De novo characterization of Lentinula edodes C(91-3) transcriptome by deep Solexa sequencing.

PubMed

Zhong, Mintao; Liu, Ben; Wang, Xiaoli; Liu, Lei; Lun, Yongzhi; Li, Xingyun; Ning, Anhong; Cao, Jing; Huang, Min

2013-02-01

Lentinula edodes, has been utilized as food, as well as, in popular medicine, moreover, its extract isolated from its mycelium and fruiting body have shown several therapeutic properties. Yet little is understood about its genes involved in these properties, and the absence of L.edodes genomes has been a barrier to the development of functional genomics research. However, high throughput sequencing technologies are now being widely applied to non-model species. To facilitate research on L.edodes, we leveraged Solexa sequencing technology in de novo assembly of L.edodes C(91-3) transcriptome. In a single run, we produced more than 57 million sequencing reads. These reads were assembled into 28,923 unigene sequences (mean size=689bp) including 18,120 unigenes with coding sequence (CDS). Based on similarity search with known proteins, assembled unigene sequences were annotated with gene descriptions, gene ontology (GO) and clusters of orthologous group (COG) terms. Our data provides the first comprehensive sequence resource available for functional genomics studies in L.edodes, and demonstrates the utility of Illumina/Solexa sequencing for de novo transcriptome characterization and gene discovery in a non-model mushroom. Copyright © 2012 Elsevier Inc. All rights reserved.

Discovery of a large-scale clumpy structure around the Lynx supercluster at z~ 1.27

NASA Astrophysics Data System (ADS)

Nakata, Fumiaki; Kodama, Tadayuki; Shimasaku, Kazuhiro; Doi, Mamoru; Furusawa, Hisanori; Hamabe, Masaru; Kimura, Masahiko; Komiyama, Yutaka; Miyazaki, Satoshi; Okamura, Sadanori; Ouchi, Masami; Sekiguchi, Maki; Ueda, Yoshihiro; Yagi, Masafumi; Yasuda, Naoki

2005-03-01

We report the discovery of a probable large-scale structure composed of many galaxy clumps around the known twin clusters at z= 1.26 and 1.27 in the Lynx region. Our analysis is based on deep, panoramic, and multicolour imaging, 26.4 × 24.1 arcmin2 in VRi'z' bands with the Suprime-Cam on the 8.2-m Subaru telescope. This unique, deep and wide-field imaging data set allows us for the first time to map out the galaxy distribution in the highest-redshift supercluster known. We apply a photometric redshift technique to extract plausible cluster members at z~ 1.27 down to i'= 26.15 (5σ) corresponding to ~M*+ 2.5 at this redshift. From the two-dimensional distribution of these photometrically selected galaxies, we newly identify seven candidates of galaxy groups or clusters where the surface density of red galaxies is significantly high (>5σ), in addition to the two known clusters. These candidates show clear red colour-magnitude sequences consistent with a passive evolution model, which suggests the existence of additional high-density regions around the Lynx superclusters.

Discovery of Pod Shatter-Resistant Associated SNPs by Deep Sequencing of a Representative Library Followed by Bulk Segregant Analysis in Rapeseed

PubMed Central

Huang, Shunmou; Yang, Hongli; Zhan, Gaomiao; Wang, Xinfa; Liu, Guihua; Wang, Hanzhong

2012-01-01

Background Single nucleotide polymorphisms (SNPs) are an important class of genetic marker for target gene mapping. As of yet, there is no rapid and effective method to identify SNPs linked with agronomic traits in rapeseed and other crop species. Methodology/Principal Findings We demonstrate a novel method for identifying SNP markers in rapeseed by deep sequencing a representative library and performing bulk segregant analysis. With this method, SNPs associated with rapeseed pod shatter-resistance were discovered. Firstly, a reduced representation of the rapeseed genome was used. Genomic fragments ranging from 450–550 bp were prepared from the susceptible bulk (ten F2 plants with the silique shattering resistance index, SSRI <0.10) and the resistance bulk (ten F2 plants with SSRI >0.90), and also Solexa sequencing-produced 90 bp reads. Approximately 50 million of these sequence reads were assembled into contigs to a depth of 20-fold coverage. Secondly, 60,396 ‘simple SNPs’ were identified, and the statistical significance was evaluated using Fisher's exact test. There were 70 associated SNPs whose –log10 p value over 16 were selected to be further analyzed. The distribution of these SNPs appeared a tight cluster, which consisted of 14 associated SNPs within a 396 kb region on chromosome A09. Our evidence indicates that this region contains a major quantitative trait locus (QTL). Finally, two associated SNPs from this region were mapped on a major QTL region. Conclusions/Significance 70 associated SNPs were discovered and a major QTL for rapeseed pod shatter-resistance was found on chromosome A09 using our novel method. The associated SNP markers were used for mapping of the QTL, and may be useful for improving pod shatter-resistance in rapeseed through marker-assisted selection and map-based cloning. This approach will accelerate the discovery of major QTLs and the cloning of functional genes for important agronomic traits in rapeseed and other crop species. PMID:22529909

New in protein structure and function annotation: hotspots, single nucleotide polymorphisms and the 'Deep Web'.

PubMed

Bromberg, Yana; Yachdav, Guy; Ofran, Yanay; Schneider, Reinhard; Rost, Burkhard

2009-05-01

The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation.

Domain-specific Web Service Discovery with Service Class Descriptions

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

Rocco, D; Caverlee, J; Liu, L

2005-02-14

This paper presents DynaBot, a domain-specific web service discovery system. The core idea of the DynaBot service discovery system is to use domain-specific service class descriptions powered by an intelligent Deep Web crawler. In contrast to current registry-based service discovery systems--like the several available UDDI registries--DynaBot promotes focused crawling of the Deep Web of services and discovers candidate services that are relevant to the domain of interest. It uses intelligent filtering algorithms to match services found by focused crawling with the domain-specific service class descriptions. We demonstrate the capability of DynaBot through the BLAST service discovery scenario and describe ourmore » initial experience with DynaBot.« less

De Novo Deep Transcriptome Analysis of Medicinal Plants for Gene Discovery in Biosynthesis of Plant Natural Products.

PubMed

Han, R; Rai, A; Nakamura, M; Suzuki, H; Takahashi, H; Yamazaki, M; Saito, K

2016-01-01

Study on transcriptome, the entire pool of transcripts in an organism or single cells at certain physiological or pathological stage, is indispensable in unraveling the connection and regulation between DNA and protein. Before the advent of deep sequencing, microarray was the main approach to handle transcripts. Despite obvious shortcomings, including limited dynamic range and difficulties to compare the results from distinct experiments, microarray was widely applied. During the past decade, next-generation sequencing (NGS) has revolutionized our understanding of genomics in a fast, high-throughput, cost-effective, and tractable manner. By adopting NGS, efficiency and fruitful outcomes concerning the efforts to elucidate genes responsible for producing active compounds in medicinal plants were profoundly enhanced. The whole process involves steps, from the plant material sampling, to cDNA library preparation, to deep sequencing, and then bioinformatics takes over to assemble enormous-yet fragmentary-data from which to comb and extract information. The unprecedentedly rapid development of such technologies provides so many choices to facilitate the task, which can cause confusion when choosing the suitable methodology for specific purposes. Here, we review the general approaches for deep transcriptome analysis and then focus on their application in discovering biosynthetic pathways of medicinal plants that produce important secondary metabolites. © 2016 Elsevier Inc. All rights reserved.

iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder.

PubMed

Xu, Zhao-Chun; Wang, Peng; Qiu, Wang-Ren; Xiao, Xuan

2017-08-15

Gene splicing is one of the most significant biological processes in eukaryotic gene expression, such as RNA splicing, which can cause a pre-mRNA to produce one or more mature messenger RNAs containing the coded information with multiple biological functions. Thus, identifying splicing sites in DNA/RNA sequences is significant for both the bio-medical research and the discovery of new drugs. However, it is expensive and time consuming based only on experimental technique, so new computational methods are needed. To identify the splice donor sites and splice acceptor sites accurately and quickly, a deep sparse auto-encoder model with two hidden layers, called iSS-PC, was constructed based on minimum error law, in which we incorporated twelve physical-chemical properties of the dinucleotides within DNA into PseDNC to formulate given sequence samples via a battery of cross-covariance and auto-covariance transformations. In this paper, five-fold cross-validation test results based on the same benchmark data-sets indicated that the new predictor remarkably outperformed the existing prediction methods in this field. Furthermore, it is expected that many other related problems can be also studied by this approach. To implement classification accurately and quickly, an easy-to-use web-server for identifying slicing sites has been established for free access at: http://www.jci-bioinfo.cn/iSS-PC.

Studies of a biochemical factory: tomato trichome deep expressed sequence tag sequencing and proteomics.

PubMed

Schilmiller, Anthony L; Miner, Dennis P; Larson, Matthew; McDowell, Eric; Gang, David R; Wilkerson, Curtis; Last, Robert L

2010-07-01

Shotgun proteomics analysis allows hundreds of proteins to be identified and quantified from a single sample at relatively low cost. Extensive DNA sequence information is a prerequisite for shotgun proteomics, and it is ideal to have sequence for the organism being studied rather than from related species or accessions. While this requirement has limited the set of organisms that are candidates for this approach, next generation sequencing technologies make it feasible to obtain deep DNA sequence coverage from any organism. As part of our studies of specialized (secondary) metabolism in tomato (Solanum lycopersicum) trichomes, 454 sequencing of cDNA was combined with shotgun proteomics analyses to obtain in-depth profiles of genes and proteins expressed in leaf and stem glandular trichomes of 3-week-old plants. The expressed sequence tag and proteomics data sets combined with metabolite analysis led to the discovery and characterization of a sesquiterpene synthase that produces beta-caryophyllene and alpha-humulene from E,E-farnesyl diphosphate in trichomes of leaf but not of stem. This analysis demonstrates the utility of combining high-throughput cDNA sequencing with proteomics experiments in a target tissue. These data can be used for dissection of other biochemical processes in these specialized epidermal cells.

Studies of a Biochemical Factory: Tomato Trichome Deep Expressed Sequence Tag Sequencing and Proteomics1[W][OA

PubMed Central

Schilmiller, Anthony L.; Miner, Dennis P.; Larson, Matthew; McDowell, Eric; Gang, David R.; Wilkerson, Curtis; Last, Robert L.

2010-01-01

Shotgun proteomics analysis allows hundreds of proteins to be identified and quantified from a single sample at relatively low cost. Extensive DNA sequence information is a prerequisite for shotgun proteomics, and it is ideal to have sequence for the organism being studied rather than from related species or accessions. While this requirement has limited the set of organisms that are candidates for this approach, next generation sequencing technologies make it feasible to obtain deep DNA sequence coverage from any organism. As part of our studies of specialized (secondary) metabolism in tomato (Solanum lycopersicum) trichomes, 454 sequencing of cDNA was combined with shotgun proteomics analyses to obtain in-depth profiles of genes and proteins expressed in leaf and stem glandular trichomes of 3-week-old plants. The expressed sequence tag and proteomics data sets combined with metabolite analysis led to the discovery and characterization of a sesquiterpene synthase that produces β-caryophyllene and α-humulene from E,E-farnesyl diphosphate in trichomes of leaf but not of stem. This analysis demonstrates the utility of combining high-throughput cDNA sequencing with proteomics experiments in a target tissue. These data can be used for dissection of other biochemical processes in these specialized epidermal cells. PMID:20431087

Deep hierarchies in the primate visual cortex: what can we learn for computer vision?

PubMed

Krüger, Norbert; Janssen, Peter; Kalkan, Sinan; Lappe, Markus; Leonardis, Ales; Piater, Justus; Rodríguez-Sánchez, Antonio J; Wiskott, Laurenz

2013-08-01

Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition, or vision-based navigation and manipulation. This paper reviews some functional principles and structures that are generally thought to underlie the primate visual cortex, and attempts to extract biological principles that could further advance computer vision research. Organized for a computer vision audience, we present functional principles of the processing hierarchies present in the primate visual system considering recent discoveries in neurophysiology. The hierarchical processing in the primate visual system is characterized by a sequence of different levels of processing (on the order of 10) that constitute a deep hierarchy in contrast to the flat vision architectures predominantly used in today's mainstream computer vision. We hope that the functional description of the deep hierarchies realized in the primate visual system provides valuable insights for the design of computer vision algorithms, fostering increasingly productive interaction between biological and computer vision research.

Emerging pathogens in the fish farming industry and sequencing-based pathogen discovery.

PubMed

Tengs, Torstein; Rimstad, Espen

2017-10-01

The use of large scale DNA/RNA sequencing has become an integral part of biomedical research. Reduced sequencing costs and the availability of efficient computational resources has led to a revolution in how problems concerning genomics and transcriptomics are addressed. Sequencing-based pathogen discovery represents one example of how genetic data can now be used in ways that were previously considered infeasible. Emerging pathogens affect both human and animal health due to a multitude of factors, including globalization, a shifting environment and an increasing human population. Fish farming represents a relevant, interesting and challenging system to study emerging pathogens. This review summarizes recent progress in pathogen discovery using sequence data, with particular emphasis on viruses in Atlantic salmon (Salmo salar). Copyright © 2017 Elsevier Ltd. All rights reserved.

Ribosome profiling reveals the what, when, where and how of protein synthesis.

PubMed

Brar, Gloria A; Weissman, Jonathan S

2015-11-01

Ribosome profiling, which involves the deep sequencing of ribosome-protected mRNA fragments, is a powerful tool for globally monitoring translation in vivo. The method has facilitated discovery of the regulation of gene expression underlying diverse and complex biological processes, of important aspects of the mechanism of protein synthesis, and even of new proteins, by providing a systematic approach for experimental annotation of coding regions. Here, we introduce the methodology of ribosome profiling and discuss examples in which this approach has been a key factor in guiding biological discovery, including its prominent role in identifying thousands of novel translated short open reading frames and alternative translation products.

Next-generation sequencing in clinical virology: Discovery of new viruses.

PubMed

Datta, Sibnarayan; Budhauliya, Raghvendra; Das, Bidisha; Chatterjee, Soumya; Vanlalhmuaka; Veer, Vijay

2015-08-12

Viruses are a cause of significant health problem worldwide, especially in the developing nations. Due to different anthropological activities, human populations are exposed to different viral pathogens, many of which emerge as outbreaks. In such situations, discovery of novel viruses is utmost important for deciding prevention and treatment strategies. Since last century, a number of different virus discovery methods, based on cell culture inoculation, sequence-independent PCR have been used for identification of a variety of viruses. However, the recent emergence and commercial availability of next-generation sequencers (NGS) has entirely changed the field of virus discovery. These massively parallel sequencing platforms can sequence a mixture of genetic materials from a very heterogeneous mix, with high sensitivity. Moreover, these platforms work in a sequence-independent manner, making them ideal tools for virus discovery. However, for their application in clinics, sample preparation or enrichment is necessary to detect low abundance virus populations. A number of techniques have also been developed for enrichment or viral nucleic acids. In this manuscript, we review the evolution of sequencing; NGS technologies available today as well as widely used virus enrichment technologies. We also discuss the challenges associated with their applications in the clinical virus discovery.

Genetics of impulsive behaviour

PubMed Central

Bevilacqua, Laura; Goldman, David

2013-01-01

Impulsivity, defined as the tendency to act without foresight, comprises a multitude of constructs and is associated with a variety of psychiatric disorders. Dissecting different aspects of impulsive behaviour and relating these to specific neurobiological circuits would improve our understanding of the etiology of complex behaviours for which impulsivity is key, and advance genetic studies in this behavioural domain. In this review, we will discuss the heritability of some impulsivity constructs and their possible use as endophenotypes (heritable, disease-associated intermediate phenotypes). Several functional genetic variants associated with impulsive behaviour have been identified by the candidate gene approach and re-sequencing, and whole genome strategies can be implemented for discovery of novel rare and common alleles influencing impulsivity. Via deep sequencing an uncommon HTR2B stop codon, common in one population, was discovered, with implications for understanding impulsive behaviour in both humans and rodents and for future gene discovery. PMID:23440466

Discovery of a new genotype of Squash mosaic virus through deep sequencing of small RNAs and development of a qRT-PCR for broad spectrum detection

USDA-ARS?s Scientific Manuscript database

Squash mosaic virus (SqMV), a seed-borne virus belonging to the genus Commovirus in the family Comoviridae, could cause a serious yield loss on cucurbit crops worldwide. SqMV has a bipartite single-stranded ribonucleic acid (RNA) genome (RNA-1 and RNA-2) encapsidated separately with two capsid prote...

Genome-wide discovery and differential regulation of conserved and novel microRNAs in chickpea via deep sequencing.

PubMed

Jain, Mukesh; Chevala, V V S Narayana; Garg, Rohini

2014-11-01

MicroRNAs (miRNAs) are essential components of complex gene regulatory networks that orchestrate plant development. Although several genomic resources have been developed for the legume crop chickpea, miRNAs have not been discovered until now. For genome-wide discovery of miRNAs in chickpea (Cicer arietinum), we sequenced the small RNA content from seven major tissues/organs employing Illumina technology. About 154 million reads were generated, which represented more than 20 million distinct small RNA sequences. We identified a total of 440 conserved miRNAs in chickpea based on sequence similarity with known miRNAs in other plants. In addition, 178 novel miRNAs were identified using a miRDeep pipeline with plant-specific scoring. Some of the conserved and novel miRNAs with significant sequence similarity were grouped into families. The chickpea miRNAs targeted a wide range of mRNAs involved in diverse cellular processes, including transcriptional regulation (transcription factors), protein modification and turnover, signal transduction, and metabolism. Our analysis revealed several miRNAs with differential spatial expression. Many of the chickpea miRNAs were expressed in a tissue-specific manner. The conserved and differential expression of members of the same miRNA family in different tissues was also observed. Some of the same family members were predicted to target different chickpea mRNAs, which suggested the specificity and complexity of miRNA-mediated developmental regulation. This study, for the first time, reveals a comprehensive set of conserved and novel miRNAs along with their expression patterns and putative targets in chickpea, and provides a framework for understanding regulation of developmental processes in legumes. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Single nucleotide polymorphism discovery in rainbow trout by deep sequencing of a reduced representation library.

PubMed

Sánchez, Cecilia Castaño; Smith, Timothy P L; Wiedmann, Ralph T; Vallejo, Roger L; Salem, Mohamed; Yao, Jianbo; Rexroad, Caird E

2009-11-25

To enhance capabilities for genomic analyses in rainbow trout, such as genomic selection, a large suite of polymorphic markers that are amenable to high-throughput genotyping protocols must be identified. Expressed Sequence Tags (ESTs) have been used for single nucleotide polymorphism (SNP) discovery in salmonids. In those strategies, the salmonid semi-tetraploid genomes often led to assemblies of paralogous sequences and therefore resulted in a high rate of false positive SNP identification. Sequencing genomic DNA using primers identified from ESTs proved to be an effective but time consuming methodology of SNP identification in rainbow trout, therefore not suitable for high throughput SNP discovery. In this study, we employed a high-throughput strategy that used pyrosequencing technology to generate data from a reduced representation library constructed with genomic DNA pooled from 96 unrelated rainbow trout that represent the National Center for Cool and Cold Water Aquaculture (NCCCWA) broodstock population. The reduced representation library consisted of 440 bp fragments resulting from complete digestion with the restriction enzyme HaeIII; sequencing produced 2,000,000 reads providing an average 6 fold coverage of the estimated 150,000 unique genomic restriction fragments (300,000 fragment ends). Three independent data analyses identified 22,022 to 47,128 putative SNPs on 13,140 to 24,627 independent contigs. A set of 384 putative SNPs, randomly selected from the sets produced by the three analyses were genotyped on individual fish to determine the validation rate of putative SNPs among analyses, distinguish apparent SNPs that actually represent paralogous loci in the tetraploid genome, examine Mendelian segregation, and place the validated SNPs on the rainbow trout linkage map. Approximately 48% (183) of the putative SNPs were validated; 167 markers were successfully incorporated into the rainbow trout linkage map. In addition, 2% of the sequences from the validated markers were associated with rainbow trout transcripts. The use of reduced representation libraries and pyrosequencing technology proved to be an effective strategy for the discovery of a high number of putative SNPs in rainbow trout; however, modifications to the technique to decrease the false discovery rate resulting from the evolutionary recent genome duplication would be desirable.

Accurate identification of RNA editing sites from primitive sequence with deep neural networks.

PubMed

Ouyang, Zhangyi; Liu, Feng; Zhao, Chenghui; Ren, Chao; An, Gaole; Mei, Chuan; Bo, Xiaochen; Shu, Wenjie

2018-04-16

RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, time-consuming identification process. Moreover, these methods have limited generalizability and applicability in species with insufficient genomic annotations or in conditions of limited prior knowledge. We developed DeepRed, a deep learning-based method that identifies RNA editing from primitive RNA sequences without prior-knowledge-based filtering steps or genomic annotations. DeepRed achieved 98.1% and 97.9% area under the curve (AUC) in training and test sets, respectively. We further validated DeepRed using experimentally verified U87 cell RNA-seq data, achieving 97.9% positive predictive value (PPV). We demonstrated that DeepRed offers better prediction accuracy and computational efficiency than current methods with large-scale, mass RNA-seq data. We used DeepRed to assess the impact of multiple factors on editing identification with RNA-seq data from the Association of Biomolecular Resource Facilities and Sequencing Quality Control projects. We explored developmental RNA editing pattern changes during human early embryogenesis and evolutionary patterns in Drosophila species and the primate lineage using DeepRed. Our work illustrates DeepRed's state-of-the-art performance; it may decipher the hidden principles behind RNA editing, making editing detection convenient and effective.

Quantitative phenotyping via deep barcode sequencing.

PubMed

Smith, Andrew M; Heisler, Lawrence E; Mellor, Joseph; Kaper, Fiona; Thompson, Michael J; Chee, Mark; Roth, Frederick P; Giaever, Guri; Nislow, Corey

2009-10-01

Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or "Bar-seq," outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that approximately 20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene-environment interactions on a genome-wide scale.

Application of industrial scale genomics to discovery of therapeutic targets in heart failure.

PubMed

Mehraban, F; Tomlinson, J E

2001-12-01

In recent years intense activity in both academic and industrial sectors has provided a wealth of information on the human genome with an associated impressive increase in the number of novel gene sequences deposited in sequence data repositories and patent applications. This genomic industrial revolution has transformed the way in which drug target discovery is now approached. In this article we discuss how various differential gene expression (DGE) technologies are being utilized for cardiovascular disease (CVD) drug target discovery. Other approaches such as sequencing cDNA from cardiovascular derived tissues and cells coupled with bioinformatic sequence analysis are used with the aim of identifying novel gene sequences that may be exploited towards target discovery. Additional leverage from gene sequence information is obtained through identification of polymorphisms that may confer disease susceptibility and/or affect drug responsiveness. Pharmacogenomic studies are described wherein gene expression-based techniques are used to evaluate drug response and/or efficacy. Industrial-scale genomics supports and addresses not only novel target gene discovery but also the burgeoning issues in pharmaceutical and clinical cardiovascular medicine relative to polymorphic gene responses.

Two combinatorial optimization problems for SNP discovery using base-specific cleavage and mass spectrometry.

PubMed

Chen, Xin; Wu, Qiong; Sun, Ruimin; Zhang, Louxin

2012-01-01

The discovery of single-nucleotide polymorphisms (SNPs) has important implications in a variety of genetic studies on human diseases and biological functions. One valuable approach proposed for SNP discovery is based on base-specific cleavage and mass spectrometry. However, it is still very challenging to achieve the full potential of this SNP discovery approach. In this study, we formulate two new combinatorial optimization problems. While both problems are aimed at reconstructing the sample sequence that would attain the minimum number of SNPs, they search over different candidate sequence spaces. The first problem, denoted as SNP - MSP, limits its search to sequences whose in silico predicted mass spectra have all their signals contained in the measured mass spectra. In contrast, the second problem, denoted as SNP - MSQ, limits its search to sequences whose in silico predicted mass spectra instead contain all the signals of the measured mass spectra. We present an exact dynamic programming algorithm for solving the SNP - MSP problem and also show that the SNP - MSQ problem is NP-hard by a reduction from a restricted variation of the 3-partition problem. We believe that an efficient solution to either problem above could offer a seamless integration of information in four complementary base-specific cleavage reactions, thereby improving the capability of the underlying biotechnology for sensitive and accurate SNP discovery.

