findings indicate that idiotopes from the complementarity determining region (CDR) 3 of MS patients on average have high predicted affinities for disease associated HLA-DRB1*15:01 molecules and are predicted to be endosomally processed by cathepsin S and L in positions that allows such HLA binding to occur. Additionally, complementarity determining region 3 sequences from cerebrospinal fluid (CSF) B cells from MS patients contain on average more rare T cell-exposed motifs that could potentially escape tolerance and stimulate CD4+ T cells than CSF B cells from OIND patients. Many of these features were associated with preferential use of the IGHV4 gene family by CSF B cells from MS patients. This is the first study to combine high-throughput sequencing of patient immune repertoires with large-scale prediction analysis and provides key indicators for future in vitro and in vivo analyses. PMID:29038659

In Silico Prediction Analysis of Idiotope-Driven T-B Cell Collaboration in Multiple Sclerosis.

PubMed

Høglund, Rune A; Lossius, Andreas; Johansen, Jorunn N; Homan, Jane; Benth, Jūratė Šaltytė; Robins, Harlan; Bogen, Bjarne; Bremel, Robert D; Holmøy, Trygve

2017-01-01

Memory B cells acting as antigen-presenting cells are believed to be important in multiple sclerosis (MS), but the antigen they present remains unknown. We hypothesized that B cells may activate CD4 + T cells in the central nervous system of MS patients by presenting idiotopes from their own immunoglobulin variable regions on human leukocyte antigen (HLA) class II molecules. Here, we use bioinformatics prediction analysis of B cell immunoglobulin variable regions from 11 MS patients and 6 controls with other inflammatory neurological disorders (OINDs), to assess whether the prerequisites for such idiotope-driven T-B cell collaboration are present. Our findings indicate that idiotopes from the complementarity determining region (CDR) 3 of MS patients on average have high predicted affinities for disease associated HLA-DRB1*15:01 molecules and are predicted to be endosomally processed by cathepsin S and L in positions that allows such HLA binding to occur. Additionally, complementarity determining region 3 sequences from cerebrospinal fluid (CSF) B cells from MS patients contain on average more rare T cell-exposed motifs that could potentially escape tolerance and stimulate CD4 + T cells than CSF B cells from OIND patients. Many of these features were associated with preferential use of the IGHV4 gene family by CSF B cells from MS patients. This is the first study to combine high-throughput sequencing of patient immune repertoires with large-scale prediction analysis and provides key indicators for future in vitro and in vivo analyses.

RNA-Seq analysis of yak ovary: improving yak gene structure information and mining reproduction-related genes.

PubMed

Lan, DaoLiang; Xiong, XianRong; Wei, YanLi; Xu, Tong; Zhong, JinCheng; Zhi, XiangDong; Wang, Yong; Li, Jian

2014-09-01

RNA-Seq, a high-throughput (HT) sequencing technique, has been used effectively in large-scale transcriptomic studies, and is particularly useful for improving gene structure information and mining of new genes. In this study, RNA-Seq HT technology was employed to analyze the transcriptome of yak ovary. After Illumina-Solexa deep sequencing, 26826516 clean reads with a total of 4828772880 bp were obtained from the ovary library. Alignment analysis showed that 16992 yak genes mapped to the yak genome and 3734 of these genes were involved in alternative splicing. Gene structure refinement analysis showed that 7340 genes that were annotated in the yak genome could be extended at the 5' or 3' ends based on the alignments been the transcripts and the genome sequence. Novel transcript prediction analysis identified 6321 new transcripts with lengths ranging from 180 to 14884 bp, and 2267 of them were predicted to code proteins. BLAST analysis of the new transcripts showed that 1200?4933 mapped to the non-redundant (nr), nucleotide (nt) and/or SwissProt sequence databases. Comparative statistical analysis of the new mapped transcripts showed that the majority of them were similar to genes in Bos taurus (41.4%), Bos grunniens mutus (33.0%), Ovis aries (6.3%), Homo sapiens (2.8%), Mus musculus (1.6%) and other species. Functional analysis showed that these expressed genes were involved in various Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes pathways. GO analysis of the new transcripts found that the largest proportion of them was associated with reproduction. The results of this study will provide a basis for describing the normal transcriptome map of yak ovary and for future studies on yak breeding performance. Moreover, the results confirmed that RNA-Seq HT technology is highly advantageous in improving gene structure information and mining of new genes, as well as in providing valuable data to expand the yak genome information.

A likelihood ratio-based method to predict exact pedigrees for complex families from next-generation sequencing data.

PubMed

Heinrich, Verena; Kamphans, Tom; Mundlos, Stefan; Robinson, Peter N; Krawitz, Peter M

2017-01-01

Next generation sequencing technology considerably changed the way we screen for pathogenic mutations in rare Mendelian disorders. However, the identification of the disease-causing mutation amongst thousands of variants of partly unknown relevance is still challenging and efficient techniques that reduce the genomic search space play a decisive role. Often segregation- or linkage analysis are used to prioritize candidates, however, these approaches require correct information about the degree of relationship among the sequenced samples. For quality assurance an automated control of pedigree structures and sample assignment is therefore highly desirable in order to detect label mix-ups that might otherwise corrupt downstream analysis. We developed an algorithm based on likelihood ratios that discriminates between different classes of relationship for an arbitrary number of genotyped samples. By identifying the most likely class we are able to reconstruct entire pedigrees iteratively, even for highly consanguineous families. We tested our approach on exome data of different sequencing studies and achieved high precision for all pedigree predictions. By analyzing the precision for varying degrees of relatedness or inbreeding we could show that a prediction is robust down to magnitudes of a few hundred loci. A java standalone application that computes the relationships between multiple samples as well as a Rscript that visualizes the pedigree information is available for download as well as a web service at www.gene-talk.de CONTACT: heinrich@molgen.mpg.deSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

A likelihood ratio-based method to predict exact pedigrees for complex families from next-generation sequencing data

PubMed Central

Kamphans, Tom; Mundlos, Stefan; Robinson, Peter N.; Krawitz, Peter M.

2017-01-01

Motivation: Next generation sequencing technology considerably changed the way we screen for pathogenic mutations in rare Mendelian disorders. However, the identification of the disease-causing mutation amongst thousands of variants of partly unknown relevance is still challenging and efficient techniques that reduce the genomic search space play a decisive role. Often segregation- or linkage analysis are used to prioritize candidates, however, these approaches require correct information about the degree of relationship among the sequenced samples. For quality assurance an automated control of pedigree structures and sample assignment is therefore highly desirable in order to detect label mix-ups that might otherwise corrupt downstream analysis. Results: We developed an algorithm based on likelihood ratios that discriminates between different classes of relationship for an arbitrary number of genotyped samples. By identifying the most likely class we are able to reconstruct entire pedigrees iteratively, even for highly consanguineous families. We tested our approach on exome data of different sequencing studies and achieved high precision for all pedigree predictions. By analyzing the precision for varying degrees of relatedness or inbreeding we could show that a prediction is robust down to magnitudes of a few hundred loci. Availability and Implementation: A java standalone application that computes the relationships between multiple samples as well as a Rscript that visualizes the pedigree information is available for download as well as a web service at www.gene-talk.de. Contact: heinrich@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27565584

Microsatellite analysis in the genome of Acanthaceae: An in silico approach.

PubMed

Kaliswamy, Priyadharsini; Vellingiri, Srividhya; Nathan, Bharathi; Selvaraj, Saravanakumar

2015-01-01

Acanthaceae is one of the advanced and specialized families with conventionally used medicinal plants. Simple sequence repeats (SSRs) play a major role as molecular markers for genome analysis and plant breeding. The microsatellites existing in the complete genome sequences would help to attain a direct role in the genome organization, recombination, gene regulation, quantitative genetic variation, and evolution of genes. The current study reports the frequency of microsatellites and appropriate markers for the Acanthaceae family genome sequences. The whole nucleotide sequences of Acanthaceae species were obtained from National Center for Biotechnology Information database and screened for the presence of SSRs. SSR Locator tool was used to predict the microsatellites and inbuilt Primer3 module was used for primer designing. Totally 110 repeats from 108 sequences of Acanthaceae family plant genomes were identified, and the occurrence of dinucleotide repeats was found to be abundant in the genome sequences. The essential amino acid isoleucine was found rich in all the sequences. We also designed the SSR-based primers/markers for 59 sequences of this family that contains microsatellite repeats in their genome. The identified microsatellites and primers might be useful for breeding and genetic studies of plants that belong to Acanthaceae family in the future.

Texture analysis of common renal masses in multiple MR sequences for prediction of pathology

NASA Astrophysics Data System (ADS)

Hoang, Uyen N.; Malayeri, Ashkan A.; Lay, Nathan S.; Summers, Ronald M.; Yao, Jianhua

2017-03-01

This pilot study performs texture analysis on multiple magnetic resonance (MR) images of common renal masses for differentiation of renal cell carcinoma (RCC). Bounding boxes are drawn around each mass on one axial slice in T1 delayed sequence to use for feature extraction and classification. All sequences (T1 delayed, venous, arterial, pre-contrast phases, T2, and T2 fat saturated sequences) are co-registered and texture features are extracted from each sequence simultaneously. Random forest is used to construct models to classify lesions on 96 normal regions, 87 clear cell RCCs, 8 papillary RCCs, and 21 renal oncocytomas; ground truths are verified through pathology reports. The highest performance is seen in random forest model when data from all sequences are used in conjunction, achieving an overall classification accuracy of 83.7%. When using data from one single sequence, the overall accuracies achieved for T1 delayed, venous, arterial, and pre-contrast phase, T2, and T2 fat saturated were 79.1%, 70.5%, 56.2%, 61.0%, 60.0%, and 44.8%, respectively. This demonstrates promising results of utilizing intensity information from multiple MR sequences for accurate classification of renal masses.

Factor structure of paediatric timed motor examination and its relationship with IQ

PubMed Central

MARTIN, REBECCA; TIGERA, CASSIE; DENCKLA, MARTHA B; MAHONE, E MARK

2012-01-01

AIM Brain systems supporting higher cognitive and motor control develop in a parallel manner, dependent on functional integrity and maturation of related regions, suggesting neighbouring neural circuitry. Concurrent examination of motor and cognitive control can provide a window into neurological development. However, identification of performance-based measures that do not correlate with IQ has been a challenge. METHOD Timed motor performance from the Physical and Neurological Examination of Subtle Signs and IQ were analysed in 136 children aged 6 to 16 (mean age 10y 2.6mo, SD 2y 6.4mo; 98 female, 38male) attending an outpatient neuropsychology clinic and 136 right-handed comparison individuals aged 6 to 16 (mean age 10y 3.1mo, SD 2y 6.1mo; 98 female, 38male). Timed activities – three repetitive movements (toe tapping, hand patting, finger tapping) and three sequenced movements (heel–toe tap, hand pronate/supinate, finger sequencing) each performed on the right and left – were included in exploratory factor analyses. RESULTS Among comparison individuals, factor analysis yielded two factors – repetitive and sequenced movements – with the sequenced factor significantly predictive of Verbal IQ (VIQ) (ΔR2=0.018, p=0.019), but not the repetitive factor (ΔR2=0.004, p=0.39). Factor analysis within the clinical group yielded two similar factors (repetitive and sequenced), both significantly predictive of VIQ, (ΔR2=0.028, p=0.015; ΔR2=0.046, p=0.002 respectively). INTERPRETATION Among typical children, repetitive timed tasks may be independent of IQ; however, sequenced tasks share more variance, implying shared neural substrates. Among neurologically vulnerable populations, however, both sequenced and repetitive movements covary with IQ, suggesting that repetitive speed is more indicative of underlying neurological integrity. PMID:20412260

DMRT gene cluster analysis in the platypus: new insights into genomic organization and regulatory regions.

PubMed

El-Mogharbel, Nisrine; Wakefield, Matthew; Deakin, Janine E; Tsend-Ayush, Enkhjargal; Grützner, Frank; Alsop, Amber; Ezaz, Tariq; Marshall Graves, Jennifer A

2007-01-01

We isolated and characterized a cluster of platypus DMRT genes and compared their arrangement, location, and sequence across vertebrates. The DMRT gene cluster on human 9p24.3 harbors, in order, DMRT1, DMRT3, and DMRT2, which share a DM domain. DMRT1 is highly conserved and involved in sexual development in vertebrates, and deletions in this region cause sex reversal in humans. Sequence comparisons of DMRT genes between species have been valuable in identifying exons, control regions, and conserved nongenic regions (CNGs). The addition of platypus sequences is expected to be particularly valuable, since monotremes fill a gap in the vertebrate genome coverage. We therefore isolated and fully sequenced platypus BAC clones containing DMRT3 and DMRT2 as well as DMRT1 and then generated multispecies alignments and ran prediction programs followed by experimental verification to annotate this gene cluster. We found that the three genes have 58-66% identity to their human orthologues, lie in the same order as in other vertebrates, and colocate on 1 of the 10 platypus sex chromosomes, X5. We also predict that optimal annotation of the newly sequenced platypus genome will be challenging. The analysis of platypus sequence revealed differences in structure and sequence of the DMRT gene cluster. Multispecies comparison was particularly effective for detecting CNGs, revealing several novel potential regulatory regions within DMRT3 and DMRT2 as well as DMRT1. RT-PCR indicated that platypus DMRT1 and DMRT3 are expressed specifically in the adult testis (and not ovary), but DMRT2 has a wider expression profile, as it does for other mammals. The platypus DMRT1 expression pattern, and its location on an X chromosome, suggests an involvement in monotreme sexual development.

Determining protein function and interaction from genome analysis

DOEpatents

Eisenberg, David; Marcotte, Edward M.; Thompson, Michael J.; Pellegrini, Matteo; Yeates, Todd O.

2004-08-03

A computational method system, and computer program are provided for inferring functional links from genome sequences. One method is based on the observation that some pairs of proteins A' and B' have homologs in another organism fused into a single protein chain AB. A trans-genome comparison of sequences can reveal these AB sequences, which are Rosetta Stone sequences because they decipher an interaction between A' and B. Another method compares the genomic sequence of two or more organisms to create a phylogenetic profile for each protein indicating its presence or absence across all the genomes. The profile provides information regarding functional links between different families of proteins. In yet another method a combination of the above two methods is used to predict functional links.

Assigning protein functions by comparative genome analysis protein phylogenetic profiles

DOEpatents

Pellegrini, Matteo; Marcotte, Edward M.; Thompson, Michael J.; Eisenberg, David; Grothe, Robert; Yeates, Todd O.

2003-05-13

A computational method system, and computer program are provided for inferring functional links from genome sequences. One method is based on the observation that some pairs of proteins A' and B' have homologs in another organism fused into a single protein chain AB. A trans-genome comparison of sequences can reveal these AB sequences, which are Rosetta Stone sequences because they decipher an interaction between A' and B. Another method compares the genomic sequence of two or more organisms to create a phylogenetic profile for each protein indicating its presence or absence across all the genomes. The profile provides information regarding functional links between different families of proteins. In yet another method a combination of the above two methods is used to predict functional links.

GeneBuilder: interactive in silico prediction of gene structure.

PubMed

Milanesi, L; D'Angelo, D; Rogozin, I B

1999-01-01

Prediction of gene structure in newly sequenced DNA becomes very important in large genome sequencing projects. This problem is complicated due to the exon-intron structure of eukaryotic genes and because gene expression is regulated by many different short nucleotide domains. In order to be able to analyse the full gene structure in different organisms, it is necessary to combine information about potential functional signals (promoter region, splice sites, start and stop codons, 3' untranslated region) together with the statistical properties of coding sequences (coding potential), information about homologous proteins, ESTs and repeated elements. We have developed the GeneBuilder system which is based on prediction of functional signals and coding regions by different approaches in combination with similarity searches in proteins and EST databases. The potential gene structure models are obtained by using a dynamic programming method. The program permits the use of several parameters for gene structure prediction and refinement. During gene model construction, selecting different exon homology levels with a protein sequence selected from a list of homologous proteins can improve the accuracy of the gene structure prediction. In the case of low homology, GeneBuilder is still able to predict the gene structure. The GeneBuilder system has been tested by using the standard set (Burset and Guigo, Genomics, 34, 353-367, 1996) and the performances are: 0.89 sensitivity and 0.91 specificity at the nucleotide level. The total correlation coefficient is 0.88. The GeneBuilder system is implemented as a part of the WebGene a the URL: http://www.itba.mi. cnr.it/webgene and TRADAT (TRAncription Database and Analysis Tools) launcher URL: http://www.itba.mi.cnr.it/tradat.

Statistical Analysis of CFD Solutions From the Fifth AIAA Drag Prediction Workshop

NASA Technical Reports Server (NTRS)

Morrison, Joseph H.

2013-01-01

A graphical framework is used for statistical analysis of the results from an extensive N-version test of a collection of Reynolds-averaged Navier-Stokes computational fluid dynamics codes. The solutions were obtained by code developers and users from North America, Europe, Asia, and South America using a common grid sequence and multiple turbulence models for the June 2012 fifth Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic configuration for this workshop was the Common Research Model subsonic transport wing-body previously used for the 4th Drag Prediction Workshop. This work continues the statistical analysis begun in the earlier workshops and compares the results from the grid convergence study of the most recent workshop with previous workshops.

Comparative Analysis of the Orphan CRISPR2 Locus in 242 Enterococcus faecalis Strains

PubMed Central

Hullahalli, Karthik; Rodrigues, Marinelle; Schmidt, Brendan D.; Li, Xiang; Bhardwaj, Pooja; Palmer, Kelli L.

2015-01-01

Clustered, Regularly Interspaced Short Palindromic Repeats and their associated Cas proteins (CRISPR-Cas) provide prokaryotes with a mechanism for defense against mobile genetic elements (MGEs). A CRISPR locus is a molecular memory of MGE encounters. It contains an array of short sequences, called spacers, that generally have sequence identity to MGEs. Three different CRISPR loci have been identified among strains of the opportunistic pathogen Enterococcus faecalis. CRISPR1 and CRISPR3 are associated with the cas genes necessary for blocking MGEs, but these loci are present in only a subset of E. faecalis strains. The orphan CRISPR2 lacks cas genes and is ubiquitous in E. faecalis, although its spacer content varies from strain to strain. Because CRISPR2 is a variable locus occurring in all E. faecalis, comparative analysis of CRISPR2 sequences may provide information about the clonality of E. faecalis strains. We examined CRISPR2 sequences from 228 E. faecalis genomes in relationship to subspecies phylogenetic lineages (sequence types; STs) determined by multilocus sequence typing (MLST), and to a genome phylogeny generated for a representative 71 genomes. We found that specific CRISPR2 sequences are associated with specific STs and with specific branches on the genome tree. To explore possible applications of CRISPR2 analysis, we evaluated 14 E. faecalis bloodstream isolates using CRISPR2 analysis and MLST. CRISPR2 analysis identified two groups of clonal strains among the 14 isolates, an assessment that was confirmed by MLST. CRISPR2 analysis was also used to accurately predict the ST of a subset of isolates. We conclude that CRISPR2 analysis, while not a replacement for MLST, is an inexpensive method to assess clonality among E. faecalis isolates, and can be used in conjunction with MLST to identify recombination events occurring between STs. PMID:26398194

Test Operations Procedure (TOP) 07-2-033 Weaponized Manned/Unmanned Aircraft

DTIC Science & Technology

2013-01-14

Uncertainty Analysis ........................................................................... 24 6. PRESENTATION OF DATA...Testing. a. The purpose of testing is to confirm the predictions of engineering analysis , simulation, and subsystem tests. It is not to be... lunar illumination + 10 percent Laser hit point + 0.2 meter Sensor resolution target + 5 percent Weapon impact sequence High-speed

Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System for RNA.

PubMed

Xu, Weijia; Ozer, Stuart; Gutell, Robin R

2009-01-01

With an increasingly large amount of sequences properly aligned, comparative sequence analysis can accurately identify not only common structures formed by standard base pairing but also new types of structural elements and constraints. However, traditional methods are too computationally expensive to perform well on large scale alignment and less effective with the sequences from diversified phylogenetic classifications. We propose a new approach that utilizes coevolutional rates among pairs of nucleotide positions using phylogenetic and evolutionary relationships of the organisms of aligned sequences. With a novel data schema to manage relevant information within a relational database, our method, implemented with a Microsoft SQL Server 2005, showed 90% sensitivity in identifying base pair interactions among 16S ribosomal RNA sequences from Bacteria, at a scale 40 times bigger and 50% better sensitivity than a previous study. The results also indicated covariation signals for a few sets of cross-strand base stacking pairs in secondary structure helices, and other subtle constraints in the RNA structure.

Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System for RNA

PubMed Central

Xu, Weijia; Ozer, Stuart; Gutell, Robin R.

2010-01-01

With an increasingly large amount of sequences properly aligned, comparative sequence analysis can accurately identify not only common structures formed by standard base pairing but also new types of structural elements and constraints. However, traditional methods are too computationally expensive to perform well on large scale alignment and less effective with the sequences from diversified phylogenetic classifications. We propose a new approach that utilizes coevolutional rates among pairs of nucleotide positions using phylogenetic and evolutionary relationships of the organisms of aligned sequences. With a novel data schema to manage relevant information within a relational database, our method, implemented with a Microsoft SQL Server 2005, showed 90% sensitivity in identifying base pair interactions among 16S ribosomal RNA sequences from Bacteria, at a scale 40 times bigger and 50% better sensitivity than a previous study. The results also indicated covariation signals for a few sets of cross-strand base stacking pairs in secondary structure helices, and other subtle constraints in the RNA structure. PMID:20502534

Protein evolution analysis of S-hydroxynitrile lyase by complete sequence design utilizing the INTMSAlign software.

PubMed

Nakano, Shogo; Asano, Yasuhisa

2015-02-03

Development of software and methods for design of complete sequences of functional proteins could contribute to studies of protein engineering and protein evolution. To this end, we developed the INTMSAlign software, and used it to design functional proteins and evaluate their usefulness. The software could assign both consensus and correlation residues of target proteins. We generated three protein sequences with S-selective hydroxynitrile lyase (S-HNL) activity, which we call designed S-HNLs; these proteins folded as efficiently as the native S-HNL. Sequence and biochemical analysis of the designed S-HNLs suggested that accumulation of neutral mutations occurs during the process of S-HNLs evolution from a low-activity form to a high-activity (native) form. Taken together, our results demonstrate that our software and the associated methods could be applied not only to design of complete sequences, but also to predictions of protein evolution, especially within families such as esterases and S-HNLs.

Protein evolution analysis of S-hydroxynitrile lyase by complete sequence design utilizing the INTMSAlign software

NASA Astrophysics Data System (ADS)

Nakano, Shogo; Asano, Yasuhisa

2015-02-01

Development of software and methods for design of complete sequences of functional proteins could contribute to studies of protein engineering and protein evolution. To this end, we developed the INTMSAlign software, and used it to design functional proteins and evaluate their usefulness. The software could assign both consensus and correlation residues of target proteins. We generated three protein sequences with S-selective hydroxynitrile lyase (S-HNL) activity, which we call designed S-HNLs; these proteins folded as efficiently as the native S-HNL. Sequence and biochemical analysis of the designed S-HNLs suggested that accumulation of neutral mutations occurs during the process of S-HNLs evolution from a low-activity form to a high-activity (native) form. Taken together, our results demonstrate that our software and the associated methods could be applied not only to design of complete sequences, but also to predictions of protein evolution, especially within families such as esterases and S-HNLs.

Secondary Structure Predictions for Long RNA Sequences Based on Inversion Excursions and MapReduce.

PubMed

Yehdego, Daniel T; Zhang, Boyu; Kodimala, Vikram K R; Johnson, Kyle L; Taufer, Michela; Leung, Ming-Ying

2013-05-01

Secondary structures of ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Experimental observations and computing limitations suggest that we can approach the secondary structure prediction problem for long RNA sequences by segmenting them into shorter chunks, predicting the secondary structures of each chunk individually using existing prediction programs, and then assembling the results to give the structure of the original sequence. The selection of cutting points is a crucial component of the segmenting step. Noting that stem-loops and pseudoknots always contain an inversion, i.e., a stretch of nucleotides followed closely by its inverse complementary sequence, we developed two cutting methods for segmenting long RNA sequences based on inversion excursions: the centered and optimized method. Each step of searching for inversions, chunking, and predictions can be performed in parallel. In this paper we use a MapReduce framework, i.e., Hadoop, to extensively explore meaningful inversion stem lengths and gap sizes for the segmentation and identify correlations between chunking methods and prediction accuracy. We show that for a set of long RNA sequences in the RFAM database, whose secondary structures are known to contain pseudoknots, our approach predicts secondary structures more accurately than methods that do not segment the sequence, when the latter predictions are possible computationally. We also show that, as sequences exceed certain lengths, some programs cannot computationally predict pseudoknots while our chunking methods can. Overall, our predicted structures still retain the accuracy level of the original prediction programs when compared with known experimental secondary structure.

Intronic splicing mutations in PTCH1 cause Gorlin syndrome.

PubMed

Bholah, Zaynab; Smith, Miriam J; Byers, Helen J; Miles, Emma K; Evans, D Gareth; Newman, William G

2014-09-01

Gorlin syndrome is an autosomal dominant disorder characterized by multiple early-onset basal cell carcinoma, odontogenic keratocysts and skeletal abnormalities. It is caused by heterozygous mutations in the tumour suppressor PTCH1. Routine clinical genetic testing, by Sanger sequencing and multiplex ligation-dependent probe amplification (MLPA) to confirm a clinical diagnosis of Gorlin syndrome, identifies a mutation in 60-90 % of cases. We undertook RNA analysis on lymphocytes from ten individuals diagnosed with Gorlin syndrome, but without known PTCH1 mutations by exonic sequencing or MLPA. Two altered PTCH1 transcripts were identified. Genomic DNA sequence analysis identified an intron 7 mutation c.1068-10T>A, which created a strong cryptic splice acceptor site, leading to an intronic insertion of eight bases; this is predicted to create a frameshift p.(His358Alafs*12). Secondly, a deep intronic mutation c.2561-2057A>G caused an inframe insertion of 78 intronic bases in the cDNA transcript, leading to a premature stop codon p.(Gly854fs*3). The mutations are predicted to cause loss of function of PTCH1, consistent with its tumour suppressor function. The findings indicate the importance of RNA analysis to detect intronic mutations in PTCH1 not identified by routine screening techniques.

Transcriptomic analysis of rice aleurone cells identified a novel abscisic acid response element.

PubMed

Watanabe, Kenneth A; Homayouni, Arielle; Gu, Lingkun; Huang, Kuan-Ying; Ho, Tuan-Hua David; Shen, Qingxi J

2017-09-01

Seeds serve as a great model to study plant responses to drought stress, which is largely mediated by abscisic acid (ABA). The ABA responsive element (ABRE) is a key cis-regulatory element in ABA signalling. However, its consensus sequence (ACGTG(G/T)C) is present in the promoters of only about 40% of ABA-induced genes in rice aleurone cells, suggesting other ABREs may exist. To identify novel ABREs, RNA sequencing was performed on aleurone cells of rice seeds treated with 20 μM ABA. Gibbs sampling was used to identify enriched elements, and particle bombardment-mediated transient expression studies were performed to verify the function. Gene ontology analysis was performed to predict the roles of genes containing the novel ABREs. This study revealed 2443 ABA-inducible genes and a novel ABRE, designated as ABREN, which was experimentally verified to mediate ABA signalling in rice aleurone cells. Many of the ABREN-containing genes are predicted to be involved in stress responses and transcription. Analysis of other species suggests that the ABREN may be monocot specific. This study also revealed interesting expression patterns of genes involved in ABA metabolism and signalling. Collectively, this study advanced our understanding of diverse cis-regulatory sequences and the transcriptomes underlying ABA responses in rice aleurone cells. © 2017 John Wiley & Sons Ltd.

Thermodynamic characterization of tandem mismatches found in naturally occurring RNA

PubMed Central

Christiansen, Martha E.; Znosko, Brent M.

2009-01-01

Although all sequence symmetric tandem mismatches and some sequence asymmetric tandem mismatches have been thermodynamically characterized and a model has been proposed to predict the stability of previously unmeasured sequence asymmetric tandem mismatches [Christiansen,M.E. and Znosko,B.M. (2008) Biochemistry, 47, 4329–4336], experimental thermodynamic data for frequently occurring tandem mismatches is lacking. Since experimental data is preferred over a predictive model, the thermodynamic parameters for 25 frequently occurring tandem mismatches were determined. These new experimental values, on average, are 1.0 kcal/mol different from the values predicted for these mismatches using the previous model. The data for the sequence asymmetric tandem mismatches reported here were then combined with the data for 72 sequence asymmetric tandem mismatches that were published previously, and the parameters used to predict the thermodynamics of previously unmeasured sequence asymmetric tandem mismatches were updated. The average absolute difference between the measured values and the values predicted using these updated parameters is 0.5 kcal/mol. This updated model improves the prediction for tandem mismatches that were predicted rather poorly by the previous model. This new experimental data and updated predictive model allow for more accurate calculations of the free energy of RNA duplexes containing tandem mismatches, and, furthermore, should allow for improved prediction of secondary structure from sequence. PMID:19509311

Predicting DNA hybridization kinetics from sequence

NASA Astrophysics Data System (ADS)

Zhang, Jinny X.; Fang, John Z.; Duan, Wei; Wu, Lucia R.; Zhang, Angela W.; Dalchau, Neil; Yordanov, Boyan; Petersen, Rasmus; Phillips, Andrew; Zhang, David Yu

2018-01-01

Hybridization is a key molecular process in biology and biotechnology, but so far there is no predictive model for accurately determining hybridization rate constants based on sequence information. Here, we report a weighted neighbour voting (WNV) prediction algorithm, in which the hybridization rate constant of an unknown sequence is predicted based on similarity reactions with known rate constants. To construct this algorithm we first performed 210 fluorescence kinetics experiments to observe the hybridization kinetics of 100 different DNA target and probe pairs (36 nt sub-sequences of the CYCS and VEGF genes) at temperatures ranging from 28 to 55 °C. Automated feature selection and weighting optimization resulted in a final six-feature WNV model, which can predict hybridization rate constants of new sequences to within a factor of 3 with ∼91% accuracy, based on leave-one-out cross-validation. Accurate prediction of hybridization kinetics allows the design of efficient probe sequences for genomics research.