Annotation-based genome-wide SNP discovery in the large and complex Aegilops tauschii genome using next-generation sequencing without a reference genome sequence

PubMed Central

2011-01-01

Background Many plants have large and complex genomes with an abundance of repeated sequences. Many plants are also polyploid. Both of these attributes typify the genome architecture in the tribe Triticeae, whose members include economically important wheat, rye and barley. Large genome sizes, an abundance of repeated sequences, and polyploidy present challenges to genome-wide SNP discovery using next-generation sequencing (NGS) of total genomic DNA by making alignment and clustering of short reads generated by the NGS platforms difficult, particularly in the absence of a reference genome sequence. Results An annotation-based, genome-wide SNP discovery pipeline is reported using NGS data for large and complex genomes without a reference genome sequence. Roche 454 shotgun reads with low genome coverage of one genotype are annotated in order to distinguish single-copy sequences and repeat junctions from repetitive sequences and sequences shared by paralogous genes. Multiple genome equivalents of shotgun reads of another genotype generated with SOLiD or Solexa are then mapped to the annotated Roche 454 reads to identify putative SNPs. A pipeline program package, AGSNP, was developed and used for genome-wide SNP discovery in Aegilops tauschii-the diploid source of the wheat D genome, and with a genome size of 4.02 Gb, of which 90% is repetitive sequences. Genomic DNA of Ae. tauschii accession AL8/78 was sequenced with the Roche 454 NGS platform. Genomic DNA and cDNA of Ae. tauschii accession AS75 was sequenced primarily with SOLiD, although some Solexa and Roche 454 genomic sequences were also generated. A total of 195,631 putative SNPs were discovered in gene sequences, 155,580 putative SNPs were discovered in uncharacterized single-copy regions, and another 145,907 putative SNPs were discovered in repeat junctions. These SNPs were dispersed across the entire Ae. tauschii genome. To assess the false positive SNP discovery rate, DNA containing putative SNPs was amplified by PCR from AL8/78 and AS75 and resequenced with the ABI 3730 xl. In a sample of 302 randomly selected putative SNPs, 84.0% in gene regions, 88.0% in repeat junctions, and 81.3% in uncharacterized regions were validated. Conclusion An annotation-based genome-wide SNP discovery pipeline for NGS platforms was developed. The pipeline is suitable for SNP discovery in genomic libraries of complex genomes and does not require a reference genome sequence. The pipeline is applicable to all current NGS platforms, provided that at least one such platform generates relatively long reads. The pipeline package, AGSNP, and the discovered 497,118 Ae. tauschii SNPs can be accessed at (http://avena.pw.usda.gov/wheatD/agsnp.shtml). PMID:21266061

In-Silico Identification Of Micro-Loops In Myelodysplastic Syndromes

NASA Astrophysics Data System (ADS)

Beck, Dominik; Brandl, Miriam; Pham, Tuan D.; Chang, Chung-Che; Zhou, Xiaobo

2011-06-01

Micro-loops are regulatory network motifs that leverage transcriptional and posttranscriptional control to effectively regulate the transcriptome. In this paper a regulatory network for Myelodysplastic Syndromes (MDSs) was constructed from the literature and publicly available data sources. The network was filtered using data from deep-sequencing of small RNAs, exon and microarrays. Motif discovery showed that micro-loops might exist in MDS. We further used the identified micro-loops and performed basic network analysis to identify the known disease gene RUNX1/AML, as well as miRNA family hsa-mir-181. This suggested that the concept of micro-loops can be applied to enhance disease gene identification and biomarker discovery.

Sequence-specific bias correction for RNA-seq data using recurrent neural networks.

PubMed

Zhang, Yao-Zhong; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

2017-01-25

The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.

Emerging Concepts and Methodologies in Cancer Biomarker Discovery.

PubMed

Lu, Meixia; Zhang, Jinxiang; Zhang, Lanjing

2017-01-01

Cancer biomarker discovery is a critical part of cancer prevention and treatment. Despite the decades of effort, only a small number of cancer biomarkers have been identified for and validated in clinical settings. Conceptual and methodological breakthroughs may help accelerate the discovery of additional cancer biomarkers, particularly their use for diagnostics. In this review, we have attempted to review the emerging concepts in cancer biomarker discovery, including real-world evidence, open access data, and data paucity in rare or uncommon cancers. We have also summarized the recent methodological progress in cancer biomarker discovery, such as high-throughput sequencing, liquid biopsy, big data, artificial intelligence (AI), and deep learning and neural networks. Much attention has been given to the methodological details and comparison of the methodologies. Notably, these concepts and methodologies interact with each other and will likely lead to synergistic effects when carefully combined. Newer, more innovative concepts and methodologies are emerging as the current emerging ones became mainstream and widely applied to the field. Some future challenges are also discussed. This review contributes to the development of future theoretical frameworks and technologies in cancer biomarker discovery and will contribute to the discovery of more useful cancer biomarkers.

Fungal diversity from deep marine subsurface sediments (IODP 317, Canterbury Basin, New Zealand)

NASA Astrophysics Data System (ADS)

Redou, V.; Arzur, D.; Burgaud, G.; Barbier, G.

2012-12-01

Recent years have seen a growing interest regarding micro-eukaryotic communities in extreme environments as a third microbial domain after Bacteria and Archaea. However, knowledge is still scarce and the diversity of micro-eukaryotes in such environments remains hidden and their ecological role unknown. Our research program is based on the deep sedimentary layers of the Canterbury Basin in New Zealand (IODP 317) from the subsurface to the record depth of 1884 meters below seafloor. The objectives of our study are (i) to assess the genetic diversity of fungi in deep-sea sediments and (ii) identify the functional part in order to better understand the origin and the ecological role of fungal communities in this extreme ecosystem. Fingerprinting-based methods using capillary electrophoresis single-strand conformation polymorphism and denaturing high-performance liquid chromatography were used as a first step to raise our objectives. Molecular fungal diversity was assessed using amplification of ITS1 (Internal Transcribed Spacer 1) as a biomarker on 11 samples sediments from 3.76 to 1884 meters below seafloor. Fungal molecular signatures were detected throughout the sediment core. The phyla Ascomycota and Basidiomycota were revealed with DNA as well as cDNA. Most of the phylotypes are affiliated to environmental sequences and some to common fungal cultured species. The discovery of a present and metabolically active fungal component in this unique ecosystem allows some interesting first hypotheses that will be further combined to culture-based methods and deeper molecular methods (454 pyrosequencing) to highlight essential informations regarding physiology and ecological role of fungal communities in deep marine sediments.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

PubMed

Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P

2017-10-01

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.

Computational functional genomics-based approaches in analgesic drug discovery and repurposing.

PubMed

Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred; Lötsch, Jörn

2018-06-01

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library 'dbtORA'.

Diverse deep-sea fungi from the South China Sea and their antimicrobial activity.

PubMed

Zhang, Xiao-Yong; Zhang, Yun; Xu, Xin-Ya; Qi, Shu-Hua

2013-11-01

We investigated the diversity of fungal communities in nine different deep-sea sediment samples of the South China Sea by culture-dependent methods followed by analysis of fungal internal transcribed spacer (ITS) sequences. Although 14 out of 27 identified species were reported in a previous study, 13 species were isolated from sediments of deep-sea environments for the first report. Moreover, these ITS sequences of six isolates shared 84-92 % similarity with their closest matches in GenBank, which suggested that they might be novel phylotypes of genera Ajellomyces, Podosordaria, Torula, and Xylaria. The antimicrobial activities of these fungal isolates were explored using a double-layer technique. A relatively high proportion (56 %) of fungal isolates exhibited antimicrobial activity against at least one pathogenic bacterium or fungus among four marine pathogenic microbes (Micrococcus luteus, Pseudoaltermonas piscida, Aspergerillus versicolor, and A. sydowii). Out of these antimicrobial fungi, the genera Arthrinium, Aspergillus, and Penicillium exhibited antibacterial and antifungal activities, while genus Aureobasidium displayed only antibacterial activity, and genera Acremonium, Cladosporium, Geomyces, and Phaeosphaeriopsis displayed only antifungal activity. To our knowledge, this is the first report to investigate the diversity and antimicrobial activity of culturable deep-sea-derived fungi in the South China Sea. These results suggest that diverse deep-sea fungi from the South China Sea are a potential source for antibiotics' discovery and further increase the pool of fungi available for natural bioactive product screening.

Discovery of Culex pipiens associated tunisia virus: a new ssRNA(+) virus representing a new insect associated virus family

PubMed Central

Bigot, Diane; Atyame, Célestine M; Weill, Mylène; Justy, Fabienne

2018-01-01

Abstract In the global context of arboviral emergence, deep sequencing unlocks the discovery of new mosquito-borne viruses. Mosquitoes of the species Culex pipiens, C. torrentium, and C. hortensis were sampled from 22 locations worldwide for transcriptomic analyses. A virus discovery pipeline was used to analyze the dataset of 0.7 billion reads comprising 22 individual transcriptomes. Two closely related 6.8 kb viral genomes were identified in C. pipiens and named as Culex pipiens associated tunisia virus (CpATV) strains Ayed and Jedaida. The CpATV genome contained four ORFs. ORF1 possessed helicase and RNA-dependent RNA polymerase (RdRp) domains related to new viral sequences recently found mainly in dipterans. ORF2 and 4 contained a capsid protein domain showing strong homology with Virgaviridae plant viruses. ORF3 displayed similarities with eukaryotic Rhoptry domain and a merozoite surface protein (MSP7) domain only found in mosquito-transmitted Plasmodium, suggesting possible interactions between CpATV and vertebrate cells. Estimation of a strong purifying selection exerted on each ORFs and the presence of a polymorphism maintained in the coding region of ORF3 suggested that both CpATV sequences are genuine functional viruses. CpATV is part of an entirely new and highly diversified group of viruses recently found in insects, and that bears the genomic hallmarks of a new viral family. PMID:29340209

Quantitative phenotyping via deep barcode sequencing

PubMed Central

Smith, Andrew M.; Heisler, Lawrence E.; Mellor, Joseph; Kaper, Fiona; Thompson, Michael J.; Chee, Mark; Roth, Frederick P.; Giaever, Guri; Nislow, Corey

2009-01-01

Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or “Bar-seq,” outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that ∼20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene–environment interactions on a genome-wide scale. PMID:19622793

Limitations and potentials of current motif discovery algorithms

PubMed Central

Hu, Jianjun; Li, Bin; Kihara, Daisuke

2005-01-01

Computational methods for de novo identification of gene regulation elements, such as transcription factor binding sites, have proved to be useful for deciphering genetic regulatory networks. However, despite the availability of a large number of algorithms, their strengths and weaknesses are not sufficiently understood. Here, we designed a comprehensive set of performance measures and benchmarked five modern sequence-based motif discovery algorithms using large datasets generated from Escherichia coli RegulonDB. Factors that affect the prediction accuracy, scalability and reliability are characterized. It is revealed that the nucleotide and the binding site level accuracy are very low, while the motif level accuracy is relatively high, which indicates that the algorithms can usually capture at least one correct motif in an input sequence. To exploit diverse predictions from multiple runs of one or more algorithms, a consensus ensemble algorithm has been developed, which achieved 6–45% improvement over the base algorithms by increasing both the sensitivity and specificity. Our study illustrates limitations and potentials of existing sequence-based motif discovery algorithms. Taking advantage of the revealed potentials, several promising directions for further improvements are discussed. Since the sequence-based algorithms are the baseline of most of the modern motif discovery algorithms, this paper suggests substantial improvements would be possible for them. PMID:16284194

Next-generation libraries for robust RNA interference-based genome-wide screens

PubMed Central

Kampmann, Martin; Horlbeck, Max A.; Chen, Yuwen; Tsai, Jordan C.; Bassik, Michael C.; Gilbert, Luke A.; Villalta, Jacqueline E.; Kwon, S. Chul; Chang, Hyeshik; Kim, V. Narry; Weissman, Jonathan S.

2015-01-01

Genetic screening based on loss-of-function phenotypes is a powerful discovery tool in biology. Although the recent development of clustered regularly interspaced short palindromic repeats (CRISPR)-based screening approaches in mammalian cell culture has enormous potential, RNA interference (RNAi)-based screening remains the method of choice in several biological contexts. We previously demonstrated that ultracomplex pooled short-hairpin RNA (shRNA) libraries can largely overcome the problem of RNAi off-target effects in genome-wide screens. Here, we systematically optimize several aspects of our shRNA library, including the promoter and microRNA context for shRNA expression, selection of guide strands, and features relevant for postscreen sample preparation for deep sequencing. We present next-generation high-complexity libraries targeting human and mouse protein-coding genes, which we grouped into 12 sublibraries based on biological function. A pilot screen suggests that our next-generation RNAi library performs comparably to current CRISPR interference (CRISPRi)-based approaches and can yield complementary results with high sensitivity and high specificity. PMID:26080438

[Current applications of high-throughput DNA sequencing technology in antibody drug research].

PubMed

Yu, Xin; Liu, Qi-Gang; Wang, Ming-Rong

2012-03-01

Since the publication of a high-throughput DNA sequencing technology based on PCR reaction was carried out in oil emulsions in 2005, high-throughput DNA sequencing platforms have been evolved to a robust technology in sequencing genomes and diverse DNA libraries. Antibody libraries with vast numbers of members currently serve as a foundation of discovering novel antibody drugs, and high-throughput DNA sequencing technology makes it possible to rapidly identify functional antibody variants with desired properties. Herein we present a review of current applications of high-throughput DNA sequencing technology in the analysis of antibody library diversity, sequencing of CDR3 regions, identification of potent antibodies based on sequence frequency, discovery of functional genes, and combination with various display technologies, so as to provide an alternative approach of discovery and development of antibody drugs.

A deep learning method for lincRNA detection using auto-encoder algorithm.

PubMed

Yu, Ning; Yu, Zeng; Pan, Yi

2017-12-06

RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly annotated lincRNA data, deep learning methods based on auto-encoder algorithm can exert their capability in knowledge learning in order to capture the useful features and the information correlation along DNA genome sequences for lincRNA detection. As our knowledge, this is the first application to adopt the deep learning techniques for identifying lincRNA transcription sequences.

Construction and Evaluation of Normalized cDNA Libraries Enriched with Full-Length Sequences for Rapid Discovery of New Genes from Sisal (Agave sisalana Perr.) Different Developmental Stages

PubMed Central

Zhou, Wen-Zhao; Zhang, Yan-Mei; Lu, Jun-Ying; Li, Jun-Feng

2012-01-01

To provide a resource of sisal-specific expressed sequence data and facilitate this powerful approach in new gene research, the preparation of normalized cDNA libraries enriched with full-length sequences is necessary. Four libraries were produced with RNA pooled from Agave sisalana multiple tissues to increase efficiency of normalization and maximize the number of independent genes by SMART™ method and the duplex-specific nuclease (DSN). This procedure kept the proportion of full-length cDNAs in the subtracted/normalized libraries and dramatically enhanced the discovery of new genes. Sequencing of 3875 cDNA clones of libraries revealed 3320 unigenes with an average insert length about 1.2 kb, indicating that the non-redundancy of libraries was about 85.7%. These unigene functions were predicted by comparing their sequences to functional domain databases and extensively annotated with Gene Ontology (GO) terms. Comparative analysis of sisal unigenes and other plant genomes revealed that four putative MADS-box genes and knotted-like homeobox (knox) gene were obtained from a total of 1162 full-length transcripts. Furthermore, real-time PCR showed that the characteristics of their transcripts mainly depended on the tight expression regulation of a number of genes during the leaf and flower development. Analysis of individual library sequence data indicated that the pooled-tissue approach was highly effective in discovering new genes and preparing libraries for efficient deep sequencing. PMID:23202944

Biosynthesis and genetic encoding of phosphothreonine through parallel selection and deep sequencing

PubMed Central

Huguenin-Dezot, Nicolas; Liang, Alexandria D.; Schmied, Wolfgang H.; Rogerson, Daniel T.; Chin, Jason W.

2017-01-01

The phosphorylation of threonine residues in proteins regulates diverse processes in eukaryotic cells, and thousands of threonine phosphorylations have been identified. An understanding of how threonine phosphorylation regulates biological function will be accelerated by general methods to bio-synthesize defined phospho-proteins. Here we address limitations in current methods for discovering aminoacyl-tRNA synthetase/tRNA pairs for incorporating non-natural amino acids into proteins, by combining parallel positive selections with deep sequencing and statistical analysis, to create a rapid approach for directly discovering aminoacyl-tRNA synthetase/tRNA pairs that selectively incorporate non-natural substrates. Our approach is scalable and enables the direct discovery of aminoacyl-tRNA synthetase/tRNA pairs with mutually orthogonal substrate specificity. We biosynthesize phosphothreonine in cells, and use our new selection approach to discover a phosphothreonyl-tRNA synthetase/tRNACUA pair. By combining these advances we create an entirely biosynthetic route to incorporating phosphothreonine in proteins and biosynthesize several phosphoproteins; enabling phosphoprotein structure determination and synthetic protein kinase activation. PMID:28553966

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs.

PubMed

Lim, Chun Shen; Brown, Chris M

2017-01-01

Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community.

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

PubMed Central

Lim, Chun Shen; Brown, Chris M.

2018-01-01

Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community. PMID:29354101

Novel Virus Discovery and Genome Reconstruction from Field RNA Samples Reveals Highly Divergent Viruses in Dipteran Hosts

PubMed Central

Bass, David; Moureau, Gregory; Tang, Shuoya; McAlister, Erica; Culverwell, C. Lorna; Glücksman, Edvard; Wang, Hui; Brown, T. David K.; Gould, Ernest A.; Harbach, Ralph E.; de Lamballerie, Xavier; Firth, Andrew E.

2013-01-01

We investigated whether small RNA (sRNA) sequenced from field-collected mosquitoes and chironomids (Diptera) can be used as a proxy signature of viral prevalence within a range of species and viral groups, using sRNAs sequenced from wild-caught specimens, to inform total RNA deep sequencing of samples of particular interest. Using this strategy, we sequenced from adult Anopheles maculipennis s.l. mosquitoes the apparently nearly complete genome of one previously undescribed virus related to chronic bee paralysis virus, and, from a pool of Ochlerotatus caspius and Oc. detritus mosquitoes, a nearly complete entomobirnavirus genome. We also reconstructed long sequences (1503-6557 nt) related to at least nine other viruses. Crucially, several of the sequences detected were reconstructed from host organisms highly divergent from those in which related viruses have been previously isolated or discovered. It is clear that viral transmission and maintenance cycles in nature are likely to be significantly more complex and taxonomically diverse than previously expected. PMID:24260463

Oral Microbiome of Deep and Shallow Dental Pockets In Chronic Periodontitis

PubMed Central

Ge, Xiuchun; Rodriguez, Rafael; Trinh, My; Gunsolley, John; Xu, Ping

2013-01-01

We examined the subgingival bacterial biodiversity in untreated chronic periodontitis patients by sequencing 16S rRNA genes. The primary purpose of the study was to compare the oral microbiome in deep (diseased) and shallow (healthy) sites. A secondary purpose was to evaluate the influences of smoking, race and dental caries on this relationship. A total of 88 subjects from two clinics were recruited. Paired subgingival plaque samples were taken from each subject, one from a probing site depth >5 mm (deep site) and the other from a probing site depth ≤3mm (shallow site). A universal primer set was designed to amplify the V4–V6 region for oral microbial 16S rRNA sequences. Differences in genera and species attributable to deep and shallow sites were determined by statistical analysis using a two-part model and false discovery rate. Fifty-one of 170 genera and 200 of 746 species were found significantly different in abundances between shallow and deep sites. Besides previously identified periodontal disease-associated bacterial species, additional species were found markedly changed in diseased sites. Cluster analysis revealed that the microbiome difference between deep and shallow sites was influenced by patient-level effects such as clinic location, race and smoking. The differences between clinic locations may be influenced by racial distribution, in that all of the African Americans subjects were seen at the same clinic. Our results suggested that there were influences from the microbiome for caries and periodontal disease and these influences are independent. PMID:23762384

Sequence-Based Genotyping for Marker Discovery and Co-Dominant Scoring in Germplasm and Populations

PubMed Central

Truong, Hoa T.; Ramos, A. Marcos; Yalcin, Feyruz; de Ruiter, Marjo; van der Poel, Hein J. A.; Huvenaars, Koen H. J.; Hogers, René C. J.; van Enckevort, Leonora. J. G.; Janssen, Antoine; van Orsouw, Nathalie J.; van Eijk, Michiel J. T.

2012-01-01

Conventional marker-based genotyping platforms are widely available, but not without their limitations. In this context, we developed Sequence-Based Genotyping (SBG), a technology for simultaneous marker discovery and co-dominant scoring, using next-generation sequencing. SBG offers users several advantages including a generic sample preparation method, a highly robust genome complexity reduction strategy to facilitate de novo marker discovery across entire genomes, and a uniform bioinformatics workflow strategy to achieve genotyping goals tailored to individual species, regardless of the availability of a reference sequence. The most distinguishing features of this technology are the ability to genotype any population structure, regardless whether parental data is included, and the ability to co-dominantly score SNP markers segregating in populations. To demonstrate the capabilities of SBG, we performed marker discovery and genotyping in Arabidopsis thaliana and lettuce, two plant species of diverse genetic complexity and backgrounds. Initially we obtained 1,409 SNPs for arabidopsis, and 5,583 SNPs for lettuce. Further filtering of the SNP dataset produced over 1,000 high quality SNP markers for each species. We obtained a genotyping rate of 201.2 genotypes/SNP and 58.3 genotypes/SNP for arabidopsis (n = 222 samples) and lettuce (n = 87 samples), respectively. Linkage mapping using these SNPs resulted in stable map configurations. We have therefore shown that the SBG approach presented provides users with the utmost flexibility in garnering high quality markers that can be directly used for genotyping and downstream applications. Until advances and costs will allow for routine whole-genome sequencing of populations, we expect that sequence-based genotyping technologies such as SBG will be essential for genotyping of model and non-model genomes alike. PMID:22662172

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

PubMed

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

deepTools: a flexible platform for exploring deep-sequencing data.

PubMed

Ramírez, Fidel; Dündar, Friederike; Diehl, Sarah; Grüning, Björn A; Manke, Thomas

2014-07-01

We present a Galaxy based web server for processing and visualizing deeply sequenced data. The web server's core functionality consists of a suite of newly developed tools, called deepTools, that enable users with little bioinformatic background to explore the results of their sequencing experiments in a standardized setting. Users can upload pre-processed files with continuous data in standard formats and generate heatmaps and summary plots in a straight-forward, yet highly customizable manner. In addition, we offer several tools for the analysis of files containing aligned reads and enable efficient and reproducible generation of normalized coverage files. As a modular and open-source platform, deepTools can easily be expanded and customized to future demands and developments. The deepTools webserver is freely available at http://deeptools.ie-freiburg.mpg.de and is accompanied by extensive documentation and tutorials aimed at conveying the principles of deep-sequencing data analysis. The web server can be used without registration. deepTools can be installed locally either stand-alone or as part of Galaxy. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Improved detection of CXCR4-using HIV by V3 genotyping: application of population-based and "deep" sequencing to plasma RNA and proviral DNA.

PubMed

Swenson, Luke C; Moores, Andrew; Low, Andrew J; Thielen, Alexander; Dong, Winnie; Woods, Conan; Jensen, Mark A; Wynhoven, Brian; Chan, Dennison; Glascock, Christopher; Harrigan, P Richard

2010-08-01

Tropism testing should rule out CXCR4-using HIV before treatment with CCR5 antagonists. Currently, the recombinant phenotypic Trofile assay (Monogram) is most widely utilized; however, genotypic tests may represent alternative methods. Independent triplicate amplifications of the HIV gp120 V3 region were made from either plasma HIV RNA or proviral DNA. These underwent standard, population-based sequencing with an ABI3730 (RNA n = 63; DNA n = 40), or "deep" sequencing with a Roche/454 Genome Sequencer-FLX (RNA n = 12; DNA n = 12). Position-specific scoring matrices (PSSMX4/R5) (-6.96 cutoff) and geno2pheno[coreceptor] (5% false-positive rate) inferred tropism from V3 sequence. These methods were then independently validated with a separate, blinded dataset (n = 278) of screening samples from the maraviroc MOTIVATE trials. Standard sequencing of HIV RNA with PSSM yielded 69% sensitivity and 91% specificity, relative to Trofile. The validation dataset gave 75% sensitivity and 83% specificity. Proviral DNA plus PSSM gave 77% sensitivity and 71% specificity. "Deep" sequencing of HIV RNA detected >2% inferred-CXCR4-using virus in 8/8 samples called non-R5 by Trofile, and <2% in 4/4 samples called R5. Triplicate analyses of V3 standard sequence data detect greater proportions of CXCR4-using samples than previously achieved. Sequencing proviral DNA and "deep" V3 sequencing may also be useful tools for assessing tropism.

AFLP fragment isolation technique as a method to produce random sequences for single nucleotide polymorphism discovery in the green turtle, Chelonia mydas.

PubMed

Roden, Suzanne E; Dutton, Peter H; Morin, Phillip A

2009-01-01

The green sea turtle, Chelonia mydas, was used as a case study for single nucleotide polymorphism (SNP) discovery in a species that has little genetic sequence information available. As green turtles have a complex population structure, additional nuclear markers other than microsatellites could add to our understanding of their complex life history. Amplified fragment length polymorphism technique was used to generate sets of random fragments of genomic DNA, which were then electrophoretically separated with precast gels, stained with SYBR green, excised, and directly sequenced. It was possible to perform this method without the use of polyacrylamide gels, radioactive or fluorescent labeled primers, or hybridization methods, reducing the time, expense, and safety hazards of SNP discovery. Within 13 loci, 2547 base pairs were screened, resulting in the discovery of 35 SNPs. Using this method, it was possible to yield a sufficient number of loci to screen for SNP markers without the availability of prior sequence information.

Unique Prokaryotic Consortia in Geochemically Distinct Sediments from Red Sea Atlantis II and Discovery Deep Brine Pools

PubMed Central

Siam, Rania; Mustafa, Ghada A.; Sharaf, Hazem; Moustafa, Ahmed; Ramadan, Adham R.; Antunes, Andre; Bajic, Vladimir B.; Stingl, Uli; Marsis, Nardine G. R.; Coolen, Marco J. L.; Sogin, Mitchell; Ferreira, Ari J. S.; Dorry, Hamza El

2012-01-01

The seafloor is a unique environment, which allows insights into how geochemical processes affect the diversity of biological life. Among its diverse ecosystems are deep-sea brine pools - water bodies characterized by a unique combination of extreme conditions. The ‘polyextremophiles’ that constitute the microbial assemblage of these deep hot brines have not been comprehensively studied. We report a comparative taxonomic analysis of the prokaryotic communities of the sediments directly below the Red Sea brine pools, namely, Atlantis II, Discovery, Chain Deep, and an adjacent brine-influenced site. Analyses of sediment samples and high-throughput pyrosequencing of PCR-amplified environmental 16S ribosomal RNA genes (16S rDNA) revealed that one sulfur (S)-rich Atlantis II and one nitrogen (N)-rich Discovery Deep section contained distinct microbial populations that differed from those found in the other sediment samples examined. Proteobacteria, Actinobacteria, Cyanobacteria, Deferribacteres, and Euryarchaeota were the most abundant bacterial and archaeal phyla in both the S- and N-rich sections. Relative abundance-based hierarchical clustering of the 16S rDNA pyrotags assigned to major taxonomic groups allowed us to categorize the archaeal and bacterial communities into three major and distinct groups; group I was unique to the S-rich Atlantis II section (ATII-1), group II was characteristic for the N-rich Discovery sample (DD-1), and group III reflected the composition of the remaining sediments. Many of the groups detected in the S-rich Atlantis II section are likely to play a dominant role in the cycling of methane and sulfur due to their phylogenetic affiliations with bacteria and archaea involved in anaerobic methane oxidation and sulfate reduction. PMID:22916172

Diff-seq: A high throughput sequencing-based mismatch detection assay for DNA variant enrichment and discovery

PubMed Central

Karas, Vlad O; Sinnott-Armstrong, Nicholas A; Varghese, Vici; Shafer, Robert W; Greenleaf, William J; Sherlock, Gavin

2018-01-01

Abstract Much of the within species genetic variation is in the form of single nucleotide polymorphisms (SNPs), typically detected by whole genome sequencing (WGS) or microarray-based technologies. However, WGS produces mostly uninformative reads that perfectly match the reference, while microarrays require genome-specific reagents. We have developed Diff-seq, a sequencing-based mismatch detection assay for SNP discovery without the requirement for specialized nucleic-acid reagents. Diff-seq leverages the Surveyor endonuclease to cleave mismatched DNA molecules that are generated after cross-annealing of a complex pool of DNA fragments. Sequencing libraries enriched for Surveyor-cleaved molecules result in increased coverage at the variant sites. Diff-seq detected all mismatches present in an initial test substrate, with specific enrichment dependent on the identity and context of the variation. Application to viral sequences resulted in increased observation of variant alleles in a biologically relevant context. Diff-Seq has the potential to increase the sensitivity and efficiency of high-throughput sequencing in the detection of variation. PMID:29361139

Haplotag: Software for Haplotype-Based Genotyping-by-Sequencing Analysis

PubMed Central

Tinker, Nicholas A.; Bekele, Wubishet A.; Hattori, Jiro

2016-01-01

Genotyping-by-sequencing (GBS), and related methods, are based on high-throughput short-read sequencing of genomic complexity reductions followed by discovery of single nucleotide polymorphisms (SNPs) within sequence tags. This provides a powerful and economical approach to whole-genome genotyping, facilitating applications in genomics, diversity analysis, and molecular breeding. However, due to the complexity of analyzing large data sets, applications of GBS may require substantial time, expertise, and computational resources. Haplotag, the novel GBS software described here, is freely available, and operates with minimal user-investment on widely available computer platforms. Haplotag is unique in fulfilling the following set of criteria: (1) operates without a reference genome; (2) can be used in a polyploid species; (3) provides a discovery mode, and a production mode; (4) discovers polymorphisms based on a model of tag-level haplotypes within sequenced tags; (5) reports SNPs as well as haplotype-based genotypes; and (6) provides an intuitive visual “passport” for each inferred locus. Haplotag is optimized for use in a self-pollinating plant species. PMID:26818073

FIRST RESULTS FROM Z -FOURGE : DISCOVERY OF A CANDIDATE CLUSTER AT z = 2.2 IN COSMOS

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

Spitler, Lee R.; Glazebrook, Karl; Poole, Gregory B.

2012-04-01

We report the first results from the Z -FOURGE survey: the discovery of a candidate galaxy cluster at z = 2.2 consisting of two compact overdensities with red galaxies detected at {approx}> 20{sigma} above the mean surface density. The discovery was made possible by a new deep (K{sub s} {approx}< 24.8 AB 5{sigma}) Magellan/FOURSTAR near-IR imaging survey with five custom medium-bandwidth filters. The filters pinpoint the location of the Balmer/4000 A break in evolved stellar populations at 1.5 < z < 3.5, yielding significantly more accurate photometric redshifts than possible with broadband imaging alone. The overdensities are within 1' ofmore » each other in the COSMOS field and appear to be embedded in a larger structure that contains at least one additional overdensity ({approx}10{sigma}). Considering the global properties of the overdensities, the z = 2.2 system appears to be the most distant example of a galaxy cluster with a population of red galaxies. A comparison to a large {Lambda}CDM simulation suggests that the system may consist of merging subclusters, with properties in between those of z > 2 protoclusters with more diffuse distributions of blue galaxies and the lower-redshift galaxy clusters with prominent red sequences. The structure is completely absent in public optical catalogs in COSMOS and only weakly visible in a shallower near-IR survey. The discovery showcases the potential of deep near-IR surveys with medium-band filters to advance the understanding of environment and galaxy evolution at z > 1.5.« less

Designing deep sequencing experiments: detecting structural variation and estimating transcript abundance.

PubMed

Bashir, Ali; Bansal, Vikas; Bafna, Vineet

2010-06-18

Massively parallel DNA sequencing technologies have enabled the sequencing of several individual human genomes. These technologies are also being used in novel ways for mRNA expression profiling, genome-wide discovery of transcription-factor binding sites, small RNA discovery, etc. The multitude of sequencing platforms, each with their unique characteristics, pose a number of design challenges, regarding the technology to be used and the depth of sequencing required for a particular sequencing application. Here we describe a number of analytical and empirical results to address design questions for two applications: detection of structural variations from paired-end sequencing and estimating mRNA transcript abundance. For structural variation, our results provide explicit trade-offs between the detection and resolution of rearrangement breakpoints, and the optimal mix of paired-read insert lengths. Specifically, we prove that optimal detection and resolution of breakpoints is achieved using a mix of exactly two insert library lengths. Furthermore, we derive explicit formulae to determine these insert length combinations, enabling a 15% improvement in breakpoint detection at the same experimental cost. On empirical short read data, these predictions show good concordance with Illumina 200 bp and 2 Kbp insert length libraries. For transcriptome sequencing, we determine the sequencing depth needed to detect rare transcripts from a small pilot study. With only 1 Million reads, we derive corrections that enable almost perfect prediction of the underlying expression probability distribution, and use this to predict the sequencing depth required to detect low expressed genes with greater than 95% probability. Together, our results form a generic framework for many design considerations related to high-throughput sequencing. We provide software tools http://bix.ucsd.edu/projects/NGS-DesignTools to derive platform independent guidelines for designing sequencing experiments (amount of sequencing, choice of insert length, mix of libraries) for novel applications of next generation sequencing.

Relationship between aging and T1 relaxation time in deep gray matter: A voxel-based analysis.

PubMed

Okubo, Gosuke; Okada, Tomohisa; Yamamoto, Akira; Fushimi, Yasutaka; Okada, Tsutomu; Murata, Katsutoshi; Togashi, Kaori

2017-09-01

To investigate age-related changes in T 1 relaxation time in deep gray matter structures in healthy volunteers using magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE). In all, 70 healthy volunteers (aged 20-76, mean age 42.6 years) were scanned at 3T magnetic resonance imaging (MRI). A MP2RAGE sequence was employed to quantify T 1 relaxation times. After the spatial normalization of T 1 maps with the diffeomorphic anatomical registration using the exponentiated Lie algebra algorithm, voxel-based regression analysis was conducted. In addition, linear and quadratic regression analyses of regions of interest (ROIs) were also performed. With aging, voxel-based analysis (VBA) revealed significant T 1 value decreases in the ventral-inferior putamen, nucleus accumbens, and amygdala, whereas T 1 values significantly increased in the thalamus and white matter as well (P < 0.05 at cluster level, false discovery rate). ROI analysis revealed that T 1 values in the nucleus accumbens linearly decreased with aging (P = 0.0016), supporting the VBA result. T 1 values in the thalamus (P < 0.0001), substantia nigra (P = 0.0003), and globus pallidus (P < 0.0001) had a best fit to quadratic curves, with the minimum T 1 values observed between 30 and 50 years of age. Age-related changes in T 1 relaxation time vary by location in deep gray matter. 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:724-731. © 2017 International Society for Magnetic Resonance in Medicine.

PolyA_DB 3 catalogs cleavage and polyadenylation sites identified by deep sequencing in multiple genomes

PubMed Central

Wang, Ruijia; Nambiar, Ram; Zheng, Dinghai

2018-01-01

Abstract PolyA_DB is a database cataloging cleavage and polyadenylation sites (PASs) in several genomes. Previous versions were based mainly on expressed sequence tags (ESTs), which had a limited amount and could lead to inaccurate PAS identification due to the presence of internal A-rich sequences in transcripts. Here, we present an updated version of the database based solely on deep sequencing data. First, PASs are mapped by the 3′ region extraction and deep sequencing (3′READS) method, ensuring unequivocal PAS identification. Second, a large volume of data based on diverse biological samples increases PAS coverage by 3.5-fold over the EST-based version and provides PAS usage information. Third, strand-specific RNA-seq data are used to extend annotated 3′ ends of genes to obtain more thorough annotations of alternative polyadenylation (APA) sites. Fourth, conservation information of PAS across mammals sheds light on significance of APA sites. The database (URL: http://www.polya-db.org/v3) currently holds PASs in human, mouse, rat and chicken, and has links to the UCSC genome browser for further visualization and for integration with other genomic data. PMID:29069441

Antibody Engineering and Therapeutics

PubMed Central

Almagro, Juan Carlos; Gilliland, Gary L; Breden, Felix; Scott, Jamie K; Sok, Devin; Pauthner, Matthias; Reichert, Janice M; Helguera, Gustavo; Andrabi, Raiees; Mabry, Robert; Bléry, Mathieu; Voss, James E; Laurén, Juha; Abuqayyas, Lubna; Barghorn, Stefan; Ben-Jacob, Eshel; Crowe, James E; Huston, James S; Johnston, Stephen Albert; Krauland, Eric; Lund-Johansen, Fridtjof; Marasco, Wayne A; Parren, Paul WHI; Xu, Kai Y

2014-01-01

The 24th Antibody Engineering & Therapeutics meeting brought together a broad range of participants who were updated on the latest advances in antibody research and development. Organized by IBC Life Sciences, the gathering is the annual meeting of The Antibody Society, which serves as the scientific sponsor. Preconference workshops on 3D modeling and delineation of clonal lineages were featured, and the conference included sessions on a wide variety of topics relevant to researchers, including systems biology; antibody deep sequencing and repertoires; the effects of antibody gene variation and usage on antibody response; directed evolution; knowledge-based design; antibodies in a complex environment; polyreactive antibodies and polyspecificity; the interface between antibody therapy and cellular immunity in cancer; antibodies in cardiometabolic medicine; antibody pharmacokinetics, distribution and off-target toxicity; optimizing antibody formats for immunotherapy; polyclonals, oligoclonals and bispecifics; antibody discovery platforms; and antibody-drug conjugates. PMID:24589717

Position-specific automated processing of V3 env ultra-deep pyrosequencing data for predicting HIV-1 tropism

PubMed Central

Jeanne, Nicolas; Saliou, Adrien; Carcenac, Romain; Lefebvre, Caroline; Dubois, Martine; Cazabat, Michelle; Nicot, Florence; Loiseau, Claire; Raymond, Stéphanie; Izopet, Jacques; Delobel, Pierre

2015-01-01

HIV-1 coreceptor usage must be accurately determined before starting CCR5 antagonist-based treatment as the presence of undetected minor CXCR4-using variants can cause subsequent virological failure. Ultra-deep pyrosequencing of HIV-1 V3 env allows to detect low levels of CXCR4-using variants that current genotypic approaches miss. However, the computation of the mass of sequence data and the need to identify true minor variants while excluding artifactual sequences generated during amplification and ultra-deep pyrosequencing is rate-limiting. Arbitrary fixed cut-offs below which minor variants are discarded are currently used but the errors generated during ultra-deep pyrosequencing are sequence-dependant rather than random. We have developed an automated processing of HIV-1 V3 env ultra-deep pyrosequencing data that uses biological filters to discard artifactual or non-functional V3 sequences followed by statistical filters to determine position-specific sensitivity thresholds, rather than arbitrary fixed cut-offs. It allows to retain authentic sequences with point mutations at V3 positions of interest and discard artifactual ones with accurate sensitivity thresholds. PMID:26585833

Position-specific automated processing of V3 env ultra-deep pyrosequencing data for predicting HIV-1 tropism.

PubMed

Jeanne, Nicolas; Saliou, Adrien; Carcenac, Romain; Lefebvre, Caroline; Dubois, Martine; Cazabat, Michelle; Nicot, Florence; Loiseau, Claire; Raymond, Stéphanie; Izopet, Jacques; Delobel, Pierre

2015-11-20

HIV-1 coreceptor usage must be accurately determined before starting CCR5 antagonist-based treatment as the presence of undetected minor CXCR4-using variants can cause subsequent virological failure. Ultra-deep pyrosequencing of HIV-1 V3 env allows to detect low levels of CXCR4-using variants that current genotypic approaches miss. However, the computation of the mass of sequence data and the need to identify true minor variants while excluding artifactual sequences generated during amplification and ultra-deep pyrosequencing is rate-limiting. Arbitrary fixed cut-offs below which minor variants are discarded are currently used but the errors generated during ultra-deep pyrosequencing are sequence-dependant rather than random. We have developed an automated processing of HIV-1 V3 env ultra-deep pyrosequencing data that uses biological filters to discard artifactual or non-functional V3 sequences followed by statistical filters to determine position-specific sensitivity thresholds, rather than arbitrary fixed cut-offs. It allows to retain authentic sequences with point mutations at V3 positions of interest and discard artifactual ones with accurate sensitivity thresholds.

Evolution of coreceptor utilization to escape CCR5 antagonist therapy.

PubMed

Zhang, Jie; Gao, Xiang; Martin, John; Rosa, Bruce; Chen, Zheng; Mitreva, Makedonka; Henrich, Timothy; Kuritzkes, Daniel; Ratner, Lee

2016-07-01

The HIV-1 envelope interacts with coreceptors CCR5 and CXCR4 in a dynamic, multi-step process, its molecular details not clearly delineated. Use of CCR5 antagonists results in tropism shift and therapeutic failure. Here we describe a novel approach using full-length patient-derived gp160 quasispecies libraries cloned into HIV-1 molecular clones, their separation based on phenotypic tropism in vitro, and deep sequencing of the resultant variants for structure-function analyses. Analysis of functionally validated envelope sequences from patients who failed CCR5 antagonist therapy revealed determinants strongly associated with coreceptor specificity, especially at the gp120-gp41 and gp41-gp41 interaction surfaces that invite future research on the roles of subunit interaction and envelope trimer stability in coreceptor usage. This study identifies important structure-function relationships in HIV-1 envelope, and demonstrates proof of concept for a new integrated analysis method that facilitates laboratory discovery of resistant mutants to aid in development of other therapeutic agents. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Quasispecies Analyses of the HIV-1 Near-full-length Genome With Illumina MiSeq

PubMed Central

Ode, Hirotaka; Matsuda, Masakazu; Matsuoka, Kazuhiro; Hachiya, Atsuko; Hattori, Junko; Kito, Yumiko; Yokomaku, Yoshiyuki; Iwatani, Yasumasa; Sugiura, Wataru

2015-01-01

Human immunodeficiency virus type-1 (HIV-1) exhibits high between-host genetic diversity and within-host heterogeneity, recognized as quasispecies. Because HIV-1 quasispecies fluctuate in terms of multiple factors, such as antiretroviral exposure and host immunity, analyzing the HIV-1 genome is critical for selecting effective antiretroviral therapy and understanding within-host viral coevolution mechanisms. Here, to obtain HIV-1 genome sequence information that includes minority variants, we sought to develop a method for evaluating quasispecies throughout the HIV-1 near-full-length genome using the Illumina MiSeq benchtop deep sequencer. To ensure the reliability of minority mutation detection, we applied an analysis method of sequence read mapping onto a consensus sequence derived from de novo assembly followed by iterative mapping and subsequent unique error correction. Deep sequencing analyses of aHIV-1 clone showed that the analysis method reduced erroneous base prevalence below 1% in each sequence position and discarded only < 1% of all collected nucleotides, maximizing the usage of the collected genome sequences. Further, we designed primer sets to amplify the HIV-1 near-full-length genome from clinical plasma samples. Deep sequencing of 92 samples in combination with the primer sets and our analysis method provided sufficient coverage to identify >1%-frequency sequences throughout the genome. When we evaluated sequences of pol genes from 18 treatment-naïve patients' samples, the deep sequencing results were in agreement with Sanger sequencing and identified numerous additional minority mutations. The results suggest that our deep sequencing method would be suitable for identifying within-host viral population dynamics throughout the genome. PMID:26617593

Identification and Characterization of Two Novel RNA Viruses from Anopheles gambiae Species Complex Mosquitoes

PubMed Central

Carissimo, Guillaume; Eiglmeier, Karin; Reveillaud, Julie; Holm, Inge; Diallo, Mawlouth; Diallo, Diawo; Vantaux, Amélie; Kim, Saorin; Ménard, Didier; Siv, Sovannaroth; Belda, Eugeni; Bischoff, Emmanuel; Antoniewski, Christophe; Vernick, Kenneth D.

2016-01-01

Mosquitoes of the Anopheles gambiae complex display strong preference for human bloodmeals and are major malaria vectors in Africa. However, their interaction with viruses or role in arbovirus transmission during epidemics has been little examined, with the exception of O’nyong-nyong virus, closely related to Chikungunya virus. Deep-sequencing has revealed different RNA viruses in natural insect viromes, but none have been previously described in the Anopheles gambiae species complex. Here, we describe two novel insect RNA viruses, a Dicistrovirus and a Cypovirus, found in laboratory colonies of An. gambiae taxa using small-RNA deep sequencing. Sequence analysis was done with Metavisitor, an open-source bioinformatic pipeline for virus discovery and de novo genome assembly. Wild-collected Anopheles from Senegal and Cambodia were positive for the Dicistrovirus and Cypovirus, displaying high sequence identity to the laboratory-derived virus. Thus, the Dicistrovirus (Anopheles C virus, AnCV) and Cypovirus (Anopheles Cypovirus, AnCPV) are components of the natural virome of at least some anopheline species. Their possible influence on mosquito immunity or transmission of other pathogens is unknown. These natural viruses could be developed as models for the study of Anopheles-RNA virus interactions in low security laboratory settings, in an analogous manner to the use of rodent malaria parasites for studies of mosquito anti-parasite immunity. PMID:27138938

BayesMotif: de novo protein sorting motif discovery from impure datasets.

PubMed

Hu, Jianjun; Zhang, Fan

2010-01-18

Protein sorting is the process that newly synthesized proteins are transported to their target locations within or outside of the cell. This process is precisely regulated by protein sorting signals in different forms. A major category of sorting signals are amino acid sub-sequences usually located at the N-terminals or C-terminals of protein sequences. Genome-wide experimental identification of protein sorting signals is extremely time-consuming and costly. Effective computational algorithms for de novo discovery of protein sorting signals is needed to improve the understanding of protein sorting mechanisms. We formulated the protein sorting motif discovery problem as a classification problem and proposed a Bayesian classifier based algorithm (BayesMotif) for de novo identification of a common type of protein sorting motifs in which a highly conserved anchor is present along with a less conserved motif regions. A false positive removal procedure is developed to iteratively remove sequences that are unlikely to contain true motifs so that the algorithm can identify motifs from impure input sequences. Experiments on both implanted motif datasets and real-world datasets showed that the enhanced BayesMotif algorithm can identify anchored sorting motifs from pure or impure protein sequence dataset. It also shows that the false positive removal procedure can help to identify true motifs even when there is only 20% of the input sequences containing true motif instances. We proposed BayesMotif, a novel Bayesian classification based algorithm for de novo discovery of a special category of anchored protein sorting motifs from impure datasets. Compared to conventional motif discovery algorithms such as MEME, our algorithm can find less-conserved motifs with short highly conserved anchors. Our algorithm also has the advantage of easy incorporation of additional meta-sequence features such as hydrophobicity or charge of the motifs which may help to overcome the limitations of PWM (position weight matrix) motif model.

Argo_CUDA: Exhaustive GPU based approach for motif discovery in large DNA datasets.

PubMed

Vishnevsky, Oleg V; Bocharnikov, Andrey V; Kolchanov, Nikolay A

2018-02-01

The development of chromatin immunoprecipitation sequencing (ChIP-seq) technology has revolutionized the genetic analysis of the basic mechanisms underlying transcription regulation and led to accumulation of information about a huge amount of DNA sequences. There are a lot of web services which are currently available for de novo motif discovery in datasets containing information about DNA/protein binding. An enormous motif diversity makes their finding challenging. In order to avoid the difficulties, researchers use different stochastic approaches. Unfortunately, the efficiency of the motif discovery programs dramatically declines with the query set size increase. This leads to the fact that only a fraction of top "peak" ChIP-Seq segments can be analyzed or the area of analysis should be narrowed. Thus, the motif discovery in massive datasets remains a challenging issue. Argo_Compute Unified Device Architecture (CUDA) web service is designed to process the massive DNA data. It is a program for the detection of degenerate oligonucleotide motifs of fixed length written in 15-letter IUPAC code. Argo_CUDA is a full-exhaustive approach based on the high-performance GPU technologies. Compared with the existing motif discovery web services, Argo_CUDA shows good prediction quality on simulated sets. The analysis of ChIP-Seq sequences revealed the motifs which correspond to known transcription factor binding sites.

Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks

PubMed Central

Avsec, Žiga; Cheng, Jun; Gagneur, Julien

2018-01-01

Abstract Motivation Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genomic landmarks in deep neural networks has not been addressed. Results Here we developed spline transformation, a neural network module based on splines to flexibly and robustly model distances. Modeling distances to various genomic landmarks with spline transformations significantly increased state-of-the-art prediction accuracy of in vivo RNA-binding protein binding sites for 120 out of 123 proteins. We also developed a deep neural network for human splice branchpoint based on spline transformations that outperformed the current best, already distance-based, machine learning model. Compared to piecewise linear transformation, as obtained by composition of rectified linear units, spline transformation yields higher prediction accuracy as well as faster and more robust training. As spline transformation can be applied to further quantities beyond distances, such as methylation or conservation, we foresee it as a versatile component in the genomics deep learning toolbox. Availability and implementation Spline transformation is implemented as a Keras layer in the CONCISE python package: https://github.com/gagneurlab/concise. Analysis code is available at https://github.com/gagneurlab/Manuscript_Avsec_Bioinformatics_2017. Contact avsec@in.tum.de or gagneur@in.tum.de Supplementary information Supplementary data are available at Bioinformatics online. PMID:29155928

Deep Sequencing in Infectious Diseases: Immune and Pathogen Repertoires for the Improvement of Patient Outcomes.

PubMed

Burkholder, William F; Newell, Evan W; Poidinger, Michael; Chen, Swaine; Fink, Katja

2017-01-01

The inaugural workshop "Deep Sequencing in Infectious Diseases: Immune and Pathogen Repertoires for the Improvement of Patient Outcomes" was held in Singapore on 13-14 October 2016. The aim of the workshop was to discuss the latest trends in using high-throughput sequencing, bioinformatics, and allied technologies to analyze immune and pathogen repertoires and their interplay within the host, bringing together key international players in the field and Singapore-based researchers and clinician-scientists. The focus was in particular on the application of these technologies for the improvement of patient diagnosis, prognosis and treatment, and for other broad public health outcomes. The presentations by scientists and clinicians showed the potential of deep sequencing technology to capture the coevolution of adaptive immunity and pathogens. For clinical applications, some key challenges remain, such as the long turnaround time and relatively high cost of deep sequencing for pathogen identification and characterization and the lack of international standardization in immune repertoire analysis.