A domain-centric solution to functional genomics via dcGO Predictor

PubMed Central

2013-01-01

Background Computational/manual annotations of protein functions are one of the first routes to making sense of a newly sequenced genome. Protein domain predictions form an essential part of this annotation process. This is due to the natural modularity of proteins with domains as structural, evolutionary and functional units. Sometimes two, three, or more adjacent domains (called supra-domains) are the operational unit responsible for a function, e.g. via a binding site at the interface. These supra-domains have contributed to functional diversification in higher organisms. Traditionally functional ontologies have been applied to individual proteins, rather than families of related domains and supra-domains. We expect, however, to some extent functional signals can be carried by protein domains and supra-domains, and consequently used in function prediction and functional genomics. Results Here we present a domain-centric Gene Ontology (dcGO) perspective. We generalize a framework for automatically inferring ontological terms associated with domains and supra-domains from full-length sequence annotations. This general framework has been applied specifically to primary protein-level annotations from UniProtKB-GOA, generating GO term associations with SCOP domains and supra-domains. The resulting 'dcGO Predictor', can be used to provide functional annotation to protein sequences. The functional annotation of sequences in the Critical Assessment of Function Annotation (CAFA) has been used as a valuable opportunity to validate our method and to be assessed by the community. The functional annotation of all completely sequenced genomes has demonstrated the potential for domain-centric GO enrichment analysis to yield functional insights into newly sequenced or yet-to-be-annotated genomes. This generalized framework we have presented has also been applied to other domain classifications such as InterPro and Pfam, and other ontologies such as mammalian phenotype and disease ontology. The dcGO and its predictor are available at http://supfam.org/SUPERFAMILY/dcGO including an enrichment analysis tool. Conclusions As functional units, domains offer a unique perspective on function prediction regardless of whether proteins are multi-domain or single-domain. The 'dcGO Predictor' holds great promise for contributing to a domain-centric functional understanding of genomes in the next generation sequencing era. PMID:23514627

How Many Protein Sequences Fold to a Given Structure? A Coevolutionary Analysis.

PubMed

Tian, Pengfei; Best, Robert B

2017-10-17

Quantifying the relationship between protein sequence and structure is key to understanding the protein universe. A fundamental measure of this relationship is the total number of amino acid sequences that can fold to a target protein structure, known as the "sequence capacity," which has been suggested as a proxy for how designable a given protein fold is. Although sequence capacity has been extensively studied using lattice models and theory, numerical estimates for real protein structures are currently lacking. In this work, we have quantitatively estimated the sequence capacity of 10 proteins with a variety of different structures using a statistical model based on residue-residue co-evolution to capture the variation of sequences from the same protein family. Remarkably, we find that even for the smallest protein folds, such as the WW domain, the number of foldable sequences is extremely large, exceeding the Avogadro constant. In agreement with earlier theoretical work, the calculated sequence capacity is positively correlated with the size of the protein, or better, the density of contacts. This allows the absolute sequence capacity of a given protein to be approximately predicted from its structure. On the other hand, the relative sequence capacity, i.e., normalized by the total number of possible sequences, is an extremely tiny number and is strongly anti-correlated with the protein length. Thus, although there may be more foldable sequences for larger proteins, it will be much harder to find them. Lastly, we have correlated the evolutionary age of proteins in the CATH database with their sequence capacity as predicted by our model. The results suggest a trade-off between the opposing requirements of high designability and the likelihood of a novel fold emerging by chance. Published by Elsevier Inc.

Complete genomic sequence of a Tobacco rattle virus isolate from Michigan-grown potatoes.

PubMed

Crosslin, James M; Hamm, Philip B; Kirk, William W; Hammond, Rosemarie W

2010-04-01

Tobacco rattle virus (TRV) causes stem mottle on potato leaves and necrotic arcs and rings in potato tubers, known as corky ringspot disease. Recently, TRV was reported in Michigan potato tubers cv. FL1879 exhibiting corky ringspot disease. Sequence analysis of the RNA-1-encoded 16-kDa gene of the Michigan isolate, designated MI-1, revealed homology to TRV isolates from Florida and Washington. Here, we report the complete genomic sequence of RNA-1 (6,791 nt) and RNA-2 (3,685 nt) of TRV MI-1. RNA-1 is predicted to contain four open reading frames, and the genome structure and phylogenetic analyses of the RNA-1 nucleotide sequence revealed significant homologies to the known sequences of other TRV-1 isolates. The relationships based on the full-length nucleotide sequence were different from than those based on the 16-kDa gene encoded on genomic RNA-1 and reflect sequence variation within a 20-25-aa residue region of the 16-kDa protein. MI-1 RNA-2 is predicted to contain three ORFs, encoding the coat protein (CP), a 37.6-kDa protein (ORF 2b), and a 33.6-kDa protein (ORF 2c). In addition, it contains a region of similarity to the 3' terminus of RNA-1, including a truncated portion of the 16-kDa cistron. Phylogenetic analysis of RNA-2, based on a comparison of nucleotide sequences with other members of the genus Tobravirus, indicates that TRV MI-1 and other North American isolates cluster as a distinct group. TRV M1-1 is only the second North American isolate for which there is a complete sequence of the genome, and it is distinct from the North American isolate TRV ORY. The relationship of the TRV MI-1 isolate to other tobravirus isolates is discussed.

De novo Assembly of the Indo-Pacific Humpback Dolphin Leucocyte Transcriptome to Identify Putative Genes Involved in the Aquatic Adaptation and Immune Response

PubMed Central

Xia, Jia; Yang, Lili; Chen, Jialin; Wu, Yuping; Yi, Meisheng

2013-01-01

Background The Indo-Pacific humpback dolphin (Sousa chinensis), a marine mammal species inhabited in the waters of Southeast Asia, South Africa and Australia, has attracted much attention because of the dramatic decline in population size in the past decades, which raises the concern of extinction. So far, this species is poorly characterized at molecular level due to little sequence information available in public databases. Recent advances in large-scale RNA sequencing provide an efficient approach to generate abundant sequences for functional genomic analyses in the species with un-sequenced genomes. Principal Findings We performed a de novo assembly of the Indo-Pacific humpback dolphin leucocyte transcriptome by Illumina sequencing. 108,751 high quality sequences from 47,840,388 paired-end reads were generated, and 48,868 and 46,587 unigenes were functionally annotated by BLAST search against the NCBI non-redundant and Swiss-Prot protein databases (E-value<10−5), respectively. In total, 16,467 unigenes were clustered into 25 functional categories by searching against the COG database, and BLAST2GO search assigned 37,976 unigenes to 61 GO terms. In addition, 36,345 unigenes were grouped into 258 KEGG pathways. We also identified 9,906 simple sequence repeats and 3,681 putative single nucleotide polymorphisms as potential molecular markers in our assembled sequences. A large number of unigenes were predicted to be involved in immune response, and many genes were predicted to be relevant to adaptive evolution and cetacean-specific traits. Conclusion This study represented the first transcriptome analysis of the Indo-Pacific humpback dolphin, an endangered species. The de novo transcriptome analysis of the unique transcripts will provide valuable sequence information for discovery of new genes, characterization of gene expression, investigation of various pathways and adaptive evolution, as well as identification of genetic markers. PMID:24015242

De novo assembly of the Indo-Pacific humpback dolphin leucocyte transcriptome to identify putative genes involved in the aquatic adaptation and immune response.

PubMed

Gui, Duan; Jia, Kuntong; Xia, Jia; Yang, Lili; Chen, Jialin; Wu, Yuping; Yi, Meisheng

2013-01-01

The Indo-Pacific humpback dolphin (Sousa chinensis), a marine mammal species inhabited in the waters of Southeast Asia, South Africa and Australia, has attracted much attention because of the dramatic decline in population size in the past decades, which raises the concern of extinction. So far, this species is poorly characterized at molecular level due to little sequence information available in public databases. Recent advances in large-scale RNA sequencing provide an efficient approach to generate abundant sequences for functional genomic analyses in the species with un-sequenced genomes. We performed a de novo assembly of the Indo-Pacific humpback dolphin leucocyte transcriptome by Illumina sequencing. 108,751 high quality sequences from 47,840,388 paired-end reads were generated, and 48,868 and 46,587 unigenes were functionally annotated by BLAST search against the NCBI non-redundant and Swiss-Prot protein databases (E-value<10(-5)), respectively. In total, 16,467 unigenes were clustered into 25 functional categories by searching against the COG database, and BLAST2GO search assigned 37,976 unigenes to 61 GO terms. In addition, 36,345 unigenes were grouped into 258 KEGG pathways. We also identified 9,906 simple sequence repeats and 3,681 putative single nucleotide polymorphisms as potential molecular markers in our assembled sequences. A large number of unigenes were predicted to be involved in immune response, and many genes were predicted to be relevant to adaptive evolution and cetacean-specific traits. This study represented the first transcriptome analysis of the Indo-Pacific humpback dolphin, an endangered species. The de novo transcriptome analysis of the unique transcripts will provide valuable sequence information for discovery of new genes, characterization of gene expression, investigation of various pathways and adaptive evolution, as well as identification of genetic markers.

Targeted Analysis of Whole Genome Sequence Data to Diagnose Genetic Cardiomyopathy

DOE PAGES

Golbus, Jessica R.; Puckelwartz, Megan J.; Dellefave-Castillo, Lisa; ...

2014-09-01

Background—Cardiomyopathy is highly heritable but genetically diverse. At present, genetic testing for cardiomyopathy uses targeted sequencing to simultaneously assess the coding regions of more than 50 genes. New genes are routinely added to panels to improve the diagnostic yield. With the anticipated $1000 genome, it is expected that genetic testing will shift towards comprehensive genome sequencing accompanied by targeted gene analysis. Therefore, we assessed the reliability of whole genome sequencing and targeted analysis to identify cardiomyopathy variants in 11 subjects with cardiomyopathy. Methods and Results—Whole genome sequencing with an average of 37× coverage was combined with targeted analysis focused onmore » 204 genes linked to cardiomyopathy. Genetic variants were scored using multiple prediction algorithms combined with frequency data from public databases. This pipeline yielded 1-14 potentially pathogenic variants per individual. Variants were further analyzed using clinical criteria and/or segregation analysis. Three of three previously identified primary mutations were detected by this analysis. In six subjects for whom the primary mutation was previously unknown, we identified mutations that segregated with disease, had clinical correlates, and/or had additional pathological correlation to provide evidence for causality. For two subjects with previously known primary mutations, we identified additional variants that may act as modifiers of disease severity. In total, we identified the likely pathological mutation in 9 of 11 (82%) subjects. We conclude that these pilot data demonstrate that ~30-40× coverage whole genome sequencing combined with targeted analysis is feasible and sensitive to identify rare variants in cardiomyopathy-associated genes.« less

Depositional sequence analysis and sedimentologic modeling for improved prediction of Pennsylvanian reservoirs (Annex 1). Annual report, February 1, 1991--January 31, 1992

DOE Office of Scientific and Technical Information (OSTI.GOV)

Watney, W.L.

1992-08-01

Interdisciplinary studies of the Upper Pennsylvanian Lansing and Kansas City groups have been undertaken in order to improve the geologic characterization of petroleum reservoirs and to develop a quantitative understanding of the processes responsible for formation of associated depositional sequences. To this end, concepts and methods of sequence stratigraphy are being used to define and interpret the three-dimensional depositional framework of the Kansas City Group. The investigation includes characterization of reservoir rocks in oil fields in western Kansas, description of analog equivalents in near-surface and surface sites in southeastern Kansas, and construction of regional structural and stratigraphic framework to linkmore » the site specific studies. Geologic inverse and simulation models are being developed to integrate quantitative estimates of controls on sedimentation to produce reconstructions of reservoir-bearing strata in an attempt to enhance our ability to predict reservoir characteristics.« less

Depositional sequence analysis and sedimentologic modeling for improved prediction of Pennsylvanian reservoirs (Annex 1)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Watney, W.L.

1992-01-01

Interdisciplinary studies of the Upper Pennsylvanian Lansing and Kansas City groups have been undertaken in order to improve the geologic characterization of petroleum reservoirs and to develop a quantitative understanding of the processes responsible for formation of associated depositional sequences. To this end, concepts and methods of sequence stratigraphy are being used to define and interpret the three-dimensional depositional framework of the Kansas City Group. The investigation includes characterization of reservoir rocks in oil fields in western Kansas, description of analog equivalents in near-surface and surface sites in southeastern Kansas, and construction of regional structural and stratigraphic framework to linkmore » the site specific studies. Geologic inverse and simulation models are being developed to integrate quantitative estimates of controls on sedimentation to produce reconstructions of reservoir-bearing strata in an attempt to enhance our ability to predict reservoir characteristics.« less

Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle.

PubMed

van Binsbergen, Rianne; Calus, Mario P L; Bink, Marco C A M; van Eeuwijk, Fred A; Schrooten, Chris; Veerkamp, Roel F

2015-09-17

In contrast to currently used single nucleotide polymorphism (SNP) panels, the use of whole-genome sequence data is expected to enable the direct estimation of the effects of causal mutations on a given trait. This could lead to higher reliabilities of genomic predictions compared to those based on SNP genotypes. Also, at each generation of selection, recombination events between a SNP and a mutation can cause decay in reliability of genomic predictions based on markers rather than on the causal variants. Our objective was to investigate the use of imputed whole-genome sequence genotypes versus high-density SNP genotypes on (the persistency of) the reliability of genomic predictions using real cattle data. Highly accurate phenotypes based on daughter performance and Illumina BovineHD Beadchip genotypes were available for 5503 Holstein Friesian bulls. The BovineHD genotypes (631,428 SNPs) of each bull were used to impute whole-genome sequence genotypes (12,590,056 SNPs) using the Beagle software. Imputation was done using a multi-breed reference panel of 429 sequenced individuals. Genomic estimated breeding values for three traits were predicted using a Bayesian stochastic search variable selection (BSSVS) model and a genome-enabled best linear unbiased prediction model (GBLUP). Reliabilities of predictions were based on 2087 validation bulls, while the other 3416 bulls were used for training. Prediction reliabilities ranged from 0.37 to 0.52. BSSVS performed better than GBLUP in all cases. Reliabilities of genomic predictions were slightly lower with imputed sequence data than with BovineHD chip data. Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low. No increase in persistency of prediction reliability using imputed sequence data was observed. Compared to BovineHD genotype data, using imputed sequence data for genomic prediction produced no advantage. To investigate the putative advantage of genomic prediction using (imputed) sequence data, a training set with a larger number of individuals that are distantly related to each other and genomic prediction models that incorporate biological information on the SNPs or that apply stricter SNP pre-selection should be considered.

Identification and positional distribution analysis of transcription factor binding sites for genes from the wheat fl-cDNA sequences.

PubMed

Chen, Zhen-Yong; Guo, Xiao-Jiang; Chen, Zhong-Xu; Chen, Wei-Ying; Wang, Ji-Rui

2017-06-01

The binding sites of transcription factors (TFs) in upstream DNA regions are called transcription factor binding sites (TFBSs). TFBSs are important elements for regulating gene expression. To date, there have been few studies on the profiles of TFBSs in plants. In total, 4,873 sequences with 5' upstream regions from 8530 wheat fl-cDNA sequences were used to predict TFBSs. We found 4572 TFBSs for the MADS TF family, which was twice as many as for bHLH (1951), B3 (1951), HB superfamily (1914), ERF (1820), and AP2/ERF (1725) TFs, and was approximately four times higher than the remaining TFBS types. The percentage of TFBSs and TF members showed a distinct distribution in different tissues. Overall, the distribution of TFBSs in the upstream regions of wheat fl-cDNA sequences had significant difference. Meanwhile, high frequencies of some types of TFBSs were found in specific regions in the upstream sequences. Both TFs and fl-cDNA with TFBSs predicted in the same tissues exhibited specific distribution preferences for regulating gene expression. The tissue-specific analysis of TFs and fl-cDNA with TFBSs provides useful information for functional research, and can be used to identify relationships between tissue-specific TFs and fl-cDNA with TFBSs. Moreover, the positional distribution of TFBSs indicates that some types of wheat TFBS have different positional distribution preferences in the upstream regions of genes.

Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.

PubMed

Park, Byungkyu; Im, Jinyong; Tuvshinjargal, Narankhuu; Lee, Wook; Han, Kyungsook

2014-11-01

As many structures of protein-DNA complexes have been known in the past years, several computational methods have been developed to predict DNA-binding sites in proteins. However, its inverse problem (i.e., predicting protein-binding sites in DNA) has received much less attention. One of the reasons is that the differences between the interaction propensities of nucleotides are much smaller than those between amino acids. Another reason is that DNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding DNA nucleotides is much harder than predicting DNA-binding amino acids. We computed the interaction propensity (IP) of nucleotide triplets with amino acids using an extensive dataset of protein-DNA complexes, and developed two support vector machine (SVM) models that predict protein-binding nucleotides from sequence data alone. One SVM model predicts protein-binding nucleotides using DNA sequence data alone, and the other SVM model predicts protein-binding nucleotides using both DNA and protein sequences. In a 10-fold cross-validation with 1519 DNA sequences, the SVM model that uses DNA sequence data only predicted protein-binding nucleotides with an accuracy of 67.0%, an F-measure of 67.1%, and a Matthews correlation coefficient (MCC) of 0.340. With an independent dataset of 181 DNAs that were not used in training, it achieved an accuracy of 66.2%, an F-measure 66.3% and a MCC of 0.324. Another SVM model that uses both DNA and protein sequences achieved an accuracy of 69.6%, an F-measure of 69.6%, and a MCC of 0.383 in a 10-fold cross-validation with 1519 DNA sequences and 859 protein sequences. With an independent dataset of 181 DNAs and 143 proteins, it showed an accuracy of 67.3%, an F-measure of 66.5% and a MCC of 0.329. Both in cross-validation and independent testing, the second SVM model that used both DNA and protein sequence data showed better performance than the first model that used DNA sequence data. To the best of our knowledge, this is the first attempt to predict protein-binding nucleotides in a given DNA sequence from the sequence data alone. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Molecular cloning and characterization of Aspergillus nidulans cyclophilin B.

PubMed

Joseph, J D; Heitman, J; Means, A R

1999-06-01

Cyclophilins are an evolutionarily conserved family of proteins which serve as the intracellular receptors for the immunosuppressive drug cyclosporin A. Here we report the characterization of the first cyclophilin cloned from the filamentous fungus Aspergillus nidulans (CYPB). Sequence analysis of the cypB gene predicts an encoded protein with highest homology to the murine cyclophilin B protein. The sequence similarity includes an N-terminal sequence predicted to target the protein to the endoplasmic reticulum (ER) as well as a C-terminal sequence predicted to retain the mature protein in the ER. The bacterially expressed hexa-histidine tagged protein displays peptidyl-prolyl isomerase activity which is inhibited by cyclosporin A. In the presence of cyclosporin A, the expressed protein also inhibits purified calcineurin. When the endogenous cypB gene was disrupted and placed under the control of the regulatable alcohol dehydrogenase promoter, the strain demonstrated no detectable growth phenotype under conditions which induce or repress cypB transcription. Induction or repression of the cypB gene also did not effect sensitivity of A. nidulans to cyclosporin A. cypB mRNA levels were significantly elevated under severe heat shock conditions, indicating a possible role for the A. nidulans cyclophilin B protein during growth in high stress environments. Copyright 1999 Academic Press.

Accelerated probabilistic inference of RNA structure evolution

PubMed Central

Holmes, Ian

2005-01-01

Background Pairwise stochastic context-free grammars (Pair SCFGs) are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage. The same problem is faced by other RNA alignment-and-folding algorithms based on Sankoff's 1985 algorithm. It is therefore desirable to constrain such algorithms, by pre-processing the sequences and using this first pass to limit the range of structures and/or alignments that can be considered. Results We demonstrate how flexible classes of constraint can be imposed, greatly reducing the computational costs while maintaining a high quality of structural homology prediction. Any score-attributed context-free grammar (e.g. energy-based scoring schemes, or conditionally normalized Pair SCFGs) is amenable to this treatment. It is now possible to combine independent structural and alignment constraints of unprecedented general flexibility in Pair SCFG alignment algorithms. We outline several applications to the bioinformatics of RNA sequence and structure, including Waterman-Eggert N-best alignments and progressive multiple alignment. We evaluate the performance of the algorithm on test examples from the RFAM database. Conclusion A program, Stemloc, that implements these algorithms for efficient RNA sequence alignment and structure prediction is available under the GNU General Public License. PMID:15790387

Improved Model for Predicting the Free Energy Contribution of Dinucleotide Bulges to RNA Duplex Stability.

PubMed

Tomcho, Jeremy C; Tillman, Magdalena R; Znosko, Brent M

2015-09-01

Predicting the secondary structure of RNA is an intermediate in predicting RNA three-dimensional structure. Commonly, determining RNA secondary structure from sequence uses free energy minimization and nearest neighbor parameters. Current algorithms utilize a sequence-independent model to predict free energy contributions of dinucleotide bulges. To determine if a sequence-dependent model would be more accurate, short RNA duplexes containing dinucleotide bulges with different sequences and nearest neighbor combinations were optically melted to derive thermodynamic parameters. These data suggested energy contributions of dinucleotide bulges were sequence-dependent, and a sequence-dependent model was derived. This model assigns free energy penalties based on the identity of nucleotides in the bulge (3.06 kcal/mol for two purines, 2.93 kcal/mol for two pyrimidines, 2.71 kcal/mol for 5'-purine-pyrimidine-3', and 2.41 kcal/mol for 5'-pyrimidine-purine-3'). The predictive model also includes a 0.45 kcal/mol penalty for an A-U pair adjacent to the bulge and a -0.28 kcal/mol bonus for a G-U pair adjacent to the bulge. The new sequence-dependent model results in predicted values within, on average, 0.17 kcal/mol of experimental values, a significant improvement over the sequence-independent model. This model and new experimental values can be incorporated into algorithms that predict RNA stability and secondary structure from sequence.

Canine Lat1: molecular structure, distribution and its expression in cancer samples.

PubMed

Ochiai, Hideharu; Morishita, Taiki; Onda, Ken; Sugiyama, Hiroki; Maruo, Takuya

2012-07-01

A full-length cDNA sequence of canine L-type amino acid transporter 1 (Lat1) was determined from a canine brain. The sequence was 1828 bp long and was predicted to encode 485 amino acid polypeptides. The deduced amino acid sequence of canine Lat1 showed 93.2% and 91.1% similarities to those of humans and rats, respectively. Northern blot analysis detected Lat1 expression in the cerebellum at 4 kb, and Western blot analysis showed a single band at 40 kDa. RT-PCR analysis revealed a distinct expression of Lat1 in the pancreas and testis in addition to the cerebrum and cerebellum. Notably, Lat1 expression was observed in the tissues of thyroid cancer, melanoma and hemangiopericytoma. Although the cancer samples examined were not enough, Lat1 may serve as a useful biomarker of cancer cells in veterinary clinic.

Identification of an additional member of the protein-tyrosine-phosphatase family: evidence for alternative splicing in the tyrosine phosphatase domain.

PubMed Central

Matthews, R J; Cahir, E D; Thomas, M L

1990-01-01

Protein-tyrosine-phosphatases (protein-tyrosine-phosphate phosphohydrolase, EC 3.13.48) have been implicated in the regulation of cell growth; however, to date few tyrosine phosphatases have been characterized. To identify additional family members, the cDNA for the human tyrosine phosphatase leukocyte common antigen (LCA; CD45) was used to screen, under low stringency, a mouse pre-B-cell cDNA library. Two cDNA clones were isolated and sequence analysis predicts a protein sequence of 793 amino acids. We have named the molecule LRP (LCA-related phosphatase). RNA transfer analysis indicates that the cDNAs were derived from a 3.2-kilobase mRNA. The LRP mRNA is transcribed in a wide variety of tissues. The predicted protein structure can be divided into the following structural features: a short 19-amino acid leader sequence, an exterior domain of 123 amino acids that is predicted to be highly glycosylated, a 24-amino acid membrane-spanning region, and a 627-amino acid cytoplasmic region. The cytoplasmic region contains two approximately 260-amino acid domains, each with homology to the tyrosine phosphatase family. One of the cDNA clones differed in that it had a 108-base-pair insertion that, while preserving the reading frame, would disrupt the first protein-tyrosine-phosphatase domain. Analysis of genomic DNA indicates that the insertion is due to an alternatively spliced exon. LRP appears to be evolutionarily conserved as a putative homologue has been identified in the invertebrate Styela plicata. Images PMID:2162042

Prediction of protein secondary structure content for the twilight zone sequences.

PubMed

Homaeian, Leila; Kurgan, Lukasz A; Ruan, Jishou; Cios, Krzysztof J; Chen, Ke

2007-11-15

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure. (c) 2007 Wiley-Liss, Inc.

Functional region prediction with a set of appropriate homologous sequences-an index for sequence selection by integrating structure and sequence information with spatial statistics

PubMed Central

2012-01-01

Background The detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions. Results We have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest index score, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence-based methods. Conclusions Appropriate homologous sequences are selected automatically and objectively by the index. Such sequence selection improved the performance of functional region prediction. As far as we know, this is the first approach in which spatial statistics have been applied to protein analyses. Such integration of structure and sequence information would be useful for other bioinformatics problems. PMID:22643026

Online interactive analysis of protein structure ensembles with Bio3D-web.

PubMed

Skjærven, Lars; Jariwala, Shashank; Yao, Xin-Qiu; Grant, Barry J

2016-11-15

Bio3D-web is an online application for analyzing the sequence, structure and conformational heterogeneity of protein families. Major functionality is provided for identifying protein structure sets for analysis, their alignment and refined structure superposition, sequence and structure conservation analysis, mapping and clustering of conformations and the quantitative comparison of their predicted structural dynamics. Bio3D-web is based on the Bio3D and Shiny R packages. All major browsers are supported and full source code is available under a GPL2 license from http://thegrantlab.org/bio3d-web CONTACT: bjgrant@umich.edu or lars.skjarven@uib.no. © The Author 2016. Published by Oxford University Press.

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.

PubMed

Song, Jiangning; Yuan, Zheng; Tan, Hao; Huber, Thomas; Burrage, Kevin

2007-12-01

Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects. The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide

Prediction of BRCA1 and BRCA2 mutation status using post-irradiation assays of lymphoblastoid cell lines is compromised by inter-cell-line phenotypic variability.

PubMed

Lovelock, Paul K; Wong, Ee Ming; Sprung, Carl N; Marsh, Anna; Hobson, Karen; French, Juliet D; Southey, Melissa; Sculley, Tom; Pandeya, Nirmala; Brown, Melissa A; Chenevix-Trench, Georgia; Spurdle, Amanda B; McKay, Michael J

2007-09-01

Assays to determine the pathogenicity of unclassified sequence variants in disease-associated genes include the analysis of lymphoblastoid cell lines (LCLs). We assessed the ability of several assays of LCLs to distinguish carriers of germline BRCA1 and BRCA2 gene mutations from mutation-negative controls to determine their utility for use in a diagnostic setting. Post-ionising radiation cell viability and micronucleus formation, and telomere length were assayed in LCLs carrying BRCA1 or BRCA2 mutations, and in unaffected mutation-negative controls. Post-irradiation cell viability and micronucleus induction assays of LCLs from individuals carrying pathogenic BRCA1 mutations, unclassified BRCA1 sequence variants or wildtype BRCA1 sequence showed significant phenotypic heterogeneity within each group. Responses were not consistent with predicted functional consequences of known pathogenic or normal sequences. Telomere length was also highly heterogeneous within groups of LCLs carrying pathogenic BRCA1 or BRCA2 mutations, and normal BRCA1 sequences, and was not predictive of mutation status. Given the significant degree of phenotypic heterogeneity of LCLs after gamma-irradiation, and the lack of association with BRCA1 or BRCA2 mutation status, we conclude that the assays evaluated in this study should not be used as a means of differentiating pathogenic and non-pathogenic sequence variants for clinical application. We suggest that a range of normal controls must be included in any functional assays of LCLs to ensure that any observed differences between samples reflect the genotype under investigation rather than generic inter-individual variation.