Deep Sequencing in Infectious Diseases: Immune and Pathogen Repertoires for the Improvement of Patient Outcomes

PubMed Central

Burkholder, William F.; Newell, Evan W.; Poidinger, Michael; Chen, Swaine; Fink, Katja

2017-01-01

The inaugural workshop “Deep Sequencing in Infectious Diseases: Immune and Pathogen Repertoires for the Improvement of Patient Outcomes” was held in Singapore on 13–14 October 2016. The aim of the workshop was to discuss the latest trends in using high-throughput sequencing, bioinformatics, and allied technologies to analyze immune and pathogen repertoires and their interplay within the host, bringing together key international players in the field and Singapore-based researchers and clinician-scientists. The focus was in particular on the application of these technologies for the improvement of patient diagnosis, prognosis and treatment, and for other broad public health outcomes. The presentations by scientists and clinicians showed the potential of deep sequencing technology to capture the coevolution of adaptive immunity and pathogens. For clinical applications, some key challenges remain, such as the long turnaround time and relatively high cost of deep sequencing for pathogen identification and characterization and the lack of international standardization in immune repertoire analysis. PMID:28620372

Evidence for Deep Regulatory Similarities in Early Developmental Programs across Highly Diverged Insects

PubMed Central

Zhang, Yinan; Samee, Md. Abul Hassan; Halfon, Marc S.; Sinha, Saurabh

2014-01-01

Many genes familiar from Drosophila development, such as the so-called gap, pair-rule, and segment polarity genes, play important roles in the development of other insects and in many cases appear to be deployed in a similar fashion, despite the fact that Drosophila-like “long germband” development is highly derived and confined to a subset of insect families. Whether or not these similarities extend to the regulatory level is unknown. Identification of regulatory regions beyond the well-studied Drosophila has been challenging as even within the Diptera (flies, including mosquitoes) regulatory sequences have diverged past the point of recognition by standard alignment methods. Here, we demonstrate that methods we previously developed for computational cis-regulatory module (CRM) discovery in Drosophila can be used effectively in highly diverged (250–350 Myr) insect species including Anopheles gambiae, Tribolium castaneum, Apis mellifera, and Nasonia vitripennis. In Drosophila, we have successfully used small sets of known CRMs as “training data” to guide the search for other CRMs with related function. We show here that although species-specific CRM training data do not exist, training sets from Drosophila can facilitate CRM discovery in diverged insects. We validate in vivo over a dozen new CRMs, roughly doubling the number of known CRMs in the four non-Drosophila species. Given the growing wealth of Drosophila CRM annotation, these results suggest that extensive regulatory sequence annotation will be possible in newly sequenced insects without recourse to costly and labor-intensive genome-scale experiments. We develop a new method, Regulus, which computes a probabilistic score of similarity based on binding site composition (despite the absence of nucleotide-level sequence alignment), and demonstrate similarity between functionally related CRMs from orthologous loci. Our work represents an important step toward being able to trace the evolutionary history of gene regulatory networks and defining the mechanisms underlying insect evolution. PMID:25173756

Evidence for deep regulatory similarities in early developmental programs across highly diverged insects.

PubMed

Kazemian, Majid; Suryamohan, Kushal; Chen, Jia-Yu; Zhang, Yinan; Samee, Md Abul Hassan; Halfon, Marc S; Sinha, Saurabh

2014-09-01

Many genes familiar from Drosophila development, such as the so-called gap, pair-rule, and segment polarity genes, play important roles in the development of other insects and in many cases appear to be deployed in a similar fashion, despite the fact that Drosophila-like "long germband" development is highly derived and confined to a subset of insect families. Whether or not these similarities extend to the regulatory level is unknown. Identification of regulatory regions beyond the well-studied Drosophila has been challenging as even within the Diptera (flies, including mosquitoes) regulatory sequences have diverged past the point of recognition by standard alignment methods. Here, we demonstrate that methods we previously developed for computational cis-regulatory module (CRM) discovery in Drosophila can be used effectively in highly diverged (250-350 Myr) insect species including Anopheles gambiae, Tribolium castaneum, Apis mellifera, and Nasonia vitripennis. In Drosophila, we have successfully used small sets of known CRMs as "training data" to guide the search for other CRMs with related function. We show here that although species-specific CRM training data do not exist, training sets from Drosophila can facilitate CRM discovery in diverged insects. We validate in vivo over a dozen new CRMs, roughly doubling the number of known CRMs in the four non-Drosophila species. Given the growing wealth of Drosophila CRM annotation, these results suggest that extensive regulatory sequence annotation will be possible in newly sequenced insects without recourse to costly and labor-intensive genome-scale experiments. We develop a new method, Regulus, which computes a probabilistic score of similarity based on binding site composition (despite the absence of nucleotide-level sequence alignment), and demonstrate similarity between functionally related CRMs from orthologous loci. Our work represents an important step toward being able to trace the evolutionary history of gene regulatory networks and defining the mechanisms underlying insect evolution. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

The promises and pitfalls of RNA-interference-based therapeutics

PubMed Central

Castanotto, Daniela; Rossi, John J.

2009-01-01

The discovery that gene expression can be controlled by the Watson–Crick base-pairing of small RNAs with messenger RNAs containing complementary sequence — a process known as RNA interference — has markedly advanced our understanding of eukaryotic gene regulation and function. The ability of short RNA sequences to modulate gene expression has provided a powerful tool with which to study gene function and is set to revolutionize the treatment of disease. Remarkably, despite being just one decade from its discovery, the phenomenon is already being used therapeutically in human clinical trials, and biotechnology companies that focus on RNA-interference-based therapeutics are already publicly traded. PMID:19158789

Hiding deep in the trees: discovery of divergent mitochondrial lineages in Malagasy chameleons of the Calumma nasutum group

PubMed Central

Gehring, Philip-Sebastian; Tolley, Krystal A; Eckhardt, Falk Sebastian; Townsend, Ted M; Ziegler, Thomas; Ratsoavina, Fanomezana; Glaw, Frank; Vences, Miguel

2012-01-01

We conducted a comprehensive molecular phylogenetic study for a group of chameleons from Madagascar (Chamaeleonidae: Calumma nasutum group, comprising seven nominal species) to examine the genetic and species diversity in this widespread genus. Based on DNA sequences of the mitochondrial gene (ND2) from 215 specimens, we reconstructed the phylogeny using a Bayesian approach. Our results show deep divergences among several unnamed mitochondrial lineages that are difficult to identify morphologically. We evaluated lineage diversification using a number of statistical phylogenetic methods (general mixed Yule-coalescent model; SpeciesIdentifier; net p-distances) to objectively delimit lineages that we here consider as operational taxonomic units (OTUs), and for which the taxonomic status remains largely unknown. In addition, we compared molecular and morphological differentiation in detail for one particularly diverse clade (the C. boettgeri complex) from northern Madagascar. To assess the species boundaries within this group we used an integrative taxonomic approach, combining evidence from two independent molecular markers (ND2 and CMOS), together with genital and other external morphological characters, and conclude that some of the newly discovered OTUs are separate species (confirmed candidate species, CCS), while others should best be considered as deep conspecific lineages (DCLs). Our analysis supports a total of 33 OTUs, of which seven correspond to described species, suggesting that the taxonomy of the C. nasutum group is in need of revision. PMID:22957155

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

PubMed

Yildirim, Özal

2018-05-01

Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems. Copyright © 2018 Elsevier Ltd. All rights reserved.

A DNA Barcode Library for North American Pyraustinae (Lepidoptera: Pyraloidea: Crambidae).

PubMed

Yang, Zhaofu; Landry, Jean-François; Hebert, Paul D N

2016-01-01

Although members of the crambid subfamily Pyraustinae are frequently important crop pests, their identification is often difficult because many species lack conspicuous diagnostic morphological characters. DNA barcoding employs sequence diversity in a short standardized gene region to facilitate specimen identifications and species discovery. This study provides a DNA barcode reference library for North American pyraustines based upon the analysis of 1589 sequences recovered from 137 nominal species, 87% of the fauna. Data from 125 species were barcode compliant (>500bp, <1% n), and 99 of these taxa formed a distinct cluster that was assigned to a single BIN. The other 26 species were assigned to 56 BINs, reflecting frequent cases of deep intraspecific sequence divergence and a few instances of barcode sharing, creating a total of 155 BINs. Two systems for OTU designation, ABGD and BIN, were examined to check the correspondence between current taxonomy and sequence clusters. The BIN system performed better than ABGD in delimiting closely related species, while OTU counts with ABGD were influenced by the value employed for relative gap width. Different species with low or no interspecific divergence may represent cases of unrecognized synonymy, whereas those with high intraspecific divergence require further taxonomic scrutiny as they may involve cryptic diversity. The barcode library developed in this study will also help to advance understanding of relationships among species of Pyraustinae.

Focused Crawling of the Deep Web Using Service Class Descriptions

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

Rocco, D; Liu, L; Critchlow, T

2004-06-21

Dynamic Web data sources--sometimes known collectively as the Deep Web--increase the utility of the Web by providing intuitive access to data repositories anywhere that Web access is available. Deep Web services provide access to real-time information, like entertainment event listings, or present a Web interface to large databases or other data repositories. Recent studies suggest that the size and growth rate of the dynamic Web greatly exceed that of the static Web, yet dynamic content is often ignored by existing search engine indexers owing to the technical challenges that arise when attempting to search the Deep Web. To address thesemore » challenges, we present DynaBot, a service-centric crawler for discovering and clustering Deep Web sources offering dynamic content. DynaBot has three unique characteristics. First, DynaBot utilizes a service class model of the Web implemented through the construction of service class descriptions (SCDs). Second, DynaBot employs a modular, self-tuning system architecture for focused crawling of the DeepWeb using service class descriptions. Third, DynaBot incorporates methods and algorithms for efficient probing of the Deep Web and for discovering and clustering Deep Web sources and services through SCD-based service matching analysis. Our experimental results demonstrate the effectiveness of the service class discovery, probing, and matching algorithms and suggest techniques for efficiently managing service discovery in the face of the immense scale of the Deep Web.« less

A new monster from southwest Oregon forests: Cryptomaster behemoth sp. n. (Opiliones, Laniatores, Travunioidea)

PubMed Central

Starrett, James; Derkarabetian, Shahan; Richart, Casey H.; Cabrero, Allan; Hedin, Marshal

2016-01-01

Abstract The monotypic genus Cryptomaster Briggs, 1969 was described based on individuals from a single locality in southwestern Oregon. The described species Cryptomaster leviathan Briggs, 1969 was named for its large body size compared to most travunioid Laniatores. However, as the generic name suggests, Cryptomaster are notoriously difficult to find, and few subsequent collections have been recorded for this genus. Here, we increase sampling of Cryptomaster to 15 localities, extending their known range from the Coast Range northeast to the western Cascade Mountains of southern Oregon. Phylogenetic analyses of mitochondrial and nuclear DNA sequence data reveal deep phylogenetic breaks consistent with independently evolving lineages. We use discovery and validation species delimitation approaches to generate and test species hypotheses, including a coalescent species delimitation method to test multi-species hypotheses. For delimited species, we use light microscopy and SEM to discover diagnostic morphological characters. Although Cryptomaster has a small geographic distribution, this taxon is consistent with other short-range endemics in having deep phylogenetic breaks indicative of species level divergences. Herein we describe Cryptomaster behemoth sp. n., and provide morphological diagnostic characters for identifying Cryptomaster leviathan and Cryptomaster behemoth. PMID:26877685

Loeffler 4.0: Diagnostic Metagenomics.

PubMed

Höper, Dirk; Wylezich, Claudia; Beer, Martin

2017-01-01

A new world of possibilities for "virus discovery" was opened up with high-throughput sequencing becoming available in the last decade. While scientifically metagenomic analysis was established before the start of the era of high-throughput sequencing, the availability of the first second-generation sequencers was the kick-off for diagnosticians to use sequencing for the detection of novel pathogens. Today, diagnostic metagenomics is becoming the standard procedure for the detection and genetic characterization of new viruses or novel virus variants. Here, we provide an overview about technical considerations of high-throughput sequencing-based diagnostic metagenomics together with selected examples of "virus discovery" for animal diseases or zoonoses and metagenomics for food safety or basic veterinary research. © 2017 Elsevier Inc. All rights reserved.

LookSeq: a browser-based viewer for deep sequencing data.

PubMed

Manske, Heinrich Magnus; Kwiatkowski, Dominic P

2009-11-01

Sequencing a genome to great depth can be highly informative about heterogeneity within an individual or a population. Here we address the problem of how to visualize the multiple layers of information contained in deep sequencing data. We propose an interactive AJAX-based web viewer for browsing large data sets of aligned sequence reads. By enabling seamless browsing and fast zooming, the LookSeq program assists the user to assimilate information at different levels of resolution, from an overview of a genomic region to fine details such as heterogeneity within the sample. A specific problem, particularly if the sample is heterogeneous, is how to depict information about structural variation. LookSeq provides a simple graphical representation of paired sequence reads that is more revealing about potential insertions and deletions than are conventional methods.

VIP: an integrated pipeline for metagenomics of virus identification and discovery

PubMed Central

Li, Yang; Wang, Hao; Nie, Kai; Zhang, Chen; Zhang, Yi; Wang, Ji; Niu, Peihua; Ma, Xuejun

2016-01-01

Identification and discovery of viruses using next-generation sequencing technology is a fast-developing area with potential wide application in clinical diagnostics, public health monitoring and novel virus discovery. However, tremendous sequence data from NGS study has posed great challenge both in accuracy and velocity for application of NGS study. Here we describe VIP (“Virus Identification Pipeline”), a one-touch computational pipeline for virus identification and discovery from metagenomic NGS data. VIP performs the following steps to achieve its goal: (i) map and filter out background-related reads, (ii) extensive classification of reads on the basis of nucleotide and remote amino acid homology, (iii) multiple k-mer based de novo assembly and phylogenetic analysis to provide evolutionary insight. We validated the feasibility and veracity of this pipeline with sequencing results of various types of clinical samples and public datasets. VIP has also contributed to timely virus diagnosis (~10 min) in acutely ill patients, demonstrating its potential in the performance of unbiased NGS-based clinical studies with demand of short turnaround time. VIP is released under GPLv3 and is available for free download at: https://github.com/keylabivdc/VIP. PMID:27026381

The Revolution Continues: Newly Discovered Systems Expand the CRISPR-Cas Toolkit.

PubMed

Murugan, Karthik; Babu, Kesavan; Sundaresan, Ramya; Rajan, Rakhi; Sashital, Dipali G

2017-10-05

CRISPR-Cas systems defend prokaryotes against bacteriophages and mobile genetic elements and serve as the basis for revolutionary tools for genetic engineering. Class 2 CRISPR-Cas systems use single Cas endonucleases paired with guide RNAs to cleave complementary nucleic acid targets, enabling programmable sequence-specific targeting with minimal machinery. Recent discoveries of previously unidentified CRISPR-Cas systems have uncovered a deep reservoir of potential biotechnological tools beyond the well-characterized Type II Cas9 systems. Here we review the current mechanistic understanding of newly discovered single-protein Cas endonucleases. Comparison of these Cas effectors reveals substantial mechanistic diversity, underscoring the phylogenetic divergence of related CRISPR-Cas systems. This diversity has enabled further expansion of CRISPR-Cas biotechnological toolkits, with wide-ranging applications from genome editing to diagnostic tools based on various Cas endonuclease activities. These advances highlight the exciting prospects for future tools based on the continually expanding set of CRISPR-Cas systems. Copyright © 2017 Elsevier Inc. All rights reserved.

MicroRNA-based biotechnology for plant improvement.

PubMed

Zhang, Baohong; Wang, Qinglian

2015-01-01

MicroRNAs (miRNAs) are an extensive class of newly discovered endogenous small RNAs, which negatively regulate gene expression at the post-transcription levels. As the application of next-generation deep sequencing and advanced bioinformatics, the miRNA-related study has been expended to non-model plant species and the number of identified miRNAs has dramatically increased in the past years. miRNAs play a critical role in almost all biological and metabolic processes, and provide a unique strategy for plant improvement. Here, we first briefly review the discovery, history, and biogenesis of miRNAs, then focus more on the application of miRNAs on plant breeding and the future directions. Increased plant biomass through controlling plant development and phase change has been one achievement for miRNA-based biotechnology; plant tolerance to abiotic and biotic stress was also significantly enhanced by regulating the expression of an individual miRNA. Both endogenous and artificial miRNAs may serve as important tools for plant improvement. © 2014 Wiley Periodicals, Inc.

Discovery and small RNA profile of Pecan mosaic-associated virus, a novel potyvirus of pecan trees.

PubMed

Su, Xiu; Fu, Shuai; Qian, Yajuan; Zhang, Liqin; Xu, Yi; Zhou, Xueping

2016-05-26

A novel potyvirus was discovered in pecan (Carya illinoensis) showing leaf mosaic symptom through the use of deep sequencing of small RNAs. The complete genome of this virus was determined to comprise of 9,310 nucleotides (nt), and shared 24.0% to 58.9% nucleotide similarities with that of other Potyviridae viruses. The genome was deduced to encode a single open reading frame (polyprotein) on the plus strand. Phylogenetic analysis based on the whole genome sequence and coat protein amino acid sequence showed that this virus is most closely related to Lettuce mosaic virus. Using electron microscopy, the typical Potyvirus filamentous particles were identified in infected pecan leaves with mosaic symptoms. Our results clearly show that this virus is a new member of the genus Potyvirus in the family Potyviridae. The virus is tentatively named Pecan mosaic-associated virus (PMaV). Additionally, profiling of the PMaV-derived small RNA (PMaV-sRNA) showed that the most abundant PMaV-sRNAs were 21-nt in length. There are several hotspots for small RNA production along the PMaV genome; two 21-nt PMaV-sRNAs starting at 811 nt and 610 nt of the minus-strand genome were highly repeated.

Discovery and small RNA profile of Pecan mosaic-associated virus, a novel potyvirus of pecan trees

PubMed Central

Su, Xiu; Fu, Shuai; Qian, Yajuan; Zhang, Liqin; Xu, Yi; Zhou, Xueping

2016-01-01

A novel potyvirus was discovered in pecan (Carya illinoensis) showing leaf mosaic symptom through the use of deep sequencing of small RNAs. The complete genome of this virus was determined to comprise of 9,310 nucleotides (nt), and shared 24.0% to 58.9% nucleotide similarities with that of other Potyviridae viruses. The genome was deduced to encode a single open reading frame (polyprotein) on the plus strand. Phylogenetic analysis based on the whole genome sequence and coat protein amino acid sequence showed that this virus is most closely related to Lettuce mosaic virus. Using electron microscopy, the typical Potyvirus filamentous particles were identified in infected pecan leaves with mosaic symptoms. Our results clearly show that this virus is a new member of the genus Potyvirus in the family Potyviridae. The virus is tentatively named Pecan mosaic-associated virus (PMaV). Additionally, profiling of the PMaV-derived small RNA (PMaV-sRNA) showed that the most abundant PMaV-sRNAs were 21-nt in length. There are several hotspots for small RNA production along the PMaV genome; two 21-nt PMaV-sRNAs starting at 811 nt and 610 nt of the minus-strand genome were highly repeated. PMID:27226228

Providing Multi-Page Data Extraction Services with XWRAPComposer

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

Liu, Ling; Zhang, Jianjun; Han, Wei

2008-04-30

Dynamic Web data sources – sometimes known collectively as the Deep Web – increase the utility of the Web by providing intuitive access to data repositories anywhere that Web access is available. Deep Web services provide access to real-time information, like entertainment event listings, or present a Web interface to large databases or other data repositories. Recent studies suggest that the size and growth rate of the dynamic Web greatly exceed that of the static Web, yet dynamic content is often ignored by existing search engine indexers owing to the technical challenges that arise when attempting to search the Deepmore » Web. To address these challenges, we present DYNABOT, a service-centric crawler for discovering and clustering Deep Web sources offering dynamic content. DYNABOT has three unique characteristics. First, DYNABOT utilizes a service class model of the Web implemented through the construction of service class descriptions (SCDs). Second, DYNABOT employs a modular, self-tuning system architecture for focused crawling of the Deep Web using service class descriptions. Third, DYNABOT incorporates methods and algorithms for efficient probing of the Deep Web and for discovering and clustering Deep Web sources and services through SCD-based service matching analysis. Our experimental results demonstrate the effectiveness of the service class discovery, probing, and matching algorithms and suggest techniques for efficiently managing service discovery in the face of the immense scale of the Deep Web.« less

Deep RNNs for video denoising

NASA Astrophysics Data System (ADS)

Chen, Xinyuan; Song, Li; Yang, Xiaokang

2016-09-01

Video denoising can be described as the problem of mapping from a specific length of noisy frames to clean one. We propose a deep architecture based on Recurrent Neural Network (RNN) for video denoising. The model learns a patch-based end-to-end mapping between the clean and noisy video sequences. It takes the corrupted video sequences as the input and outputs the clean one. Our deep network, which we refer to as deep Recurrent Neural Networks (deep RNNs or DRNNs), stacks RNN layers where each layer receives the hidden state of the previous layer as input. Experiment shows (i) the recurrent architecture through temporal domain extracts motion information and does favor to video denoising, and (ii) deep architecture have large enough capacity for expressing mapping relation between corrupted videos as input and clean videos as output, furthermore, (iii) the model has generality to learned different mappings from videos corrupted by different types of noise (e.g., Poisson-Gaussian noise). By training on large video databases, we are able to compete with some existing video denoising methods.

Complete genome sequence of 'Halanaeroarchaeum sulfurireducens' M27-SA2, a sulfur-reducing and acetate-oxidizing haloarchaeon from the deep-sea hypersaline anoxic lake Medee.

PubMed

Messina, Enzo; Sorokin, Dimitry Y; Kublanov, Ilya V; Toshchakov, Stepan; Lopatina, Anna; Arcadi, Erika; Smedile, Francesco; La Spada, Gina; La Cono, Violetta; Yakimov, Michail M

2016-01-01

Strain M27-SA2 was isolated from the deep-sea salt-saturated anoxic lake Medee, which represents one of the most hostile extreme environments on our planet. On the basis of physiological studies and phylogenetic positioning this extremely halophilic euryarchaeon belongs to a novel genus 'Halanaeroarchaeum' within the family Halobacteriaceae. All members of this genus cultivated so far are strict anaerobes using acetate as the sole carbon and energy source and elemental sulfur as electron acceptor. Here we report the complete genome sequence of the strain M27-SA2 which is composed of a 2,129,244-bp chromosome and a 124,256-bp plasmid. This is the second complete genome sequence within the genus Halanaeroarchaeum. We demonstrate that genome of 'Halanaeroarchaeum sulfurireducens' M27-SA2 harbors complete metabolic pathways for acetate and sulfur catabolism and for de novo biosynthesis of 19 amino acids. The genomic analysis also reveals that 'Halanaeroarchaeum sulfurireducens' M27-SA2 harbors two prophage loci and one CRISPR locus, highly similar to that of Kulunda Steppe (Altai, Russia) isolate 'H. sulfurireducens' HSR2(T). The discovery of sulfur-respiring acetate-utilizing haloarchaeon in deep-sea hypersaline anoxic lakes has certain significance for understanding the biogeochemical functioning of these harsh ecosystems, which are incompatible with life for common organisms. Moreover, isolations of Halanaeroarchaeum members from geographically distant salt-saturated sites of different origin suggest a high degree of evolutionary success in their adaptation to this type of extreme biotopes around the world.

Drilling through the Messinian evaporites: the beginning of a new adventure?

NASA Astrophysics Data System (ADS)

Bassetti, M. A.; Lofi, J.