Primary and secondary structural analyses of glutathione S-transferase pi from human placenta.

PubMed

Ahmad, H; Wilson, D E; Fritz, R R; Singh, S V; Medh, R D; Nagle, G T; Awasthi, Y C; Kurosky, A

1990-05-01

The primary structure of glutathione S-transferase (GST) pi from a single human placenta was determined. The structure was established by chemical characterization of tryptic and cyanogen bromide peptides as well as automated sequence analysis of the intact enzyme. The structural analysis indicated that the protein is comprised of 209 amino acid residues and gave no evidence of post-translational modifications. The amino acid sequence differed from that of the deduced amino acid sequence determined by nucleotide sequence analysis of a cDNA clone (Kano, T., Sakai, M., and Muramatsu, M., 1987, Cancer Res. 47, 5626-5630) at position 104 which contained both valine and isoleucine whereas the deduced sequence from nucleotide sequence analysis identified only isoleucine at this position. These results demonstrated that in the one individual placenta studied at least two GST pi genes are coexpressed, probably as a result of allelomorphism. Computer assisted consensus sequence evaluation identified a hydrophobic region in GST pi (residues 155-181) that was predicted to be either a buried transmembrane helical region or a signal sequence region. The significance of this hydrophobic region was interpreted in relation to the mode of action of the enzyme especially in regard to the potential involvement of a histidine in the active site mechanism. A comparison of the chemical similarity of five known human GST complete enzyme structures, one of pi, one of mu, two of alpha, and one microsomal, gave evidence that all five enzymes have evolved by a divergent evolutionary process after gene duplication, with the microsomal enzyme representing the most divergent form.

Complete Genome Sequence and Comparative Analysis of the Fish Pathogen Lactococcus garvieae

PubMed Central

Oshima, Kenshiro; Yoshizaki, Mariko; Kawanishi, Michiko; Nakaya, Kohei; Suzuki, Takehito; Miyauchi, Eiji; Ishii, Yasuo; Tanabe, Soichi; Murakami, Masaru; Hattori, Masahira

2011-01-01

Lactococcus garvieae causes fatal haemorrhagic septicaemia in fish such as yellowtail. The comparative analysis of genomes of a virulent strain Lg2 and a non-virulent strain ATCC 49156 of L. garvieae revealed that the two strains shared a high degree of sequence identity, but Lg2 had a 16.5-kb capsule gene cluster that is absent in ATCC 49156. The capsule gene cluster was composed of 15 genes, of which eight genes are highly conserved with those in exopolysaccharide biosynthesis gene cluster often found in Lactococcus lactis strains. Sequence analysis of the capsule gene cluster in the less virulent strain L. garvieae Lg2-S, Lg2-derived strain, showed that two conserved genes were disrupted by a single base pair deletion, respectively. These results strongly suggest that the capsule is crucial for virulence of Lg2. The capsule gene cluster of Lg2 may be a genomic island from several features such as the presence of insertion sequences flanked on both ends, different GC content from the chromosomal average, integration into the locus syntenic to other lactococcal genome sequences, and distribution in human gut microbiomes. The analysis also predicted other potential virulence factors such as haemolysin. The present study provides new insights into understanding of the virulence mechanisms of L. garvieae in fish. PMID:21829716

Nucleotide sequence determination of guinea-pig casein B mRNA reveals homology with bovine and rat alpha s1 caseins and conservation of the non-coding regions of the mRNA.

PubMed Central

Hall, L; Laird, J E; Craig, R K

1984-01-01

Nucleotide sequence analysis of cloned guinea-pig casein B cDNA sequences has identified two casein B variants related to the bovine and rat alpha s1 caseins. Amino acid homology was largely confined to the known bovine or predicted rat phosphorylation sites and within the 'signal' precursor sequence. Comparison of the deduced nucleotide sequence of the guinea-pig and rat alpha s1 casein mRNA species showed greater sequence conservation in the non-coding than in the coding regions, suggesting a functional and possibly regulatory role for the non-coding regions of casein mRNA. The results provide insight into the evolution of the casein genes, and raise questions as to the role of conserved nucleotide sequences within the non-coding regions of mRNA species. Images Fig. 1. PMID:6548375

Predicted stem-loop structures and variation in nucleotide sequence of 3' noncoding regions among animal calicivirus genomes.

PubMed

Seal, B S; Neill, J D; Ridpath, J F

1994-07-01

Caliciviruses are nonenveloped with a polyadenylated genome of approximately 7.6 kb and a single capsid protein. The "RNA Fold" computer program was used to analyze 3'-terminal noncoding sequences of five feline calicivirus (FCV), rabbit hemorrhagic disease virus (RHDV), and two San Miguel sea lion virus (SMSV) isolates. The FCV 3'-terminal sequences are 40-46 nucleotides in length and 72-91% similar. The FCV sequences were predicted to contain two possible duplex structures and one stem-loop structure with free energies of -2.1 to -18.2 kcal/mole. The RHDV genomic 3'-terminal RNA sequences are 54 nucleotides in length and share 49% sequence similarity to homologous regions of the FCV genome. The RHDV sequence was predicted to form two duplex structures in the 3'-terminal noncoding region with a single stem-loop structure, resembling that of FCV. In contrast, the SMSV 1 and 4 genomic 3'-terminal noncoding sequences were 185 and 182 nucleotides in length, respectively. Ten possible duplex structures were predicted with an average structural free energy of -35 kcal/mole. Sequence similarity between the two SMSV isolates was 75%. Furthermore, extensive cloverleaflike structures are predicted in the 3' noncoding region of the SMSV genome, in contrast to the predicted single stem-loop structures of FCV or RHDV.

The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v.4)

DOE PAGES

Huntemann, Marcel; Ivanova, Natalia N.; Mavromatis, Konstantinos; ...

2016-02-24

The DOE-JGI Metagenome Annotation Pipeline (MAP v.4) performs structural and functional annotation for metagenomic sequences that are submitted to the Integrated Microbial Genomes with Microbiomes (IMG/M) system for comparative analysis. The pipeline runs on nucleotide sequences provide d via the IMG submission site. Users must first define their analysis projects in GOLD and then submit the associated sequence datasets consisting of scaffolds/contigs with optional coverage information and/or unassembled reads in fasta and fastq file formats. The MAP processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNAs, as well as CRISPR elements. Structural annotation ismore » followed by functional annotation including assignment of protein product names and connection to various protein family databases.« less

The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v.4)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huntemann, Marcel; Ivanova, Natalia N.; Mavromatis, Konstantinos

The DOE-JGI Metagenome Annotation Pipeline (MAP v.4) performs structural and functional annotation for metagenomic sequences that are submitted to the Integrated Microbial Genomes with Microbiomes (IMG/M) system for comparative analysis. The pipeline runs on nucleotide sequences provide d via the IMG submission site. Users must first define their analysis projects in GOLD and then submit the associated sequence datasets consisting of scaffolds/contigs with optional coverage information and/or unassembled reads in fasta and fastq file formats. The MAP processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNAs, as well as CRISPR elements. Structural annotation ismore » followed by functional annotation including assignment of protein product names and connection to various protein family databases.« less

Mutational analysis of the Wolfram syndrome gene in two families with chromosome 4p-linked bipolar affective disorder.

PubMed

Evans, K L; Lawson, D; Meitinger, T; Blackwood, D H; Porteous, D J

2000-04-03

Bipolar affective disorder (BPAD) is a complex disease with a significant genetic component. Heterozygous carriers of Wolfram syndrome (WFS) are at increased risk of psychiatric illness. A gene for WFS (WFS1) has recently been cloned and mapped to chromosome 4p, in the general region we previously reported as showing linkage to BPAD. Here we present sequence analysis of the WFS1 coding sequence in five affected individuals from two chromosome 4p-linked families. This resulted in the identification of six polymorphisms, two of which are predicted to change the amino acid sequence of the WFS1 protein, however none of the changes segregated with disease status. Am. J. Med. Genet. (Neuropsychiatr. Genet.) 96:158-160, 2000. Copyright 2000 Wiley-Liss, Inc.

HIV drug resistance testing among patients failing second line antiretroviral therapy. Comparison of in-house and commercial sequencing.

PubMed

Chimukangara, Benjamin; Varyani, Bhavini; Shamu, Tinei; Mutsvangwa, Junior; Manasa, Justen; White, Elizabeth; Chimbetete, Cleophas; Luethy, Ruedi; Katzenstein, David

2017-05-01

HIV genotyping is often unavailable in low and middle-income countries due to infrastructure requirements and cost. We compared genotype resistance testing in patients with virologic failure, by amplification of HIV pol gene, followed by "in-house" sequencing and commercial sequencing. Remnant plasma samples from adults and children failing second-line ART were amplified and sequenced using in-house and commercial di-deoxysequencing, and analyzed in Harare, Zimbabwe and at Stanford, U.S.A, respectively. HIV drug resistance mutations were determined using the Stanford HIV drug resistance database. Twenty-six of 28 samples were amplified and 25 were successfully genotyped. Comparison of average percent nucleotide and amino acid identities between 23 pairs sequenced in both laboratories were 99.51 (±0.56) and 99.11 (±0.95), respectively. All pairs clustered together in phylogenetic analysis. Sequencing analysis identified 6/23 pairs with mutation discordances resulting in differences in phenotype, but these did not impact future regimens. The results demonstrate our ability to produce good quality drug resistance data in-house. Despite discordant mutations in some sequence pairs, the phenotypic predictions were not clinically significant. Copyright © 2016 Elsevier B.V. All rights reserved.

RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction

PubMed Central

Cruz, José Almeida; Blanchet, Marc-Frédérick; Boniecki, Michal; Bujnicki, Janusz M.; Chen, Shi-Jie; Cao, Song; Das, Rhiju; Ding, Feng; Dokholyan, Nikolay V.; Flores, Samuel Coulbourn; Huang, Lili; Lavender, Christopher A.; Lisi, Véronique; Major, François; Mikolajczak, Katarzyna; Patel, Dinshaw J.; Philips, Anna; Puton, Tomasz; Santalucia, John; Sijenyi, Fredrick; Hermann, Thomas; Rother, Kristian; Rother, Magdalena; Serganov, Alexander; Skorupski, Marcin; Soltysinski, Tomasz; Sripakdeevong, Parin; Tuszynska, Irina; Weeks, Kevin M.; Waldsich, Christina; Wildauer, Michael; Leontis, Neocles B.; Westhof, Eric

2012-01-01

We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises. PMID:22361291

Accuracy of the high-throughput amplicon sequencing to identify species within the genus Aspergillus.

PubMed

Lee, Seungeun; Yamamoto, Naomichi

2015-12-01

This study characterized the accuracy of high-throughput amplicon sequencing to identify species within the genus Aspergillus. To this end, we sequenced the internal transcribed spacer 1 (ITS1), β-tubulin (BenA), and calmodulin (CaM) gene encoding sequences as DNA markers from eight reference Aspergillus strains with known identities using 300-bp sequencing on the Illumina MiSeq platform, and compared them with the BLASTn outputs. The identifications with the sequences longer than 250 bp were accurate at the section rank, with some ambiguities observed at the species rank due to mostly cross detection of sibling species. Additionally, in silico analysis was performed to predict the identification accuracy for all species in the genus Aspergillus, where 107, 210, and 187 species were predicted to be identifiable down to the species rank based on ITS1, BenA, and CaM, respectively. Finally, air filter samples were analysed to quantify the relative abundances of Aspergillus species in outdoor air. The results were reproducible across biological duplicates both at the species and section ranks, but not strongly correlated between ITS1 and BenA, suggesting the Aspergillus detection can be taxonomically biased depending on the selection of the DNA markers and/or primers. Copyright © 2015 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.

shRNA target prediction informed by comprehensive enquiry (SPICE): a supporting system for high-throughput screening of shRNA library.

PubMed

Kamatuka, Kenta; Hattori, Masahiro; Sugiyama, Tomoyasu

2016-12-01

RNA interference (RNAi) screening is extensively used in the field of reverse genetics. RNAi libraries constructed using random oligonucleotides have made this technology affordable. However, the new methodology requires exploration of the RNAi target gene information after screening because the RNAi library includes non-natural sequences that are not found in genes. Here, we developed a web-based tool to support RNAi screening. The system performs short hairpin RNA (shRNA) target prediction that is informed by comprehensive enquiry (SPICE). SPICE automates several tasks that are laborious but indispensable to evaluate the shRNAs obtained by RNAi screening. SPICE has four main functions: (i) sequence identification of shRNA in the input sequence (the sequence might be obtained by sequencing clones in the RNAi library), (ii) searching the target genes in the database, (iii) demonstrating biological information obtained from the database, and (iv) preparation of search result files that can be utilized in a local personal computer (PC). Using this system, we demonstrated that genes targeted by random oligonucleotide-derived shRNAs were not different from those targeted by organism-specific shRNA. The system facilitates RNAi screening, which requires sequence analysis after screening. The SPICE web application is available at http://www.spice.sugysun.org/.

Deep sequencing and in silico analysis of small RNA library reveals novel miRNA from leaf Persicaria minor transcriptome.

PubMed

Samad, Abdul Fatah A; Nazaruddin, Nazaruddin; Murad, Abdul Munir Abdul; Jani, Jaeyres; Zainal, Zamri; Ismail, Ismanizan

2018-03-01

In current era, majority of microRNA (miRNA) are being discovered through computational approaches which are more confined towards model plants. Here, for the first time, we have described the identification and characterization of novel miRNA in a non-model plant, Persicaria minor ( P . minor ) using computational approach. Unannotated sequences from deep sequencing were analyzed based on previous well-established parameters. Around 24 putative novel miRNAs were identified from 6,417,780 reads of the unannotated sequence which represented 11 unique putative miRNA sequences. PsRobot target prediction tool was deployed to identify the target transcripts of putative novel miRNAs. Most of the predicted target transcripts (mRNAs) were known to be involved in plant development and stress responses. Gene ontology showed that majority of the putative novel miRNA targets involved in cellular component (69.07%), followed by molecular function (30.08%) and biological process (0.85%). Out of 11 unique putative miRNAs, 7 miRNAs were validated through semi-quantitative PCR. These novel miRNAs discoveries in P . minor may develop and update the current public miRNA database.

Biosynthesis of Lipoic Acid in Arabidopsis: Cloning and Characterization of the cDNA for Lipoic Acid Synthase1

PubMed Central

Yasuno, Rie; Wada, Hajime

1998-01-01

Lipoic acid is a coenzyme that is essential for the activity of enzyme complexes such as those of pyruvate dehydrogenase and glycine decarboxylase. We report here the isolation and characterization of LIP1 cDNA for lipoic acid synthase of Arabidopsis. The Arabidopsis LIP1 cDNA was isolated using an expressed sequence tag homologous to the lipoic acid synthase of Escherichia coli. This cDNA was shown to code for Arabidopsis lipoic acid synthase by its ability to complement a lipA mutant of E. coli defective in lipoic acid synthase. DNA-sequence analysis of the LIP1 cDNA revealed an open reading frame predicting a protein of 374 amino acids. Comparisons of the deduced amino acid sequence with those of E. coli and yeast lipoic acid synthase homologs showed a high degree of sequence similarity and the presence of a leader sequence presumably required for import into the mitochondria. Southern-hybridization analysis suggested that LIP1 is a single-copy gene in Arabidopsis. Western analysis with an antibody against lipoic acid synthase demonstrated that this enzyme is located in the mitochondrial compartment in Arabidopsis cells as a 43-kD polypeptide. PMID:9808738

Predicting ecological roles in the rhizosphere using metabolome and transportome modeling

DOE PAGES

Larsen, Peter E.; Collart, Frank R.; Dai, Yang; ...

2015-09-02

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. New algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad’s ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter ofmore » plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism’s transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.« less

Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, Peter E.; Collart, Frank R.; Dai, Yang

2015-09-02

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. However, new algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad's ecological role in the rhizosphere: a biofilm, biocontrol agent, promotermore » of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism's transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.« less

Software for optimization of SNP and PCR-RFLP genotyping to discriminate many genomes with the fewest assays

PubMed Central

Gardner, Shea N; Wagner, Mark C

2005-01-01

Background Microbial forensics is important in tracking the source of a pathogen, whether the disease is a naturally occurring outbreak or part of a criminal investigation. Results A method and SPR Opt (SNP and PCR-RFLP Optimization) software to perform a comprehensive, whole-genome analysis to forensically discriminate multiple sequences is presented. Tools for the optimization of forensic typing using Single Nucleotide Polymorphism (SNP) and PCR-Restriction Fragment Length Polymorphism (PCR-RFLP) analyses across multiple isolate sequences of a species are described. The PCR-RFLP analysis includes prediction and selection of optimal primers and restriction enzymes to enable maximum isolate discrimination based on sequence information. SPR Opt calculates all SNP or PCR-RFLP variations present in the sequences, groups them into haplotypes according to their co-segregation across those sequences, and performs combinatoric analyses to determine which sets of haplotypes provide maximal discrimination among all the input sequences. Those set combinations requiring that membership in the fewest haplotypes be queried (i.e. the fewest assays be performed) are found. These analyses highlight variable regions based on existing sequence data. These markers may be heterogeneous among unsequenced isolates as well, and thus may be useful for characterizing the relationships among unsequenced as well as sequenced isolates. The predictions are multi-locus. Analyses of mumps and SARS viruses are summarized. Phylogenetic trees created based on SNPs, PCR-RFLPs, and full genomes are compared for SARS virus, illustrating that purported phylogenies based only on SNP or PCR-RFLP variations do not match those based on multiple sequence alignment of the full genomes. Conclusion This is the first software to optimize the selection of forensic markers to maximize information gained from the fewest assays, accepting whole or partial genome sequence data as input. As more sequence data becomes available for multiple strains and isolates of a species, automated, computational approaches such as those described here will be essential to make sense of large amounts of information, and to guide and optimize efforts in the laboratory. The software and source code for SPR Opt is publicly available and free for non-profit use at . PMID:15904493

In-silico and in-vivo analyses of EST databases unveil conserved miRNAs from Carthamus tinctorius and Cynara cardunculus

PubMed Central

2012-01-01

Background MicroRNAs (miRNAs) are small RNAs (21-24 bp) providing an RNA-based system of gene regulation highly conserved in plants and animals. In plants, miRNAs control mRNA degradation or restrain translation, affecting development and responses to stresses. Plant miRNAs show imperfect but extensive complementarity to mRNA targets, making their computational prediction possible, useful when data mining is applied on different species. In this study we used a comparative approach to identify both miRNAs and their targets, in artichoke and safflower. Results Two complete expressed sequence tags (ESTs) datasets from artichoke (3.6·104 entries) and safflower (4.2·104), were analysed with a bioinformatic pipeline and in vitro experiments, identifying 17 potential miRNAs. For each EST, using RNAhybrid program and 953 non redundant miRNA mature sequences, available in mirBase as reference, we searched matching putative targets. 8730 out of 42011 ESTs from safflower and 7145 of 36323 ESTs from artichoke showed at least one predicted miRNA target. BLAST analysis showed that 75% of all ESTs shared at least a common homologous region (E-value < 10-4) and about 50% of these displayed 400 bp or longer aligned sequences as conserved homologous/orthologous (COS) regions. 960 and 890 ESTs of safflower and artichoke organized in COS shared 79 different miRNA targets, considered functionally conserved, and statistically significant when compared with random sequences (signal to noise ratio > 2 and specificity ≥ 0.85). Four highly significant miRNAs selected from in silico data were experimentally validated in globe artichoke leaves. Conclusions Mature miRNAs and targets were predicted within EST sequences of safflower and artichoke. Most of the miRNA targets appeared highly/moderately conserved, highlighting an important and conserved function. In this study we introduce a stringent parameter for the comparative sequence analysis, represented by the identification of the same target in the COS region. After statistical analysis 79 targets, found on the COS regions and belonging to 60 miRNA families, have a signal to noise ratio > 2, with ≥ 0.85 specificity. The putative miRNAs identified belong to 55 dicotyledon plants and to 24 families only in monocotyledon. PMID:22536958

The importance of proper bioinformatics analysis and clinical interpretation of tumor genomic profiling: a case study of undifferentiated sarcoma and a constitutional pathogenic BRCA2 mutation and an MLH1 variant of uncertain significance.

PubMed

Varga, Elizabeth; Chao, Elizabeth C; Yeager, Nicholas D

2015-09-01

Next-generation sequencing (NGS) technology is increasingly utilized to identify therapeutic targets for patients with malignancy. This technology also has the capability to reveal the presence of constitutional genetic alterations, which may have significant implications for patients and their family members. Here we present the case of a 23 year old Caucasian patient with recurrent undifferentiated sarcoma who had NGS-based tumor analysis using an assay which simultaneously analyzed the entire coding sequence of 236 cancer-related genes (3769 exons) plus 47 introns from 19 genes often rearranged or altered in cancer. Pathogenic alterations were reported in tumor as the predicted protein alterations, BRCA2 "R645fs*15″ and MLH1 "E694*". Because constitutional BRCA2 and MLH1 gene mutations are associated with Hereditary Breast Ovarian Cancer Syndrome (HBOCS) and Lynch syndrome respectively, sequence analysis of DNA isolated from peripheral blood was performed. The presence of the alterations, BRCA2 c.1929delG and MLH1 c.2080G>T, corresponding to the previously reported predicted protein alterations, were confirmed by Sanger sequencing in the constitutional DNA. An additional DNA finding was reported in this analysis, MLH1 c.2081A>C at the neighboring nucleotide. Further evaluation of the family revealed that all alterations were paternally inherited and the two MLH1 substitutions were in cis, more appropriately referred to as MLH1 c.2080_2081delGAinsTC, which is classified as a variant of uncertain significance. This case illustrates important considerations related to appropriate interpretation of NGS tumor results and follow-up of patients with potentially deleterious constitutional alterations.

Gene and translation initiation site prediction in metagenomic sequences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hyatt, Philip Douglas; LoCascio, Philip F; Hauser, Loren John

2012-01-01

Gene prediction in metagenomic sequences remains a difficult problem. Current sequencing technologies do not achieve sufficient coverage to assemble the individual genomes in a typical sample; consequently, sequencing runs produce a large number of short sequences whose exact origin is unknown. Since these sequences are usually smaller than the average length of a gene, algorithms must make predictions based on very little data. We present MetaProdigal, a metagenomic version of the gene prediction program Prodigal, that can identify genes in short, anonymous coding sequences with a high degree of accuracy. The novel value of the method consists of enhanced translationmore » initiation site identification, ability to identify sequences that use alternate genetic codes and confidence values for each gene call. We compare the results of MetaProdigal with other methods and conclude with a discussion of future improvements.« less

Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing.

PubMed

Willems, Sander; Fraiture, Marie-Alice; Deforce, Dieter; De Keersmaecker, Sigrid C J; De Loose, Marc; Ruttink, Tom; Herman, Philippe; Van Nieuwerburgh, Filip; Roosens, Nancy

2016-02-01

Because the number and diversity of genetically modified (GM) crops has significantly increased, their analysis based on real-time PCR (qPCR) methods is becoming increasingly complex and laborious. While several pioneers already investigated Next Generation Sequencing (NGS) as an alternative to qPCR, its practical use has not been assessed for routine analysis. In this study a statistical framework was developed to predict the number of NGS reads needed to detect transgene sequences, to prove their integration into the host genome and to identify the specific transgene event in a sample with known composition. This framework was validated by applying it to experimental data from food matrices composed of pure GM rice, processed GM rice (noodles) or a 10% GM/non-GM rice mixture, revealing some influential factors. Finally, feasibility of NGS for routine analysis of GM crops was investigated by applying the framework to samples commonly encountered in routine analysis of GM crops. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Gene Unprediction with Spurio: A tool to identify spurious protein sequences.

PubMed

Höps, Wolfram; Jeffryes, Matt; Bateman, Alex

2018-01-01

We now have access to the sequences of tens of millions of proteins. These protein sequences are essential for modern molecular biology and computational biology. The vast majority of protein sequences are derived from gene prediction tools and have no experimental supporting evidence for their translation.  Despite the increasing accuracy of gene prediction tools there likely exists a large number of spurious protein predictions in the sequence databases.  We have developed the Spurio tool to help identify spurious protein predictions in prokaryotes.  Spurio searches the query protein sequence against a prokaryotic nucleotide database using tblastn and identifies homologous sequences. The tblastn matches are used to score the query sequence's likelihood of being a spurious protein prediction using a Gaussian process model. The most informative feature is the appearance of stop codons within the presumed translation of homologous DNA sequences. Benchmarking shows that the Spurio tool is able to distinguish spurious from true proteins. However, transposon proteins are prone to be predicted as spurious because of the frequency of degraded homologs found in the DNA sequence databases. Our initial experiments suggest that less than 1% of the proteins in the UniProtKB sequence database are likely to be spurious and that Spurio is able to identify over 60 times more spurious proteins than the AntiFam resource. The Spurio software and source code is available under an MIT license at the following URL: https://bitbucket.org/bateman-group/spurio.

Rotational motions from the 2016, Central Italy seismic sequence, as observed by an underground ring laser gyroscope

NASA Astrophysics Data System (ADS)

Simonelli, A.; Igel, H.; Wassermann, J.; Belfi, J.; Di Virgilio, A.; Beverini, N.; De Luca, G.; Saccorotti, G.

2018-05-01

We present the analysis of rotational and translational ground motions from earthquakes recorded during October/November, 2016, in association with the Central Italy seismic-sequence. We use co-located measurements of the vertical ground rotation rate from a large ring laser gyroscope (RLG), and the three components of ground velocity from a broadband seismometer. Both instruments are positioned in a deep underground environment, within the Gran Sasso National Laboratories (LNGS) of the Istituto Nazionale di Fisica Nucleare (INFN). We collected dozens of events spanning the 3.5-5.9 Magnitude range, and epicentral distances between 30 km and 70 km. This data set constitutes an unprecedented observation of the vertical rotational motions associated with an intense seismic sequence at local distance. Under the plane wave approximation we process the data set in order to get an experimental estimation of the events back azimuth. Peak values of rotation rate (PRR) and horizontal acceleration (PGA) are markedly correlated, according to a scaling constant which is consistent with previous measurements from different earthquake sequences. We used a prediction model in use for Italy to calculate the expected PGA at the recording site, obtaining consequently predictions for PRR. Within the modeling uncertainties, predicted rotations are consistent with the observed ones, suggesting the possibility of establishing specific attenuation models for ground rotations, like the scaling of peak velocity and peak acceleration in empirical ground-motion prediction relationships. In a second step, after identifying the direction of the incoming wave-field, we extract phase velocity data using the spectral ratio of the translational and rotational components.. This analysis is performed over time windows associated with the P-coda, S-coda and Lg phase. Results are consistent with independent estimates of shear-wave velocities in the shallow crust of the Central Apennines.

An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions

PubMed Central

Churkin, Alexander; Barash, Danny

2008-01-01

Background RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm) for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure. Results Using RNAsubopt, the suboptimal solutions for a given wild-type sequence are calculated once. Then, specific mutations are selected that are most likely to cause a conformational rearrangement. For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3), for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations. Conclusion A highly efficient addition to RNAmute that is as user friendly as the original application but that facilitates the practical analysis of multiple-point mutations is presented. Such an extension can now be exploited prior to site-directed mutagenesis experiments by virologists, for example, who investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure. A complete explanation of the application, called MultiRNAmute, is available at [1]. PMID:18445289

Analysis of microRNA profile of Anopheles sinensis by deep sequencing and bioinformatic approaches.

PubMed

Feng, Xinyu; Zhou, Xiaojian; Zhou, Shuisen; Wang, Jingwen; Hu, Wei

2018-03-12

microRNAs (miRNAs) are small non-coding RNAs widely identified in many mosquitoes. They are reported to play important roles in development, differentiation and innate immunity. However, miRNAs in Anopheles sinensis, one of the Chinese malaria mosquitoes, remain largely unknown. We investigated the global miRNA expression profile of An. sinensis using Illumina Hiseq 2000 sequencing. Meanwhile, we applied a bioinformatic approach to identify potential miRNAs in An. sinensis. The identified miRNA profiles were compared and analyzed by two approaches. The selected miRNAs from the sequencing result and the bioinformatic approach were confirmed with qRT-PCR. Moreover, target prediction, GO annotation and pathway analysis were carried out to understand the role of miRNAs in An. sinensis. We identified 49 conserved miRNAs and 12 novel miRNAs by next-generation high-throughput sequencing technology. In contrast, 43 miRNAs were predicted by the bioinformatic approach, of which two were assigned as novel. Comparative analysis of miRNA profiles by two approaches showed that 21 miRNAs were shared between them. Twelve novel miRNAs did not match any known miRNAs of any organism, indicating that they are possibly species-specific. Forty miRNAs were found in many mosquito species, indicating that these miRNAs are evolutionally conserved and may have critical roles in the process of life. Both the selected known and novel miRNAs (asi-miR-281, asi-miR-184, asi-miR-14, asi-miR-nov5, asi-miR-nov4, asi-miR-9383, and asi-miR-2a) could be detected by quantitative real-time PCR (qRT-PCR) in the sequenced sample, and the expression patterns of these miRNAs measured by qRT-PCR were in concordance with the original miRNA sequencing data. The predicted targets for the known and the novel miRNAs covered many important biological roles and pathways indicating the diversity of miRNA functions. We also found 21 conserved miRNAs and eight counterparts of target immune pathway genes in An. sinensis based on the analysis of An. gambiae. Our results provide the first lead to the elucidation of the miRNA profile in An. sinensis. Unveiling the roles of mosquito miRNAs will undoubtedly lead to a better understanding of mosquito biology and mosquito-pathogen interactions. This work lays the foundation for the further functional study of An. sinensis miRNAs and will facilitate their application in vector control.

Tissue-specific Proteogenomic Analysis of Plutella xylostella Larval Midgut Using a Multialgorithm Pipeline.

PubMed

Zhu, Xun; Xie, Shangbo; Armengaud, Jean; Xie, Wen; Guo, Zhaojiang; Kang, Shi; Wu, Qingjun; Wang, Shaoli; Xia, Jixing; He, Rongjun; Zhang, Youjun

2016-06-01

The diamondback moth, Plutella xylostella (L.), is the major cosmopolitan pest of brassica and other cruciferous crops. Its larval midgut is a dynamic tissue that interfaces with a wide variety of toxicological and physiological processes. The draft sequence of the P. xylostella genome was recently released, but its annotation remains challenging because of the low sequence coverage of this branch of life and the poor description of exon/intron splicing rules for these insects. Peptide sequencing by computational assignment of tandem mass spectra to genome sequence information provides an experimental independent approach for confirming or refuting protein predictions, a concept that has been termed proteogenomics. In this study, we carried out an in-depth proteogenomic analysis to complement genome annotation of P. xylostella larval midgut based on shotgun HPLC-ESI-MS/MS data by means of a multialgorithm pipeline. A total of 876,341 tandem mass spectra were searched against the predicted P. xylostella protein sequences and a whole-genome six-frame translation database. Based on a data set comprising 2694 novel genome search specific peptides, we discovered 439 novel protein-coding genes and corrected 128 existing gene models. To get the most accurate data to seed further insect genome annotation, more than half of the novel protein-coding genes, i.e. 235 over 439, were further validated after RT-PCR amplification and sequencing of the corresponding transcripts. Furthermore, we validated 53 novel alternative splicings. Finally, a total of 6764 proteins were identified, resulting in one of the most comprehensive proteogenomic study of a nonmodel animal. As the first tissue-specific proteogenomics analysis of P. xylostella, this study provides the fundamental basis for high-throughput proteomics and functional genomics approaches aimed at deciphering the molecular mechanisms of resistance and controlling this pest. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Introduction to bioinformatics.

PubMed

Can, Tolga

2014-01-01

Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks.

Computational Identification and Functional Predictions of Long Noncoding RNA in Zea mays

PubMed Central

Boerner, Susan; McGinnis, Karen M.

2012-01-01

Background Computational analysis of cDNA sequences from multiple organisms suggests that a large portion of transcribed DNA does not code for a functional protein. In mammals, noncoding transcription is abundant, and often results in functional RNA molecules that do not appear to encode proteins. Many long noncoding RNAs (lncRNAs) appear to have epigenetic regulatory function in humans, including HOTAIR and XIST. While epigenetic gene regulation is clearly an essential mechanism in plants, relatively little is known about the presence or function of lncRNAs in plants. Methodology/Principal Findings To explore the connection between lncRNA and epigenetic regulation of gene expression in plants, a computational pipeline using the programming language Python has been developed and applied to maize full length cDNA sequences to identify, classify, and localize potential lncRNAs. The pipeline was used in parallel with an SVM tool for identifying ncRNAs to identify the maximal number of ncRNAs in the dataset. Although the available library of sequences was small and potentially biased toward protein coding transcripts, 15% of the sequences were predicted to be noncoding. Approximately 60% of these sequences appear to act as precursors for small RNA molecules and may function to regulate gene expression via a small RNA dependent mechanism. ncRNAs were predicted to originate from both genic and intergenic loci. Of the lncRNAs that originated from genic loci, ∼20% were antisense to the host gene loci. Conclusions/Significance Consistent with similar studies in other organisms, noncoding transcription appears to be widespread in the maize genome. Computational predictions indicate that maize lncRNAs may function to regulate expression of other genes through multiple RNA mediated mechanisms. PMID:22916204

miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets

PubMed Central

Paicu, Claudia; Mohorianu, Irina; Stocks, Matthew; Xu, Ping; Coince, Aurore; Billmeier, Martina; Dalmay, Tamas; Moulton, Vincent; Moxon, Simon

2017-01-01

Abstract Motivation MicroRNAs are a class of ∼21–22 nt small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction algorithm, miRCat2. miRCat2 incorporates a new entropy-based approach to detect miRNA loci, which is designed to cope with the high sequencing depth of current next-generation sequencing datasets. It has a user-friendly interface and produces graphical representations of the hairpin structure and plots depicting the alignment of sequences on the secondary structure. Results We test miRCat2 on a number of animal and plant datasets and present a comparative analysis with miRCat, miRDeep2, miRPlant and miReap. We also use mutants in the miRNA biogenesis pathway to evaluate the predictions of these tools. Results indicate that miRCat2 has an improved accuracy compared with other methods tested. Moreover, miRCat2 predicts several new miRNAs that are differentially expressed in wild-type versus mutants in the miRNA biogenesis pathway. Availability and Implementation miRCat2 is part of the UEA small RNA Workbench and is freely available from http://srna-workbench.cmp.uea.ac.uk/. Contact v.moulton@uea.ac.uk or s.moxon@uea.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:28407097

Identification of (R)-selective ω-aminotransferases by exploring evolutionary sequence space.

PubMed

Kim, Eun-Mi; Park, Joon Ho; Kim, Byung-Gee; Seo, Joo-Hyun

2018-03-01

Several (R)-selective ω-aminotransferases (R-ωATs) have been reported. The existence of additional R-ωATs having different sequence characteristics from previous ones is highly expected. In addition, it is generally accepted that R-ωATs are variants of aminotransferase group III. Based on these backgrounds, sequences in RefSeq database were scored using family profiles of branched-chain amino acid aminotransferase (BCAT) and d-alanine aminotransferase (DAT) to predict and identify putative R-ωATs. Sequences with two profile analysis scores were plotted on two-dimensional score space. Candidates with relatively similar scores in both BCAT and DAT profiles (i.e., profile analysis score using BCAT profile was similar to profile analysis score using DAT profile) were selected. Experimental results for selected candidates showed that putative R-ωATs from Saccharopolyspora erythraea (R-ωAT_Sery), Bacillus cellulosilyticus (R-ωAT_Bcel), and Bacillus thuringiensis (R-ωAT_Bthu) had R-ωAT activity. Additional experiments revealed that R-ωAT_Sery also possessed DAT activity while R-ωAT_Bcel and R-ωAT_Bthu had BCAT activity. Selecting putative R-ωATs from regions with similar profile analysis scores identified potential R-ωATs. Therefore, R-ωATs could be efficiently identified by using simple family profile analysis and exploring evolutionary sequence space. Copyright © 2017 Elsevier Inc. All rights reserved.

Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis

PubMed Central

2013-01-01

Background Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs. Results We present here a novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences. In the proposed method, 11188 protein pairs retrieved from the DIP database were encoded into feature vectors by using four kinds of protein sequences information. Focusing on dimension reduction, an effective feature extraction method PCA was then employed to construct the most discriminative new feature set. Finally, multiple extreme learning machines were trained and then aggregated into a consensus classifier by majority voting. The ensembling of extreme learning machine removes the dependence of results on initial random weights and improves the prediction performance. Conclusions When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 87.00% prediction accuracy with 86.15% sensitivity at the precision of 87.59%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Experimental results demonstrate that proposed PCA-EELM outperforms the SVM method by 5-fold cross-validation. Besides, PCA-EELM performs faster than PCA-SVM based method. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time. PMID:23815620

Efficient use of unlabeled data for protein sequence classification: a comparative study

PubMed Central

Kuksa, Pavel; Huang, Pai-Hsi; Pavlovic, Vladimir

2009-01-01

Background Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags–the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers. Results Combined with state-of-the-art string kernels, our proposed computational framework achieves very accurate semi-supervised protein remote fold and homology detection on three large unlabeled databases. It outperforms current state-of-the-art methods and exhibits significant reduction in running time. Conclusion The unlabeled sequences used under the semi-supervised setting resemble the unpolished gemstones; when used as-is, they may carry unnecessary features and hence compromise the classification accuracy but once cut and polished, they improve the accuracy of the classifiers considerably. PMID:19426450

Ariadne: a database search engine for identification and chemical analysis of RNA using tandem mass spectrometry data.

PubMed

Nakayama, Hiroshi; Akiyama, Misaki; Taoka, Masato; Yamauchi, Yoshio; Nobe, Yuko; Ishikawa, Hideaki; Takahashi, Nobuhiro; Isobe, Toshiaki

2009-04-01

We present here a method to correlate tandem mass spectra of sample RNA nucleolytic fragments with an RNA nucleotide sequence in a DNA/RNA sequence database, thereby allowing tandem mass spectrometry (MS/MS)-based identification of RNA in biological samples. Ariadne, a unique web-based database search engine, identifies RNA by two probability-based evaluation steps of MS/MS data. In the first step, the software evaluates the matches between the masses of product ions generated by MS/MS of an RNase digest of sample RNA and those calculated from a candidate nucleotide sequence in a DNA/RNA sequence database, which then predicts the nucleotide sequences of these RNase fragments. In the second step, the candidate sequences are mapped for all RNA entries in the database, and each entry is scored for a function of occurrences of the candidate sequences to identify a particular RNA. Ariadne can also predict post-transcriptional modifications of RNA, such as methylation of nucleotide bases and/or ribose, by estimating mass shifts from the theoretical mass values. The method was validated with MS/MS data of RNase T1 digests of in vitro transcripts. It was applied successfully to identify an unknown RNA component in a tRNA mixture and to analyze post-transcriptional modification in yeast tRNA(Phe-1).

UET: a database of evolutionarily-predicted functional determinants of protein sequences that cluster as functional sites in protein structures.

PubMed

Lua, Rhonald C; Wilson, Stephen J; Konecki, Daniel M; Wilkins, Angela D; Venner, Eric; Morgan, Daniel H; Lichtarge, Olivier

2016-01-04

The structure and function of proteins underlie most aspects of biology and their mutational perturbations often cause disease. To identify the molecular determinants of function as well as targets for drugs, it is central to characterize the important residues and how they cluster to form functional sites. The Evolutionary Trace (ET) achieves this by ranking the functional and structural importance of the protein sequence positions. ET uses evolutionary distances to estimate functional distances and correlates genotype variations with those in the fitness phenotype. Thus, ET ranks are worse for sequence positions that vary among evolutionarily closer homologs but better for positions that vary mostly among distant homologs. This approach identifies functional determinants, predicts function, guides the mutational redesign of functional and allosteric specificity, and interprets the action of coding sequence variations in proteins, people and populations. Now, the UET database offers pre-computed ET analyses for the protein structure databank, and on-the-fly analysis of any protein sequence. A web interface retrieves ET rankings of sequence positions and maps results to a structure to identify functionally important regions. This UET database integrates several ways of viewing the results on the protein sequence or structure and can be found at http://mammoth.bcm.tmc.edu/uet/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

The diploid genome sequence of an Asian individual

PubMed Central

Wang, Jun; Wang, Wei; Li, Ruiqiang; Li, Yingrui; Tian, Geng; Goodman, Laurie; Fan, Wei; Zhang, Junqing; Li, Jun; Zhang, Juanbin; Guo, Yiran; Feng, Binxiao; Li, Heng; Lu, Yao; Fang, Xiaodong; Liang, Huiqing; Du, Zhenglin; Li, Dong; Zhao, Yiqing; Hu, Yujie; Yang, Zhenzhen; Zheng, Hancheng; Hellmann, Ines; Inouye, Michael; Pool, John; Yi, Xin; Zhao, Jing; Duan, Jinjie; Zhou, Yan; Qin, Junjie; Ma, Lijia; Li, Guoqing; Yang, Zhentao; Zhang, Guojie; Yang, Bin; Yu, Chang; Liang, Fang; Li, Wenjie; Li, Shaochuan; Li, Dawei; Ni, Peixiang; Ruan, Jue; Li, Qibin; Zhu, Hongmei; Liu, Dongyuan; Lu, Zhike; Li, Ning; Guo, Guangwu; Zhang, Jianguo; Ye, Jia; Fang, Lin; Hao, Qin; Chen, Quan; Liang, Yu; Su, Yeyang; san, A.; Ping, Cuo; Yang, Shuang; Chen, Fang; Li, Li; Zhou, Ke; Zheng, Hongkun; Ren, Yuanyuan; Yang, Ling; Gao, Yang; Yang, Guohua; Li, Zhuo; Feng, Xiaoli; Kristiansen, Karsten; Wong, Gane Ka-Shu; Nielsen, Rasmus; Durbin, Richard; Bolund, Lars; Zhang, Xiuqing; Li, Songgang; Yang, Huanming; Wang, Jian

2009-01-01

Here we present the first diploid genome sequence of an Asian individual. The genome was sequenced to 36-fold average coverage using massively parallel sequencing technology. We aligned the short reads onto the NCBI human reference genome to 99.97% coverage, and guided by the reference genome, we used uniquely mapped reads to assemble a high-quality consensus sequence for 92% of the Asian individual's genome. We identified approximately 3 million single-nucleotide polymorphisms (SNPs) inside this region, of which 13.6% were not in the dbSNP database. Genotyping analysis showed that SNP identification had high accuracy and consistency, indicating the high sequence quality of this assembly. We also carried out heterozygote phasing and haplotype prediction against HapMap CHB and JPT haplotypes (Chinese and Japanese, respectively), sequence comparison with the two available individual genomes (J. D. Watson and J. C. Venter), and structural variation identification. These variations were considered for their potential biological impact. Our sequence data and analyses demonstrate the potential usefulness of next-generation sequencing technologies for personal genomics. PMID:18987735

Representation of DNA sequences in genetic codon context with applications in exon and intron prediction.

PubMed

Yin, Changchuan

2015-04-01

To apply digital signal processing (DSP) methods to analyze DNA sequences, the sequences first must be specially mapped into numerical sequences. Thus, effective numerical mappings of DNA sequences play key roles in the effectiveness of DSP-based methods such as exon prediction. Despite numerous mappings of symbolic DNA sequences to numerical series, the existing mapping methods do not include the genetic coding features of DNA sequences. We present a novel numerical representation of DNA sequences using genetic codon context (GCC) in which the numerical values are optimized by simulation annealing to maximize the 3-periodicity signal to noise ratio (SNR). The optimized GCC representation is then applied in exon and intron prediction by Short-Time Fourier Transform (STFT) approach. The results show the GCC method enhances the SNR values of exon sequences and thus increases the accuracy of predicting protein coding regions in genomes compared with the commonly used 4D binary representation. In addition, this study offers a novel way to reveal specific features of DNA sequences by optimizing numerical mappings of symbolic DNA sequences.

Rapid Identification of Sequences for Orphan Enzymes to Power Accurate Protein Annotation

PubMed Central

Ojha, Sunil; Watson, Douglas S.; Bomar, Martha G.; Galande, Amit K.; Shearer, Alexander G.

2013-01-01

The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the “back catalog” of enzymology – “orphan enzymes,” those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC) database alone. In this study, we demonstrate how this orphan enzyme “back catalog” is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis) to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology’s “back catalog” another powerful tool to drive accurate genome annotation. PMID:24386392

Rapid identification of sequences for orphan enzymes to power accurate protein annotation.

PubMed

Ramkissoon, Kevin R; Miller, Jennifer K; Ojha, Sunil; Watson, Douglas S; Bomar, Martha G; Galande, Amit K; Shearer, Alexander G

2013-01-01

The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the "back catalog" of enzymology--"orphan enzymes," those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC) database alone. In this study, we demonstrate how this orphan enzyme "back catalog" is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis) to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology's "back catalog" another powerful tool to drive accurate genome annotation.

SIBIS: a Bayesian model for inconsistent protein sequence estimation.

PubMed

Khenoussi, Walyd; Vanhoutrève, Renaud; Poch, Olivier; Thompson, Julie D

2014-09-01

The prediction of protein coding genes is a major challenge that depends on the quality of genome sequencing, the accuracy of the model used to elucidate the exonic structure of the genes and the complexity of the gene splicing process leading to different protein variants. As a consequence, today's protein databases contain a huge amount of inconsistency, due to both natural variants and sequence prediction errors. We have developed a new method, called SIBIS, to detect such inconsistencies based on the evolutionary information in multiple sequence alignments. A Bayesian framework, combined with Dirichlet mixture models, is used to estimate the probability of observing specific amino acids and to detect inconsistent or erroneous sequence segments. We evaluated the performance of SIBIS on a reference set of protein sequences with experimentally validated errors and showed that the sensitivity is significantly higher than previous methods, with only a small loss of specificity. We also assessed a large set of human sequences from the UniProt database and found evidence of inconsistency in 48% of the previously uncharacterized sequences. We conclude that the integration of quality control methods like SIBIS in automatic analysis pipelines will be critical for the robust inference of structural, functional and phylogenetic information from these sequences. Source code, implemented in C on a linux system, and the datasets of protein sequences are freely available for download at http://www.lbgi.fr/∼julie/SIBIS. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Sensor image prediction techniques

NASA Astrophysics Data System (ADS)

Stenger, A. J.; Stone, W. R.; Berry, L.; Murray, T. J.

1981-02-01

The preparation of prediction imagery is a complex, costly, and time consuming process. Image prediction systems which produce a detailed replica of the image area require the extensive Defense Mapping Agency data base. The purpose of this study was to analyze the use of image predictions in order to determine whether a reduced set of more compact image features contains enough information to produce acceptable navigator performance. A job analysis of the navigator's mission tasks was performed. It showed that the cognitive and perceptual tasks he performs during navigation are identical to those performed for the targeting mission function. In addition, the results of the analysis of his performance when using a particular sensor can be extended to the analysis of this mission tasks using any sensor. An experimental approach was used to determine the relationship between navigator performance and the type of amount of information in the prediction image. A number of subjects were given image predictions containing varying levels of scene detail and different image features, and then asked to identify the predicted targets in corresponding dynamic flight sequences over scenes of cultural, terrain, and mixed (both cultural and terrain) content.

Towards pathogenomics: a web-based resource for pathogenicity islands

PubMed Central

Yoon, Sung Ho; Park, Young-Kyu; Lee, Soohyun; Choi, Doil; Oh, Tae Kwang; Hur, Cheol-Goo; Kim, Jihyun F.

2007-01-01

Pathogenicity islands (PAIs) are genetic elements whose products are essential to the process of disease development. They have been horizontally (laterally) transferred from other microbes and are important in evolution of pathogenesis. In this study, a comprehensive database and search engines specialized for PAIs were established. The pathogenicity island database (PAIDB) is a comprehensive relational database of all the reported PAIs and potential PAI regions which were predicted by a method that combines feature-based analysis and similarity-based analysis. Also, using the PAI Finder search application, a multi-sequence query can be analyzed onsite for the presence of potential PAIs. As of April 2006, PAIDB contains 112 types of PAIs and 889 GenBank accessions containing either partial or all PAI loci previously reported in the literature, which are present in 497 strains of pathogenic bacteria. The database also offers 310 candidate PAIs predicted from 118 sequenced prokaryotic genomes. With the increasing number of prokaryotic genomes without functional inference and sequenced genetic regions of suspected involvement in diseases, this web-based, user-friendly resource has the potential to be of significant use in pathogenomics. PAIDB is freely accessible at . PMID:17090594

Identification of functional candidates amongst hypothetical proteins of Mycobacterium leprae Br4923, a causative agent of leprosy.

PubMed

Naqvi, Ahmad Abu Turab; Ahmad, Faizan; Hassan, Md Imtaiyaz

2015-01-01

Mycobacterium leprae is an intracellular obligate parasite that causes leprosy in humans, and it leads to the destruction of peripheral nerves and skin deformation. Here, we report an extensive analysis of the hypothetical proteins (HPs) from M. leprae strain Br4923, assigning their functions to better understand the mechanism of pathogenesis and to search for potential therapeutic interventions. The genome of M. leprae encodes 1604 proteins, of which the functions of 632 are not known (HPs). In this paper, we predicted the probable functions of 312 HPs. First, we classified all HPs into families and subfamilies on the basis of sequence similarity, followed by domain assignment, which provides many clues for their possible function. However, the functions of 320 proteins were not predicted because of low sequence similarity with proteins of known function. Annotated HPs were categorized into enzymes, binding proteins, transporters, and proteins involved in cellular processes. We found several novel proteins whose functions were unknown for M. leprae. These proteins have a requisite association with bacterial virulence and pathogenicity. Finally, our sequence-based analysis will be helpful for further validation and the search for potential drug targets while developing effective drugs to cure leprosy.

The Phyre2 web portal for protein modelling, prediction and analysis

PubMed Central

Kelley, Lawrence A; Mezulis, Stefans; Yates, Christopher M; Wass, Mark N; Sternberg, Michael JE

2017-01-01

Summary Phyre2 is a suite of tools available on the web to predict and analyse protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a protocol. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites, and analyse the effect of amino-acid variants (e.g. nsSNPs) for a user’s protein sequence. Users are guided through results by a simple interface at a level of detail determined by them. This protocol will guide a user from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30mins and 2 hours after submission. PMID:25950237

Human Splicing Finder: an online bioinformatics tool to predict splicing signals.

PubMed

Desmet, François-Olivier; Hamroun, Dalil; Lalande, Marine; Collod-Béroud, Gwenaëlle; Claustres, Mireille; Béroud, Christophe

2009-05-01

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-beta Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5' and 3' splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project.

Human Splicing Finder: an online bioinformatics tool to predict splicing signals

PubMed Central

Desmet, François-Olivier; Hamroun, Dalil; Lalande, Marine; Collod-Béroud, Gwenaëlle; Claustres, Mireille; Béroud, Christophe

2009-01-01

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-β Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5′ and 3′ splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project. PMID:19339519

Adaptive compressive learning for prediction of protein-protein interactions from primary sequence.

PubMed

Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin

2011-08-21

Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction

PubMed Central

Cocco, Simona; Monasson, Remi; Weigt, Martin

2013-01-01

Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analysis (DCA), a global inference method based on the maximum entropy principle, which aims at predicting residue-residue contacts. In this paper, inspired by the statistical physics of disordered systems, we introduce the Hopfield-Potts model to naturally interpolate between these two approaches. The Hopfield-Potts model allows us to identify relevant ‘patterns’ of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix. We show how the computation of such statistical patterns makes it possible to accurately predict residue-residue contacts with a much smaller number of parameters than DCA. This dimensional reduction allows us to avoid overfitting and to extract contact information from multiple-sequence alignments of reduced size. In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact in the three-dimensional protein fold. PMID:23990764

A bioinformatics prediction approach towards analyzing the glycosylation, co-expression and interaction patterns of epithelial membrane antigen (EMA/MUC1)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kalra, Rajkumar S., E-mail: renu-wadhwa@aist.go.jp; Wadhwa, Renu, E-mail: renu-wadhwa@aist.go.jp

2015-02-27

Epithelial membrane antigen (EMA or MUC1) is a heavily glycosylated, type I transmembrane glycoprotein commonly expressed by epithelial cells of duct organs. It has been shown to be aberrantly glycosylated in several diseases including cancer. Protein sequence based annotation and analysis of glycosylation profile of glycoproteins by robust computational and comprehensive algorithms provides possible insights to the mechanism(s) of anomalous glycosylation. In present report, by using a number of bioinformatics applications we studied EMA/MUC1 and explored its trans-membrane structural domain sequence that is widely subjected to glycosylation. Exploration of different extracellular motifs led to prediction of N and O-linked glycosylationmore » target sites. Based on the putative O-linked target sites, glycosylated moieties and pathways were envisaged. Furthermore, Protein network analysis demonstrated physical interaction of EMA with a number of proteins and confirmed its functional involvement in cell growth and proliferation pathways. Gene Ontology analysis suggested an involvement of EMA in a number of functions including signal transduction, protein binding, processing and transport along with glycosylation. Thus, present study explored potential of bioinformatics prediction approach in analyzing glycosylation, co-expression and interaction patterns of EMA/MUC1 glycoprotein.« less

BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference.

PubMed

Garcia-Garcia, Javier; Schleker, Sylvia; Klein-Seetharaman, Judith; Oliva, Baldo

2012-07-01

Protein-protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

Predicting overload-affected fatigue crack growth in steels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Skorupa, M.; Skorupa, A.; Ladecki, B.

1996-12-01

The ability of semi-empirical crack closure models to predict the effect of overloads on fatigue crack growth in low-alloy steels has been investigated. With this purpose, the CORPUS model developed for aircraft metals and spectra has been checked first through comparisons between the simulated and observed results for a low-alloy steel. The CORPUS predictions of crack growth under several types of simple load histories containing overloads appeared generally unconservative which prompted the authors to formulate a new model, more suitable for steels. With the latter approach, the assumed evolution of the crack opening stress during the delayed retardation stage hasmore » been based on experimental results reported for various steels. For all the load sequences considered, the predictions from the proposed model appeared to be by far more accurate than those from CORPUS. Based on the analysis results, the capability of semi-empirical prediction concepts to cover experimentally observed trends that have been reported for sequences with overloads is discussed. Finally, possibilities of improving the model performance are considered.« less

Discriminating between stabilizing and destabilizing protein design mutations via recombination and simulation.

PubMed

Johnson, Lucas B; Gintner, Lucas P; Park, Sehoo; Snow, Christopher D

2015-08-01

Accuracy of current computational protein design (CPD) methods is limited by inherent approximations in energy potentials and sampling. These limitations are often used to qualitatively explain design failures; however, relatively few studies provide specific examples or quantitative details that can be used to improve future CPD methods. Expanding the design method to include a library of sequences provides data that is well suited for discriminating between stabilizing and destabilizing design elements. Using thermophilic endoglucanase E1 from Acidothermus cellulolyticus as a model enzyme, we computationally designed a sequence with 60 mutations. The design sequence was rationally divided into structural blocks and recombined with the wild-type sequence. Resulting chimeras were assessed for activity and thermostability. Surprisingly, unlike previous chimera libraries, regression analysis based on one- and two-body effects was not sufficient for predicting chimera stability. Analysis of molecular dynamics simulations proved helpful in distinguishing stabilizing and destabilizing mutations. Reverting to the wild-type amino acid at destabilized sites partially regained design stability, and introducing predicted stabilizing mutations in wild-type E1 significantly enhanced thermostability. The ability to isolate stabilizing and destabilizing elements in computational design offers an opportunity to interpret previous design failures and improve future CPD methods. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Molecular analysis of Acinetobacter baumannii strains isolated in Lebanon using four different typing methods.

PubMed

Rafei, Rayane; Dabboussi, Fouad; Hamze, Monzer; Eveillard, Matthieu; Lemarié, Carole; Gaultier, Marie-Pierre; Mallat, Hassan; Moghnieh, Rima; Husni-Samaha, Rola; Joly-Guillou, Marie-Laure; Kempf, Marie

2014-01-01

This study analyzed 42 Acinetobacter baumannii strains collected between 2009-2012 from different hospitals in Beyrouth and North Lebanon to better understand the epidemiology and carbapenem resistance mechanisms in our collection and to compare the robustness of pulsed field gel electrophoresis (PFGE), multilocus sequence typing (MLST), repetitive sequence-based PCR (rep-PCR) and blaOXA-51 sequence-based typing (SBT). Among 31 carbapenem resistant strains, we have detected three carbapenem resistance genes: 28 carried the blaOXA-23 gene, 1 the blaOXA-24 gene and 2 strains the blaOXA-58 gene. This is the first detection of blaOXA-23 and blaOXA-24 in Lebanon. PFGE identified 11 types and was the most discriminating technique followed by rep-PCR (9 types), blaOXA-51 SBT (8 types) and MLST (7 types). The PFGE type A'/ST2 was the dominant genotype in our collection present in Beyrouth and North Lebanon. The clustering agreement between all techniques was measured by adjust Wallace coefficient. An overall agreement has been demonstrated. High values of adjust Wallace coefficient were found with followed combinations: PFGE to predict MLST types  = 100%, PFGE to predict blaOXA-51 SBT = 100%, blaOXA-51 SBT to predict MLST = 100%, MLST to predict blaOXA-51 SBT = 84.7%, rep-PCR to predict MLST = 81.5%, PFGE to predict rep-PCR = 69% and rep-PCR to predict blaOXA-51 SBT = 67.2%. PFGE and MLST are gold standard methods for outbreaks investigation and population structure studies respectively. Otherwise, these two techniques are technically, time and cost demanding. We recommend the use of blaOXA-51 SBT as first typing method to screen isolates and assign them to their corresponding clonal lineages. Repetitive sequence-based PCR is a rapid tool to access outbreaks but careful interpretation of results must be always performed.