2009-04-01

The sensitivity of past environments tell us a lot about the nature of changes, either of climatic or geodynamic origin. In this respect, the Mediterranean basin represents the ideal natural laboratory for studying the interaction between deep processes, tectonics, sedimentary fluxes and sea-level oscillation that are at the origin of the sedimentary records. A spectacular example of reactivity of this system have been experienced less than 6 Myrs ago, when the pan-Mediterranean realm underwent rapid and abrupt changes of paleo-environmental parameters that led to the well known Messinian Salinity Crisis (MSC, Hsü et al., 1973). This short-term event at the geological scale (~5.96-5.32 Ma) results from the progressive closure of the two-way connection between the Atlantic Ocean and the Mediterranean Sea. The most important characteristics of this event are: (1) a reduction of the Atlantic water supply having as a consequence, an increased salinity and in the precipitation of thick evaporites within shallow water marginal basins (presently disconnected from the deep basins); (2) a subsequent major sea-level fall exceeding 2000 m and resulting in the massive erosion of the margins and the development of deep subaerial canyons; (3) the accumulation of the product of the erosion in the downslope domain of the margins; (4) the deposition of thick evaporites (up to 3000 m thick) above the deep Mediterranean abyssal plains and (5) and a very rapid refilling of the Mediterranean basin during the Latest Miocene/Lower Pliocene, following the re-connection between Atlantic and Mediterranean through the Gibraltar straight. Timing, causes and chronology of the MSC are not yet fully understood, but different scenarii have been proposed to explain in details the modalities of this catastrophic event. Certainly, the ongoing discussion about not fully conclusive interpretations are mainly linked to the fact that so far, only the deepest and buried Mediterranean basins might offer the most complete sequence from the Messinian to the Quaternary. Anywhere else, the MSC mostly generated a sedimentary/time lag corresponding to a widespread erosion surface extending from onshore down to the lower slopes of the margins. Onland, Messinian outcrops (e.g. Morocco, Cyprus, Spain, Italy…) are all incomplete and pre-date the drawdown phase and/or are tectonically/geometrically disconnected from the deep basin sequence. Correlations with the offshore depositional units are thus complex, preventing the construction of a coherent scenario of the MSC linking the outcropping evaporites, the erosion of the margins, and the deposition of clastics and deep evaporites in the abyssal plains. The discovery of the Messinian evaporites in the Mediterranenan is probably one of the major achievements of the DSDP program. Unfortunately, the Joides Resolution never drilled through evaporites because of technical impossibility (non-riser drilling vessel). Only the upper few meters of the pinch out of the deep basin sequence has been recovered. Thus, all hypothesis are based on onland outcropping evaporites and offshore seismic data interpretations. Improved quality of seismic data allowed some important advances in the recognition and understanding of Messinian markers (erosion surfaces, depositional units and bounding surfaces) but without the recovery of the full succession, all interpretations lack lithological and stratigraphical calibrations. At present, several basic questions are still open: - What are the true nature of the deep basin depositional units? What are their ages and chronologies? - What was the water depth before, during and after halite deposition in the deep basin? Did the basin(s) completely dried out? What are the associated amplitude and dynamics of the base-level changes? - Did the desiccation impact the regional climate and river run-off? What about climatic variability during the drawdown phase? - What was the balance between erosion and sedimentation during the crisis? What are the vertical movements (tectonic/isostatic responses) associated to margin unloading and basin loading? - What are the present-day fluid dynamics related to the salt layer? Their impact on the deep biosphere? The response to all of these questions would only come from drilling through the complete Messinian succession. It would represent an outstanding opportunity to unravel the history of extreme environmental changes during the Messinian and a unique chance to constrain the age, nature and paleo-environment of deposition of the deep-basin Messinian sequence. For that reason, in the framework of the IODP drilling program, we propose to sample and log two different sites in the western and eastern Mediterranean basins, with the new scientific riser drillship Chikyu perfectly adapted to overcome all safety problems. In order to promote a continuous sedimentary record of the MSC since the pre-crisis paleo-environmental changes, the sites should be drilled in areas where the Messinian salt is tabular and exempted of significant tectonic influence. A complete set of integrated studies (sedimentology, geochemistry, micropaleontology, bio-and cyclostratigraphy) should be carried out. This project opens the perspective of a new intellectual and scientific adventure that we expect to be as rich and exciting as the discovery of this unusual event was.

The Expanding Family of Virophages.

PubMed

Bekliz, Meriem; Colson, Philippe; La Scola, Bernard

2016-11-23

Virophages replicate with giant viruses in the same eukaryotic cells. They are a major component of the specific mobilome of mimiviruses. Since their discovery in 2008, five other representatives have been isolated, 18 new genomes have been described, two of which being nearly completely sequenced, and they have been classified in a new viral family, Lavidaviridae . Virophages are small viruses with approximately 35-74 nm large icosahedral capsids and 17-29 kbp large double-stranded DNA genomes with 16-34 genes, among which a very small set is shared with giant viruses. Virophages have been isolated or detected in various locations and in a broad range of habitats worldwide, including the deep ocean and inland. Humans, therefore, could be commonly exposed to virophages, although currently limited evidence exists of their presence in humans based on serology and metagenomics. The distribution of virophages, the consequences of their infection and the interactions with their giant viral hosts within eukaryotic cells deserve further research.

Pharmacological Potential of Phylogenetically Diverse Actinobacteria Isolated from Deep-Sea Coral Ecosystems of the Submarine Avilés Canyon in the Cantabrian Sea.

PubMed

Sarmiento-Vizcaíno, Aida; González, Verónica; Braña, Alfredo F; Palacios, Juan J; Otero, Luis; Fernández, Jonathan; Molina, Axayacatl; Kulik, Andreas; Vázquez, Fernando; Acuña, José L; García, Luis A; Blanco, Gloria

2017-02-01

Marine Actinobacteria are emerging as an unexplored source for natural product discovery. Eighty-seven deep-sea coral reef invertebrates were collected during an oceanographic expedition at the submarine Avilés Canyon (Asturias, Spain) in a range of 1500 to 4700 m depth. From these, 18 cultivable bioactive Actinobacteria were isolated, mainly from corals, phylum Cnidaria, and some specimens of phyla Echinodermata, Porifera, Annelida, Arthropoda, Mollusca and Sipuncula. As determined by 16S rRNA sequencing and phylogenetic analyses, all isolates belong to the phylum Actinobacteria, mainly to the Streptomyces genus and also to Micromonospora, Pseudonocardia and Myceligenerans. Production of bioactive compounds of pharmacological interest was investigated by high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS) techniques and subsequent database comparison. Results reveal that deep-sea isolated Actinobacteria display a wide repertoire of secondary metabolite production with a high chemical diversity. Most identified products (both diffusible and volatiles) are known by their contrasted antibiotic or antitumor activities. Bioassays with ethyl acetate extracts from isolates displayed strong antibiotic activities against a panel of important resistant clinical pathogens, including Gram-positive and Gram-negative bacteria, as well as fungi, all of them isolated at two main hospitals (HUCA and Cabueñes) from the same geographical region. The identity of the active extracts components of these producing Actinobacteria is currently being investigated, given its potential for the discovery of pharmaceuticals and other products of biotechnological interest.

Deep Impact Sequence Planning Using Multi-Mission Adaptable Planning Tools With Integrated Spacecraft Models

NASA Technical Reports Server (NTRS)

Wissler, Steven S.; Maldague, Pierre; Rocca, Jennifer; Seybold, Calina

2006-01-01

The Deep Impact mission was ambitious and challenging. JPL's well proven, easily adaptable multi-mission sequence planning tools combined with integrated spacecraft subsystem models enabled a small operations team to develop, validate, and execute extremely complex sequence-based activities within very short development times. This paper focuses on the core planning tool used in the mission, APGEN. It shows how the multi-mission design and adaptability of APGEN made it possible to model spacecraft subsystems as well as ground assets throughout the lifecycle of the Deep Impact project, starting with models of initial, high-level mission objectives, and culminating in detailed predictions of spacecraft behavior during mission-critical activities.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

PubMed

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

Applications of Deep Learning in Biomedicine.

PubMed

Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

2016-05-02

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

Discovery of asphalt seeps in the deep Southwest Atlantic off Brazil

NASA Astrophysics Data System (ADS)

Fujikura, Katsunori; Yamanaka, Toshiro; Sumida, Paulo Y. G.; Bernardino, Angelo F.; Pereira, Olivia S.; Kanehara, Toshiyuki; Nagano, Yuriko; Nakayama, Cristina R.; Nobrega, Marcos; Pellizari, Vivian H.; Shigeno, Shuichi; Yoshida, Takao; Zhang, Jing; Kitazato, Hiroshi

2017-12-01

The discovery and description of cold seeps with deep-sea chemosynthetic communities in the Southwest Atlantic Ocean are still incomplete, despite the large proven oil and gas reserves off the coast of Brazil. In the southeastern Brazilian continental margin, where over 71% of the country's oil and gas production takes place, there are previous geological and qualitative biological evidence of seep biota associated with pockmarks on the upper slope of the Santos Basin. In order to further study seep ecosystems on the Brazilian margin, a deep-sea investigation named Iatá-Piúna cruise was conducted using the human-occupied vehicle Shinkai 6500 off Brazil's southeast continental margin. Asphalt seeps were discovered on the seafloor of the North São Paulo Plateau from depths of 2652-2752 m, representing only the third discovery of this type of seep worldwide, following those in the Gulf of Mexico and off Angola. Video and isotopic analyses indicated a number of megabenthic animals in the asphalt seeps in the North São Paulo Plateau and revealed typical deep-sea heterotrophic and photosynthesis-based fauna occupying hard substrates provided by the asphalt seep. There was no evidence of chemosynthesis-based megabenthic fauna such as vesicomyid clams, Bathymodiolus mussels, and siboglinid tube worms, or any sediment bacterial mats, gas seepage, and carbonate rock in/around the seeps. The benthic fauna was composed mainly of sponges (ca. 15 species), such as the hexactinellids Caulophacus sp., Poliopogon amadou, Saccocalyx pedunculatus, Farrea occa and cf. Chonelasma choanoides; besides typical deep-sea isidid octocorals, brisingid starfishes and galatheid crabs. The δ13C values of poriferan sponges suggested a heterotrophic and pelagic nutrition. Geochemical analyses of asphalt revealed a heavy biodegradation of hydrocarbon molecules, supported by the depletion of light n-alkanes and other labile compounds. This advanced asphalt biodegradation is the likely reason for the absence of chemosynthetic communities at these seep sites.

Guiding principles for peptide nanotechnology through directed discovery.

PubMed

Lampel, A; Ulijn, R V; Tuttle, T

2018-05-21

Life's diverse molecular functions are largely based on only a small number of highly conserved building blocks - the twenty canonical amino acids. These building blocks are chemically simple, but when they are organized in three-dimensional structures of tremendous complexity, new properties emerge. This review explores recent efforts in the directed discovery of functional nanoscale systems and materials based on these same amino acids, but that are not guided by copying or editing biological systems. The review summarises insights obtained using three complementary approaches of searching the sequence space to explore sequence-structure relationships for assembly, reactivity and complexation, namely: (i) strategic editing of short peptide sequences; (ii) computational approaches to predicting and comparing assembly behaviours; (iii) dynamic peptide libraries that explore the free energy landscape. These approaches give rise to guiding principles on controlling order/disorder, complexation and reactivity by peptide sequence design.

k-merSNP discovery: Software for alignment-and reference-free scalable SNP discovery, phylogenetics, and annotation for hundreds of microbial genomes

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

With the flood of whole genome finished and draft microbial sequences, we need faster, more scalable bioinformatics tools for sequence comparison. An algorithm is described to find single nucleotide polymorphisms (SNPs) in whole genome data. It scales to hundreds of bacterial or viral genomes, and can be used for finished and/or draft genomes available as unassembled contigs or raw, unassembled reads. The method is fast to compute, finding SNPs and building a SNP phylogeny in minutes to hours, depending on the size and diversity of the input sequences. The SNP-based trees that result are consistent with known taxonomy and treesmore » determined in other studies. The approach we describe can handle many gigabases of sequence in a single run. The algorithm is based on k-mer analysis.« less

The third annual BRDS on research and development of nucleic acid-based nanomedicines

PubMed Central

Chaudhary, Amit Kumar

2017-01-01

The completion of human genome project, decrease in the sequencing cost, and correlation of genome sequencing data with specific diseases led to the exponential rise in the nucleic acid-based therapeutic approaches. In the third annual Biopharmaceutical Research and Development Symposium (BRDS) held at the Center for Drug Discovery and Lozier Center for Pharmacy Sciences and Education at the University of Nebraska Medical Center (UNMC), we highlighted the remarkable features of the nucleic acid-based nanomedicines, their significance, NIH funding opportunities on nanomedicines and gene therapy research, challenges and opportunities in the clinical translation of nucleic acids into therapeutics, and the role of intellectual property (IP) in drug discovery and development. PMID:27848223

Discovering discovery patterns with Predication-based Semantic Indexing.

PubMed

Cohen, Trevor; Widdows, Dominic; Schvaneveldt, Roger W; Davies, Peter; Rindflesch, Thomas C

2012-12-01

In this paper we utilize methods of hyperdimensional computing to mediate the identification of therapeutically useful connections for the purpose of literature-based discovery. Our approach, named Predication-based Semantic Indexing, is utilized to identify empirically sequences of relationships known as "discovery patterns", such as "drug x INHIBITS substance y, substance y CAUSES disease z" that link pharmaceutical substances to diseases they are known to treat. These sequences are derived from semantic predications extracted from the biomedical literature by the SemRep system, and subsequently utilized to direct the search for known treatments for a held out set of diseases. Rapid and efficient inference is accomplished through the application of geometric operators in PSI space, allowing for both the derivation of discovery patterns from a large set of known TREATS relationships, and the application of these discovered patterns to constrain search for therapeutic relationships at scale. Our results include the rediscovery of discovery patterns that have been constructed manually by other authors in previous research, as well as the discovery of a set of previously unrecognized patterns. The application of these patterns to direct search through PSI space results in better recovery of therapeutic relationships than is accomplished with models based on distributional statistics alone. These results demonstrate the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships, suggesting a role of these methods in drug repurposing efforts. In addition, the results provide strong support for the utility of the discovery pattern approach pioneered by Hristovski and his colleagues. Copyright © 2012 Elsevier Inc. All rights reserved.

Discovering discovery patterns with predication-based Semantic Indexing

PubMed Central

Cohen, Trevor; Widdows, Dominic; Schvaneveldt, Roger W.; Davies, Peter; Rindflesch, Thomas C.

2012-01-01

In this paper we utilize methods of hyperdimensional computing to mediate the identification of therapeutically useful connections for the purpose of literature-based discovery. Our approach, named Predication-based Semantic Indexing, is utilized to identify empirically sequences of relationships known as “discovery patterns”, such as “drug x INHIBITS substance y, substance y CAUSES disease z” that link pharmaceutical substances to diseases they are known to treat. These sequences are derived from semantic predications extracted from the biomedical literature by the SemRep system, and subsequently utilized to direct the search for known treatments for a held out set of diseases. Rapid and efficient inference is accomplished through the application of geometric operators in PSI space, allowing for both the derivation of discovery patterns from a large set of known TREATS relationships, and the application of these discovered patterns to constrain search for therapeutic relationships at scale. Our results include the rediscovery of discovery patterns that have been constructed manually by other authors in previous research, as well as the discovery of a set of previously unrecognized patterns. The application of these patterns to direct search through PSI space results in better recovery of therapeutic relationships than is accomplished with models based on distributional statistics alone. These results demonstrate the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships, suggesting a role of these methods in drug repurposing efforts. In addition, the results provide strong support for the utility of the discovery pattern approach pioneered by Hristovski and his colleagues. PMID:22841748

Molecular Networking and Pattern-Based Genome Mining Improves Discovery of Biosynthetic Gene Clusters and their Products from Salinispora Species

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

Duncan, Katherine R.; Crüsemann, Max; Lechner, Anna

Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. In this paper, we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains, including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated themore » identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. Finally, these efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches.« less

Molecular Networking and Pattern-Based Genome Mining Improves Discovery of Biosynthetic Gene Clusters and their Products from Salinispora Species

DOE PAGES

Duncan, Katherine R.; Crüsemann, Max; Lechner, Anna; ...

2015-04-09

Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. In this paper, we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains, including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated themore » identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. Finally, these efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches.« less

Molecular Networking and Pattern-Based Genome Mining Improves discovery of biosynthetic gene clusters and their products from Salinispora species

PubMed Central

Duncan, Katherine R.; Crüsemann, Max; Lechner, Anna; Sarkar, Anindita; Li, Jie; Ziemert, Nadine; Wang, Mingxun; Bandeira, Nuno; Moore, Bradley S.; Dorrestein, Pieter C.; Jensen, Paul R.

2015-01-01

Summary Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. Here we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated the identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. These efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches. PMID:25865308

Geoseq: a tool for dissecting deep-sequencing datasets.

PubMed

Gurtowski, James; Cancio, Anthony; Shah, Hardik; Levovitz, Chaya; George, Ajish; Homann, Robert; Sachidanandam, Ravi

2010-10-12

Datasets generated on deep-sequencing platforms have been deposited in various public repositories such as the Gene Expression Omnibus (GEO), Sequence Read Archive (SRA) hosted by the NCBI, or the DNA Data Bank of Japan (ddbj). Despite being rich data sources, they have not been used much due to the difficulty in locating and analyzing datasets of interest. Geoseq http://geoseq.mssm.edu provides a new method of analyzing short reads from deep sequencing experiments. Instead of mapping the reads to reference genomes or sequences, Geoseq maps a reference sequence against the sequencing data. It is web-based, and holds pre-computed data from public libraries. The analysis reduces the input sequence to tiles and measures the coverage of each tile in a sequence library through the use of suffix arrays. The user can upload custom target sequences or use gene/miRNA names for the search and get back results as plots and spreadsheet files. Geoseq organizes the public sequencing data using a controlled vocabulary, allowing identification of relevant libraries by organism, tissue and type of experiment. Analysis of small sets of sequences against deep-sequencing datasets, as well as identification of public datasets of interest, is simplified by Geoseq. We applied Geoseq to, a) identify differential isoform expression in mRNA-seq datasets, b) identify miRNAs (microRNAs) in libraries, and identify mature and star sequences in miRNAS and c) to identify potentially mis-annotated miRNAs. The ease of using Geoseq for these analyses suggests its utility and uniqueness as an analysis tool.

Sensitive Deep-Sequencing-Based HIV-1 Genotyping Assay To Simultaneously Determine Susceptibility to Protease, Reverse Transcriptase, Integrase, and Maturation Inhibitors, as Well as HIV-1 Coreceptor Tropism

PubMed Central

Gibson, Richard M.; Meyer, Ashley M.; Winner, Dane; Archer, John; Feyertag, Felix; Ruiz-Mateos, Ezequiel; Leal, Manuel; Robertson, David L.; Schmotzer, Christine L.

2014-01-01

With 29 individual antiretroviral drugs available from six classes that are approved for the treatment of HIV-1 infection, a combination of different phenotypic and genotypic tests is currently needed to monitor HIV-infected individuals. In this study, we developed a novel HIV-1 genotypic assay based on deep sequencing (DeepGen HIV) to simultaneously assess HIV-1 susceptibilities to all drugs targeting the three viral enzymes and to predict HIV-1 coreceptor tropism. Patient-derived gag-p2/NCp7/p1/p6/pol-PR/RT/IN- and env-C2V3 PCR products were sequenced using the Ion Torrent Personal Genome Machine. Reads spanning the 3′ end of the Gag, protease (PR), reverse transcriptase (RT), integrase (IN), and V3 regions were extracted, truncated, translated, and assembled for genotype and HIV-1 coreceptor tropism determination. DeepGen HIV consistently detected both minority drug-resistant viruses and non-R5 HIV-1 variants from clinical specimens with viral loads of ≥1,000 copies/ml and from B and non-B subtypes. Additional mutations associated with resistance to PR, RT, and IN inhibitors, previously undetected by standard (Sanger) population sequencing, were reliably identified at frequencies as low as 1%. DeepGen HIV results correlated with phenotypic (original Trofile, 92%; enhanced-sensitivity Trofile assay [ESTA], 80%; TROCAI, 81%; and VeriTrop, 80%) and genotypic (population sequencing/Geno2Pheno with a 10% false-positive rate [FPR], 84%) HIV-1 tropism test results. DeepGen HIV (83%) and Trofile (85%) showed similar concordances with the clinical response following an 8-day course of maraviroc monotherapy (MCT). In summary, this novel all-inclusive HIV-1 genotypic and coreceptor tropism assay, based on deep sequencing of the PR, RT, IN, and V3 regions, permits simultaneous multiplex detection of low-level drug-resistant and/or non-R5 viruses in up to 96 clinical samples. This comprehensive test, the first of its class, will be instrumental in the development of new antiretroviral drugs and, more importantly, will aid in the treatment and management of HIV-infected individuals. PMID:24468782

Deep sequencing of evolving pathogen populations: applications, errors, and bioinformatic solutions

PubMed Central

2014-01-01

Deep sequencing harnesses the high throughput nature of next generation sequencing technologies to generate population samples, treating information contained in individual reads as meaningful. Here, we review applications of deep sequencing to pathogen evolution. Pioneering deep sequencing studies from the virology literature are discussed, such as whole genome Roche-454 sequencing analyses of the dynamics of the rapidly mutating pathogens hepatitis C virus and HIV. Extension of the deep sequencing approach to bacterial populations is then discussed, including the impacts of emerging sequencing technologies. While it is clear that deep sequencing has unprecedented potential for assessing the genetic structure and evolutionary history of pathogen populations, bioinformatic challenges remain. We summarise current approaches to overcoming these challenges, in particular methods for detecting low frequency variants in the context of sequencing error and reconstructing individual haplotypes from short reads. PMID:24428920

Counting of oligomers in sequences generated by markov chains for DNA motif discovery.

PubMed

Shan, Gao; Zheng, Wei-Mou

2009-02-01

By means of the technique of the imbedded Markov chain, an efficient algorithm is proposed to exactly calculate first, second moments of word counts and the probability for a word to occur at least once in random texts generated by a Markov chain. A generating function is introduced directly from the imbedded Markov chain to derive asymptotic approximations for the problem. Two Z-scores, one based on the number of sequences with hits and the other on the total number of word hits in a set of sequences, are examined for discovery of motifs on a set of promoter sequences extracted from A. thaliana genome. Source code is available at http://www.itp.ac.cn/zheng/oligo.c.

Deep Molecular Diversity of Mammalian Synapses: Why It Matters and How to Measure It

PubMed Central

O’Rourke, Nancy A.; Weiler, Nick C.; Micheva, Kristina D.; Smith, Stephen J

2013-01-01

Summary Pioneering studies during the middle of the twentieth century revealed substantial diversity amongst mammalian chemical synapses and led to a widely accepted synapse type classification based on neurotransmitter molecule identity. Subsequently, powerful new physiological, genetic and structural methods have enabled the discovery of much deeper functional and molecular diversity within each traditional neurotransmitter type. Today, this deep diversity continues to pose both daunting challenges and exciting new opportunities for neuroscience. Our growing understanding of deep synapse diversity may transform how we think about and study neural circuit development, structure and function. PMID:22573027

Fungal diversity in deep-sea sediments of a hydrothermal vent system in the Southwest Indian Ridge

NASA Astrophysics Data System (ADS)

Xu, Wei; Gong, Lin-feng; Pang, Ka-Lai; Luo, Zhu-Hua

2018-01-01

Deep-sea hydrothermal sediment is known to support remarkably diverse microbial consortia. In deep sea environments, fungal communities remain less studied despite their known taxonomic and functional diversity. High-throughput sequencing methods have augmented our capacity to assess eukaryotic diversity and their functions in microbial ecology. Here we provide the first description of the fungal community diversity found in deep sea sediments collected at the Southwest Indian Ridge (SWIR) using culture-dependent and high-throughput sequencing approaches. A total of 138 fungal isolates were cultured from seven different sediment samples using various nutrient media, and these isolates were identified to 14 fungal taxa, including 11 Ascomycota taxa (7 genera) and 3 Basidiomycota taxa (2 genera) based on internal transcribed spacers (ITS1, ITS2 and 5.8S) of rDNA. Using illumina HiSeq sequencing, a total of 757,467 fungal ITS2 tags were recovered from the samples and clustered into 723 operational taxonomic units (OTUs) belonging to 79 taxa (Ascomycota and Basidiomycota contributed to 99% of all samples) based on 97% sequence similarity. Results from both approaches suggest that there is a high fungal diversity in the deep-sea sediments collected in the SWIR and fungal communities were shown to be slightly different by location, although all were collected from adjacent sites at the SWIR. This study provides baseline data of the fungal diversity and biogeography, and a glimpse to the microbial ecology associated with the deep-sea sediments of the hydrothermal vent system of the Southwest Indian Ridge.

Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning.

PubMed

Teng, Haotian; Cao, Minh Duc; Hall, Michael B; Duarte, Tania; Wang, Sheng; Coin, Lachlan J M

2018-05-01

Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

2016 Year in Review Video- NASA’s Marshall Space Flight Center

NASA Image and Video Library

2016-12-22

The work underway today at NASA’s Marshall Space Flight Center is making it possible to send humans beyond Earth’s orbit and into deep space on bold new missions of space exploration. Marshall teams are designing and building NASA’s Space Launch System, the most powerful rocket ever built and the only launch vehicle capable of launching human explorers to Mars. Using the International Space Station’s orbiting lab, Marshall flight controllers provided round-the-clock oversight of science experiments, supporting the first-ever DNA sequencing in space, pioneering 3-D printing capabilities and advancing human health research. Several successful New Frontiers deep-space robotic missions including OSIRIS-REx, New Horizons and Juno, made new discoveries and refined theories of the solar system. And Marshall collaborations with outside partners are yielding innovative technologies and solving technical challenges that are making the Journey to Mars a reality.

Low Data Drug Discovery with One-Shot Learning.

PubMed

Altae-Tran, Han; Ramsundar, Bharath; Pappu, Aneesh S; Pande, Vijay

2017-04-26

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. 2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016).

Discovery of a Novel Periodontal Disease-Associated Bacterium.