PlantTFDB: a comprehensive plant transcription factor database

PubMed Central

Guo, An-Yuan; Chen, Xin; Gao, Ge; Zhang, He; Zhu, Qi-Hui; Liu, Xiao-Chuan; Zhong, Ying-Fu; Gu, Xiaocheng; He, Kun; Luo, Jingchu

2008-01-01

Transcription factors (TFs) play key roles in controlling gene expression. Systematic identification and annotation of TFs, followed by construction of TF databases may serve as useful resources for studying the function and evolution of transcription factors. We developed a comprehensive plant transcription factor database PlantTFDB (http://planttfdb.cbi.pku.edu.cn), which contains 26 402 TFs predicted from 22 species, including five model organisms with available whole genome sequence and 17 plants with available EST sequences. To provide comprehensive information for those putative TFs, we made extensive annotation at both family and gene levels. A brief introduction and key references were presented for each family. Functional domain information and cross-references to various well-known public databases were available for each identified TF. In addition, we predicted putative orthologs of those TFs among the 22 species. PlantTFDB has a simple interface to allow users to search the database by IDs or free texts, to make sequence similarity search against TFs of all or individual species, and to download TF sequences for local analysis. PMID:17933783

Prediction of a common beta-propeller catalytic domain for fructosyltransferases of different origin and substrate specificity.

PubMed

Pons, T; Hernández, L; Batista, F R; Chinea, G

2000-11-01

The three-dimensional (3D) structure of fructan biosynthetic enzymes is still unknown. Here, we have explored folding similarities between reported microbial and plant enzymes that catalyze transfructosylation reactions. A sequence-structure compatibility search using TOPITS, SDP, 3D-PSSM, and SAM-T98 programs identified a beta-propeller fold with scores above the confidence threshold that indicate a structurally conserved catalytic domain in fructosyltransferases (FTFs) of diverse origin and substrate specificity. The predicted fold appeared related to that of neuraminidase and sialidase, of glycoside hydrolase families 33 and 34, respectively. The most reliable structural model was obtained using the crystal structure of neuraminidase (Protein Data Bank file: 5nn9) as template, and it is consistent with the location of previously identified functional residues of bacterial levansucrases (Batista et al., 1999; Song & Jacques, 1999). The sequence-sequence analysis presented here reinforces the recent inclusion of fungal and plant FTFs into glycoside hydrolase family 32, and suggests a modified sequence pattern H-x (2)-[PTV]-x (4)-[LIVMA]-[NSCAYG]-[DE]-P-[NDSC][GA]3 for this family.

Prediction of a common beta-propeller catalytic domain for fructosyltransferases of different origin and substrate specificity.

PubMed Central

Pons, T.; Hernández, L.; Batista, F. R.; Chinea, G.

2000-01-01

The three-dimensional (3D) structure of fructan biosynthetic enzymes is still unknown. Here, we have explored folding similarities between reported microbial and plant enzymes that catalyze transfructosylation reactions. A sequence-structure compatibility search using TOPITS, SDP, 3D-PSSM, and SAM-T98 programs identified a beta-propeller fold with scores above the confidence threshold that indicate a structurally conserved catalytic domain in fructosyltransferases (FTFs) of diverse origin and substrate specificity. The predicted fold appeared related to that of neuraminidase and sialidase, of glycoside hydrolase families 33 and 34, respectively. The most reliable structural model was obtained using the crystal structure of neuraminidase (Protein Data Bank file: 5nn9) as template, and it is consistent with the location of previously identified functional residues of bacterial levansucrases (Batista et al., 1999; Song & Jacques, 1999). The sequence-sequence analysis presented here reinforces the recent inclusion of fungal and plant FTFs into glycoside hydrolase family 32, and suggests a modified sequence pattern H-x (2)-[PTV]-x (4)-[LIVMA]-[NSCAYG]-[DE]-P-[NDSC][GA]3 for this family. PMID:11305239

DMINDA: an integrated web server for DNA motif identification and analyses

PubMed Central

Ma, Qin; Zhang, Hanyuan; Mao, Xizeng; Zhou, Chuan; Liu, Bingqiang; Chen, Xin; Xu, Ying

2014-01-01

DMINDA (DNA motif identification and analyses) is an integrated web server for DNA motif identification and analyses, which is accessible at http://csbl.bmb.uga.edu/DMINDA/. This web site is freely available to all users and there is no login requirement. This server provides a suite of cis-regulatory motif analysis functions on DNA sequences, which are important to elucidation of the mechanisms of transcriptional regulation: (i) de novo motif finding for a given set of promoter sequences along with statistical scores for the predicted motifs derived based on information extracted from a control set, (ii) scanning motif instances of a query motif in provided genomic sequences, (iii) motif comparison and clustering of identified motifs, and (iv) co-occurrence analyses of query motifs in given promoter sequences. The server is powered by a backend computer cluster with over 150 computing nodes, and is particularly useful for motif prediction and analyses in prokaryotic genomes. We believe that DMINDA, as a new and comprehensive web server for cis-regulatory motif finding and analyses, will benefit the genomic research community in general and prokaryotic genome researchers in particular. PMID:24753419

The evolution, diversity, and host associations of rhabdoviruses.

PubMed

Longdon, Ben; Murray, Gemma G R; Palmer, William J; Day, Jonathan P; Parker, Darren J; Welch, John J; Obbard, Darren J; Jiggins, Francis M

2015-01-01

Metagenomic studies are leading to the discovery of a hidden diversity of RNA viruses. These new viruses are poorly characterized and new approaches are needed predict the host species these viruses pose a risk to. The rhabdoviruses are a diverse family of RNA viruses that includes important pathogens of humans, animals, and plants. We have discovered thirty-two new rhabdoviruses through a combination of our own RNA sequencing of insects and searching public sequence databases. Combining these with previously known sequences we reconstructed the phylogeny of 195 rhabdovirus sequences, and produced the most in depth analysis of the family to date. In most cases we know nothing about the biology of the viruses beyond the host they were identified from, but our dataset provides a powerful phylogenetic approach to predict which are vector-borne viruses and which are specific to vertebrates or arthropods. By reconstructing ancestral and present host states we found that switches between major groups of hosts have occurred rarely during rhabdovirus evolution. This allowed us to propose seventy-six new likely vector-borne vertebrate viruses among viruses identified from vertebrates or biting insects. Based on currently available data, our analysis suggests it is likely there was a single origin of the known plant viruses and arthropod-borne vertebrate viruses, while vertebrate- and arthropod-specific viruses arose at least twice. There are also few transitions between aquatic and terrestrial ecosystems. Viruses also cluster together at a finer scale, with closely related viruses tending to be found in closely related hosts. Our data therefore suggest that throughout their evolution, rhabdoviruses have occasionally jumped between distantly related host species before spreading through related hosts in the same environment. This approach offers a way to predict the most probable biology and key traits of newly discovered viruses.

Microbial genomic island discovery, visualization and analysis.

PubMed

Bertelli, Claire; Tilley, Keith E; Brinkman, Fiona S L

2018-06-03

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Version 3.0 User Guide

EPA Science Inventory

User Guide to describe the complete functionality of the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Version 3.0 online tool. The US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility tool (SeqAPASS; https://seqa...

Genome sequence of Plasmopara viticola and insight into the pathogenic mechanism

PubMed Central

Yin, Ling; An, Yunhe; Qu, Junjie; Li, Xinlong; Zhang, Yali; Dry, Ian; Wu, Huijuan; Lu, Jiang

2017-01-01

Plasmopara viticola causes downy mildew disease of grapevine which is one of the most devastating diseases of viticulture worldwide. Here we report a 101.3 Mb whole genome sequence of P. viticola isolate ‘JL-7-2’ obtained by a combination of Illumina and PacBio sequencing technologies. The P. viticola genome contains 17,014 putative protein-coding genes and has ~26% repetitive sequences. A total of 1,301 putative secreted proteins, including 100 putative RXLR effectors and 90 CRN effectors were identified in this genome. In the secretome, 261 potential pathogenicity genes and 95 carbohydrate-active enzymes were predicted. Transcriptional analysis revealed that most of the RXLR effectors, pathogenicity genes and carbohydrate-active enzymes were significantly up-regulated during infection. Comparative genomic analysis revealed that P. viticola evolved independently from the Arabidopsis downy mildew pathogen Hyaloperonospora arabidopsidis. The availability of the P. viticola genome provides a valuable resource not only for comparative genomic analysis and evolutionary studies among oomycetes, but also enhance our knowledge on the mechanism of interactions between this biotrophic pathogen and its host. PMID:28417959

Microsatellite analysis in the genome of Acanthaceae: An in silico approach

PubMed Central

Kaliswamy, Priyadharsini; Vellingiri, Srividhya; Nathan, Bharathi; Selvaraj, Saravanakumar

2015-01-01

Background: Acanthaceae is one of the advanced and specialized families with conventionally used medicinal plants. Simple sequence repeats (SSRs) play a major role as molecular markers for genome analysis and plant breeding. The microsatellites existing in the complete genome sequences would help to attain a direct role in the genome organization, recombination, gene regulation, quantitative genetic variation, and evolution of genes. Objective: The current study reports the frequency of microsatellites and appropriate markers for the Acanthaceae family genome sequences. Materials and Methods: The whole nucleotide sequences of Acanthaceae species were obtained from National Center for Biotechnology Information database and screened for the presence of SSRs. SSR Locator tool was used to predict the microsatellites and inbuilt Primer3 module was used for primer designing. Results: Totally 110 repeats from 108 sequences of Acanthaceae family plant genomes were identified, and the occurrence of dinucleotide repeats was found to be abundant in the genome sequences. The essential amino acid isoleucine was found rich in all the sequences. We also designed the SSR-based primers/markers for 59 sequences of this family that contains microsatellite repeats in their genome. Conclusion: The identified microsatellites and primers might be useful for breeding and genetic studies of plants that belong to Acanthaceae family in the future. PMID:25709226

Evaluation and integration of functional annotation pipelines for newly sequenced organisms: the potato genome as a test case.

PubMed

Amar, David; Frades, Itziar; Danek, Agnieszka; Goldberg, Tatyana; Sharma, Sanjeev K; Hedley, Pete E; Proux-Wera, Estelle; Andreasson, Erik; Shamir, Ron; Tzfadia, Oren; Alexandersson, Erik

2014-12-05

For most organisms, even if their genome sequence is available, little functional information about individual genes or proteins exists. Several annotation pipelines have been developed for functional analysis based on sequence, 'omics', and literature data. However, researchers encounter little guidance on how well they perform. Here, we used the recently sequenced potato genome as a case study. The potato genome was selected since its genome is newly sequenced and it is a non-model plant even if there is relatively ample information on individual potato genes, and multiple gene expression profiles are available. We show that the automatic gene annotations of potato have low accuracy when compared to a "gold standard" based on experimentally validated potato genes. Furthermore, we evaluate six state-of-the-art annotation pipelines and show that their predictions are markedly dissimilar (Jaccard similarity coefficient of 0.27 between pipelines on average). To overcome this discrepancy, we introduce a simple GO structure-based algorithm that reconciles the predictions of the different pipelines. We show that the integrated annotation covers more genes, increases by over 50% the number of highly co-expressed GO processes, and obtains much higher agreement with the gold standard. We find that different annotation pipelines produce different results, and show how to integrate them into a unified annotation that is of higher quality than each single pipeline. We offer an improved functional annotation of both PGSC and ITAG potato gene models, as well as tools that can be applied to additional pipelines and improve annotation in other organisms. This will greatly aid future functional analysis of '-omics' datasets from potato and other organisms with newly sequenced genomes. The new potato annotations are available with this paper.

Evolutionary insight into the ionotropic glutamate receptor superfamily of photosynthetic organisms.

PubMed

De Bortoli, Sara; Teardo, Enrico; Szabò, Ildikò; Morosinotto, Tomas; Alboresi, Alessandro

2016-11-01

Photosynthetic eukaryotes have a complex evolutionary history shaped by multiple endosymbiosis events that required a tight coordination between the organelles and the rest of the cell. Plant ionotropic glutamate receptors (iGLRs) form a large superfamily of proteins with a predicted or proven non-selective cation channel activity regulated by a broad range of amino acids. They are involved in different physiological processes such as C/N sensing, resistance against fungal infection, root and pollen tube growth and response to wounding and pathogens. Most of the present knowledge is limited to iGLRs located in plasma membranes. However, recent studies localized different iGLR isoforms to mitochondria and/or chloroplasts, suggesting the possibility that they play a specific role in bioenergetic processes. In this work, we performed a comparative analysis of GLR sequences from bacteria and various photosynthetic eukaryotes. In particular, novel types of selectivity filters of bacteria are reported adding new examples of the great diversity of the GLR superfamily. The highest variability in GLR sequences was found among the algal sequences (cryptophytes, diatoms, brown and green algae). GLRs of land plants are not closely related to the GLRs of green algae analyzed in this work. The GLR family underwent a great expansion in vascular plants. Among plant GLRs, Clade III includes sequences from Physcomitrella patens, Marchantia polymorpha and gymnosperms and can be considered the most ancient, while other clades likely emerged later. In silico analysis allowed the identification of sequences with a putative target to organelles. Sequences with a predicted localization to mitochondria and chloroplasts are randomly distributed among different type of GLRs, suggesting that no compartment-related specific function has been maintained across the species. Copyright © 2016 Elsevier B.V. All rights reserved.

A mechanistic insight into the amyloidogenic structure of hIAPP peptide revealed from sequence analysis and molecular dynamics simulation.

PubMed

Chakraborty, Sandipan; Chatterjee, Barnali; Basu, Soumalee

2012-07-01

A collective approach of sequence analysis, phylogenetic tree and in silico prediction of amyloidogenecity using bioinformatics tools have been used to correlate the observed species-specific variations in IAPP sequences with the amyloid forming propensity. Observed substitution patterns indicate that probable changes in local hydrophobicity are instrumental in altering the aggregation propensity of the peptide. In particular, residues at 17th, 22nd and 23rd positions of the IAPP peptide are found to be crucial for amyloid formation. Proline25 primarily dictates the observed non-amyloidogenecity in rodents. Furthermore, extensive molecular dynamics simulation of 0.24 μs have been carried out with human IAPP (hIAPP) fragment 19-27, the portion showing maximum sequence variation across different species, to understand the native folding characteristic of this region. Principal component analysis in combination with free energy landscape analysis illustrates a four residue turn spanning from residue 22 to 25. The results provide a structural insight into the intramolecular β-sheet structure of amylin which probably is the template for nucleation of fibril formation and growth, a pathogenic feature of type II diabetes. Copyright © 2012 Elsevier B.V. All rights reserved.

Application of the MIDAS approach for analysis of lysine acetylation sites.

PubMed

Evans, Caroline A; Griffiths, John R; Unwin, Richard D; Whetton, Anthony D; Corfe, Bernard M

2013-01-01

Multiple Reaction Monitoring Initiated Detection and Sequencing (MIDAS™) is a mass spectrometry-based technique for the detection and characterization of specific post-translational modifications (Unwin et al. 4:1134-1144, 2005), for example acetylated lysine residues (Griffiths et al. 18:1423-1428, 2007). The MIDAS™ technique has application for discovery and analysis of acetylation sites. It is a hypothesis-driven approach that requires a priori knowledge of the primary sequence of the target protein and a proteolytic digest of this protein. MIDAS essentially performs a targeted search for the presence of modified, for example acetylated, peptides. The detection is based on the combination of the predicted molecular weight (measured as mass-charge ratio) of the acetylated proteolytic peptide and a diagnostic fragment (product ion of m/z 126.1), which is generated by specific fragmentation of acetylated peptides during collision induced dissociation performed in tandem mass spectrometry (MS) analysis. Sequence information is subsequently obtained which enables acetylation site assignment. The technique of MIDAS was later trademarked by ABSciex for targeted protein analysis where an MRM scan is combined with full MS/MS product ion scan to enable sequence confirmation.

Evolutionary Analysis Predicts Sensitive Positions of MMP20 and Validates Newly- and Previously-Identified MMP20 Mutations Causing Amelogenesis Imperfecta

PubMed Central

Gasse, Barbara; Prasad, Megana; Delgado, Sidney; Huckert, Mathilde; Kawczynski, Marzena; Garret-Bernardin, Annelyse; Lopez-Cazaux, Serena; Bailleul-Forestier, Isabelle; Manière, Marie-Cécile; Stoetzel, Corinne; Bloch-Zupan, Agnès; Sire, Jean-Yves

2017-01-01

Amelogenesis imperfecta (AI) designates a group of genetic diseases characterized by a large range of enamel disorders causing important social and health problems. These defects can result from mutations in enamel matrix proteins or protease encoding genes. A range of mutations in the enamel cleavage enzyme matrix metalloproteinase-20 gene (MMP20) produce enamel defects of varying severity. To address how various alterations produce a range of AI phenotypes, we performed a targeted analysis to find MMP20 mutations in French patients diagnosed with non-syndromic AI. Genomic DNA was isolated from saliva and MMP20 exons and exon-intron boundaries sequenced. We identified several homozygous or heterozygous mutations, putatively involved in the AI phenotypes. To validate missense mutations and predict sensitive positions in the MMP20 sequence, we evolutionarily compared 75 sequences extracted from the public databases using the Datamonkey webserver. These sequences were representative of mammalian lineages, covering more than 150 million years of evolution. This analysis allowed us to find 324 sensitive positions (out of the 483 MMP20 residues), pinpoint functionally important domains, and build an evolutionary chart of important conserved MMP20 regions. This is an efficient tool to identify new- and previously-identified mutations. We thus identified six functional MMP20 mutations in unrelated families, finding two novel mutated sites. The genotypes and phenotypes of these six mutations are described and compared. To date, 13 MMP20 mutations causing AI have been reported, making these genotypes and associated hypomature enamel phenotypes the most frequent in AI. PMID:28659819

Evolutionary Analysis Predicts Sensitive Positions of MMP20 and Validates Newly- and Previously-Identified MMP20 Mutations Causing Amelogenesis Imperfecta.

PubMed

Gasse, Barbara; Prasad, Megana; Delgado, Sidney; Huckert, Mathilde; Kawczynski, Marzena; Garret-Bernardin, Annelyse; Lopez-Cazaux, Serena; Bailleul-Forestier, Isabelle; Manière, Marie-Cécile; Stoetzel, Corinne; Bloch-Zupan, Agnès; Sire, Jean-Yves

2017-01-01

Amelogenesis imperfecta (AI) designates a group of genetic diseases characterized by a large range of enamel disorders causing important social and health problems. These defects can result from mutations in enamel matrix proteins or protease encoding genes. A range of mutations in the enamel cleavage enzyme matrix metalloproteinase-20 gene ( MMP20 ) produce enamel defects of varying severity. To address how various alterations produce a range of AI phenotypes, we performed a targeted analysis to find MMP20 mutations in French patients diagnosed with non-syndromic AI. Genomic DNA was isolated from saliva and MMP20 exons and exon-intron boundaries sequenced. We identified several homozygous or heterozygous mutations, putatively involved in the AI phenotypes. To validate missense mutations and predict sensitive positions in the MMP20 sequence, we evolutionarily compared 75 sequences extracted from the public databases using the Datamonkey webserver. These sequences were representative of mammalian lineages, covering more than 150 million years of evolution. This analysis allowed us to find 324 sensitive positions (out of the 483 MMP20 residues), pinpoint functionally important domains, and build an evolutionary chart of important conserved MMP20 regions. This is an efficient tool to identify new- and previously-identified mutations. We thus identified six functional MMP20 mutations in unrelated families, finding two novel mutated sites. The genotypes and phenotypes of these six mutations are described and compared. To date, 13 MMP20 mutations causing AI have been reported, making these genotypes and associated hypomature enamel phenotypes the most frequent in AI.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Golbus, Jessica R.; Puckelwartz, Megan J.; Dellefave-Castillo, Lisa

Background—Cardiomyopathy is highly heritable but genetically diverse. At present, genetic testing for cardiomyopathy uses targeted sequencing to simultaneously assess the coding regions of more than 50 genes. New genes are routinely added to panels to improve the diagnostic yield. With the anticipated $1000 genome, it is expected that genetic testing will shift towards comprehensive genome sequencing accompanied by targeted gene analysis. Therefore, we assessed the reliability of whole genome sequencing and targeted analysis to identify cardiomyopathy variants in 11 subjects with cardiomyopathy. Methods and Results—Whole genome sequencing with an average of 37× coverage was combined with targeted analysis focused onmore » 204 genes linked to cardiomyopathy. Genetic variants were scored using multiple prediction algorithms combined with frequency data from public databases. This pipeline yielded 1-14 potentially pathogenic variants per individual. Variants were further analyzed using clinical criteria and/or segregation analysis. Three of three previously identified primary mutations were detected by this analysis. In six subjects for whom the primary mutation was previously unknown, we identified mutations that segregated with disease, had clinical correlates, and/or had additional pathological correlation to provide evidence for causality. For two subjects with previously known primary mutations, we identified additional variants that may act as modifiers of disease severity. In total, we identified the likely pathological mutation in 9 of 11 (82%) subjects. We conclude that these pilot data demonstrate that ~30-40× coverage whole genome sequencing combined with targeted analysis is feasible and sensitive to identify rare variants in cardiomyopathy-associated genes.« less

Comparative Genome Sequence Analysis of the Bpa/Str Region in Mouse and Man

PubMed Central

Mallon, A.-M.; Platzer, M.; Bate, R.; Gloeckner, G.; Botcherby, M.R.M.; Nordsiek, G.; Strivens, M.A.; Kioschis, P.; Dangel, A.; Cunningham, D.; Straw, R.N.A.; Weston, P.; Gilbert, M.; Fernando, S.; Goodall, K.; Hunter, G.; Greystrong, J.S.; Clarke, D.; Kimberley, C.; Goerdes, M.; Blechschmidt, K.; Rump, A.; Hinzmann, B.; Mundy, C.R.; Miller, W.; Poustka, A.; Herman, G.E.; Rhodes, M.; Denny, P.; Rosenthal, A.; Brown, S.D.M.

2000-01-01

The progress of human and mouse genome sequencing programs presages the possibility of systematic cross-species comparison of the two genomes as a powerful tool for gene and regulatory element identification. As the opportunities to perform comparative sequence analysis emerge, it is important to develop parameters for such analyses and to examine the outcomes of cross-species comparison. Our analysis used gene prediction and a database search of 430 kb of genomic sequence covering the Bpa/Str region of the mouse X chromosome, and 745 kb of genomic sequence from the homologous human X chromosome region. We identified 11 genes in mouse and 13 genes and two pseudogenes in human. In addition, we compared the mouse and human sequences using pairwise alignment and searches for evolutionary conserved regions (ECRs) exceeding a defined threshold of sequence identity. This approach aided the identification of at least four further putative conserved genes in the region. Comparative sequencing revealed that this region is a mosaic in evolutionary terms, with considerably more rearrangement between the two species than realized previously from comparative mapping studies. Surprisingly, this region showed an extremely high LINE and low SINE content, low G+C content, and yet a relatively high gene density, in contrast to the low gene density usually associated with such regions. [The sequence data described in this paper have been submitted to EMBL under the following accession nos.: Mouse Genomic Sequence: Mouse contig A (AL021127), Mouse contig B (AL049866), BAC41M10 (AL136328), PAC303O11(AL136329). Human Genomic Sequence: Human contig 1 (U82671, U82670), Human contig 2 (U82695).] PMID:10854409

BCM Search Launcher--an integrated interface to molecular biology data base search and analysis services available on the World Wide Web.

PubMed

Smith, R F; Wiese, B A; Wojzynski, M K; Davison, D B; Worley, K C

1996-05-01

The BCM Search Launcher is an integrated set of World Wide Web (WWW) pages that organize molecular biology-related search and analysis services available on the WWW by function, and provide a single point of entry for related searches. The Protein Sequence Search Page, for example, provides a single sequence entry form for submitting sequences to WWW servers that offer remote access to a variety of different protein sequence search tools, including BLAST, FASTA, Smith-Waterman, BEAUTY, PROSITE, and BLOCKS searches. Other Launch pages provide access to (1) nucleic acid sequence searches, (2) multiple and pair-wise sequence alignments, (3) gene feature searches, (4) protein secondary structure prediction, and (5) miscellaneous sequence utilities (e.g., six-frame translation). The BCM Search Launcher also provides a mechanism to extend the utility of other WWW services by adding supplementary hypertext links to results returned by remote servers. For example, links to the NCBI's Entrez data base and to the Sequence Retrieval System (SRS) are added to search results returned by the NCBI's WWW BLAST server. These links provide easy access to auxiliary information, such as Medline abstracts, that can be extremely helpful when analyzing BLAST data base hits. For new or infrequent users of sequence data base search tools, we have preset the default search parameters to provide the most informative first-pass sequence analysis possible. We have also developed a batch client interface for Unix and Macintosh computers that allows multiple input sequences to be searched automatically as a background task, with the results returned as individual HTML documents directly to the user's system. The BCM Search Launcher and batch client are available on the WWW at URL http:@gc.bcm.tmc.edu:8088/search-launcher.html.

Computational prediction and biochemical characterization of novel RNA aptamers to Rift Valley fever virus nucleocapsid protein.

PubMed

Ellenbecker, Mary; St Goddard, Jeremy; Sundet, Alec; Lanchy, Jean-Marc; Raiford, Douglas; Lodmell, J Stephen

2015-10-01

Rift Valley fever virus (RVFV) is a potent human and livestock pathogen endemic to sub-Saharan Africa and the Arabian Peninsula that has potential to spread to other parts of the world. Although there is no proven effective and safe treatment for RVFV infections, a potential therapeutic target is the virally encoded nucleocapsid protein (N). During the course of infection, N binds to viral RNA, and perturbation of this interaction can inhibit viral replication. To gain insight into how N recognizes viral RNA specifically, we designed an algorithm that uses a distance matrix and multidimensional scaling to compare the predicted secondary structures of known N-binding RNAs, or aptamers, that were isolated and characterized in previous in vitro evolution experiment. These aptamers did not exhibit overt sequence or predicted structure similarity, so we employed bioinformatic methods to propose novel aptamers based on analysis and clustering of secondary structures. We screened and scored the predicted secondary structures of novel randomly generated RNA sequences in silico and selected several of these putative N-binding RNAs whose secondary structures were similar to those of known N-binding RNAs. We found that overall the in silico generated RNA sequences bound well to N in vitro. Furthermore, introduction of these RNAs into cells prior to infection with RVFV inhibited viral replication in cell culture. This proof of concept study demonstrates how the predictive power of bioinformatics and the empirical power of biochemistry can be jointly harnessed to discover, synthesize, and test new RNA sequences that bind tightly to RVFV N protein. The approach would be easily generalizable to other applications. Copyright © 2015 Elsevier Ltd. All rights reserved.

Statistical physics of interacting neural networks

NASA Astrophysics Data System (ADS)

Kinzel, Wolfgang; Metzler, Richard; Kanter, Ido

2001-12-01

Recent results on the statistical physics of time series generation and prediction are presented. A neural network is trained on quasi-periodic and chaotic sequences and overlaps to the sequence generator as well as the prediction errors are calculated numerically. For each network there exists a sequence for which it completely fails to make predictions. Two interacting networks show a transition to perfect synchronization. A pool of interacting networks shows good coordination in the minority game-a model of competition in a closed market. Finally, as a demonstration, a perceptron predicts bit sequences produced by human beings.

Prediction and analysis of beta-turns in proteins by support vector machine.

PubMed

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2003-01-01

Tight turn has long been recognized as one of the three important features of proteins after the alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns. Analysis and prediction of beta-turns in particular and tight turns in general are very useful for the design of new molecules such as drugs, pesticides, and antigens. In this paper, we introduce a support vector machine (SVM) approach to prediction and analysis of beta-turns. We have investigated two aspects of applying SVM to the prediction and analysis of beta-turns. First, we developed a new SVM method, called BTSVM, which predicts beta-turns of a protein from its sequence. The prediction results on the dataset of 426 non-homologous protein chains by sevenfold cross-validation technique showed that our method is superior to the other previous methods. Second, we analyzed how amino acid positions support (or prevent) the formation of beta-turns based on the "multivariable" classification model of a linear SVM. This model is more general than the other ones of previous statistical methods. Our analysis results are more comprehensive and easier to use than previously published analysis results.

Genomic cloning and promoter functional analysis of myostatin-2 in shi drum, Umbrina cirrosa: conservation of muscle-specific promoter activity.

PubMed

Nadjar-Boger, Elisabeth; Maccatrozzo, Lisa; Radaelli, Giuseppe; Funkenstein, Bruria

2013-02-01

Myostatin (MSTN) is a member of the transforming growth factor-ß superfamily, known as a negative regulator of skeletal muscle development and growth in mammals. In contrast to mammals, fish possess at least two paralogs of MSTN: MSTN-1 and MSTN-2. Here we describe the cloning and sequence analysis of spliced and precursor (unspliced) transcripts as well as the 5' flanking region of MSTN-2 from the marine fish Umbrina cirrosa (ucMSTN-2). In silico analysis revealed numerous putative cis regulatory elements including several E-boxes known as binding sites to myogenic transcription factors. Transient transfection experiments using non-muscle and muscle cell lines showed high transcriptional activity in muscle cells and in differentiated neural cells, in accordance with our previous findings in MSTN-2 promoter from Sparus aurata. Comparative informatics analysis of MSTN-2 from several fish species revealed high conservation of the predicted amino acid sequence as well as the gene structure (exon length) although intron length varied between species. The proximal promoter of MSTN-2 gene was found to be conserved among Perciforms. In conclusion, this study reinforces our conclusion that MSTN-2 promoter is a very strong promoter, especially in muscle cells. In addition, we show that the MSTN-2 gene structure is highly conserved among fishes as is the predicted amino acid sequence of the peptide. Copyright © 2012 Elsevier Inc. All rights reserved.

Single-step methods for predicting orbital motion considering its periodic components

NASA Astrophysics Data System (ADS)

Lavrov, K. N.

1989-01-01

Modern numerical methods for integration of ordinary differential equations can provide accurate and universal solutions to celestial mechanics problems. The implicit single sequence algorithms of Everhart and multiple step computational schemes using a priori information on periodic components can be combined to construct implicit single sequence algorithms which combine their advantages. The construction and analysis of the properties of such algorithms are studied, utilizing trigonometric approximation of the solutions of differential equations containing periodic components. The algorithms require 10 percent more machine memory than the Everhart algorithms, but are twice as fast, and yield short term predictions valid for five to ten orbits with good accuracy and five to six times faster than algorithms using other methods.

SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

PubMed

Yang, Xiaoxia; Wang, Jia; Sun, Jun; Liu, Rong

2015-01-01

Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

Mutation Detection in Patients With Advanced Cancer by Universal Sequencing of Cancer-Related Genes in Tumor and Normal DNA vs Guideline-Based Germline Testing.

PubMed

Mandelker, Diana; Zhang, Liying; Kemel, Yelena; Stadler, Zsofia K; Joseph, Vijai; Zehir, Ahmet; Pradhan, Nisha; Arnold, Angela; Walsh, Michael F; Li, Yirong; Balakrishnan, Anoop R; Syed, Aijazuddin; Prasad, Meera; Nafa, Khedoudja; Carlo, Maria I; Cadoo, Karen A; Sheehan, Meg; Fleischut, Megan H; Salo-Mullen, Erin; Trottier, Magan; Lipkin, Steven M; Lincoln, Anne; Mukherjee, Semanti; Ravichandran, Vignesh; Cambria, Roy; Galle, Jesse; Abida, Wassim; Arcila, Marcia E; Benayed, Ryma; Shah, Ronak; Yu, Kenneth; Bajorin, Dean F; Coleman, Jonathan A; Leach, Steven D; Lowery, Maeve A; Garcia-Aguilar, Julio; Kantoff, Philip W; Sawyers, Charles L; Dickler, Maura N; Saltz, Leonard; Motzer, Robert J; O'Reilly, Eileen M; Scher, Howard I; Baselga, Jose; Klimstra, David S; Solit, David B; Hyman, David M; Berger, Michael F; Ladanyi, Marc; Robson, Mark E; Offit, Kenneth

2017-09-05

Guidelines for cancer genetic testing based on family history may miss clinically actionable genetic changes with established implications for cancer screening or prevention. To determine the proportion and potential clinical implications of inherited variants detected using simultaneous sequencing of the tumor and normal tissue ("tumor-normal sequencing") compared with genetic test results based on current guidelines. From January 2014 until May 2016 at Memorial Sloan Kettering Cancer Center, 10 336 patients consented to tumor DNA sequencing. Since May 2015, 1040 of these patients with advanced cancer were referred by their oncologists for germline analysis of 76 cancer predisposition genes. Patients with clinically actionable inherited mutations whose genetic test results would not have been predicted by published decision rules were identified. Follow-up for potential clinical implications of mutation detection was through May 2017. Tumor and germline sequencing compared with the predicted yield of targeted germline sequencing based on clinical guidelines. Proportion of clinically actionable germline mutations detected by universal tumor-normal sequencing that would not have been detected by guideline-directed testing. Of 1040 patients, the median age was 58 years (interquartile range, 50.5-66 years), 65.3% were male, and 81.3% had stage IV disease at the time of genomic analysis, with prostate, renal, pancreatic, breast, and colon cancer as the most common diagnoses. Of the 1040 patients, 182 (17.5%; 95% CI, 15.3%-19.9%) had clinically actionable mutations conferring cancer susceptibility, including 149 with moderate- to high-penetrance mutations; 101 patients tested (9.7%; 95% CI, 8.1%-11.7%) would not have had these mutations detected using clinical guidelines, including 65 with moderate- to high-penetrance mutations. Frequency of inherited mutations was related to case mix, stage, and founder mutations. Germline findings led to discussion or initiation of change to targeted therapy in 38 patients tested (3.7%) and predictive testing in the families of 13 individuals (1.3%), including 6 for whom genetic evaluation would not have been initiated by guideline-based testing. In this referral population with selected advanced cancers, universal sequencing of a broad panel of cancer-related genes in paired germline and tumor DNA samples was associated with increased detection of individuals with potentially clinically significant heritable mutations over the predicted yield of targeted germline testing based on current clinical guidelines. Knowledge of these additional mutations can help guide therapeutic and preventive interventions, but whether all of these interventions would improve outcomes for patients with cancer or their family members requires further study. clinicaltrials.gov Identifier: NCT01775072.

T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ernest A. Mancini

Characterization of stratigraphic sequences (T-R cycles or sequences) included outcrop studies, well log analysis and seismic reflection interpretation. These studies were performed by researchers at the University of Alabama, Wichita State University and McGill University. The outcrop, well log and seismic characterization studies were used to develop a depositional sequence model, a T-R cycle (sequence) model, and a sequence stratigraphy predictive model. The sequence stratigraphy predictive model developed in this study is based primarily on the modified T-R cycle (sequence) model. The T-R cycle (sequence) model using transgressive and regressive systems tracts and aggrading, backstepping, and infilling intervals or sectionsmore » was found to be the most appropriate sequence stratigraphy model for the strata in the onshore interior salt basins of the Gulf of Mexico to improve petroleum stratigraphic trap and specific reservoir facies imaging, detection and delineation. The known petroleum reservoirs of the Mississippi Interior and North Louisiana Salt Basins were classified using T-R cycle (sequence) terminology. The transgressive backstepping reservoirs have been the most productive of oil, and the transgressive backstepping and regressive infilling reservoirs have been the most productive of gas. Exploration strategies were formulated using the sequence stratigraphy predictive model and the classification of the known petroleum reservoirs utilizing T-R cycle (sequence) terminology. The well log signatures and seismic reflector patterns were determined to be distinctive for the aggrading, backstepping and infilling sections of the T-R cycle (sequence) and as such, well log and seismic data are useful for recognizing and defining potential reservoir facies. The use of the sequence stratigraphy predictive model, in combination with the knowledge of how the distinctive characteristics of the T-R system tracts and their subdivisions are expressed in well log patterns and seismic reflection configurations and terminations, improves the ability to identify and define the limits of potential stratigraphic traps and the stratigraphic component of combination stratigraphic and structural traps and the associated continental, coastal plain and marine potential reservoir facies. The assessment of the underdeveloped and undiscovered reservoirs and resources in the Mississippi Interior and North Louisiana Salt Basins resulted in the confirmation of the Monroe Uplift as a feature characterized by a major regional unconformity, which serves as a combination stratigraphic and structural trap with a significant stratigraphic component, and the characterization of a developing play in southwest Alabama, which involves a stratigraphic trap, located updip near the pinchout of the potential reservoir facies. Potential undiscovered and underdeveloped reservoirs in the onshore interior salt basins are identified as Jurassic and Cretaceous aggrading continental and coastal, backstepping nearshore marine and marine shelf, and infilling fluvial, deltaic, coastal plain and marine shelf.« less

Photovoltaic System Modeling. Uncertainty and Sensitivity Analyses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hansen, Clifford W.; Martin, Curtis E.

2015-08-01

We report an uncertainty and sensitivity analysis for modeling AC energy from ph otovoltaic systems . Output from a PV system is predicted by a sequence of models. We quantify u ncertainty i n the output of each model using empirical distribution s of each model's residuals. We propagate uncertainty through the sequence of models by sampli ng these distributions to obtain a n empirical distribution of a PV system's output. We consider models that: (1) translate measured global horizontal, direct and global diffuse irradiance to plane - of - array irradiance; (2) estimate effective irradiance; (3) predict cell temperature;more » (4) estimate DC voltage, current and power ; (5) reduce DC power for losses due to inefficient maximum power point tracking or mismatch among modules; and (6) convert DC to AC power . O ur analysis consider s a notional PV system com prising an array of FirstSolar FS - 387 modules and a 250 kW AC inverter ; we use measured irradiance and weather at Albuquerque, NM. We found the uncertainty in PV syste m output to be relatively small, on the order of 1% for daily energy. We found that unce rtainty in the models for POA irradiance and effective irradiance to be the dominant contributors to uncertainty in predicted daily energy. Our analysis indicates that efforts to reduce the uncertainty in PV system output predictions may yield the greatest improvements by focusing on the POA and effective irradiance models.« less

Large-scale analysis of full-length cDNAs from the tomato (Solanum lycopersicum) cultivar Micro-Tom, a reference system for the Solanaceae genomics.

PubMed

Aoki, Koh; Yano, Kentaro; Suzuki, Ayako; Kawamura, Shingo; Sakurai, Nozomu; Suda, Kunihiro; Kurabayashi, Atsushi; Suzuki, Tatsuya; Tsugane, Taneaki; Watanabe, Manabu; Ooga, Kazuhide; Torii, Maiko; Narita, Takanori; Shin-I, Tadasu; Kohara, Yuji; Yamamoto, Naoki; Takahashi, Hideki; Watanabe, Yuichiro; Egusa, Mayumi; Kodama, Motoichiro; Ichinose, Yuki; Kikuchi, Mari; Fukushima, Sumire; Okabe, Akiko; Arie, Tsutomu; Sato, Yuko; Yazawa, Katsumi; Satoh, Shinobu; Omura, Toshikazu; Ezura, Hiroshi; Shibata, Daisuke

2010-03-30

The Solanaceae family includes several economically important vegetable crops. The tomato (Solanum lycopersicum) is regarded as a model plant of the Solanaceae family. Recently, a number of tomato resources have been developed in parallel with the ongoing tomato genome sequencing project. In particular, a miniature cultivar, Micro-Tom, is regarded as a model system in tomato genomics, and a number of genomics resources in the Micro-Tom-background, such as ESTs and mutagenized lines, have been established by an international alliance. To accelerate the progress in tomato genomics, we developed a collection of fully-sequenced 13,227 Micro-Tom full-length cDNAs. By checking redundant sequences, coding sequences, and chimeric sequences, a set of 11,502 non-redundant full-length cDNAs (nrFLcDNAs) was generated. Analysis of untranslated regions demonstrated that tomato has longer 5'- and 3'-untranslated regions than most other plants but rice. Classification of functions of proteins predicted from the coding sequences demonstrated that nrFLcDNAs covered a broad range of functions. A comparison of nrFLcDNAs with genes of sixteen plants facilitated the identification of tomato genes that are not found in other plants, most of which did not have known protein domains. Mapping of the nrFLcDNAs onto currently available tomato genome sequences facilitated prediction of exon-intron structure. Introns of tomato genes were longer than those of Arabidopsis and rice. According to a comparison of exon sequences between the nrFLcDNAs and the tomato genome sequences, the frequency of nucleotide mismatch in exons between Micro-Tom and the genome-sequencing cultivar (Heinz 1706) was estimated to be 0.061%. The collection of Micro-Tom nrFLcDNAs generated in this study will serve as a valuable genomic tool for plant biologists to bridge the gap between basic and applied studies. The nrFLcDNA sequences will help annotation of the tomato whole-genome sequence and aid in tomato functional genomics and molecular breeding. Full-length cDNA sequences and their annotations are provided in the database KaFTom http://www.pgb.kazusa.or.jp/kaftom/ via the website of the National Bioresource Project Tomato http://tomato.nbrp.jp.

Identification of sequence motifs significantly associated with antisense activity.

PubMed

McQuisten, Kyle A; Peek, Andrew S

2007-06-07

Predicting the suppression activity of antisense oligonucleotide sequences is the main goal of the rational design of nucleic acids. To create an effective predictive model, it is important to know what properties of an oligonucleotide sequence associate significantly with antisense activity. Also, for the model to be efficient we must know what properties do not associate significantly and can be omitted from the model. This paper will discuss the results of a randomization procedure to find motifs that associate significantly with either high or low antisense suppression activity, analysis of their properties, as well as the results of support vector machine modelling using these significant motifs as features. We discovered 155 motifs that associate significantly with high antisense suppression activity and 202 motifs that associate significantly with low suppression activity. The motifs range in length from 2 to 5 bases, contain several motifs that have been previously discovered as associating highly with antisense activity, and have thermodynamic properties consistent with previous work associating thermodynamic properties of sequences with their antisense activity. Statistical analysis revealed no correlation between a motif's position within an antisense sequence and that sequences antisense activity. Also, many significant motifs existed as subwords of other significant motifs. Support vector regression experiments indicated that the feature set of significant motifs increased correlation compared to all possible motifs as well as several subsets of the significant motifs. The thermodynamic properties of the significantly associated motifs support existing data correlating the thermodynamic properties of the antisense oligonucleotide with antisense efficiency, reinforcing our hypothesis that antisense suppression is strongly associated with probe/target thermodynamics, as there are no enzymatic mediators to speed the process along like the RNA Induced Silencing Complex (RISC) in RNAi. The independence of motif position and antisense activity also allows us to bypass consideration of this feature in the modelling process, promoting model efficiency and reducing the chance of overfitting when predicting antisense activity. The increase in SVR correlation with significant features compared to nearest-neighbour features indicates that thermodynamics alone is likely not the only factor in determining antisense efficiency.

Method of identifying hairpin DNA probes by partial fold analysis

DOEpatents

Miller, Benjamin L [Penfield, NY; Strohsahl, Christopher M [Saugerties, NY

2009-10-06

Method of identifying molecular beacons in which a secondary structure prediction algorithm is employed to identify oligonucleotide sequences within a target gene having the requisite hairpin structure. Isolated oligonucleotides, molecular beacons prepared from those oligonucleotides, and their use are also disclosed.

Method of identifying hairpin DNA probes by partial fold analysis

DOEpatents

Miller, Benjamin L.; Strohsahl, Christopher M.

2008-10-28

Methods of identifying molecular beacons in which a secondary structure prediction algorithm is employed to identify oligonucleotide sequences within a target gene having the requisite hairpin structure. Isolated oligonucleotides, molecular beacons prepared from those oligonucleotides, and their use are also disclosed.

Discriminative prediction of mammalian enhancers from DNA sequence

PubMed Central

Lee, Dongwon; Karchin, Rachel; Beer, Michael A.

2011-01-01

Accurately predicting regulatory sequences and enhancers in entire genomes is an important but difficult problem, especially in large vertebrate genomes. With the advent of ChIP-seq technology, experimental detection of genome-wide EP300/CREBBP bound regions provides a powerful platform to develop predictive tools for regulatory sequences and to study their sequence properties. Here, we develop a support vector machine (SVM) framework which can accurately identify EP300-bound enhancers using only genomic sequence and an unbiased set of general sequence features. Moreover, we find that the predictive sequence features identified by the SVM classifier reveal biologically relevant sequence elements enriched in the enhancers, but we also identify other features that are significantly depleted in enhancers. The predictive sequence features are evolutionarily conserved and spatially clustered, providing further support of their functional significance. Although our SVM is trained on experimental data, we also predict novel enhancers and show that these putative enhancers are significantly enriched in both ChIP-seq signal and DNase I hypersensitivity signal in the mouse brain and are located near relevant genes. Finally, we present results of comparisons between other EP300/CREBBP data sets using our SVM and uncover sequence elements enriched and/or depleted in the different classes of enhancers. Many of these sequence features play a role in specifying tissue-specific or developmental-stage-specific enhancer activity, but our results indicate that some features operate in a general or tissue-independent manner. In addition to providing a high confidence list of enhancer targets for subsequent experimental investigation, these results contribute to our understanding of the general sequence structure of vertebrate enhancers. PMID:21875935

Cloning and characterization of the Cerasus humilis sucrose phosphate synthase gene (ChSPS1)

PubMed Central

Du, Junjie; Mu, Xiaopeng; Wang, Pengfei

2017-01-01

Sucrose is crucial to the growth and development of plants, and sucrose phosphate synthase (SPS) plays a key role in sucrose synthesis. To understand the genetic and molecular mechanisms of sucrose synthesis in Cerasus humilis, ChSPS1, a homologue of SPS, was cloned using RT-PCR. Sequence analysis showed that the open reading frame (ORF) sequence of ChSPS1 is 3174 bp in length, encoding a predicted protein of 1057 amino acids. The predicted protein showed a high degree of sequence identity with SPS homologues from other species. Real-time RT-PCR analysis showed that ChSPS1 mRNA was detected in all tissues and the transcription level was the highest in mature fruit. There is a significant positive correlation between expression of ChSPS1 and sucrose content. Prokaryotic expression of ChSPS1 indicated that ChSPS1 protein was expressed in E. coli and it had the SPS activity. Overexpression of ChSPS1 in tobacco led to upregulation of enzyme activity and increased sucrose contents in transgenic plants. Real-time RT-PCR analysis showed that the expression of ChSPS1 in transgenic tobacco was significantly higher than in wild type plants. These results suggested that ChSPS1 plays an important role in sucrose synthesis in Cerasus humilis. PMID:29036229

Functional metagenomics reveals novel β-galactosidases not predictable from gene sequences.

PubMed

Cheng, Jiujun; Romantsov, Tatyana; Engel, Katja; Doxey, Andrew C; Rose, David R; Neufeld, Josh D; Charles, Trevor C

2017-01-01

The techniques of metagenomics have allowed researchers to access the genomic potential of uncultivated microbes, but there remain significant barriers to determination of gene function based on DNA sequence alone. Functional metagenomics, in which DNA is cloned and expressed in surrogate hosts, can overcome these barriers, and make important contributions to the discovery of novel enzymes. In this study, a soil metagenomic library carried in an IncP cosmid was used for functional complementation for β-galactosidase activity in both Sinorhizobium meliloti (α-Proteobacteria) and Escherichia coli (γ-Proteobacteria) backgrounds. One β-galactosidase, encoded by six overlapping clones that were selected in both hosts, was identified as a member of glycoside hydrolase family 2. We could not identify ORFs obviously encoding possible β-galactosidases in 19 other sequenced clones that were only able to complement S. meliloti. Based on low sequence identity to other known glycoside hydrolases, yet not β-galactosidases, three of these ORFs were examined further. Biochemical analysis confirmed that all three encoded β-galactosidase activity. Lac36W_ORF11 and Lac161_ORF7 had conserved domains, but lacked similarities to known glycoside hydrolases. Lac161_ORF10 had neither conserved domains nor similarity to known glycoside hydrolases. Bioinformatic and structural modeling implied that Lac161_ORF10 protein represented a novel enzyme family with a five-bladed propeller glycoside hydrolase domain. By discovering founding members of three novel β-galactosidase families, we have reinforced the value of functional metagenomics for isolating novel genes that could not have been predicted from DNA sequence analysis alone.

Genome Sequence of Microbulbifer mangrovi DD-13T Reveals Its Versatility to Degrade Multiple Polysaccharides.

PubMed

Imran, Md; Pant, Poonam; Shanbhag, Yogini P; Sawant, Samir V; Ghadi, Sanjeev C

2017-02-01

Microbulbifer mangrovi strain DD-13 T is a novel-type species isolated from the mangroves of Goa, India. The draft genome sequence of strain DD-13 comprised 4,528,106 bp with G+C content of 57.15%. Out of 3479 open reading frames, functions for 3488 protein coding sequences were predicted on the basis of similarity with the cluster of orthologous groups. In addition to protein coding sequences, 34 tRNA genes and 3 rRNA genes were detected. Analysis of nucleotide sequence of predicted gene using a Carbohydrate-Active Enzymes (CAZymes) Analysis Toolkit indicates that strain DD-13 encodes a large set of CAZymes including 255 glycoside hydrolases, 76 carbohydrate esterases, 17 polysaccharide lyases, and 113 carbohydrate-binding modules (CBMs). Many genes from strain DD-13 were annotated as carbohydrases specific for degradation of agar, alginate, carrageenan, chitin, xylan, pullulan, cellulose, starch, β-glucan, pectin, etc. Some of polysaccharide-degrading genes were highly modular and were appended at least with one CBM indicating the versatility of strain DD-13 to degrade complex polysaccharides. The cell growth of strain DD-13 was validated using pure polysaccharides such as agarose or alginate as carbon source as well as by using red and brown seaweed powder as substrate. The homologous carbohydrase produced by strain DD-13 during growth degraded the polysaccharide, ensuring the production of metabolizable reducing sugars. Additionally, several other polysaccharides such as carrageenan, xylan, pullulan, pectin, starch, and carboxymethyl cellulose were also corroborated as growth substrate for strain DD-13 and were associated with concomitant production of homologous carbohydrase.

Sequence-Dependent Structure/Function Relationships of Catalytic Peptide-Enabled Gold Nanoparticles Generated under Ambient Synthetic Conditions.

PubMed

Bedford, Nicholas M; Hughes, Zak E; Tang, Zhenghua; Li, Yue; Briggs, Beverly D; Ren, Yang; Swihart, Mark T; Petkov, Valeri G; Naik, Rajesh R; Knecht, Marc R; Walsh, Tiffany R

2016-01-20

Peptide-enabled nanoparticle (NP) synthesis routes can create and/or assemble functional nanomaterials under environmentally friendly conditions, with properties dictated by complex interactions at the biotic/abiotic interface. Manipulation of this interface through sequence modification can provide the capability for material properties to be tailored to create enhanced materials for energy, catalysis, and sensing applications. Fully realizing the potential of these materials requires a comprehensive understanding of sequence-dependent structure/function relationships that is presently lacking. In this work, the atomic-scale structures of a series of peptide-capped Au NPs are determined using a combination of atomic pair distribution function analysis of high-energy X-ray diffraction data and advanced molecular dynamics (MD) simulations. The Au NPs produced with different peptide sequences exhibit varying degrees of catalytic activity for the exemplar reaction 4-nitrophenol reduction. The experimentally derived atomic-scale NP configurations reveal sequence-dependent differences in structural order at the NP surface. Replica exchange with solute-tempering MD simulations are then used to predict the morphology of the peptide overlayer on these Au NPs and identify factors determining the structure/catalytic properties relationship. We show that the amount of exposed Au surface, the underlying surface structural disorder, and the interaction strength of the peptide with the Au surface all influence catalytic performance. A simplified computational prediction of catalytic performance is developed that can potentially serve as a screening tool for future studies. Our approach provides a platform for broadening the analysis of catalytic peptide-enabled metallic NP systems, potentially allowing for the development of rational design rules for property enhancement.

EFICAz2: enzyme function inference by a combined approach enhanced by machine learning.

PubMed

Arakaki, Adrian K; Huang, Ying; Skolnick, Jeffrey

2009-04-13

We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment. We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i) the new SVM-based components outperform their FDR-based counterparts, and ii) both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz2, exhibits a highly improved prediction precision at MTTSI < 30% compared to the original EFICAz, with only a slight decrease in prediction recall. A comparative analysis of enzyme function annotation of the human proteome by EFICAz2 and KEGG shows that: i) when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii) EFICAz2 generates considerably more unique assignments than KEGG. Performance benchmarks and the comparison with KEGG demonstrate that EFICAz2 is a powerful and precise tool for enzyme function annotation, with multiple applications in genome analysis and metabolic pathway reconstruction. The EFICAz2 web service is available at: http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.html.

Acyl carrier protein structural classification and normal mode analysis

PubMed Central

Cantu, David C; Forrester, Michael J; Charov, Katherine; Reilly, Peter J

2012-01-01

All acyl carrier protein primary and tertiary structures were gathered into the ThYme database. They are classified into 16 families by amino acid sequence similarity, with members of the different families having sequences with statistically highly significant differences. These classifications are supported by tertiary structure superposition analysis. Tertiary structures from a number of families are very similar, suggesting that these families may come from a single distant ancestor. Normal vibrational mode analysis was conducted on experimentally determined freestanding structures, showing greater fluctuations at chain termini and loops than in most helices. Their modes overlap more so within families than between different families. The tertiary structures of three acyl carrier protein families that lacked any known structures were predicted as well. PMID:22374859

Insight into the Structure of Amyloid Fibrils from the Analysis of Globular Proteins

PubMed Central

Trovato, Antonio; Chiti, Fabrizio; Maritan, Amos; Seno, Flavio

2006-01-01

The conversion from soluble states into cross-β fibrillar aggregates is a property shared by many different proteins and peptides and was hence conjectured to be a generic feature of polypeptide chains. Increasing evidence is now accumulating that such fibrillar assemblies are generally characterized by a parallel in-register alignment of β-strands contributed by distinct protein molecules. Here we assume a universal mechanism is responsible for β-structure formation and deduce sequence-specific interaction energies between pairs of protein fragments from a statistical analysis of the native folds of globular proteins. The derived fragment–fragment interaction was implemented within a novel algorithm, prediction of amyloid structure aggregation (PASTA), to investigate the role of sequence heterogeneity in driving specific aggregation into ordered self-propagating cross-β structures. The algorithm predicts that the parallel in-register arrangement of sequence portions that participate in the fibril cross-β core is favoured in most cases. However, the antiparallel arrangement is correctly discriminated when present in fibrils formed by short peptides. The predictions of the most aggregation-prone portions of initially unfolded polypeptide chains are also in excellent agreement with available experimental observations. These results corroborate the recent hypothesis that the amyloid structure is stabilised by the same physicochemical determinants as those operating in folded proteins. They also suggest that side chain–side chain interaction across neighbouring β-strands is a key determinant of amyloid fibril formation and of their self-propagating ability. PMID:17173479

Identification of Streptococcus mitis321A vaccine antigens based on reverse vaccinology

PubMed Central

Zhang, Qiao; Lin, Kexiong; Wang, Changzheng; Xu, Zhi; Yang, Li; Ma, Qianli

2018-01-01

Streptococcus mitis (S. mitis) may transform into highly pathogenic bacteria. The aim of the present study was to identify potential antigen targets for designing an effective vaccine against the pathogenic S. mitis321A. The genome of S. mitis321A was sequenced using an Illumina Hiseq2000 instrument. Subsequently, Glimmer 3.02 and Tandem Repeat Finder (TRF) 4.04 were used to predict genes and tandem repeats, respectively, with DNA sequence function analysis using the Basic Local Alignment Search Tool (BLAST) in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups of proteins (COG) databases. Putative gene antigen candidates were screened with BLAST ahead of phylogenetic tree analysis. The DNA sequence assembly size was 2,110,680 bp with 40.12% GC, 6 scaffolds and 9 contig. Consequently, 1,944 genes were predicted, and 119 TRF, 56 microsatellite DNA, 10 minisatellite DNA and 154 transposons were acquired. The predicted genes were associated with various pathways and functions concerning membrane transport and energy metabolism. Multiple putative genes encoding surface proteins, secreted proteins and virulence factors, as well as essential genes were determined. The majority of essential genes belonged to a phylogenetic lineage, while 321AGL000129 and 321AGL000299 were on the same branch. The current study provided useful information regarding the biological function of the S. mitis321A genome and recommends putative antigen candidates for developing a potent vaccine against S. mitis. PMID:29620181

NCBI prokaryotic genome annotation pipeline.

PubMed

Tatusova, Tatiana; DiCuccio, Michael; Badretdin, Azat; Chetvernin, Vyacheslav; Nawrocki, Eric P; Zaslavsky, Leonid; Lomsadze, Alexandre; Pruitt, Kim D; Borodovsky, Mark; Ostell, James

2016-08-19

Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/. Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

A Concise Atlas of Thyroid Cancer Next-Generation Sequencing Panel ThyroSeq v.2

PubMed Central

Alsina, Jorge; Alsina, Raul; Gulec, Seza

2017-01-01

The next-generation sequencing technology allows high out-put genomic analysis. An innovative assay in thyroid cancer, ThyroSeq® was developed for targeted mutation detection by next generation sequencing technology in fine needle aspiration and tissue samples. ThyroSeq v.2 next generation sequencing panel offers simultaneous sequencing and detection in >1000 hotspots of 14 thyroid cancer-related genes and for 42 types of gene fusions known to occur in thyroid cancer. ThyroSeq is being increasingly used to further narrow the indeterminate category defined by cytology for thyroid nodules. From a surgical perspective, genomic profiling also provides prognostic and predictive information and closely relates to determination of surgical strategy. Both the genomic analysis technology and the informatics for the cancer genome data base are rapidly developing. In this paper, we have gathered existing information on the thyroid cancer-related genes involved in the initiation and progression of thyroid cancer. Our goal is to assemble a glossary for the current ThyroSeq genomic panel that can help elucidate the role genomics play in thyroid cancer oncogenesis. PMID:28117295

Selecting sequence variants to improve genomic predictions for dairy cattle

USDA-ARS?s Scientific Manuscript database

Millions of genetic variants have been identified by population-scale sequencing projects, but subsets are needed for routine genomic predictions or to include on genotyping arrays. Methods of selecting sequence variants were compared using both simulated sequence genotypes and actual data from run ...