PubMed

Torres, Pedro J; Thompson, John; McLean, Jeffrey S; Kelley, Scott T; Edlund, Anna

2018-06-02

One of the world's most common infectious disease, periodontitis (PD), derives from largely uncharacterized communities of oral bacteria growing as biofilms (a.k.a. plaque) on teeth and gum surfaces in periodontal pockets. Bacteria associated with periodontal disease trigger inflammatory responses in immune cells, which in later stages of the disease cause loss of both soft and hard tissue structures supporting teeth. Thus far, only a handful of bacteria have been characterized as infectious agents of PD. Although deep sequencing technologies, such as whole community shotgun sequencing have the potential to capture a detailed picture of highly complex bacterial communities in any given environment, we still lack major reference genomes for the oral microbiome associated with PD and other diseases. In recent work, by using a combination of supervised machine learning and genome assembly, we identified a genome from a novel member of the Bacteroidetes phylum in periodontal samples. Here, by applying a comparative metagenomics read-classification approach, including 272 metagenomes from various human body sites, and our previously assembled draft genome of the uncultivated Candidatus Bacteroides periocalifornicus (CBP) bacterium, we show CBP's ubiquitous distribution in dental plaque, as well as its strong association with the well-known pathogenic "red complex" that resides in deep periodontal pockets.

Performance Evaluation of an Expanded Range XIPS Ion Thruster System for NASA Science Missions

NASA Technical Reports Server (NTRS)

Oh, David Y.; Goebel, Dan M.

2006-01-01

This paper examines the benefit that a solar electric propulsion (SEP) system based on the 5 kW Xenon Ion Propulsion System (XIPS) could have for NASA's Discovery class deep space missions. The relative cost and performance of the commercial heritage XIPS system is compared to NSTAR ion thruster based systems on three Discovery class reference missions: 1) a Near Earth Asteroid Sample Return, 2) a Comet Rendezvous and 3) a Main Belt Asteroid Rendezvous. It is found that systems utilizing a single operating XIPS thruster provides significant performance advantages over a single operating NSTAR thruster. In fact, XIPS performs as well as systems utilizing two operating NSTAR thrusters, and still costs less than the NSTAR system with a single operating thruster. This makes XIPS based SEP a competitive and attractive candidate for Discovery class science missions.

Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data.

PubMed

Lasko, Thomas A; Denny, Joshua C; Levy, Mia A

2013-01-01

Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don't think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data - Electronic Medical Records - typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data-driven phenotypes to expose unknown disease variants and subtypes and to provide rich targets for genetic association studies.

Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data

PubMed Central

Lasko, Thomas A.; Denny, Joshua C.; Levy, Mia A.

2013-01-01

Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don’t think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data – Electronic Medical Records – typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data-driven phenotypes to expose unknown disease variants and subtypes and to provide rich targets for genetic association studies. PMID:23826094

Instant Integrated Ultradeep Quantitative-structural Membrane Proteomics Discovered Post-translational Modification Signatures for Human Cys-loop Receptor Subunit Bias*

PubMed Central

Zhang, Xi

2016-01-01

Neurotransmitter ligand-gated ion channels (LGICs) are widespread and pivotal in brain functions. Unveiling their structure-function mechanisms is crucial to drive drug discovery, and demands robust proteomic quantitation of expression, post-translational modifications (PTMs) and dynamic structures. Yet unbiased digestion of these modified transmembrane proteins—at high efficiency and peptide reproducibility—poses the obstacle. Targeting both enzyme-substrate contacts and PTMs for peptide formation and detection, we devised flow-and-detergent-facilitated protease and de-PTM digestions for deep sequencing (FDD) method that combined omni-compatible detergent, tandem immobilized protease/PNGase columns, and Cys-selective reduction/alkylation, to achieve streamlined ultradeep peptide preparation within minutes not days, at high peptide reproducibility and low abundance-bias. FDD transformed enzyme-protein contacts into equal catalytic travel paths through enzyme-excessive columns regardless of protein abundance, removed products instantly preventing inhibition, tackled intricate structures via sequential multiple micro-digestions along the flow, and precisely controlled peptide formation by flow rate. Peptide-stage reactions reduced steric bias; low contamination deepened MS/MS scan; distinguishing disulfide from M oxidation and avoiding gain/loss artifacts unmasked protein-endogenous oxidation states. Using a recent interactome of 285-kDa human GABA type A receptor, this pilot study validated FDD platform's applicability to deep sequencing (up to 99% coverage), H/D-exchange and TMT-based structural mapping. FDD discovered novel subunit-specific PTM signatures, including unusual nontop-surface N-glycosylations, that may drive subunit biases in human Cys-loop LGIC assembly and pharmacology, by redefining subunit/ligand interfaces and connecting function domains. PMID:27073180

A natively paired antibody library yields drug leads with higher sensitivity and specificity than a randomly paired antibody library.

PubMed

Adler, Adam S; Bedinger, Daniel; Adams, Matthew S; Asensio, Michael A; Edgar, Robert C; Leong, Renee; Leong, Jackson; Mizrahi, Rena A; Spindler, Matthew J; Bandi, Srinivasa Rao; Huang, Haichun; Tawde, Pallavi; Brams, Peter; Johnson, David S

2018-04-01

Deep sequencing and single-chain variable fragment (scFv) yeast display methods are becoming more popular for discovery of therapeutic antibody candidates in mouse B cell repertoires. In this study, we compare a deep sequencing and scFv display method that retains native heavy and light chain pairing with a related method that randomly pairs heavy and light chain. We performed the studies in a humanized mouse, using interleukin 21 receptor (IL-21R) as a test immunogen. We identified 44 high-affinity binder scFv with the native pairing method and 100 high-affinity binder scFv with the random pairing method. 30% of the natively paired scFv binders were also discovered with the randomly paired method, and 13% of the randomly paired binders were also discovered with the natively paired method. Additionally, 33% of the scFv binders discovered only in the randomly paired library were initially present in the natively paired pre-sort library. Thus, a significant proportion of "randomly paired" scFv were actually natively paired. We synthesized and produced 46 of the candidates as full-length antibodies and subjected them to a panel of binding assays to characterize their therapeutic potential. 87% of the antibodies were verified as binding IL-21R by at least one assay. We found that antibodies with native light chains were more likely to bind IL-21R than antibodies with non-native light chains, suggesting a higher false positive rate for antibodies from the randomly paired library. Additionally, the randomly paired method failed to identify nearly half of the true natively paired binders, suggesting a higher false negative rate. We conclude that natively paired libraries have critical advantages in sensitivity and specificity for antibody discovery programs.

A natively paired antibody library yields drug leads with higher sensitivity and specificity than a randomly paired antibody library

PubMed Central

Adler, Adam S.; Bedinger, Daniel; Adams, Matthew S.; Asensio, Michael A.; Edgar, Robert C.; Leong, Renee; Leong, Jackson; Mizrahi, Rena A.; Spindler, Matthew J.; Bandi, Srinivasa Rao; Huang, Haichun; Brams, Peter; Johnson, David S.

2018-01-01

ABSTRACT Deep sequencing and single-chain variable fragment (scFv) yeast display methods are becoming more popular for discovery of therapeutic antibody candidates in mouse B cell repertoires. In this study, we compare a deep sequencing and scFv display method that retains native heavy and light chain pairing with a related method that randomly pairs heavy and light chain. We performed the studies in a humanized mouse, using interleukin 21 receptor (IL-21R) as a test immunogen. We identified 44 high-affinity binder scFv with the native pairing method and 100 high-affinity binder scFv with the random pairing method. 30% of the natively paired scFv binders were also discovered with the randomly paired method, and 13% of the randomly paired binders were also discovered with the natively paired method. Additionally, 33% of the scFv binders discovered only in the randomly paired library were initially present in the natively paired pre-sort library. Thus, a significant proportion of “randomly paired” scFv were actually natively paired. We synthesized and produced 46 of the candidates as full-length antibodies and subjected them to a panel of binding assays to characterize their therapeutic potential. 87% of the antibodies were verified as binding IL-21R by at least one assay. We found that antibodies with native light chains were more likely to bind IL-21R than antibodies with non-native light chains, suggesting a higher false positive rate for antibodies from the randomly paired library. Additionally, the randomly paired method failed to identify nearly half of the true natively paired binders, suggesting a higher false negative rate. We conclude that natively paired libraries have critical advantages in sensitivity and specificity for antibody discovery programs. PMID:29376776

DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations.

PubMed

Yuan, Yuchen; Shi, Yi; Li, Changyang; Kim, Jinman; Cai, Weidong; Han, Zeguang; Feng, David Dagan

2016-12-23

With the developments of DNA sequencing technology, large amounts of sequencing data have become available in recent years and provide unprecedented opportunities for advanced association studies between somatic point mutations and cancer types/subtypes, which may contribute to more accurate somatic point mutation based cancer classification (SMCC). However in existing SMCC methods, issues like high data sparsity, small volume of sample size, and the application of simple linear classifiers, are major obstacles in improving the classification performance. To address the obstacles in existing SMCC studies, we propose DeepGene, an advanced deep neural network (DNN) based classifier, that consists of three steps: firstly, the clustered gene filtering (CGF) concentrates the gene data by mutation occurrence frequency, filtering out the majority of irrelevant genes; secondly, the indexed sparsity reduction (ISR) converts the gene data into indexes of its non-zero elements, thereby significantly suppressing the impact of data sparsity; finally, the data after CGF and ISR is fed into a DNN classifier, which extracts high-level features for accurate classification. Experimental results on our curated TCGA-DeepGene dataset, which is a reformulated subset of the TCGA dataset containing 12 selected types of cancer, show that CGF, ISR and DNN all contribute in improving the overall classification performance. We further compare DeepGene with three widely adopted classifiers and demonstrate that DeepGene has at least 24% performance improvement in terms of testing accuracy. Based on deep learning and somatic point mutation data, we devise DeepGene, an advanced cancer type classifier, which addresses the obstacles in existing SMCC studies. Experiments indicate that DeepGene outperforms three widely adopted existing classifiers, which is mainly attributed to its deep learning module that is able to extract the high level features between combinatorial somatic point mutations and cancer types.

Computational drug discovery

PubMed Central

Ou-Yang, Si-sheng; Lu, Jun-yan; Kong, Xiang-qian; Liang, Zhong-jie; Luo, Cheng; Jiang, Hualiang

2012-01-01

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field. PMID:22922346

Deep Learning and Its Applications in Biomedicine.

PubMed

Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

2018-02-01

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

Unified Deep Learning Architecture for Modeling Biology Sequence.

PubMed

Wu, Hongjie; Cao, Chengyuan; Xia, Xiaoyan; Lu, Qiang

2017-10-09

Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences. Additionally, the merge and pooling operators enhanced the ability to capture short-range interactions between basic units of biological sequences. The proposed deep-learning model and its training algorithm might be capable of solving currently known biological sequence-modeling problems through the use of a unified framework. We validated our model on one of the most difficult biological sequence-modeling problems currently known, with our results indicating the ability of the model to obtain predictions of protein residue interactions that exceeded the accuracy of current popular approaches by 10% based on multiple benchmarks.

An Integrated Microfluidic Processor for DNA-Encoded Combinatorial Library Functional Screening

PubMed Central

2017-01-01

DNA-encoded synthesis is rekindling interest in combinatorial compound libraries for drug discovery and in technology for automated and quantitative library screening. Here, we disclose a microfluidic circuit that enables functional screens of DNA-encoded compound beads. The device carries out library bead distribution into picoliter-scale assay reagent droplets, photochemical cleavage of compound from the bead, assay incubation, laser-induced fluorescence-based assay detection, and fluorescence-activated droplet sorting to isolate hits. DNA-encoded compound beads (10-μm diameter) displaying a photocleavable positive control inhibitor pepstatin A were mixed (1920 beads, 729 encoding sequences) with negative control beads (58 000 beads, 1728 encoding sequences) and screened for cathepsin D inhibition using a biochemical enzyme activity assay. The circuit sorted 1518 hit droplets for collection following 18 min incubation over a 240 min analysis. Visual inspection of a subset of droplets (1188 droplets) yielded a 24% false discovery rate (1166 pepstatin A beads; 366 negative control beads). Using template barcoding strategies, it was possible to count hit collection beads (1863) using next-generation sequencing data. Bead-specific barcodes enabled replicate counting, and the false discovery rate was reduced to 2.6% by only considering hit-encoding sequences that were observed on >2 beads. This work represents a complete distributable small molecule discovery platform, from microfluidic miniaturized automation to ultrahigh-throughput hit deconvolution by sequencing. PMID:28199790

An Integrated Microfluidic Processor for DNA-Encoded Combinatorial Library Functional Screening.

PubMed

MacConnell, Andrew B; Price, Alexander K; Paegel, Brian M

2017-03-13

DNA-encoded synthesis is rekindling interest in combinatorial compound libraries for drug discovery and in technology for automated and quantitative library screening. Here, we disclose a microfluidic circuit that enables functional screens of DNA-encoded compound beads. The device carries out library bead distribution into picoliter-scale assay reagent droplets, photochemical cleavage of compound from the bead, assay incubation, laser-induced fluorescence-based assay detection, and fluorescence-activated droplet sorting to isolate hits. DNA-encoded compound beads (10-μm diameter) displaying a photocleavable positive control inhibitor pepstatin A were mixed (1920 beads, 729 encoding sequences) with negative control beads (58 000 beads, 1728 encoding sequences) and screened for cathepsin D inhibition using a biochemical enzyme activity assay. The circuit sorted 1518 hit droplets for collection following 18 min incubation over a 240 min analysis. Visual inspection of a subset of droplets (1188 droplets) yielded a 24% false discovery rate (1166 pepstatin A beads; 366 negative control beads). Using template barcoding strategies, it was possible to count hit collection beads (1863) using next-generation sequencing data. Bead-specific barcodes enabled replicate counting, and the false discovery rate was reduced to 2.6% by only considering hit-encoding sequences that were observed on >2 beads. This work represents a complete distributable small molecule discovery platform, from microfluidic miniaturized automation to ultrahigh-throughput hit deconvolution by sequencing.

Comparative Single-Cell Genomics of Chloroflexi from the Okinawa Trough Deep-Subsurface Biosphere.

PubMed

Fullerton, Heather; Moyer, Craig L

2016-05-15

Chloroflexi small-subunit (SSU) rRNA gene sequences are frequently recovered from subseafloor environments, but the metabolic potential of the phylum is poorly understood. The phylum Chloroflexi is represented by isolates with diverse metabolic strategies, including anoxic phototrophy, fermentation, and reductive dehalogenation; therefore, function cannot be attributed to these organisms based solely on phylogeny. Single-cell genomics can provide metabolic insights into uncultured organisms, like the deep-subsurface Chloroflexi Nine SSU rRNA gene sequences were identified from single-cell sorts of whole-round core material collected from the Okinawa Trough at Iheya North hydrothermal field as part of Integrated Ocean Drilling Program (IODP) expedition 331 (Deep Hot Biosphere). Previous studies of subsurface Chloroflexi single amplified genomes (SAGs) suggested heterotrophic or lithotrophic metabolisms and provided no evidence for growth by reductive dehalogenation. Our nine Chloroflexi SAGs (seven of which are from the order Anaerolineales) indicate that, in addition to genes for the Wood-Ljungdahl pathway, exogenous carbon sources can be actively transported into cells. At least one subunit for pyruvate ferredoxin oxidoreductase was found in four of the Chloroflexi SAGs. This protein can provide a link between the Wood-Ljungdahl pathway and other carbon anabolic pathways. Finally, one of the seven Anaerolineales SAGs contains a distinct reductive dehalogenase homologous (rdhA) gene. Through the use of single amplified genomes (SAGs), we have extended the metabolic potential of an understudied group of subsurface microbes, the Chloroflexi These microbes are frequently detected in the subsurface biosphere, though their metabolic capabilities have remained elusive. In contrast to previously examined Chloroflexi SAGs, our genomes (several are from the order Anaerolineales) were recovered from a hydrothermally driven system and therefore provide a unique window into the metabolic potential of this type of habitat. In addition, a reductive dehalogenase gene (rdhA) has been directly linked to marine subsurface Chloroflexi, suggesting that reductive dehalogenation is not limited to the class Dehalococcoidia This discovery expands the nutrient-cycling and metabolic potential present within the deep subsurface and provides functional gene information relating to this enigmatic group. Copyright © 2016 Fullerton and Moyer.

High-Throughput Sequencing Reveals Differential Expression of miRNAs in Intestine from Sea Cucumber during Aestivation

PubMed Central

Chen, Muyan; Zhang, Xiumei; Liu, Jianning; Storey, Kenneth B.

2013-01-01

The regulatory role of miRNA in gene expression is an emerging hot new topic in the control of hypometabolism. Sea cucumber aestivation is a complicated physiological process that includes obvious hypometabolism as evidenced by a decrease in the rates of oxygen consumption and ammonia nitrogen excretion, as well as a serious degeneration of the intestine into a very tiny filament. To determine whether miRNAs play regulatory roles in this process, the present study analyzed profiles of miRNA expression in the intestine of the sea cucumber (Apostichopus japonicus), using Solexa deep sequencing technology. We identified 308 sea cucumber miRNAs, including 18 novel miRNAs specific to sea cucumber. Animals sampled during deep aestivation (DA) after at least 15 days of continuous torpor, were compared with animals from a non-aestivation (NA) state (animals that had passed through aestivation and returned to the active state). We identified 42 differentially expressed miRNAs [RPM (reads per million) >10, |FC| (|fold change|) ≥1, FDR (false discovery rate) <0.01] during aestivation, which were validated by two other miRNA profiling methods: miRNA microarray and real-time PCR. Among the most prominent miRNA species, miR-200-3p, miR-2004, miR-2010, miR-22, miR-252a, miR-252a-3p and miR-92 were significantly over-expressed during deep aestivation compared with non-aestivation animals. Preliminary analyses of their putative target genes and GO analysis suggest that these miRNAs could play important roles in global transcriptional depression and cell differentiation during aestivation. High-throughput sequencing data and microarray data have been submitted to GEO database. PMID:24143179

A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology.

PubMed

Kravatsky, Yuri; Chechetkin, Vladimir; Fedoseeva, Daria; Gorbacheva, Maria; Kravatskaya, Galina; Kretova, Olga; Tchurikov, Nickolai

2017-11-23

The efficient development of antiviral drugs, including efficient antiviral small interfering RNAs (siRNAs), requires continuous monitoring of the strict correspondence between a drug and the related highly variable viral DNA/RNA target(s). Deep sequencing is able to provide an assessment of both the general target conservation and the frequency of particular mutations in the different target sites. The aim of this study was to develop a reliable bioinformatic pipeline for the analysis of millions of short, deep sequencing reads corresponding to selected highly variable viral sequences that are drug target(s). The suggested bioinformatic pipeline combines the available programs and the ad hoc scripts based on an original algorithm of the search for the conserved targets in the deep sequencing data. We also present the statistical criteria for the threshold of reliable mutation detection and for the assessment of variations between corresponding data sets. These criteria are robust against the possible sequencing errors in the reads. As an example, the bioinformatic pipeline is applied to the study of the conservation of RNA interference (RNAi) targets in human immunodeficiency virus 1 (HIV-1) subtype A. The developed pipeline is freely available to download at the website http://virmut.eimb.ru/. Brief comments and comparisons between VirMut and other pipelines are also presented.

TrawlerWeb: an online de novo motif discovery tool for next-generation sequencing datasets.

PubMed

Dang, Louis T; Tondl, Markus; Chiu, Man Ho H; Revote, Jerico; Paten, Benedict; Tano, Vincent; Tokolyi, Alex; Besse, Florence; Quaife-Ryan, Greg; Cumming, Helen; Drvodelic, Mark J; Eichenlaub, Michael P; Hallab, Jeannette C; Stolper, Julian S; Rossello, Fernando J; Bogoyevitch, Marie A; Jans, David A; Nim, Hieu T; Porrello, Enzo R; Hudson, James E; Ramialison, Mirana

2018-04-05

A strong focus of the post-genomic era is mining of the non-coding regulatory genome in order to unravel the function of regulatory elements that coordinate gene expression (Nat 489:57-74, 2012; Nat 507:462-70, 2014; Nat 507:455-61, 2014; Nat 518:317-30, 2015). Whole-genome approaches based on next-generation sequencing (NGS) have provided insight into the genomic location of regulatory elements throughout different cell types, organs and organisms. These technologies are now widespread and commonly used in laboratories from various fields of research. This highlights the need for fast and user-friendly software tools dedicated to extracting cis-regulatory information contained in these regulatory regions; for instance transcription factor binding site (TFBS) composition. Ideally, such tools should not require prior programming knowledge to ensure they are accessible for all users. We present TrawlerWeb, a web-based version of the Trawler_standalone tool (Nat Methods 4:563-5, 2007; Nat Protoc 5:323-34, 2010), to allow for the identification of enriched motifs in DNA sequences obtained from next-generation sequencing experiments in order to predict their TFBS composition. TrawlerWeb is designed for online queries with standard options common to web-based motif discovery tools. In addition, TrawlerWeb provides three unique new features: 1) TrawlerWeb allows the input of BED files directly generated from NGS experiments, 2) it automatically generates an input-matched biologically relevant background, and 3) it displays resulting conservation scores for each instance of the motif found in the input sequences, which assists the researcher in prioritising the motifs to validate experimentally. Finally, to date, this web-based version of Trawler_standalone remains the fastest online de novo motif discovery tool compared to other popular web-based software, while generating predictions with high accuracy. TrawlerWeb provides users with a fast, simple and easy-to-use web interface for de novo motif discovery. This will assist in rapidly analysing NGS datasets that are now being routinely generated. TrawlerWeb is freely available and accessible at: http://trawler.erc.monash.edu.au .

Molecular biology and immunology of head and neck cancer.

PubMed

Guo, Theresa; Califano, Joseph A

2015-07-01

In recent years, our knowledge and understanding of head and neck squamous cell carcinoma (HNSCC) has expanded dramatically. New high-throughput sequencing technologies have accelerated these discoveries since the first reports of whole-exome sequencing of HNSCC tumors in 2011. In addition, the discovery of human papillomavirus in relationship with oropharyngeal squamous cell carcinoma has shifted our molecular understanding of the disease. New investigation into the role of immune evasion in HNSCC has also led to potential novel therapies based on immune-specific systemic therapies. Copyright © 2015 Elsevier Inc. All rights reserved.

Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

PubMed

Adhikari, Badri; Hou, Jie; Cheng, Jianlin

2018-03-01

In this study, we report the evaluation of the residue-residue contacts predicted by our three different methods in the CASP12 experiment, focusing on studying the impact of multiple sequence alignment, residue coevolution, and machine learning on contact prediction. The first method (MULTICOM-NOVEL) uses only traditional features (sequence profile, secondary structure, and solvent accessibility) with deep learning to predict contacts and serves as a baseline. The second method (MULTICOM-CONSTRUCT) uses our new alignment algorithm to generate deep multiple sequence alignment to derive coevolution-based features, which are integrated by a neural network method to predict contacts. The third method (MULTICOM-CLUSTER) is a consensus combination of the predictions of the first two methods. We evaluated our methods on 94 CASP12 domains. On a subset of 38 free-modeling domains, our methods achieved an average precision of up to 41.7% for top L/5 long-range contact predictions. The comparison of the three methods shows that the quality and effective depth of multiple sequence alignments, coevolution-based features, and machine learning integration of coevolution-based features and traditional features drive the quality of predicted protein contacts. On the full CASP12 dataset, the coevolution-based features alone can improve the average precision from 28.4% to 41.6%, and the machine learning integration of all the features further raises the precision to 56.3%, when top L/5 predicted long-range contacts are evaluated. And the correlation between the precision of contact prediction and the logarithm of the number of effective sequences in alignments is 0.66. © 2017 Wiley Periodicals, Inc.

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

PubMed

Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

2018-02-01

Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg .

SSHscreen and SSHdb, generic software for microarray based gene discovery: application to the stress response in cowpea

PubMed Central

2010-01-01

Background Suppression subtractive hybridization is a popular technique for gene discovery from non-model organisms without an annotated genome sequence, such as cowpea (Vigna unguiculata (L.) Walp). We aimed to use this method to enrich for genes expressed during drought stress in a drought tolerant cowpea line. However, current methods were inefficient in screening libraries and management of the sequence data, and thus there was a need to develop software tools to facilitate the process. Results Forward and reverse cDNA libraries enriched for cowpea drought response genes were screened on microarrays, and the R software package SSHscreen 2.0.1 was developed (i) to normalize the data effectively using spike-in control spot normalization, and (ii) to select clones for sequencing based on the calculation of enrichment ratios with associated statistics. Enrichment ratio 3 values for each clone showed that 62% of the forward library and 34% of the reverse library clones were significantly differentially expressed by drought stress (adjusted p value < 0.05). Enrichment ratio 2 calculations showed that > 88% of the clones in both libraries were derived from rare transcripts in the original tester samples, thus supporting the notion that suppression subtractive hybridization enriches for rare transcripts. A set of 118 clones were chosen for sequencing, and drought-induced cowpea genes were identified, the most interesting encoding a late embryogenesis abundant Lea5 protein, a glutathione S-transferase, a thaumatin, a universal stress protein, and a wound induced protein. A lipid transfer protein and several components of photosynthesis were down-regulated by the drought stress. Reverse transcriptase quantitative PCR confirmed the enrichment ratio values for the selected cowpea genes. SSHdb, a web-accessible database, was developed to manage the clone sequences and combine the SSHscreen data with sequence annotations derived from BLAST and Blast2GO. The self-BLAST function within SSHdb grouped redundant clones together and illustrated that the SSHscreen plots are a useful tool for choosing anonymous clones for sequencing, since redundant clones cluster together on the enrichment ratio plots. Conclusions We developed the SSHscreen-SSHdb software pipeline, which greatly facilitates gene discovery using suppression subtractive hybridization by improving the selection of clones for sequencing after screening the library on a small number of microarrays. Annotation of the sequence information and collaboration was further enhanced through a web-based SSHdb database, and we illustrated this through identification of drought responsive genes from cowpea, which can now be investigated in gene function studies. SSH is a popular and powerful gene discovery tool, and therefore this pipeline will have application for gene discovery in any biological system, particularly non-model organisms. SSHscreen 2.0.1 and a link to SSHdb are available from http://microarray.up.ac.za/SSHscreen. PMID:20359330

Molecular Evolution in Historical Perspective.

PubMed

Suárez-Díaz, Edna

2016-12-01

In the 1960s, advances in protein chemistry and molecular genetics provided new means for the study of biological evolution. Amino acid sequencing, nucleic acid hybridization, zone gel electrophoresis, and immunochemistry were some of the experimental techniques that brought about new perspectives to the study of the patterns and mechanisms of evolution. New concepts, such as the molecular evolutionary clock, and the discovery of unexpected molecular phenomena, like the presence of repetitive sequences in eukaryotic genomes, eventually led to the realization that evolution might occur at a different pace at the organismic and the molecular levels, and according to different mechanisms. These developments sparked important debates between defendants of the molecular and organismic approaches. The most vocal confrontations focused on the relation between primates and humans, and the neutral theory of molecular evolution. By the 1980s and 1990s, the construction of large protein and DNA sequences databases, and the development of computer-based statistical tools, facilitated the coming together of molecular and evolutionary biology. Although in its contemporary form the field of molecular evolution can be traced back to the last five decades, the field has deep roots in twentieth century experimental life sciences. For historians of science, the origins and consolidation of molecular evolution provide a privileged field for the study of scientific debates, the relation between technological advances and scientific knowledge, and the connection between science and broader social concerns.

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.

Systematic Optimization of Long Gradient Chromatography Mass Spectrometry for Deep Analysis of Brain Proteome

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

Wang, Hong; Yang, Yanling; Li, Yuxin

2015-02-06

Development of high resolution liquid chromatography (LC) is essential for improving the sensitivity and throughput of mass spectrometry (MS)-based proteomics. Here we present systematic optimization of a long gradient LC-MS/MS platform to enhance protein identification from a complex mixture. The platform employed an in-house fabricated, reverse phase column (100 μm x 150 cm) coupled with Q Exactive MS. The column was capable of achieving a peak capacity of approximately 700 in a 720 min gradient of 10-45% acetonitrile. The optimal loading level was about 6 micrograms of peptides, although the column allowed loading as many as 20 micrograms. Gas phasemore » fractionation of peptide ions further increased the number of peptide identification by ~10%. Moreover, the combination of basic pH LC pre-fractionation with the long gradient LC-MS/MS platform enabled the identification of 96,127 peptides and 10,544 proteins at 1% protein false discovery rate in a postmortem brain sample of Alzheimer’s disease. As deep RNA sequencing of the same specimen suggested that ~16,000 genes were expressed, current analysis covered more than 60% of the expressed proteome. Further improvement strategies of the LC/LC-MS/MS platform were also discussed.« less

Indel variant analysis of short-read sequencing data with Scalpel

PubMed Central

Fang, Han; Bergmann, Ewa A; Arora, Kanika; Vacic, Vladimir; Zody, Michael C; Iossifov, Ivan; O’Rawe, Jason A; Wu, Yiyang; Barron, Laura T Jimenez; Rosenbaum, Julie; Ronemus, Michael; Lee, Yoon-ha; Wang, Zihua; Dikoglu, Esra; Jobanputra, Vaidehi; Lyon, Gholson J; Wigler, Michael; Schatz, Michael C; Narzisi, Giuseppe

2017-01-01

As the second most common type of variation in the human genome, insertions and deletions (indels) have been linked to many diseases, but the discovery of indels of more than a few bases in size from short-read sequencing data remains challenging. Scalpel (http://scalpel.sourceforge.net) is an open-source software for reliable indel detection based on the microassembly technique. It has been successfully used to discover mutations in novel candidate genes for autism, and it is extensively used in other large-scale studies of human diseases. This protocol gives an overview of the algorithm and describes how to use Scalpel to perform highly accurate indel calling from whole-genome and whole-exome sequencing data. We provide detailed instructions for an exemplary family-based de novo study, but we also characterize the other two supported modes of operation: single-sample and somatic analysis. Indel normalization, visualization and annotation of the mutations are also illustrated. Using a standard server, indel discovery and characterization in the exonic regions of the example sequencing data can be completed in ~5 h after read mapping. PMID:27854363

Culturing of respiratory viruses in well-differentiated pseudostratified human airway epithelium as a tool to detect unknown viruses

PubMed Central

Jazaeri Farsani, Seyed Mohammad; Deijs, Martin; Dijkman, Ronald; Molenkamp, Richard; Jeeninga, Rienk E; Ieven, Margareta; Goossens, Herman; van der Hoek, Lia

2015-01-01

Background Currently, virus discovery is mainly based on molecular techniques. Here, we propose a method that relies on virus culturing combined with state-of-the-art sequencing techniques. The most natural ex vivo culture system was used to enable replication of respiratory viruses. Method Three respiratory clinical samples were tested on well-differentiated pseudostratified tracheobronchial human airway epithelial (HAE) cultures grown at an air–liquid interface, which resemble the airway epithelium. Cells were stained with convalescent serum of the patients to identify infected cells and apical washes were analyzed by VIDISCA-454, a next-generation sequencing virus discovery technique. Results Infected cells were observed for all three samples. Sequencing subsequently indicated that the cells were infected by either human coronavirus OC43, influenzavirus B, or influenzavirus A. The sequence reads covered a large part of the genome (52%, 82%, and 57%, respectively). Conclusion We present here a new method for virus discovery that requires a virus culture on primary cells and an antibody detection. The virus in the harvest can be used to characterize the viral genome sequence and cell tropism, but also provides progeny virus to initiate experiments to fulfill the Koch's postulates. PMID:25482367

Exome Sequencing and the Management of Neurometabolic Disorders.

PubMed

Tarailo-Graovac, Maja; Shyr, Casper; Ross, Colin J; Horvath, Gabriella A; Salvarinova, Ramona; Ye, Xin C; Zhang, Lin-Hua; Bhavsar, Amit P; Lee, Jessica J Y; Drögemöller, Britt I; Abdelsayed, Mena; Alfadhel, Majid; Armstrong, Linlea; Baumgartner, Matthias R; Burda, Patricie; Connolly, Mary B; Cameron, Jessie; Demos, Michelle; Dewan, Tammie; Dionne, Janis; Evans, A Mark; Friedman, Jan M; Garber, Ian; Lewis, Suzanne; Ling, Jiqiang; Mandal, Rupasri; Mattman, Andre; McKinnon, Margaret; Michoulas, Aspasia; Metzger, Daniel; Ogunbayo, Oluseye A; Rakic, Bojana; Rozmus, Jacob; Ruben, Peter; Sayson, Bryan; Santra, Saikat; Schultz, Kirk R; Selby, Kathryn; Shekel, Paul; Sirrs, Sandra; Skrypnyk, Cristina; Superti-Furga, Andrea; Turvey, Stuart E; Van Allen, Margot I; Wishart, David; Wu, Jiang; Wu, John; Zafeiriou, Dimitrios; Kluijtmans, Leo; Wevers, Ron A; Eydoux, Patrice; Lehman, Anna M; Vallance, Hilary; Stockler-Ipsiroglu, Sylvia; Sinclair, Graham; Wasserman, Wyeth W; van Karnebeek, Clara D

2016-06-09

Whole-exome sequencing has transformed gene discovery and diagnosis in rare diseases. Translation into disease-modifying treatments is challenging, particularly for intellectual developmental disorder. However, the exception is inborn errors of metabolism, since many of these disorders are responsive to therapy that targets pathophysiological features at the molecular or cellular level. To uncover the genetic basis of potentially treatable inborn errors of metabolism, we combined deep clinical phenotyping (the comprehensive characterization of the discrete components of a patient's clinical and biochemical phenotype) with whole-exome sequencing analysis through a semiautomated bioinformatics pipeline in consecutively enrolled patients with intellectual developmental disorder and unexplained metabolic phenotypes. We performed whole-exome sequencing on samples obtained from 47 probands. Of these patients, 6 were excluded, including 1 who withdrew from the study. The remaining 41 probands had been born to predominantly nonconsanguineous parents of European descent. In 37 probands, we identified variants in 2 genes newly implicated in disease, 9 candidate genes, 22 known genes with newly identified phenotypes, and 9 genes with expected phenotypes; in most of the genes, the variants were classified as either pathogenic or probably pathogenic. Complex phenotypes of patients in five families were explained by coexisting monogenic conditions. We obtained a diagnosis in 28 of 41 probands (68%) who were evaluated. A test of a targeted intervention was performed in 18 patients (44%). Deep phenotyping and whole-exome sequencing in 41 probands with intellectual developmental disorder and unexplained metabolic abnormalities led to a diagnosis in 68%, the identification of 11 candidate genes newly implicated in neurometabolic disease, and a change in treatment beyond genetic counseling in 44%. (Funded by BC Children's Hospital Foundation and others.).

Exome Sequencing and the Management of Neurometabolic Disorders

PubMed Central

Tarailo-Graovac, M.; Shyr, C.; Ross, C.J.; Horvath, G.A.; Salvarinova, R.; Ye, X.C.; Zhang, L.-H.; Bhavsar, A.P.; Lee, J.J.Y.; Drögemöller, B.I.; Abdelsayed, M.; Alfadhel, M.; Armstrong, L.; Baumgartner, M.R.; Burda, P.; Connolly, M.B.; Cameron, J.; Demos, M.; Dewan, T.; Dionne, J.; Evans, A.M.; Friedman, J.M.; Garber, I.; Lewis, S.; Ling, J.; Mandal, R.; Mattman, A.; McKinnon, M.; Michoulas, A.; Metzger, D.; Ogunbayo, O.A.; Rakic, B.; Rozmus, J.; Ruben, P.; Sayson, B.; Santra, S.; Schultz, K.R.; Selby, K.; Shekel, P.; Sirrs, S.; Skrypnyk, C.; Superti-Furga, A.; Turvey, S.E.; Van Allen, M.I.; Wishart, D.; Wu, J.; Wu, J.; Zafeiriou, D.; Kluijtmans, L.; Wevers, R.A.; Eydoux, P.; Lehman, A.M.; Vallance, H.; Stockler-Ipsiroglu, S.; Sinclair, G.; Wasserman, W.W.; van Karnebeek, C.D.

2016-01-01

BACKGROUND Whole-exome sequencing has transformed gene discovery and diagnosis in rare diseases. Translation into disease-modifying treatments is challenging, particularly for intellectual developmental disorder. However, the exception is inborn errors of metabolism, since many of these disorders are responsive to therapy that targets pathophysiological features at the molecular or cellular level. METHODS To uncover the genetic basis of potentially treatable inborn errors of metabolism, we combined deep clinical phenotyping (the comprehensive characterization of the discrete components of a patient’s clinical and biochemical phenotype) with whole-exome sequencing analysis through a semiautomated bioinformatics pipeline in consecutively enrolled patients with intellectual developmental disorder and unexplained metabolic phenotypes. RESULTS We performed whole-exome sequencing on samples obtained from 47 probands. Of these patients, 6 were excluded, including 1 who withdrew from the study. The remaining 41 probands had been born to predominantly nonconsanguineous parents of European descent. In 37 probands, we identified variants in 2 genes newly implicated in disease, 9 candidate genes, 22 known genes with newly identified phenotypes, and 9 genes with expected phenotypes; in most of the genes, the variants were classified as either pathogenic or probably pathogenic. Complex phenotypes of patients in five families were explained by coexisting monogenic conditions. We obtained a diagnosis in 28 of 41 probands (68%) who were evaluated. A test of a targeted intervention was performed in 18 patients (44%). CONCLUSIONS Deep phenotyping and whole-exome sequencing in 41 probands with intellectual developmental disorder and unexplained metabolic abnormalities led to a diagnosis in 68%, the identification of 11 candidate genes newly implicated in neurometabolic disease, and a change in treatment beyond genetic counseling in 44%. (Funded by BC Children’s Hospital Foundation and others.) PMID:27276562

SSRPrimer and SSR Taxonomy Tree: Biome SSR discovery

PubMed Central

Jewell, Erica; Robinson, Andrew; Savage, David; Erwin, Tim; Love, Christopher G.; Lim, Geraldine A. C.; Li, Xi; Batley, Jacqueline; Spangenberg, German C.; Edwards, David

2006-01-01

Simple sequence repeat (SSR) molecular genetic markers have become important tools for a broad range of applications such as genome mapping and genetic diversity studies. SSRs are readily identified within DNA sequence data and PCR primers can be designed for their amplification. These PCR primers frequently cross amplify within related species. We report a web-based tool, SSR Primer, that integrates SPUTNIK, an SSR repeat finder, with Primer3, a primer design program, within one pipeline. On submission of multiple FASTA formatted sequences, the script screens each sequence for SSRs using SPUTNIK. Results are then parsed to Primer3 for locus specific primer design. We have applied this tool for the discovery of SSRs within the complete GenBank database, and have designed PCR amplification primers for over 13 million SSRs. The SSR Taxonomy Tree server provides web-based searching and browsing of species and taxa for the visualisation and download of these SSR amplification primers. These tools are available at . PMID:16845092

SSRPrimer and SSR Taxonomy Tree: Biome SSR discovery.

PubMed

Jewell, Erica; Robinson, Andrew; Savage, David; Erwin, Tim; Love, Christopher G; Lim, Geraldine A C; Li, Xi; Batley, Jacqueline; Spangenberg, German C; Edwards, David

2006-07-01

Simple sequence repeat (SSR) molecular genetic markers have become important tools for a broad range of applications such as genome mapping and genetic diversity studies. SSRs are readily identified within DNA sequence data and PCR primers can be designed for their amplification. These PCR primers frequently cross amplify within related species. We report a web-based tool, SSR Primer, that integrates SPUTNIK, an SSR repeat finder, with Primer3, a primer design program, within one pipeline. On submission of multiple FASTA formatted sequences, the script screens each sequence for SSRs using SPUTNIK. Results are then parsed to Primer3 for locus specific primer design. We have applied this tool for the discovery of SSRs within the complete GenBank database, and have designed PCR amplification primers for over 13 million SSRs. The SSR Taxonomy Tree server provides web-based searching and browsing of species and taxa for the visualisation and download of these SSR amplification primers. These tools are available at http://bioinformatics.pbcbasc.latrobe.edu.au/ssrdiscovery.html.

Energy Gradients Structure Microbial Communities Across Sediment Horizons in Deep Marine Sediments of the South China Sea

PubMed Central

Graw, Michael F.; D'Angelo, Grace; Borchers, Matthew; Thurber, Andrew R.; Johnson, Joel E.; Zhang, Chuanlun; Liu, Haodong; Colwell, Frederick S.

2018-01-01

The deep marine subsurface is a heterogeneous environment in which the assembly of microbial communities is thought to be controlled by a combination of organic matter deposition, electron acceptor availability, and sedimentology. However, the relative importance of these factors in structuring microbial communities in marine sediments remains unclear. The South China Sea (SCS) experiences significant variability in sedimentation across the basin and features discrete changes in sedimentology as a result of episodic deposition of turbidites and volcanic ashes within lithogenic clays and siliceous or calcareous ooze deposits throughout the basin's history. Deep subsurface microbial communities were recently sampled by the International Ocean Discovery Program (IODP) at three locations in the SCS with sedimentation rates of 5, 12, and 20 cm per thousand years. Here, we used Illumina sequencing of the 16S ribosomal RNA gene to characterize deep subsurface microbial communities from distinct sediment types at these sites. Communities across all sites were dominated by several poorly characterized taxa implicated in organic matter degradation, including Atribacteria, Dehalococcoidia, and Aerophobetes. Sulfate-reducing bacteria comprised only 4% of the community across sulfate-bearing sediments from multiple cores and did not change in abundance in sediments from the methanogenic zone at the site with the lowest sedimentation rate. Microbial communities were significantly structured by sediment age and the availability of sulfate as an electron acceptor in pore waters. However, microbial communities demonstrated no partitioning based on the sediment type they inhabited. These results indicate that microbial communities in the SCS are structured by the availability of electron donors and acceptors rather than sedimentological characteristics. PMID:29696012

Boosting compound-protein interaction prediction by deep learning.

PubMed

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

DNA Barcode Analysis of Thrips (Thysanoptera) Diversity in Pakistan Reveals Cryptic Species Complexes.

PubMed

Iftikhar, Romana; Ashfaq, Muhammad; Rasool, Akhtar; Hebert, Paul D N

2016-01-01

Although thrips are globally important crop pests and vectors of viral disease, species identifications are difficult because of their small size and inconspicuous morphological differences. Sequence variation in the mitochondrial COI-5' (DNA barcode) region has proven effective for the identification of species in many groups of insect pests. We analyzed barcode sequence variation among 471 thrips from various plant hosts in north-central Pakistan. The Barcode Index Number (BIN) system assigned these sequences to 55 BINs, while the Automatic Barcode Gap Discovery detected 56 partitions, a count that coincided with the number of monophyletic lineages recognized by Neighbor-Joining analysis and Bayesian inference. Congeneric species showed an average of 19% sequence divergence (range = 5.6% - 27%) at COI, while intraspecific distances averaged 0.6% (range = 0.0% - 7.6%). BIN analysis suggested that all intraspecific divergence >3.0% actually involved a species complex. In fact, sequences for three major pest species (Haplothrips reuteri, Thrips palmi, Thrips tabaci), and one predatory thrips (Aeolothrips intermedius) showed deep intraspecific divergences, providing evidence that each is a cryptic species complex. The study compiles the first barcode reference library for the thrips of Pakistan, and examines global haplotype diversity in four important pest thrips.

BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone.

PubMed

Yang, Bite; Liu, Feng; Ren, Chao; Ouyang, Zhangyi; Xie, Ziwei; Bo, Xiaochen; Shu, Wenjie

2017-07-01

Enhancer elements are noncoding stretches of DNA that play key roles in controlling gene expression programmes. Despite major efforts to develop accurate enhancer prediction methods, identifying enhancer sequences continues to be a challenge in the annotation of mammalian genomes. One of the major issues is the lack of large, sufficiently comprehensive and experimentally validated enhancers for humans or other species. Thus, the development of computational methods based on limited experimentally validated enhancers and deciphering the transcriptional regulatory code encoded in the enhancer sequences is urgent. We present a deep-learning-based hybrid architecture, BiRen, which predicts enhancers using the DNA sequence alone. Our results demonstrate that BiRen can learn common enhancer patterns directly from the DNA sequence and exhibits superior accuracy, robustness and generalizability in enhancer prediction relative to other state-of-the-art enhancer predictors based on sequence characteristics. Our BiRen will enable researchers to acquire a deeper understanding of the regulatory code of enhancer sequences. Our BiRen method can be freely accessed at https://github.com/wenjiegroup/BiRen . shuwj@bmi.ac.cn or boxc@bmi.ac.cn. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Modelling and enhanced molecular dynamics to steer structure-based drug discovery.

PubMed

Kalyaanamoorthy, Subha; Chen, Yi-Ping Phoebe

2014-05-01

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Deep learning for neuroimaging: a validation study.

PubMed

Plis, Sergey M; Hjelm, Devon R; Salakhutdinov, Ruslan; Allen, Elena A; Bockholt, Henry J; Long, Jeffrey D; Johnson, Hans J; Paulsen, Jane S; Turner, Jessica A; Calhoun, Vince D

2014-01-01

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

Low Data Drug Discovery with One-Shot Learning

PubMed Central

2017-01-01

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. Model.2015, 55, 263–27425635324). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016). PMID:28470045

Is Multitask Deep Learning Practical for Pharma?

PubMed

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

Deep Recurrent Neural Networks for Human Activity Recognition

PubMed Central

Murad, Abdulmajid

2017-01-01

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs. PMID:29113103

Deep Recurrent Neural Networks for Human Activity Recognition.

PubMed

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

DCO-VIVO: A Collaborative Data Platform for the Deep Carbon Science Communities

NASA Astrophysics Data System (ADS)

Wang, H.; Chen, Y.; West, P.; Erickson, J. S.; Ma, X.; Fox, P. A.

2014-12-01

Deep Carbon Observatory (DCO) is a decade-long scientific endeavor to understand carbon in the complex deep Earth system. Thousands of DCO scientists from institutions across the globe are organized into communities representing four domains of exploration: Extreme Physics and Chemistry, Reservoirs and Fluxes, Deep Energy, and Deep Life. Cross-community and cross-disciplinary collaboration is one of the most distinctive features in DCO's flexible research framework. VIVO is an open-source Semantic Web platform that facilitates cross-institutional researcher and research discovery. it includes a number of standard ontologies that interconnect people, organizations, publications, activities, locations, and other entities of research interest to enable browsing, searching, visualizing, and generating Linked Open (research) Data. The DCO-VIVO solution expedites research collaboration between DCO scientists and communities. Based on DCO's specific requirements, the DCO Data Science team developed a series of extensions to the VIVO platform including extending the VIVO information model, extended query over the semantic information within VIVO, integration with other open source collaborative environments and data management systems, using single sign-on, assigning of unique Handles to DCO objects, and publication and dataset ingesting extensions using existing publication systems. We present here the iterative development of these requirements that are now in daily use by the DCO community of scientists for research reporting, information sharing, and resource discovery in support of research activities and program management.

Deep-sea vent chemoautotrophs: diversity, biochemistry and ecological significance.

PubMed

Nakagawa, Satoshi; Takai, Ken

2008-07-01

Deep-sea vents support productive ecosystems driven primarily by chemoautotrophs. Chemoautotrophs are organisms that are able to fix inorganic carbon using a chemical energy obtained through the oxidation of reduced compounds. Following the discovery of deep-sea vent ecosystems in 1977, there has been an increasing knowledge that deep-sea vent chemoautotrophs display remarkable physiological and phylogenetic diversity. Cultivation-dependent and -independent studies have led to an emerging view that the majority of deep-sea vent chemoautotrophs have the ability to derive energy from a variety of redox couples other than the conventional sulfur-oxygen couple, and fix inorganic carbon via the reductive tricarboxylic acid cycle. In addition, recent genomic, metagenomic and postgenomic studies have considerably accelerated the comprehensive understanding of molecular mechanisms of deep-sea vent chemoautotrophy, even in yet uncultivable endosymbionts of vent fauna. Genomic analysis also suggested that there are previously unrecognized evolutionary links between deep-sea vent chemoautotrophs and important human/animal pathogens. This review summarizes chemoautotrophy in deep-sea vents, highlighting recent biochemical and genomic discoveries.

Transcription profile of boar spermatozoa as revealed by RNA-sequencing

USDA-ARS?s Scientific Manuscript database

High-throughput RNA sequencing (RNA-Seq) overcomes the limitations of the current hybridization-based techniques to detect the actual pool of RNA transcripts in spermatozoa. The application of this technology in livestock can speed the discovery of potential predictors of male fertility. As a first ...

Application of circular consensus sequencing and network analysis to characterize the bovine IgG repertoire

USDA-ARS?s Scientific Manuscript database

Background: Vertebrate immune systems generate diverse repertoires of antibodies capable of mediating response to a variety of antigens. Next generation sequencing methods provide unique approaches to a number of immuno-based research areas including antibody discovery and engineering, disease surve...

Next generation sequencing applications for microRNA biomarker discovery in toxicological studies

EPA Science Inventory

Next Generation Sequencing (NGS) technology will be reviewed for its base pair resolution, wide dynamic range, and insights into the genome and transcriptome, with special focus upon the biomarker potential of microRNAs (miRNAs). The first part of this presentation reviews commo...

SNPServer: a real-time SNP discovery tool.

PubMed

Savage, David; Batley, Jacqueline; Erwin, Tim; Logan, Erica; Love, Christopher G; Lim, Geraldine A C; Mongin, Emmanuel; Barker, Gary; Spangenberg, German C; Edwards, David

2005-07-01

SNPServer is a real-time flexible tool for the discovery of SNPs (single nucleotide polymorphisms) within DNA sequence data. The program uses BLAST, to identify related sequences, and CAP3, to cluster and align these sequences. The alignments are parsed to the SNP discovery software autoSNP, a program that detects SNPs and insertion/deletion polymorphisms (indels). Alternatively, lists of related sequences or pre-assembled sequences may be entered for SNP discovery. SNPServer and autoSNP use redundancy to differentiate between candidate SNPs and sequence errors. For each candidate SNP, two measures of confidence are calculated, the redundancy of the polymorphism at a SNP locus and the co-segregation of the candidate SNP with other SNPs in the alignment. SNPServer is available at http://hornbill.cspp.latrobe.edu.au/snpdiscovery.html.

Direct AUC optimization of regulatory motifs.