Novel splice mutation in microthalmia-associated transcription factor in Waardenburg Syndrome.

PubMed

Brenner, Laura; Burke, Kelly; Leduc, Charles A; Guha, Saurav; Guo, Jiancheng; Chung, Wendy K

2011-01-01

Waardenburg Syndrome (WS) is a syndromic form of hearing loss associated with mutations in six different genes. We identified a large family with WS that had previously undergone clinical testing, with no reported pathogenic mutation. Using linkage analysis, a region on 3p14.1 with an LOD score of 6.6 was identified. Microthalmia-Associated Transcription Factor, a gene known to cause WS, is located within this region of linkage. Sequencing of Microthalmia-Associated Transcription Factor demonstrated a c.1212 G>A synonymous variant that segregated with the WS in the family and was predicted to cause a novel splicing site that was confirmed with expression analysis of the mRNA. This case illustrates the need to computationally analyze novel synonymous sequence variants for possible effects on splicing to maximize the clinical sensitivity of sequence-based genetic testing.

Comparative Genomic Analysis of Lactobacillus plantarum GB-LP1 Isolated from Traditional Korean Fermented Food.

PubMed

Yu, Jihyun; Ahn, Sojin; Kim, Kwondo; Caetano-Anolles, Kelsey; Lee, Chanho; Kang, Jungsun; Cho, Kyungjin; Yoon, Sook Hee; Kang, Dae-Kyung; Kim, Heebal

2017-08-28

As probiotics play an important role in maintaining a healthy gut flora environment through antitoxin activity and inhibition of pathogen colonization, they have been of interest to the medical research community for quite some time now. Probiotic bacteria such as Lactobacillus plantarum , which can be found in fermented food, are of particular interest given their easy accessibility. We performed whole-genome sequencing and genomic analysis on a GB-LP1 strain of L. plantarum isolated from Korean traditional fermented food; this strain is well known for its functions in immune response, suppression of pathogen growth, and antitoxin effects. The complete genome sequence of GB-LP1 is a single chromosome of 3,040,388 bp with 2,899 predicted open reading frames. Genomic analysis of GB-LP1 revealed two CRISPR regions and genes showing accelerated evolution, which may have antibiotic and antitoxin functions. The aim of the present study was to predict strain specific-genomic characteristics and assess the potential of this new strain as lactic acid bacteria at the genomic level using in silico analysis. These results provide insight into the L. plantarum species as well as confirm the possibility of its utility as a candidate probiotic.

IMPACT: a whole-exome sequencing analysis pipeline for integrating molecular profiles with actionable therapeutics in clinical samples

PubMed Central

Hintzsche, Jennifer; Kim, Jihye; Yadav, Vinod; Amato, Carol; Robinson, Steven E; Seelenfreund, Eric; Shellman, Yiqun; Wisell, Joshua; Applegate, Allison; McCarter, Martin; Box, Neil; Tentler, John; De, Subhajyoti

2016-01-01

Objective Currently, there is a disconnect between finding a patient’s relevant molecular profile and predicting actionable therapeutics. Here we develop and implement the Integrating Molecular Profiles with Actionable Therapeutics (IMPACT) analysis pipeline, linking variants detected from whole-exome sequencing (WES) to actionable therapeutics. Methods and materials The IMPACT pipeline contains 4 analytical modules: detecting somatic variants, calling copy number alterations, predicting drugs against deleterious variants, and analyzing tumor heterogeneity. We tested the IMPACT pipeline on whole-exome sequencing data in The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples with known EGFR mutations. We also used IMPACT to analyze melanoma patient tumor samples before treatment, after BRAF-inhibitor treatment, and after BRAF- and MEK-inhibitor treatment. Results IMPACT Food and Drug Administration (FDA) correctly identified known EGFR mutations in the TCGA lung adenocarcinoma samples. IMPACT linked these EGFR mutations to the appropriate FDA-approved EGFR inhibitors. For the melanoma patient samples, we identified NRAS p.Q61K as an acquired resistance mutation to BRAF-inhibitor treatment. We also identified CDKN2A deletion as a novel acquired resistance mutation to BRAFi/MEKi inhibition. The IMPACT analysis pipeline predicts these somatic variants to actionable therapeutics. We observed the clonal dynamic in the tumor samples after various treatments. We showed that IMPACT not only helped in successful prioritization of clinically relevant variants but also linked these variations to possible targeted therapies. Conclusion IMPACT provides a new bioinformatics strategy to delineate candidate somatic variants and actionable therapies. This approach can be applied to other patient tumor samples to discover effective drug targets for personalized medicine. IMPACT is publicly available at http://tanlab.ucdenver.edu/IMPACT. PMID:27026619

IMPACT: a whole-exome sequencing analysis pipeline for integrating molecular profiles with actionable therapeutics in clinical samples.

PubMed

Hintzsche, Jennifer; Kim, Jihye; Yadav, Vinod; Amato, Carol; Robinson, Steven E; Seelenfreund, Eric; Shellman, Yiqun; Wisell, Joshua; Applegate, Allison; McCarter, Martin; Box, Neil; Tentler, John; De, Subhajyoti; Robinson, William A; Tan, Aik Choon

2016-07-01

Currently, there is a disconnect between finding a patient's relevant molecular profile and predicting actionable therapeutics. Here we develop and implement the Integrating Molecular Profiles with Actionable Therapeutics (IMPACT) analysis pipeline, linking variants detected from whole-exome sequencing (WES) to actionable therapeutics. The IMPACT pipeline contains 4 analytical modules: detecting somatic variants, calling copy number alterations, predicting drugs against deleterious variants, and analyzing tumor heterogeneity. We tested the IMPACT pipeline on whole-exome sequencing data in The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples with known EGFR mutations. We also used IMPACT to analyze melanoma patient tumor samples before treatment, after BRAF-inhibitor treatment, and after BRAF- and MEK-inhibitor treatment. IMPACT Food and Drug Administration (FDA) correctly identified known EGFR mutations in the TCGA lung adenocarcinoma samples. IMPACT linked these EGFR mutations to the appropriate FDA-approved EGFR inhibitors. For the melanoma patient samples, we identified NRAS p.Q61K as an acquired resistance mutation to BRAF-inhibitor treatment. We also identified CDKN2A deletion as a novel acquired resistance mutation to BRAFi/MEKi inhibition. The IMPACT analysis pipeline predicts these somatic variants to actionable therapeutics. We observed the clonal dynamic in the tumor samples after various treatments. We showed that IMPACT not only helped in successful prioritization of clinically relevant variants but also linked these variations to possible targeted therapies. IMPACT provides a new bioinformatics strategy to delineate candidate somatic variants and actionable therapies. This approach can be applied to other patient tumor samples to discover effective drug targets for personalized medicine.IMPACT is publicly available at http://tanlab.ucdenver.edu/IMPACT. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The nonlinear, complex sequential organization of behavior in schizophrenic patients: neurocognitive strategies and clinical correlations.

PubMed

Paulus, M P; Perry, W; Braff, D L

1999-09-01

Thought disorder is a hallmark of schizophrenia and can be inferred from disorganized behavior. Measures of the sequential organization of behavior are important because they reflect the cognitive processes of the selection and sequencing of behavioral elements, which generate observable and analyzable behavioral patterns. In this context, sequences of choices generated by schizophrenic patients in a two-choice guessing task fluctuate significantly, which reflects an "oscillating dysregulation" between highly predictable and highly unpredictable subsequences within a single test session. In this study, we aimed to clarify the significance of dysregulation by seeing whether demographic, clinical, neuropsychological, and psychological measures predict the degree of dysregulation observed on this two-choice task. Thirty schizophrenic patients repeatedly performed a LEFT or RIGHT key press that was followed by a stimulus, which occurred randomly on the left or right side of the computer screen. Thus, the stimulus location had nothing to do with the key press behavior. The range of key press sequence predictabilities as measured by the dynamical entropy was used to quantify the dysregulation of response sequences and reflects the range of fixity and randomness of the responses. A factor analysis was performed and step-wise multiple regression analyses were used to relate the factor scores to demographic, clinical, symptomatic, Wisconsin Card Sorting Test (WCST), and Rorschach variables. The LEFT/RIGHT key press sequences were determined by three factors: 1) the degree of win-stay/lose-shift strategy; 2) the degree of contextual influence on the current choice; and 3) the degree of dysregulation on the choice task. Demographic and clinical variables did not predict any of the three response patterns on the choice task. In contrast, the WCST and Rorschach test predicted performance on various factors of choice task response patterns. Schizophrenic patients employ several rules, i.e., "win-stay/lose-shift" and "decide according to the previous choice," that fluctuate significantly when generating sequences on this task, confirming that a basic behavioral dysregulation occurs in a single schizophrenic subject across a single test session. The organization or the "temporal architecture" of the behavioral sequences is not related to symptoms per se, but is related to deficits in executive functioning, problem solving, and perceptual organizational abilities.

Duffy blood group phenotype-genotype correlations using high-resolution melting analysis PCR and microarray reveal complex cases including a new null FY*A allele: the role for sequencing in genotyping algorithms.

PubMed

Lopez, G H; Morrison, J; Condon, J A; Wilson, B; Martin, J R; Liew, Y-W; Flower, R L; Hyland, C A

2015-10-01

Duffy blood group phenotypes can be predicted by genotyping for single nucleotide polymorphisms (SNPs) responsible for the Fy(a) /Fy(b) polymorphism, for weak Fy(b) antigen, and for the red cell null Fy(a-b-) phenotype. This study correlates Duffy phenotype predictions with serotyping to assess the most reliable procedure for typing. Samples, n = 155 (135 donors and 20 patients), were genotyped by high-resolution melt PCR and by microarray. Samples were in three serology groups: 1) Duffy patterns expected n = 79, 2) weak and equivocal Fy(b) patterns n = 29 and 3) Fy(a-b-) n = 47 (one with anti-Fy3 antibody). Discrepancies were observed for five samples. For two, SNP genotyping predicted weak Fy(b) expression discrepant with Fy(b-) (Group 1 and 3). For three, SNP genotyping predicted Fy(a) , discrepant with Fy(a-b-) (Group 3). DNA sequencing identified silencing mutations in these FY*A alleles. One was a novel FY*A 719delG. One, the sample with the anti-Fy3, was homozygous for a 14-bp deletion (FY*01N.02); a true null. Both the high-resolution melting analysis and SNP microarray assays were concordant and showed genotyping, as well as phenotyping, is essential to ensure 100% accuracy for Duffy blood group assignments. Sequencing is important to resolve phenotype/genotype conflicts which here identified alleles, one novel, that carry silencing mutations. The risk of alloimmunisation may be dependent on this zygosity status. © 2015 International Society of Blood Transfusion.

Data mining of enzymes using specific peptides

PubMed Central

2009-01-01

Background Predicting the function of a protein from its sequence is a long-standing challenge of bioinformatic research, typically addressed using either sequence-similarity or sequence-motifs. We employ the novel motif method that consists of Specific Peptides (SPs) that are unique to specific branches of the Enzyme Commission (EC) functional classification. We devise the Data Mining of Enzymes (DME) methodology that allows for searching SPs on arbitrary proteins, determining from its sequence whether a protein is an enzyme and what the enzyme's EC classification is. Results We extract novel SP sets from Swiss-Prot enzyme data. Using a training set of July 2006, and test sets of July 2008, we find that the predictive power of SPs, both for true-positives (enzymes) and true-negatives (non-enzymes), depends on the coverage length of all SP matches (the number of amino-acids matched on the protein sequence). DME is quite different from BLAST. Comparing the two on an enzyme test set of July 2008, we find that DME has lower recall. On the other hand, DME can provide predictions for proteins regarded by BLAST as having low homologies with known enzymes, thus supplying complementary information. We test our method on a set of proteins belonging to 10 bacteria, dated July 2008, establishing the usefulness of the coverage-length cutoff to determine true-negatives. Moreover, sifting through our predictions we find that some of them have been substantiated by Swiss-Prot annotations by July 2009. Finally we extract, for production purposes, a novel SP set trained on all Swiss-Prot enzymes as of July 2009. This new set increases considerably the recall of DME. The new SP set is being applied to three metagenomes: Sargasso Sea with over 1,000,000 proteins, producing predictions of over 220,000 enzymes, and two human gut metagenomes. The outcome of these analyses can be characterized by the enzymatic profile of the metagenomes, describing the relative numbers of enzymes observed for different EC categories. Conclusions Employing SPs for predicting enzymatic activity of proteins works well once one utilizes coverage-length criteria. In our analysis, L ≥ 7 has led to highly accurate results. PMID:20034383

Characteristics of the Lotus japonicus gene repertoire deduced from large-scale expressed sequence tag (EST) analysis.

PubMed

Asamizu, Erika; Nakamura, Yasukazu; Sato, Shusei; Tabata, Satoshi

2004-02-01

To perform a comprehensive analysis of genes expressed in a model legume, Lotus japonicus, a total of 74472 3'-end expressed sequence tags (EST) were generated from cDNA libraries produced from six different organs. Clustering of sequences was performed with an identity criterion of 95% for 50 bases, and a total of 20457 non-redundant sequences, 8503 contigs and 11954 singletons were generated. EST sequence coverage was analyzed by using the annotated L. japonicus genomic sequence and 1093 of the 1889 predicted protein-encoding genes (57.9%) were hit by the EST sequence(s). Gene content was compared to several plant species. Among the 8503 contigs, 471 were identified as sequences conserved only in leguminous species and these included several disease resistance-related genes. This suggested that in legumes, these genes may have evolved specifically to resist pathogen attack. The rate of gene sequence divergence was assessed by comparing similarity level and functional category based on the Gene Ontology (GO) annotation of Arabidopsis genes. This revealed that genes encoding ribosomal proteins, as well as those related to translation, photosynthesis, and cellular structure were more abundantly represented in the highly conserved class, and that genes encoding transcription factors and receptor protein kinases were abundantly represented in the less conserved class. To make the sequence information and the cDNA clones available to the research community, a Web database with useful services was created at http://www.kazusa.or.jp/en/plant/lotus/EST/.

MICRA: an automatic pipeline for fast characterization of microbial genomes from high-throughput sequencing data.

PubMed

Caboche, Ségolène; Even, Gaël; Loywick, Alexandre; Audebert, Christophe; Hot, David

2017-12-19

The increase in available sequence data has advanced the field of microbiology; however, making sense of these data without bioinformatics skills is still problematic. We describe MICRA, an automatic pipeline, available as a web interface, for microbial identification and characterization through reads analysis. MICRA uses iterative mapping against reference genomes to identify genes and variations. Additional modules allow prediction of antibiotic susceptibility and resistance and comparing the results of several samples. MICRA is fast, producing few false-positive annotations and variant calls compared to current methods, making it a tool of great interest for fully exploiting sequencing data.

P2RP: a Web-based framework for the identification and analysis of regulatory proteins in prokaryotic genomes.

PubMed

Barakat, Mohamed; Ortet, Philippe; Whitworth, David E

2013-04-20

Regulatory proteins (RPs) such as transcription factors (TFs) and two-component system (TCS) proteins control how prokaryotic cells respond to changes in their external and/or internal state. Identification and annotation of TFs and TCSs is non-trivial, and between-genome comparisons are often confounded by different standards in annotation. There is a need for user-friendly, fast and convenient tools to allow researchers to overcome the inherent variability in annotation between genome sequences. We have developed the web-server P2RP (Predicted Prokaryotic Regulatory Proteins), which enables users to identify and annotate TFs and TCS proteins within their sequences of interest. Users can input amino acid or genomic DNA sequences, and predicted proteins therein are scanned for the possession of DNA-binding domains and/or TCS domains. RPs identified in this manner are categorised into families, unambiguously annotated, and a detailed description of their features generated, using an integrated software pipeline. P2RP results can then be outputted in user-specified formats. Biologists have an increasing need for fast and intuitively usable tools, which is why P2RP has been developed as an interactive system. As well as assisting experimental biologists to interrogate novel sequence data, it is hoped that P2RP will be built into genome annotation pipelines and re-annotation processes, to increase the consistency of RP annotation in public genomic sequences. P2RP is the first publicly available tool for predicting and analysing RP proteins in users' sequences. The server is freely available and can be accessed along with documentation at http://www.p2rp.org.

Sleep-stage sequencing of sleep-onset REM periods in MSLT predicts treatment response in patients with narcolepsy.

PubMed

Drakatos, Panagis; Patel, Kishankumar; Thakrar, Chiraag; Williams, Adrian J; Kent, Brian D; Leschziner, Guy D

2016-04-01

Current treatment recommendations for narcolepsy suggest that modafinil should be used as a first-line treatment ahead of conventional stimulants or sodium oxybate. In this study, performed in a tertiary sleep disorders centre, treatment responses were examined following these recommendations, and the ability of sleep-stage sequencing of sleep-onset rapid eye movement periods in the multiple sleep latency test to predict treatment response. Over a 3.5-year period, 255 patients were retrospectively identified in the authors' database as patients diagnosed with narcolepsy, type 1 (with cataplexy) or type 2 (without) using clinical and polysomnographic criteria. Eligible patients were examined in detail, sleep study data were abstracted and sleep-stage sequencing of sleep-onset rapid eye movement periods were analysed. Response to treatment was graded utilizing an internally developed scale. Seventy-five patients were included (39% males). Forty (53%) were diagnosed with type 1 narcolepsy with a mean follow-up of 2.37 ± 1.35 years. Ninety-seven percent of the patients were initially started on modafinil, and overall 59% reported complete response on the last follow-up. Twenty-nine patients (39%) had the sequence of sleep stage 1 or wake to rapid eye movement in all of their sleep-onset rapid eye movement periods, with most of these diagnosed as narcolepsy type 1 (72%). The presence of this specific sleep-stage sequence in all sleep-onset rapid eye movement periods was associated with worse treatment response (P = 0.0023). Sleep-stage sequence analysis of sleep-onset rapid eye movement periods in the multiple sleep latency test may aid the prediction of treatment response in narcoleptics and provide a useful prognostic tool in clinical practice, above and beyond their classification as narcolepsy type 1 or 2. © 2015 European Sleep Research Society.

Extensive complementarity between gene function prediction methods.

PubMed

Vidulin, Vedrana; Šmuc, Tomislav; Supek, Fran

2016-12-01

The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions. Our pipeline amalgamates 5 133 543 genes from 2071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1227 Gene Ontology (GO) terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse genomic AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to rely on a single most-confident prediction per gene/function, rather than enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known Escherichia coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the Critical Assessment of Function Annotation 2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them. The individual and integrated GO predictions for the complete set of genes are available from http://gorbi.irb.hr/ CONTACT: fran.supek@irb.hrSupplementary information: Supplementary materials are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Evaluating approaches to find exon chains based on long reads.

PubMed

Kuosmanen, Anna; Norri, Tuukka; Mäkinen, Veli

2018-05-01

Transcript prediction can be modeled as a graph problem where exons are modeled as nodes and reads spanning two or more exons are modeled as exon chains. Pacific Biosciences third-generation sequencing technology produces significantly longer reads than earlier second-generation sequencing technologies, which gives valuable information about longer exon chains in a graph. However, with the high error rates of third-generation sequencing, aligning long reads correctly around the splice sites is a challenging task. Incorrect alignments lead to spurious nodes and arcs in the graph, which in turn lead to incorrect transcript predictions. We survey several approaches to find the exon chains corresponding to long reads in a splicing graph, and experimentally study the performance of these methods using simulated data to allow for sensitivity/precision analysis. Our experiments show that short reads from second-generation sequencing can be used to significantly improve exon chain correctness either by error-correcting the long reads before splicing graph creation, or by using them to create a splicing graph on which the long-read alignments are then projected. We also study the memory and time consumption of various modules, and show that accurate exon chains lead to significantly increased transcript prediction accuracy. The simulated data and in-house scripts used for this article are available at http://www.cs.helsinki.fi/group/gsa/exon-chains/exon-chains-bib.tar.bz2.

Eukaryotic Protein Kinases (ePKs) of the Helminth Parasite Schistosoma mansoni

PubMed Central

2011-01-01

Background Schistosomiasis remains an important parasitic disease and a major economic problem in many countries. The Schistosoma mansoni genome and predicted proteome sequences were recently published providing the opportunity to identify new drug candidates. Eukaryotic protein kinases (ePKs) play a central role in mediating signal transduction through complex networks and are considered druggable targets from the medical and chemical viewpoints. Our work aimed at analyzing the S. mansoni predicted proteome in order to identify and classify all ePKs of this parasite through combined computational approaches. Functional annotation was performed mainly to yield insights into the parasite signaling processes relevant to its complex lifestyle and to select some ePKs as potential drug targets. Results We have identified 252 ePKs, which corresponds to 1.9% of the S. mansoni predicted proteome, through sequence similarity searches using HMMs (Hidden Markov Models). Amino acid sequences corresponding to the conserved catalytic domain of ePKs were aligned by MAFFT and further used in distance-based phylogenetic analysis as implemented in PHYLIP. Our analysis also included the ePK homologs from six other eukaryotes. The results show that S. mansoni has proteins in all ePK groups. Most of them are clearly clustered with known ePKs in other eukaryotes according to the phylogenetic analysis. None of the ePKs are exclusively found in S. mansoni or belong to an expanded family in this parasite. Only 16 S. mansoni ePKs were experimentally studied, 12 proteins are predicted to be catalytically inactive and approximately 2% of the parasite ePKs remain unclassified. Some proteins were mentioned as good target for drug development since they have a predicted essential function for the parasite. Conclusions Our approach has improved the functional annotation of 40% of S. mansoni ePKs through combined similarity and phylogenetic-based approaches. As we continue this work, we will highlight the biochemical and physiological adaptations of S. mansoni in response to diverse environments during the parasite development, vector interaction, and host infection. PMID:21548963

Cleavage sites within the poliovirus capsid protein precursors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, G.R.; Anderson, C.W.; Dorner, A.J.

1982-01-01

Partial amino-terminal sequence analysis was performed on radiolabeled poliovirus capsid proteins VP1, VP2, and VP3. A computer-assisted comparison of the amino acid sequences obtained with that predicted by the nucleotide sequence of the poliovirus genome allows assignment of the amino terminus of each capsid protein to a unique position within the virus polyprotein. Sequence analysis of trypsin-digested VP4, which has a blocked amino terminus, demonstrates that VP4 is encoded at or very near to the amino terminus of the polyprotein. The gene order of the capsid proteins is VP4-VP2-VP3-VP1. Cleavage of VP0 to VP4 and VP2 is shown to occurmore » between asparagine and serine, whereas the cleavages that separate VP2/VP3 and VP3/VP1 occur between glutamine and glycine residues. This finding supports the hypothesis that the cleavage of VP0, which occurs during virion morphogenesis, is distinct from the cleavages that separate functional regions of the polyprotein.« less

A survey of the sorghum transcriptome using single-molecule long reads

DOE PAGES

Abdel-Ghany, Salah E.; Hamilton, Michael; Jacobi, Jennifer L.; ...

2016-06-24

Alternative splicing and alternative polyadenylation (APA) of pre-mRNAs greatly contribute to transcriptome diversity, coding capacity of a genome and gene regulatory mechanisms in eukaryotes. Second-generation sequencing technologies have been extensively used to analyse transcriptomes. However, a major limitation of short-read data is that it is difficult to accurately predict full-length splice isoforms. Here we sequenced the sorghum transcriptome using Pacific Biosciences single-molecule real-time long-read isoform sequencing and developed a pipeline called TAPIS (Transcriptome Analysis Pipeline for Isoform Sequencing) to identify full-length splice isoforms and APA sites. Our analysis reveals transcriptome-wide full-length isoforms at an unprecedented scale with over 11,000 novelmore » splice isoforms. Additionally, we uncover APA ofB11,000 expressed genes and more than 2,100 novel genes. Lastly, these results greatly enhance sorghum gene annotations and aid in studying gene regulation in this important bioenergy crop. The TAPIS pipeline will serve as a useful tool to analyse Iso-Seq data from any organism.« less

A survey of the sorghum transcriptome using single-molecule long reads

PubMed Central

Abdel-Ghany, Salah E.; Hamilton, Michael; Jacobi, Jennifer L.; Ngam, Peter; Devitt, Nicholas; Schilkey, Faye; Ben-Hur, Asa; Reddy, Anireddy S. N.

2016-01-01

Alternative splicing and alternative polyadenylation (APA) of pre-mRNAs greatly contribute to transcriptome diversity, coding capacity of a genome and gene regulatory mechanisms in eukaryotes. Second-generation sequencing technologies have been extensively used to analyse transcriptomes. However, a major limitation of short-read data is that it is difficult to accurately predict full-length splice isoforms. Here we sequenced the sorghum transcriptome using Pacific Biosciences single-molecule real-time long-read isoform sequencing and developed a pipeline called TAPIS (Transcriptome Analysis Pipeline for Isoform Sequencing) to identify full-length splice isoforms and APA sites. Our analysis reveals transcriptome-wide full-length isoforms at an unprecedented scale with over 11,000 novel splice isoforms. Additionally, we uncover APA of ∼11,000 expressed genes and more than 2,100 novel genes. These results greatly enhance sorghum gene annotations and aid in studying gene regulation in this important bioenergy crop. The TAPIS pipeline will serve as a useful tool to analyse Iso-Seq data from any organism. PMID:27339290

Optical Processing Techniques For Pseudorandom Sequence Prediction

NASA Astrophysics Data System (ADS)

Gustafson, Steven C.

1983-11-01

Pseudorandom sequences are series of apparently random numbers generated, for example, by linear or nonlinear feedback shift registers. An important application of these sequences is in spread spectrum communication systems, in which, for example, the transmitted carrier phase is digitally modulated rapidly and pseudorandomly and in which the information to be transmitted is incorporated as a slow modulation in the pseudorandom sequence. In this case the transmitted information can be extracted only by a receiver that uses for demodulation the same pseudorandom sequence used by the transmitter, and thus this type of communication system has a very high immunity to third-party interference. However, if a third party can predict in real time the probable future course of the transmitted pseudorandom sequence given past samples of this sequence, then interference immunity can be significantly reduced.. In this application effective pseudorandom sequence prediction techniques should be (1) applicable in real time to rapid (e.g., megahertz) sequence generation rates, (2) applicable to both linear and nonlinear pseudorandom sequence generation processes, and (3) applicable to error-prone past sequence samples of limited number and continuity. Certain optical processing techniques that may meet these requirements are discussed in this paper. In particular, techniques based on incoherent optical processors that perform general linear transforms or (more specifically) matrix-vector multiplications are considered. Computer simulation examples are presented which indicate that significant prediction accuracy can be obtained using these transforms for simple pseudorandom sequences. However, the useful prediction of more complex pseudorandom sequences will probably require the application of more sophisticated optical processing techniques.

Protein location prediction using atomic composition and global features of the amino acid sequence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cherian, Betsy Sheena, E-mail: betsy.skb@gmail.com; Nair, Achuthsankar S.

2010-01-22

Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. A new biological feature, distribution of atomic composition is effectivelymore » used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Our results provide evidence that the prediction based on the biological features derived from the full length amino acid sequence gives better accuracy than those derived from N-terminal alone. Considering the features as a distribution within the entire sequence will bring out underlying property distribution to a greater detail to enhance the prediction accuracy.« less

Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.

PubMed

Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui

2012-11-07

RNA-protein interaction plays an important role in various cellular processes, such as protein synthesis, gene regulation, post-transcriptional gene regulation, alternative splicing, and infections by RNA viruses. In this study, using Gene Ontology Annotated (GOA) and Structural Classification of Proteins (SCOP) databases an automatic procedure was designed to capture structurally solved RNA-binding protein domains in different subclasses. Subsequently, we applied tuned multi-class SVM (TMCSVM), Random Forest (RF), and multi-class ℓ1/ℓq-regularized logistic regression (MCRLR) for analysis and classifying RNA-binding protein domains based on a comprehensive set of sequence and structural features. In this study, we compared prediction accuracy of three different state-of-the-art predictor methods. From our results, TMCSVM outperforms the other methods and suggests the potential of TMCSVM as a useful tool for facilitating the multi-class prediction of RNA-binding protein domains. On the other hand, MCRLR by elucidating importance of features for their contribution in predictive accuracy of RNA-binding protein domains subclasses, helps us to provide some biological insights into the roles of sequences and structures in protein-RNA interactions.

Computational sequence analysis of predicted long dsRNA transcriptomes of major crops reveals sequence complementarity with human genes.

PubMed

Jensen, Peter D; Zhang, Yuanji; Wiggins, B Elizabeth; Petrick, Jay S; Zhu, Jin; Kerstetter, Randall A; Heck, Gregory R; Ivashuta, Sergey I

2013-01-01

Long double-stranded RNAs (long dsRNAs) are precursors for the effector molecules of sequence-specific RNA-based gene silencing in eukaryotes. Plant cells can contain numerous endogenous long dsRNAs. This study demonstrates that such endogenous long dsRNAs in plants have sequence complementarity to human genes. Many of these complementary long dsRNAs have perfect sequence complementarity of at least 21 nucleotides to human genes; enough complementarity to potentially trigger gene silencing in targeted human cells if delivered in functional form. However, the number and diversity of long dsRNA molecules in plant tissue from crops such as lettuce, tomato, corn, soy and rice with complementarity to human genes that have a long history of safe consumption supports a conclusion that long dsRNAs do not present a significant dietary risk.

Sequence fingerprints distinguish erroneous from correct predictions of intrinsically disordered protein regions.

PubMed

Saravanan, Konda Mani; Dunker, A Keith; Krishnaswamy, Sankaran

2017-12-27

More than 60 prediction methods for intrinsically disordered proteins (IDPs) have been developed over the years, many of which are accessible on the World Wide Web. Nearly, all of these predictors give balanced accuracies in the ~65%-~80% range. Since predictors are not perfect, further studies are required to uncover the role of amino acid residues in native IDP as compared to predicted IDP regions. In the present work, we make use of sequences of 100% predicted IDP regions, false positive disorder predictions, and experimentally determined IDP regions to distinguish the characteristics of native versus predicted IDP regions. A higher occurrence of asparagine is observed in sequences of native IDP regions but not in sequences of false positive predictions of IDP regions. The occurrences of certain combinations of amino acids at the pentapeptide level provide a distinguishing feature in the IDPs with respect to globular proteins. The distinguishing features presented in this paper provide insights into the sequence fingerprints of amino acid residues in experimentally determined as compared to predicted IDP regions. These observations and additional work along these lines should enable the development of improvements in the accuracy of disorder prediction algorithm.

Application of meta-omics techniques to understand greenhouse gas emissions originating from ruminal metabolism.

PubMed

Wallace, Robert J; Snelling, Timothy J; McCartney, Christine A; Tapio, Ilma; Strozzi, Francesco

2017-01-16

Methane emissions from ruminal fermentation contribute significantly to total anthropological greenhouse gas (GHG) emissions. New meta-omics technologies are beginning to revolutionise our understanding of the rumen microbial community structure, metabolic potential and metabolic activity. Here we explore these developments in relation to GHG emissions. Microbial rumen community analyses based on small subunit ribosomal RNA sequence analysis are not yet predictive of methane emissions from individual animals or treatments. Few metagenomics studies have been directly related to GHG emissions. In these studies, the main genes that differed in abundance between high and low methane emitters included archaeal genes involved in methanogenesis, with others that were not apparently related to methane metabolism. Unlike the taxonomic analysis up to now, the gene sets from metagenomes may have predictive value. Furthermore, metagenomic analysis predicts metabolic function better than only a taxonomic description, because different taxa share genes with the same function. Metatranscriptomics, the study of mRNA transcript abundance, should help to understand the dynamic of microbial activity rather than the gene abundance; to date, only one study has related the expression levels of methanogenic genes to methane emissions, where gene abundance failed to do so. Metaproteomics describes the proteins present in the ecosystem, and is therefore arguably a better indication of microbial metabolism. Both two-dimensional polyacrylamide gel electrophoresis and shotgun peptide sequencing methods have been used for ruminal analysis. In our unpublished studies, both methods showed an abundance of archaeal methanogenic enzymes, but neither was able to discriminate high and low emitters. Metabolomics can take several forms that appear to have predictive value for methane emissions; ruminal metabolites, milk fatty acid profiles, faecal long-chain alcohols and urinary metabolites have all shown promising results. Rumen microbial amino acid metabolism lies at the root of excessive nitrogen emissions from ruminants, yet only indirect inferences for nitrogen emissions can be drawn from meta-omics studies published so far. Annotation of meta-omics data depends on databases that are generally weak in rumen microbial entries. The Hungate 1000 project and Global Rumen Census initiatives are therefore essential to improve the interpretation of sequence/metabolic information.

GenePRIMP: Improving Microbial Gene Prediction Quality

ScienceCinema

Pati, Amrita

2018-01-24

Amrita Pati of the DOE Joint Genome Institute's Genome Biology group talks about a computational pipeline that evaluates the accuracy of gene models in genomes and metagenomes at different stages of finishing at the "Sequencing, Finishing, Analysis in the Future" meeting in Santa Fe, NM.

Cloning and expression of a cDNA coding for a human monocyte-derived plasminogen activator inhibitor.

PubMed

Antalis, T M; Clark, M A; Barnes, T; Lehrbach, P R; Devine, P L; Schevzov, G; Goss, N H; Stephens, R W; Tolstoshev, P

1988-02-01

Human monocyte-derived plasminogen activator inhibitor (mPAI-2) was purified to homogeneity from the U937 cell line and partially sequenced. Oligonucleotide probes derived from this sequence were used to screen a cDNA library prepared from U937 cells. One positive clone was sequenced and contained most of the coding sequence as well as a long incomplete 3' untranslated region (1112 base pairs). This cDNA sequence was shown to encode mPAI-2 by hybrid-select translation. A cDNA clone encoding the remainder of the mPAI-2 mRNA was obtained by primer extension of U937 poly(A)+ RNA using a probe complementary to the mPAI-2 coding region. The coding sequence for mPAI-2 was placed under the control of the lambda PL promoter, and the protein expressed in Escherichia coli formed a complex with urokinase that could be detected immunologically. By nucleotide sequence analysis, mPAI-2 cDNA encodes a protein containing 415 amino acids with a predicted unglycosylated Mr of 46,543. The predicted amino acid sequence of mPAI-2 is very similar to placental PAI-2 (3 amino acid differences) and shows extensive homology with members of the serine protease inhibitor (serpin) superfamily. mPAI-2 was found to be more homologous to ovalbumin (37%) than the endothelial plasminogen activator inhibitor, PAI-1 (26%). Like ovalbumin, mPAI-2 appears to have no typical amino-terminal signal sequence. The 3' untranslated region of the mPAI-2 cDNA contains a putative regulatory sequence that has been associated with the inflammatory mediators.

Cloning and expression of a cDNA coding for a human monocyte-derived plasminogen activator inhibitor.

PubMed Central

Antalis, T M; Clark, M A; Barnes, T; Lehrbach, P R; Devine, P L; Schevzov, G; Goss, N H; Stephens, R W; Tolstoshev, P

1988-01-01

Human monocyte-derived plasminogen activator inhibitor (mPAI-2) was purified to homogeneity from the U937 cell line and partially sequenced. Oligonucleotide probes derived from this sequence were used to screen a cDNA library prepared from U937 cells. One positive clone was sequenced and contained most of the coding sequence as well as a long incomplete 3' untranslated region (1112 base pairs). This cDNA sequence was shown to encode mPAI-2 by hybrid-select translation. A cDNA clone encoding the remainder of the mPAI-2 mRNA was obtained by primer extension of U937 poly(A)+ RNA using a probe complementary to the mPAI-2 coding region. The coding sequence for mPAI-2 was placed under the control of the lambda PL promoter, and the protein expressed in Escherichia coli formed a complex with urokinase that could be detected immunologically. By nucleotide sequence analysis, mPAI-2 cDNA encodes a protein containing 415 amino acids with a predicted unglycosylated Mr of 46,543. The predicted amino acid sequence of mPAI-2 is very similar to placental PAI-2 (3 amino acid differences) and shows extensive homology with members of the serine protease inhibitor (serpin) superfamily. mPAI-2 was found to be more homologous to ovalbumin (37%) than the endothelial plasminogen activator inhibitor, PAI-1 (26%). Like ovalbumin, mPAI-2 appears to have no typical amino-terminal signal sequence. The 3' untranslated region of the mPAI-2 cDNA contains a putative regulatory sequence that has been associated with the inflammatory mediators. Images PMID:3257578

Tertiary structural propensities reveal fundamental sequence/structure relationships.

PubMed

Zheng, Fan; Zhang, Jian; Grigoryan, Gevorg

2015-05-05

Extracting useful generalizations from the continually growing Protein Data Bank (PDB) is of central importance. We hypothesize that the PDB contains valuable quantitative information on the level of local tertiary structural motifs (TERMs). We show that by breaking a protein structure into its constituent TERMs, and querying the PDB to characterize the natural ensemble matching each, we can estimate the compatibility of the structure with a given amino acid sequence through a metric we term "structure score." Considering submissions from recent Critical Assessment of Structure Prediction (CASP) experiments, we found a strong correlation (R = 0.69) between structure score and model accuracy, with poorly predicted regions readily identifiable. This performance exceeds that of leading atomistic statistical energy functions. Furthermore, TERM-based analysis of two prototypical multi-state proteins rapidly produced structural insights fully consistent with prior extensive experimental studies. We thus find that TERM-based analysis should have considerable utility for protein structural biology. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of HIV-1 coreceptor usage (tropism) by sequence analysis using a genotypic approach.

PubMed

Sierra, Saleta; Kaiser, Rolf; Lübke, Nadine; Thielen, Alexander; Schuelter, Eugen; Heger, Eva; Däumer, Martin; Reuter, Stefan; Esser, Stefan; Fätkenheuer, Gerd; Pfister, Herbert; Oette, Mark; Lengauer, Thomas

2011-12-01

Maraviroc (MVC) is the first licensed antiretroviral drug from the class of coreceptor antagonists. It binds to the host coreceptor CCR5, which is used by the majority of HIV strains in order to infect the human immune cells (Fig. 1). Other HIV isolates use a different coreceptor, the CXCR4. Which receptor is used, is determined in the virus by the Env protein (Fig. 2). Depending on the coreceptor used, the viruses are classified as R5 or X4, respectively. MVC binds to the CCR5 receptor inhibiting the entry of R5 viruses into the target cell. During the course of disease, X4 viruses may emerge and outgrow the R5 viruses. Determination of coreceptor usage (also called tropism) is therefore mandatory prior to administration of MVC, as demanded by EMA and FDA. The studies for MVC efficiency MOTIVATE, MERIT and 1029 have been performed with the Trofile assay from Monogram, San Francisco, U.S.A. This is a high quality assay based on sophisticated recombinant tests. The acceptance for this test for daily routine is rather low outside of the U.S.A., since the European physicians rather tend to work with decentralized expert laboratories, which also provide concomitant resistance testing. These laboratories have undergone several quality assurance evaluations, the last one being presented in 2011. For several years now, we have performed tropism determinations based on sequence analysis from the HIV env-V3 gene region (V3). This region carries enough information to perform a reliable prediction. The genotypic determination of coreceptor usage presents advantages such as: shorter turnover time (equivalent to resistance testing), lower costs, possibility to adapt the results to the patients' needs and possibility of analysing clinical samples with very low or even undetectable viral load (VL), particularly since the number of samples analysed with VL < 1000 copies/μl roughly increased in the last years (Fig. 3). The main steps for tropism testing (Fig. 4) demonstrated in this video: Collection of a blood sample Isolation of the HIV RNA from the plasma and/or HIV proviral DNA from blood mononuclear cells Amplification of the env region Amplification of the V3 region Sequence reaction of the V3 amplicon Purification of the sequencing samples Sequencing the purified samples Sequence editing Sequencing data interpretation and tropism prediction.

A multivariate prediction model for Rho-dependent termination of transcription.

PubMed

Nadiras, Cédric; Eveno, Eric; Schwartz, Annie; Figueroa-Bossi, Nara; Boudvillain, Marc

2018-06-21

Bacterial transcription termination proceeds via two main mechanisms triggered either by simple, well-conserved (intrinsic) nucleic acid motifs or by the motor protein Rho. Although bacterial genomes can harbor hundreds of termination signals of either type, only intrinsic terminators are reliably predicted. Computational tools to detect the more complex and diversiform Rho-dependent terminators are lacking. To tackle this issue, we devised a prediction method based on Orthogonal Projections to Latent Structures Discriminant Analysis [OPLS-DA] of a large set of in vitro termination data. Using previously uncharacterized genomic sequences for biochemical evaluation and OPLS-DA, we identified new Rho-dependent signals and quantitative sequence descriptors with significant predictive value. Most relevant descriptors specify features of transcript C>G skewness, secondary structure, and richness in regularly-spaced 5'CC/UC dinucleotides that are consistent with known principles for Rho-RNA interaction. Descriptors collectively warrant OPLS-DA predictions of Rho-dependent termination with a ∼85% success rate. Scanning of the Escherichia coli genome with the OPLS-DA model identifies significantly more termination-competent regions than anticipated from transcriptomics and predicts that regions intrinsically refractory to Rho are primarily located in open reading frames. Altogether, this work delineates features important for Rho activity and describes the first method able to predict Rho-dependent terminators in bacterial genomes.

Prediction of Transcriptional Terminators in Bacillus subtilis and Related Species

PubMed Central

de Hoon, Michiel J. L.; Makita, Yuko; Nakai, Kenta; Miyano, Satoru

2005-01-01

In prokaryotes, genes belonging to the same operon are transcribed in a single mRNA molecule. Transcription starts as the RNA polymerase binds to the promoter and continues until it reaches a transcriptional terminator. Some terminators rely on the presence of the Rho protein, whereas others function independently of Rho. Such Rho-independent terminators consist of an inverted repeat followed by a stretch of thymine residues, allowing us to predict their presence directly from the DNA sequence. Unlike in Escherichia coli, the Rho protein is dispensable in Bacillus subtilis, suggesting a limited role for Rho-dependent termination in this organism and possibly in other Firmicutes. We analyzed 463 experimentally known terminating sequences in B. subtilis and found a decision rule to distinguish Rho-independent transcriptional terminators from non-terminating sequences. The decision rule allowed us to find the boundaries of operons in B. subtilis with a sensitivity and specificity of about 94%. Using the same decision rule, we found an average sensitivity of 94% for 57 bacteria belonging to the Firmicutes phylum, and a considerably lower sensitivity for other bacteria. Our analysis shows that Rho-independent termination is dominant for Firmicutes in general, and that the properties of the transcriptional terminators are conserved. Terminator prediction can be used to reliably predict the operon structure in these organisms, even in the absence of experimentally known operons. Genome-wide predictions of Rho-independent terminators for the 57 Firmicutes are available in the Supporting Information section. PMID:16110342

TMFoldWeb: a web server for predicting transmembrane protein fold class.

PubMed

Kozma, Dániel; Tusnády, Gábor E

2015-09-17

Here we present TMFoldWeb, the web server implementation of TMFoldRec, a transmembrane protein fold recognition algorithm. TMFoldRec uses statistical potentials and utilizes topology filtering and a gapless threading algorithm. It ranks template structures and selects the most likely candidates and estimates the reliability of the obtained lowest energy model. The statistical potential was developed in a maximum likelihood framework on a representative set of the PDBTM database. According to the benchmark test the performance of TMFoldRec is about 77 % in correctly predicting fold class for a given transmembrane protein sequence. An intuitive web interface has been developed for the recently published TMFoldRec algorithm. The query sequence goes through a pipeline of topology prediction and a systematic sequence to structure alignment (threading). Resulting templates are ordered by energy and reliability values and are colored according to their significance level. Besides the graphical interface, a programmatic access is available as well, via a direct interface for developers or for submitting genome-wide data sets. The TMFoldWeb web server is unique and currently the only web server that is able to predict the fold class of transmembrane proteins while assigning reliability scores for the prediction. This method is prepared for genome-wide analysis with its easy-to-use interface, informative result page and programmatic access. Considering the info-communication evolution in the last few years, the developed web server, as well as the molecule viewer, is responsive and fully compatible with the prevalent tablets and mobile devices.

DWARF – a data warehouse system for analyzing protein families

PubMed Central

Fischer, Markus; Thai, Quan K; Grieb, Melanie; Pleiss, Jürgen

2006-01-01

Background The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allows to gain a novel understanding of complex biological systems. The data warehouse DWARF applies integrative bioinformatics approaches to the analysis of large protein families. Description The data warehouse system DWARF integrates data on sequence, structure, and functional annotation for protein fold families. The underlying relational data model consists of three major sections representing entities related to the protein (biochemical function, source organism, classification to homologous families and superfamilies), the protein sequence (position-specific annotation, mutant information), and the protein structure (secondary structure information, superimposed tertiary structure). Tools for extracting, transforming and loading data from public available resources (ExPDB, GenBank, DSSP) are provided to populate the database. The data can be accessed by an interface for searching and browsing, and by analysis tools that operate on annotation, sequence, or structure. We applied DWARF to the family of α/β-hydrolases to host the Lipase Engineering database. Release 2.3 contains 6138 sequences and 167 experimentally determined protein structures, which are assigned to 37 superfamilies 103 homologous families. Conclusion DWARF has been designed for constructing databases of large structurally related protein families and for evaluating their sequence-structure-function relationships by a systematic analysis of sequence, structure and functional annotation. It has been applied to predict biochemical properties from sequence, and serves as a valuable tool for protein engineering. PMID:17094801

Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.

PubMed

Li, Man; Ling, Cheng; Xu, Qi; Gao, Jingyang

2018-02-01

Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

Analysis of deep learning methods for blind protein contact prediction in CASP12.

PubMed

Wang, Sheng; Sun, Siqi; Xu, Jinbo

2018-03-01

Here we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median family size of around 58 effective sequences, our server obtained an average top L/5 long- and medium-range contact accuracy of 47% and 44%, respectively (L = length). A complete implementation has an average accuracy of 59% and 57%, respectively. Our deep learning method formulates contact prediction as a pixel-level image labeling problem and simultaneously predicts all residue pairs of a protein using a combination of two deep residual neural networks, taking as input the residue conservation information, predicted secondary structure and solvent accessibility, contact potential, and coevolution information. Our approach differs from existing methods mainly in (1) formulating contact prediction as a pixel-level image labeling problem instead of an image-level classification problem; (2) simultaneously predicting all contacts of an individual protein to make effective use of contact occurrence patterns; and (3) integrating both one-dimensional and two-dimensional deep convolutional neural networks to effectively learn complex sequence-structure relationship including high-order residue correlation. This paper discusses the RaptorX-Contact pipeline, both contact prediction and contact-based folding results, and finally the strength and weakness of our method. © 2017 Wiley Periodicals, Inc.

A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.

PubMed

Pandey, Gaurav; Pandey, Om P; Rogers, Angela J; Ahsen, Mehmet E; Hoffman, Gabriel E; Raby, Benjamin A; Weiss, Scott T; Schadt, Eric E; Bunyavanich, Supinda

2018-06-11

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

PROSPECT improves cis-acting regulatory element prediction by integrating expression profile data with consensus pattern searches

PubMed Central

Fujibuchi, Wataru; Anderson, John S. J.; Landsman, David

2001-01-01

Consensus pattern and matrix-based searches designed to predict cis-acting transcriptional regulatory sequences have historically been subject to large numbers of false positives. We sought to decrease false positives by incorporating expression profile data into a consensus pattern-based search method. We have systematically analyzed the expression phenotypes of over 6000 yeast genes, across 121 expression profile experiments, and correlated them with the distribution of 14 known regulatory elements over sequences upstream of the genes. Our method is based on a metric we term probabilistic element assessment (PEA), which is a ranking of potential sites based on sequence similarity in the upstream regions of genes with similar expression phenotypes. For eight of the 14 known elements that we examined, our method had a much higher selectivity than a naïve consensus pattern search. Based on our analysis, we have developed a web-based tool called PROSPECT, which allows consensus pattern-based searching of gene clusters obtained from microarray data. PMID:11574681

Identification, isolation, and N-terminal sequencing of style glycoproteins associated with self-incompatibility in Nicotiana alata.

PubMed

Jahnen, W; Batterham, M P; Clarke, A E; Moritz, R L; Simpson, R J

1989-05-01

S-Gene-associated glycoproteins (S-glycoproteins) from styles of Nicotiana alata, identified by non-equilibrium two-dimensional electrophoresis, were purified by cation exchange fast protein liquid chromatography with yields of 0.5 to 8 micrograms of protein per style, depending on the S-genotype of the plant. The method relies on the highly basic nature of the S-glycoproteins. The elution profiles of the different S-glycoproteins from the fast protein liquid chromatography column were characteristic of each S-glycoprotein, and could be used to establish the S-genotype of plants in outbreeding populations. In all cases, the S-genotype predicted from the style protein profile corresponded to that predicted from DNA gel blot analysis using S-allele-specific DNA probes and to that established by conventional breeding tests. Amino-terminal sequences of five purified S-glycoproteins showed a high degree of homology with the previously published sequences of N. alata and Lycopersicon esculentum S-glycoproteins.

The genome of woodland strawberry (Fragaria vesca)

PubMed Central

Shulaev, Vladimir; Sargent, Daniel J; Crowhurst, Ross N; Mockler, Todd C; Folkerts, Otto; Delcher, Arthur L; Jaiswal, Pankaj; Mockaitis, Keithanne; Liston, Aaron; Mane, Shrinivasrao P; Burns, Paul; Davis, Thomas M; Slovin, Janet P; Bassil, Nahla; Hellens, Roger P; Evans, Clive; Harkins, Tim; Kodira, Chinnappa; Desany, Brian; Crasta, Oswald R; Jensen, Roderick V; Allan, Andrew C; Michael, Todd P; Setubal, Joao Carlos; Celton, Jean-Marc; Rees, D Jasper G; Williams, Kelly P; Holt, Sarah H; Ruiz Rojas, Juan Jairo; Chatterjee, Mithu; Liu, Bo; Silva, Herman; Meisel, Lee; Adato, Avital; Filichkin, Sergei A; Troggio, Michela; Viola, Roberto; Ashman, Tia-Lynn; Wang, Hao; Dharmawardhana, Palitha; Elser, Justin; Raja, Rajani; Priest, Henry D; Bryant, Douglas W; Fox, Samuel E; Givan, Scott A; Wilhelm, Larry J; Naithani, Sushma; Christoffels, Alan; Salama, David Y; Carter, Jade; Girona, Elena Lopez; Zdepski, Anna; Wang, Wenqin; Kerstetter, Randall A; Schwab, Wilfried; Korban, Schuyler S; Davik, Jahn; Monfort, Amparo; Denoyes-Rothan, Beatrice; Arus, Pere; Mittler, Ron; Flinn, Barry; Aharoni, Asaph; Bennetzen, Jeffrey L; Salzberg, Steven L; Dickerman, Allan W; Velasco, Riccardo; Borodovsky, Mark; Veilleux, Richard E; Folta, Kevin M

2012-01-01

The woodland strawberry, Fragaria vesca (2n = 2x = 14), is a versatile experimental plant system. This diminutive herbaceous perennial has a small genome (240 Mb), is amenable to genetic transformation and shares substantial sequence identity with the cultivated strawberry (Fragaria × ananassa) and other economically important rosaceous plants. Here we report the draft F. vesca genome, which was sequenced to ×39 coverage using second-generation technology, assembled de novo and then anchored to the genetic linkage map into seven pseudochromosomes. This diploid strawberry sequence lacks the large genome duplications seen in other rosids. Gene prediction modeling identified 34,809 genes, with most being supported by transcriptome mapping. Genes critical to valuable horticultural traits including flavor, nutritional value and flowering time were identified. Macrosyntenic relationships between Fragaria and Prunus predict a hypothetical ancestral Rosaceae genome that had nine chromosomes. New phylogenetic analysis of 154 protein-coding genes suggests that assignment of Populus to Malvidae, rather than Fabidae, is warranted. PMID:21186353

PrionScan: an online database of predicted prion domains in complete proteomes.

PubMed

Espinosa Angarica, Vladimir; Angulo, Alfonso; Giner, Arturo; Losilla, Guillermo; Ventura, Salvador; Sancho, Javier

2014-02-05

Prions are a particular type of amyloids related to a large variety of important processes in cells, but also responsible for serious diseases in mammals and humans. The number of experimentally characterized prions is still low and corresponds to a handful of examples in microorganisms and mammals. Prion aggregation is mediated by specific protein domains with a remarkable compositional bias towards glutamine/asparagine and against charged residues and prolines. These compositional features have been used to predict new prion proteins in the genomes of different organisms. Despite these efforts, there are only a few available data sources containing prion predictions at a genomic scale. Here we present PrionScan, a new database of predicted prion-like domains in complete proteomes. We have previously developed a predictive methodology to identify and score prionogenic stretches in protein sequences. In the present work, we exploit this approach to scan all the protein sequences in public databases and compile a repository containing relevant information of proteins bearing prion-like domains. The database is updated regularly alongside UniprotKB and in its present version contains approximately 28000 predictions in proteins from different functional categories in more than 3200 organisms from all the taxonomic subdivisions. PrionScan can be used in two different ways: database query and analysis of protein sequences submitted by the users. In the first mode, simple queries allow to retrieve a detailed description of the properties of a defined protein. Queries can also be combined to generate more complex and specific searching patterns. In the second mode, users can submit and analyze their own sequences. It is expected that this database would provide relevant insights on prion functions and regulation from a genome-wide perspective, allowing researches performing cross-species prion biology studies. Our database might also be useful for guiding experimentalists in the identification of new candidates for further experimental characterization.

In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool

EPA Science Inventory

The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to address needs for rapid, cost effective methods of species extrapolation of chemical susceptibility. Specifically, the SeqAPASS tool compares the primary sequence (Level 1), functiona...

Theory on the mechanism of site-specific DNA-protein interactions in the presence of traps

NASA Astrophysics Data System (ADS)

Niranjani, G.; Murugan, R.

2016-08-01

The speed of site-specific binding of transcription factor (TFs) proteins with genomic DNA seems to be strongly retarded by the randomly occurring sequence traps. Traps are those DNA sequences sharing significant similarity with the original specific binding sites (SBSs). It is an intriguing question how the naturally occurring TFs and their SBSs are designed to manage the retarding effects of such randomly occurring traps. We develop a simple random walk model on the site-specific binding of TFs with genomic DNA in the presence of sequence traps. Our dynamical model predicts that (a) the retarding effects of traps will be minimum when the traps are arranged around the SBS such that there is a negative correlation between the binding strength of TFs with traps and the distance of traps from the SBS and (b) the retarding effects of sequence traps can be appeased by the condensed conformational state of DNA. Our computational analysis results on the distribution of sequence traps around the putative binding sites of various TFs in mouse and human genome clearly agree well the theoretical predictions. We propose that the distribution of traps can be used as an additional metric to efficiently identify the SBSs of TFs on genomic DNA.

Draft genome sequence of bitter gourd (Momordica charantia), a vegetable and medicinal plant in tropical and subtropical regions.

PubMed

Urasaki, Naoya; Takagi, Hiroki; Natsume, Satoshi; Uemura, Aiko; Taniai, Naoki; Miyagi, Norimichi; Fukushima, Mai; Suzuki, Shouta; Tarora, Kazuhiko; Tamaki, Moritoshi; Sakamoto, Moriaki; Terauchi, Ryohei; Matsumura, Hideo

2017-02-01

Bitter gourd (Momordica charantia) is an important vegetable and medicinal plant in tropical and subtropical regions globally. In this study, the draft genome sequence of a monoecious bitter gourd inbred line, OHB3-1, was analyzed. Through Illumina sequencing and de novo assembly, scaffolds of 285.5 Mb in length were generated, corresponding to ∼84% of the estimated genome size of bitter gourd (339 Mb). In this draft genome sequence, 45,859 protein-coding gene loci were identified, and transposable elements accounted for 15.3% of the whole genome. According to synteny mapping and phylogenetic analysis of conserved genes, bitter gourd was more related to watermelon (Citrullus lanatus) than to cucumber (Cucumis sativus) or melon (C. melo). Using RAD-seq analysis, 1507 marker loci were genotyped in an F2 progeny of two bitter gourd lines, resulting in an improved linkage map, comprising 11 linkage groups. By anchoring RAD tag markers, 255 scaffolds were assigned to the linkage map. Comparative analysis of genome sequences and predicted genes determined that putative trypsin-inhibitor and ribosome-inactivating genes were distinctive in the bitter gourd genome. These genes could characterize the bitter gourd as a medicinal plant. © The Author 2016. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

Survey of predictors of propensity for protein production and crystallization with application to predict resolution of crystal structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Jianzhao; Wu, Zhonghua; Hu, Gang

Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accuratemore » propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute error when compared to the seven considered methods. We conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.« less

Interferon-gamma of the giant panda (Ailuropoda melanoleuca): complementary DNA cloning, expression, and phylogenetic analysis.

PubMed

Tao, Yaqiong; Zeng, Bo; Xu, Liu; Yue, Bisong; Yang, Dong; Zou, Fangdong

2010-01-01

Interferon-gamma (IFN-gamma) is the only member of type II IFN and is vital in the regulation of immune and inflammatory responses. Herein we report the cloning, expression, and sequence analysis of IFN-gamma from the giant panda (Ailuropoda melanoleuca). The open reading frame of this gene is 501 base pair in length and encodes a polypeptide consisting of 166 amino acids. All conserved N-linked glycosylation sites and cysteine residues among carnivores were found in the predicted amino acid sequence of the giant panda. Recombinant giant panda IFN-gamma with a V5 epitope and polyhistidine tag was expressed in HEK293 host cells and confirmed by Western blotting. Phylogenetic analysis of mammalian IFN-gamma-coding sequences indicated that the giant panda IFN-gamma was closest to that of carnivores, then to ungulates and dolphin, and shared a distant relationship with mouse and human. These results represent a first step into the study of IFN-gamma in giant panda.