PubMed

Zhu, Lin; Zhang, Hong-Bo; Huang, De-Shuang

2017-07-15

The discovery of transcription factor binding site (TFBS) motifs is essential for untangling the complex mechanism of genetic variation under different developmental and environmental conditions. Among the huge amount of computational approaches for de novo identification of TFBS motifs, discriminative motif learning (DML) methods have been proven to be promising for harnessing the discovery power of accumulated huge amount of high-throughput binding data. However, they have to sacrifice accuracy for speed and could fail to fully utilize the information of the input sequences. We propose a novel algorithm called CDAUC for optimizing DML-learned motifs based on the area under the receiver-operating characteristic curve (AUC) criterion, which has been widely used in the literature to evaluate the significance of extracted motifs. We show that when the considered AUC loss function is optimized in a coordinate-wise manner, the cost function of each resultant sub-problem is a piece-wise constant function, whose optimal value can be found exactly and efficiently. Further, a key step of each iteration of CDAUC can be efficiently solved as a computational geometry problem. Experimental results on real world high-throughput datasets illustrate that CDAUC outperforms competing methods for refining DML motifs, while being one order of magnitude faster. Meanwhile, preliminary results also show that CDAUC may also be useful for improving the interpretability of convolutional kernels generated by the emerging deep learning approaches for predicting TF sequences specificities. CDAUC is available at: https://drive.google.com/drive/folders/0BxOW5MtIZbJjNFpCeHlBVWJHeW8 . dshuang@tongji.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Making sense of deep sequencing

PubMed Central

Goldman, D.; Domschke, K.

2016-01-01

This review, the first of an occasional series, tries to make sense of the concepts and uses of deep sequencing of polynucleic acids (DNA and RNA). Deep sequencing, synonymous with next-generation sequencing, high-throughput sequencing and massively parallel sequencing, includes whole genome sequencing but is more often and diversely applied to specific parts of the genome captured in different ways, for example the highly expressed portion of the genome known as the exome and portions of the genome that are epigenetically marked either by DNA methylation, the binding of proteins including histones, or that are in different configurations and thus more or less accessible to enzymes that cleave DNA. Deep sequencing of RNA (RNASeq) reverse-transcribed to complementary DNA is invaluable for measuring RNA expression and detecting changes in RNA structure. Important concepts in deep sequencing include the length and depth of sequence reads, mapping and assembly of reads, sequencing error, haplotypes, and the propensity of deep sequencing, as with other types of ‘big data’, to generate large numbers of errors, requiring monitoring for methodologic biases and strategies for replication and validation. Deep sequencing yields a unique genetic fingerprint that can be used to identify a person, and a trove of predictors of genetic medical diseases. Deep sequencing to identify epigenetic events including changes in DNA methylation and RNA expression can reveal the history and impact of environmental exposures. Because of the power of sequencing to identify and deliver biomedically significant information about a person and their blood relatives, it creates ethical dilemmas and practical challenges in research and clinical care, for example the decision and procedures to report incidental findings that will increasingly and frequently be discovered. PMID:24925306

MOSAIK: a hash-based algorithm for accurate next-generation sequencing short-read mapping.

PubMed

Lee, Wan-Ping; Stromberg, Michael P; Ward, Alistair; Stewart, Chip; Garrison, Erik P; Marth, Gabor T

2014-01-01

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery. All variant discovery benefits from an accurate description of the read placement confidence. To this end, MOSAIK uses a neural-network based training scheme to provide well-calibrated mapping quality scores, demonstrated by a correlation coefficient between MOSAIK assigned and actual mapping qualities greater than 0.98. In order to ensure that studies of any genome are supported, a training pipeline is provided to ensure optimal mapping quality scores for the genome under investigation. MOSAIK is multi-threaded, open source, and incorporated into our command and pipeline launcher system GKNO (http://gkno.me).

MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping

PubMed Central

Lee, Wan-Ping; Stromberg, Michael P.; Ward, Alistair; Stewart, Chip; Garrison, Erik P.; Marth, Gabor T.

2014-01-01

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery. All variant discovery benefits from an accurate description of the read placement confidence. To this end, MOSAIK uses a neural-network based training scheme to provide well-calibrated mapping quality scores, demonstrated by a correlation coefficient between MOSAIK assigned and actual mapping qualities greater than 0.98. In order to ensure that studies of any genome are supported, a training pipeline is provided to ensure optimal mapping quality scores for the genome under investigation. MOSAIK is multi-threaded, open source, and incorporated into our command and pipeline launcher system GKNO (http://gkno.me). PMID:24599324

Molecular Mapping of Restriction-Site Associated DNA Markers In Allotetraploid Upland Cotton.

PubMed

Wang, Yangkun; Ning, Zhiyuan; Hu, Yan; Chen, Jiedan; Zhao, Rui; Chen, Hong; Ai, Nijiang; Guo, Wangzhen; Zhang, Tianzhen

2015-01-01

Upland cotton (Gossypium hirsutum L., 2n = 52, AADD) is an allotetraploid, therefore the discovery of single nucleotide polymorphism (SNP) markers is difficult. The recent emergence of genome complexity reduction technologies based on the next-generation sequencing (NGS) platform has greatly expedited SNP discovery in crops with highly repetitive and complex genomes. Here we applied restriction-site associated DNA (RAD) sequencing technology for de novo SNP discovery in allotetraploid cotton. We identified 21,109 SNPs between the two parents and used these for genotyping of 161 recombinant inbred lines (RILs). Finally, a high dense linkage map comprising 4,153 loci over 3500-cM was developed based on the previous result. Using this map quantitative trait locus (QTLs) conferring fiber strength and Verticillium Wilt (VW) resistance were mapped to a more accurate region in comparison to the 1576-cM interval determined using the simple sequence repeat (SSR) genetic map. This suggests that the newly constructed map has more power and resolution than the previous SSR map. It will pave the way for the rapid identification of the marker-assisted selection in cotton breeding and cloning of QTL of interest traits.

Motif-based analysis of large nucleotide data sets using MEME-ChIP

PubMed Central

Ma, Wenxiu; Noble, William S; Bailey, Timothy L

2014-01-01

MEME-ChIP is a web-based tool for analyzing motifs in large DNA or RNA data sets. It can analyze peak regions identified by ChIP-seq, cross-linking sites identified by cLIP-seq and related assays, as well as sets of genomic regions selected using other criteria. MEME-ChIP performs de novo motif discovery, motif enrichment analysis, motif location analysis and motif clustering, providing a comprehensive picture of the DNA or RNA motifs that are enriched in the input sequences. MEME-ChIP performs two complementary types of de novo motif discovery: weight matrix–based discovery for high accuracy; and word-based discovery for high sensitivity. Motif enrichment analysis using DNA or RNA motifs from human, mouse, worm, fly and other model organisms provides even greater sensitivity. MEME-ChIP’s interactive HTML output groups and aligns significant motifs to ease interpretation. this protocol takes less than 3 h, and it provides motif discovery approaches that are distinct and complementary to other online methods. PMID:24853928

Identifying active foraminifera in the Sea of Japan using metatranscriptomic approach

NASA Astrophysics Data System (ADS)

Lejzerowicz, Franck; Voltsky, Ivan; Pawlowski, Jan

2013-02-01

Metagenetics represents an efficient and rapid tool to describe environmental diversity patterns of microbial eukaryotes based on ribosomal DNA sequences. However, the results of metagenetic studies are often biased by the presence of extracellular DNA molecules that are persistent in the environment, especially in deep-sea sediment. As an alternative, short-lived RNA molecules constitute a good proxy for the detection of active species. Here, we used a metatranscriptomic approach based on RNA-derived (cDNA) sequences to study the diversity of the deep-sea benthic foraminifera and compared it to the metagenetic approach. We analyzed 257 ribosomal DNA and cDNA sequences obtained from seven sediments samples collected in the Sea of Japan at depths ranging from 486 to 3665 m. The DNA and RNA-based approaches gave a similar view of the taxonomic composition of foraminiferal assemblage, but differed in some important points. First, the cDNA dataset was dominated by sequences of rotaliids and robertiniids, suggesting that these calcareous species, some of which have been observed in Rose Bengal stained samples, are the most active component of foraminiferal community. Second, the richness of monothalamous (single-chambered) foraminifera was particularly high in DNA extracts from the deepest samples, confirming that this group of foraminifera is abundant but not necessarily very active in the deep-sea sediments. Finally, the high divergence of undetermined sequences in cDNA dataset indicate the limits of our database and lack of knowledge about some active but possibly rare species. Our study demonstrates the capability of the metatranscriptomic approach to detect active foraminiferal species and prompt its use in future high-throughput sequencing-based environmental surveys.

Advances in the understanding and use of the genomic base of microbial secondary metabolite biosynthesis for the discovery of new natural products.

PubMed

McAlpine, James B

2009-03-27

Over the past decade major changes have occurred in the access to genome sequences that encode the enzymes responsible for the biosynthesis of secondary metabolites, knowledge of how those sequences translate into the final structure of the metabolite, and the ability to alter the sequence to obtain predicted products via both homologous and heterologous expression. Novel genera have been discovered leading to new chemotypes, but more surprisingly several instances have been uncovered where the apparently general rules of modular translation have not applied. Several new biosynthetic pathways have been unearthed, and our general knowledge grows rapidly. This review aims to highlight some of the more striking discoveries and advances of the decade.

The complete mitochondrial genome of the deep-sea sponge Poecillastra laminaris (Astrophorida, Vulcanellidae).

PubMed

Zeng, Cong; Thomas, Leighton J; Kelly, Michelle; Gardner, Jonathan P A

2016-05-01

The complete mitochondrial genome of a New Zealand specimen of the deep-sea sponge Poecillastra laminaris (Sollas, 1886) (Astrophorida, Vulcanellidae), from the Colville Ridge, New Zealand, was sequenced using the 454 Life Science pyrosequencing system. To identify homologous mitochondrial sequences, the 454 reads were mapped to the complete mitochondrial genome sequence of Geodia neptuni (GeneBank No. NC_006990). The P. laminaris genome is 18,413 bp in length and includes 14 protein-coding genes, 24 transfer RNA genes and 2 ribosomal RNA genes. Gene order resembled that of other demosponges. The base composition of the genome is A (29.1%), T (35.2%), C (14.0%) and G (21.7%). This is the second published mitogenome for a sponge of the order Astrophorida and will be useful in future phylogenetic analysis of deep-sea sponges.

Drug Discovery in Fish, Flies, and Worms

PubMed Central

Strange, Kevin

2016-01-01

Abstract Nonmammalian model organisms such as the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the zebrafish Danio rerio provide numerous experimental advantages for drug discovery including genetic and molecular tractability, amenability to high-throughput screening methods and reduced experimental costs and increased experimental throughput compared to traditional mammalian models. An interdisciplinary approach that strategically combines the study of nonmammalian and mammalian animal models with diverse experimental tools has and will continue to provide deep molecular and genetic understanding of human disease and will significantly enhance the discovery and application of new therapies to treat those diseases. This review will provide an overview of C. elegans, Drosophila, and zebrafish biology and husbandry and will discuss how these models are being used for phenotype-based drug screening and for identification of drug targets and mechanisms of action. The review will also describe how these and other nonmammalian model organisms are uniquely suited for the discovery of drug-based regenerative medicine therapies. PMID:28053067

Strain-Level Diversity of Secondary Metabolism in Streptomyces albus

PubMed Central

Seipke, Ryan F.

2015-01-01

Streptomyces spp. are robust producers of medicinally-, industrially- and agriculturally-important small molecules. Increased resistance to antibacterial agents and the lack of new antibiotics in the pipeline have led to a renaissance in natural product discovery. This endeavor has benefited from inexpensive high quality DNA sequencing technology, which has generated more than 140 genome sequences for taxonomic type strains and environmental Streptomyces spp. isolates. Many of the sequenced streptomycetes belong to the same species. For instance, Streptomyces albus has been isolated from diverse environmental niches and seven strains have been sequenced, consequently this species has been sequenced more than any other streptomycete, allowing valuable analyses of strain-level diversity in secondary metabolism. Bioinformatics analyses identified a total of 48 unique biosynthetic gene clusters harboured by Streptomyces albus strains. Eighteen of these gene clusters specify the core secondary metabolome of the species. Fourteen of the gene clusters are contained by one or more strain and are considered auxiliary, while 16 of the gene clusters encode the production of putative strain-specific secondary metabolites. Analysis of Streptomyces albus strains suggests that each strain of a Streptomyces species likely harbours at least one strain-specific biosynthetic gene cluster. Importantly, this implies that deep sequencing of a species will not exhaust gene cluster diversity and will continue to yield novelty. PMID:25635820

A fortran program for Monte Carlo simulation of oil-field discovery sequences

USGS Publications Warehouse

Bohling, Geoffrey C.; Davis, J.C.

1993-01-01

We have developed a program for performing Monte Carlo simulation of oil-field discovery histories. A synthetic parent population of fields is generated as a finite sample from a distribution of specified form. The discovery sequence then is simulated by sampling without replacement from this parent population in accordance with a probabilistic discovery process model. The program computes a chi-squared deviation between synthetic and actual discovery sequences as a function of the parameters of the discovery process model, the number of fields in the parent population, and the distributional parameters of the parent population. The program employs the three-parameter log gamma model for the distribution of field sizes and employs a two-parameter discovery process model, allowing the simulation of a wide range of scenarios. ?? 1993.

De novo peptide sequencing by deep learning

PubMed Central

Tran, Ngoc Hieu; Zhang, Xianglilan; Xin, Lei; Shan, Baozhen; Li, Ming

2017-01-01

De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7–22.9% higher accuracy at the amino acid level and 38.1–64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5–100% coverage and 97.2–99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming. PMID:28720701

The Expanding Family of Virophages

PubMed Central

Bekliz, Meriem; Colson, Philippe; La Scola, Bernard

2016-01-01

Virophages replicate with giant viruses in the same eukaryotic cells. They are a major component of the specific mobilome of mimiviruses. Since their discovery in 2008, five other representatives have been isolated, 18 new genomes have been described, two of which being nearly completely sequenced, and they have been classified in a new viral family, Lavidaviridae. Virophages are small viruses with approximately 35–74 nm large icosahedral capsids and 17–29 kbp large double-stranded DNA genomes with 16–34 genes, among which a very small set is shared with giant viruses. Virophages have been isolated or detected in various locations and in a broad range of habitats worldwide, including the deep ocean and inland. Humans, therefore, could be commonly exposed to virophages, although currently limited evidence exists of their presence in humans based on serology and metagenomics. The distribution of virophages, the consequences of their infection and the interactions with their giant viral hosts within eukaryotic cells deserve further research. PMID:27886075

Less is More: Membrane Protein Digestion Beyond Urea–Trypsin Solution for Next-level Proteomics*

PubMed Central

Zhang, Xi

2015-01-01

The goal of next-level bottom-up membrane proteomics is protein function investigation, via high-coverage high-throughput peptide-centric quantitation of expression, modifications and dynamic structures at systems scale. Yet efficient digestion of mammalian membrane proteins presents a daunting barrier, and prevalent day-long urea–trypsin in-solution digestion proved insufficient to reach this goal. Many efforts contributed incremental advances over past years, but involved protein denaturation that disconnected measurement from functional states. Beyond denaturation, the recent discovery of structure/proteomics omni-compatible detergent n-dodecyl-β-d-maltopyranoside, combined with pepsin and PNGase F columns, enabled breakthroughs in membrane protein digestion: a 2010 DDM-low-TCEP (DLT) method for H/D-exchange (HDX) using human G protein-coupled receptor, and a 2015 flow/detergent-facilitated protease and de-PTM digestions (FDD) for integrative deep sequencing and quantitation using full-length human ion channel complex. Distinguishing protein solubilization from denaturation, protease digestion reliability from theoretical specificity, and reduction from alkylation, these methods shifted day(s)-long paradigms into minutes, and afforded fully automatable (HDX)-protein-peptide-(tandem mass tag)-HPLC pipelines to instantly measure functional proteins at deep coverage, high peptide reproducibility, low artifacts and minimal leakage. Promoting—not destroying—structures and activities harnessed membrane proteins for the next-level streamlined functional proteomics. This review analyzes recent advances in membrane protein digestion methods and highlights critical discoveries for future proteomics. PMID:26081834

Discovery sequence and the nature of low permeability gas accumulations

USGS Publications Warehouse

Attanasi, E.D.

2005-01-01

There is an ongoing discussion regarding the geologic nature of accumulations that host gas in low-permeability sandstone environments. This note examines the discovery sequence of the accumulations in low permeability sandstone plays that were classified as continuous-type by the U.S. Geological Survey for the 1995 National Oil and Gas Assessment. It compares the statistical character of historical discovery sequences of accumulations associated with continuous-type sandstone gas plays to those of conventional plays. The seven sandstone plays with sufficient data exhibit declining size with sequence order, on average, and in three of the seven the trend is statistically significant. Simulation experiments show that both a skewed endowment size distribution and a discovery process that mimics sampling proportional to size are necessary to generate a discovery sequence that consistently produces a statistically significant negative size order relationship. The empirical findings suggest that discovery sequence could be used to constrain assessed gas in untested areas. The plays examined represent 134 of the 265 trillion cubic feet of recoverable gas assessed in undeveloped areas of continuous-type gas plays in low permeability sandstone environments reported in the 1995 National Assessment. ?? 2005 International Association for Mathematical Geology.

Integrating functional genomics to accelerate mechanistic personalized medicine.

PubMed

Tyner, Jeffrey W

2017-03-01

The advent of deep sequencing technologies has resulted in the deciphering of tremendous amounts of genetic information. These data have led to major discoveries, and many anecdotes now exist of individual patients whose clinical outcomes have benefited from novel, genetically guided therapeutic strategies. However, the majority of genetic events in cancer are currently undrugged, leading to a biological gap between understanding of tumor genetic etiology and translation to improved clinical approaches. Functional screening has made tremendous strides in recent years with the development of new experimental approaches to studying ex vivo and in vivo drug sensitivity. Numerous discoveries and anecdotes also exist for translation of functional screening into novel clinical strategies; however, the current clinical application of functional screening remains largely confined to small clinical trials at specific academic centers. The intersection between genomic and functional approaches represents an ideal modality to accelerate our understanding of drug sensitivities as they relate to specific genetic events and further understand the full mechanisms underlying drug sensitivity patterns.

Deep Space Mission Applications for NEXT: NASA's Evolutionary Xenon Thruster

NASA Technical Reports Server (NTRS)

Oh, David; Benson, Scott; Witzberger, Kevin; Cupples, Michael

2004-01-01

NASA's Evolutionary Xenon Thruster (NEXT) is designed to address a need for advanced ion propulsion systems on certain future NASA deep space missions. This paper surveys seven potential missions that have been identified as being able to take advantage of the unique capabilities of NEXT. Two conceptual missions to Titan and Neptune are analyzed, and it is shown that ion thrusters could decrease launch mass and shorten trip time, to Titan compared to chemical propulsion. A potential Mars Sample return mission is described, and compassion made between a chemical mission and a NEXT based mission. Four possible near term applications to New Frontiers and Discovery class missions are described, and comparisons are made to chemical systems or existing NSTAR ion propulsion system performance. The results show that NEXT has potential performance and cost benefits for missions in the Discovery, New Frontiers, and larger mission classes.

In silico mining and PCR-based approaches to transcription factor discovery in non-model plants: gene discovery of the WRKY transcription factors in conifers.

PubMed

Liu, Jun-Jun; Xiang, Yu

2011-01-01

WRKY transcription factors are key regulators of numerous biological processes in plant growth and development, as well as plant responses to abiotic and biotic stresses. Research on biological functions of plant WRKY genes has focused in the past on model plant species or species with largely characterized transcriptomes. However, a variety of non-model plants, such as forest conifers, are essential as feed, biofuel, and wood or for sustainable ecosystems. Identification of WRKY genes in these non-model plants is equally important for understanding the evolutionary and function-adaptive processes of this transcription factor family. Because of limited genomic information, the rarity of regulatory gene mRNAs in transcriptomes, and the sequence divergence to model organism genes, identification of transcription factors in non-model plants using methods similar to those generally used for model plants is difficult. This chapter describes a gene family discovery strategy for identification of WRKY transcription factors in conifers by a combination of in silico-based prediction and PCR-based experimental approaches. Compared to traditional cDNA library screening or EST sequencing at transcriptome scales, this integrated gene discovery strategy provides fast, simple, reliable, and specific methods to unveil the WRKY gene family at both genome and transcriptome levels in non-model plants.

Compositional Bias in Naïve and Chemically-modified Phage-Displayed Libraries uncovered by Paired-end Deep Sequencing.

PubMed

He, Bifang; Tjhung, Katrina F; Bennett, Nicholas J; Chou, Ying; Rau, Andrea; Huang, Jian; Derda, Ratmir

2018-01-19

Understanding the composition of a genetically-encoded (GE) library is instrumental to the success of ligand discovery. In this manuscript, we investigate the bias in GE-libraries of linear, macrocyclic and chemically post-translationally modified (cPTM) tetrapeptides displayed on the M13KE platform, which are produced via trinucleotide cassette synthesis (19 codons) and NNK-randomized codon. Differential enrichment of synthetic DNA {S}, ligated vector {L} (extension and ligation of synthetic DNA into the vector), naïve libraries {N} (transformation of the ligated vector into the bacteria followed by expression of the library for 4.5 hours to yield a "naïve" library), and libraries chemically modified by aldehyde ligation and cysteine macrocyclization {M} characterized by paired-end deep sequencing, detected a significant drop in diversity in {L} → {N}, but only a minor compositional difference in {S} → {L} and {N} → {M}. Libraries expressed at the N-terminus of phage protein pIII censored positively charged amino acids Arg and Lys; libraries expressed between pIII domains N1 and N2 overcame Arg/Lys-censorship but introduced new bias towards Gly and Ser. Interrogation of biases arising from cPTM by aldehyde ligation and cysteine macrocyclization unveiled censorship of sequences with Ser/Phe. Analogous analysis can be used to explore library diversity in new display platforms and optimize cPTM of these libraries.

The Deepwater Horizon Oil Spill: Ecogenomics of the Deep-Sea Plume

NASA Astrophysics Data System (ADS)

Hazen, T. C.

2012-12-01

The explosion on April 20, 2010 at the BP-leased Deepwater Horizon drilling rig in the Gulf of Mexico off the coast of Louisiana, resulted in oil and gas rising to the surface and the oil coming ashore in many parts of the Gulf, it also resulted in the dispersment of an immense oil plume 4,000 feet below the surface of the water. Despite spanning more than 600 feet in the water column and extending more than 10 miles from the wellhead, the dispersed oil plume was gone within weeks after the wellhead was capped - degraded and diluted to undetectable levels. Furthermore, this degradation took place without significant oxygen depletion. Ecogenomics enabled discovery of new and unclassified species of oil-eating bacteria that apparently lives in the deep Gulf where oil seeps are common. Using 16s microarrays, functional gene arrays, clone libraries, lipid analysis and a variety of hydrocarbon and micronutrient analyses we were able to characterize the oil degraders. Metagenomic sequence data was obtained for the deep-water samples using the Illumina platform. In addition, single cells were sorted and sequenced for the some of the most dominant bacteria that were represented in the oil plume; namely uncultivated representatives of Colwellia and Oceanospirillum. In addition, we performed laboratory microcosm experiments using uncontaminated water collected from The Gulf at the depth of the oil plume to which we added oil and COREXIT. These samples were characterized by 454 pyrotag. The results provide information about the key players and processes involved in degradation of oil, with and without COREXIT, in different impacted environments in The Gulf of Mexico. We are also extending these studies to explore dozens of deep sediment samples that were also collected after the oil spill around the wellhead. This data suggests that a great potential for intrinsic bioremediation of oil plumes exists in the deep-sea and other environs in the Gulf of Mexico.

Instant Integrated Ultradeep Quantitative-structural Membrane Proteomics Discovered Post-translational Modification Signatures for Human Cys-loop Receptor Subunit Bias.

PubMed

Zhang, Xi

2016-12-01

Neurotransmitter ligand-gated ion channels (LGICs) are widespread and pivotal in brain functions. Unveiling their structure-function mechanisms is crucial to drive drug discovery, and demands robust proteomic quantitation of expression, post-translational modifications (PTMs) and dynamic structures. Yet unbiased digestion of these modified transmembrane proteins-at high efficiency and peptide reproducibility-poses the obstacle. Targeting both enzyme-substrate contacts and PTMs for peptide formation and detection, we devised flow-and-detergent-facilitated protease and de-PTM digestions for deep sequencing (FDD) method that combined omni-compatible detergent, tandem immobilized protease/PNGase columns, and Cys-selective reduction/alkylation, to achieve streamlined ultradeep peptide preparation within minutes not days, at high peptide reproducibility and low abundance-bias. FDD transformed enzyme-protein contacts into equal catalytic travel paths through enzyme-excessive columns regardless of protein abundance, removed products instantly preventing inhibition, tackled intricate structures via sequential multiple micro-digestions along the flow, and precisely controlled peptide formation by flow rate. Peptide-stage reactions reduced steric bias; low contamination deepened MS/MS scan; distinguishing disulfide from M oxidation and avoiding gain/loss artifacts unmasked protein-endogenous oxidation states. Using a recent interactome of 285-kDa human GABA type A receptor, this pilot study validated FDD platform's applicability to deep sequencing (up to 99% coverage), H/D-exchange and TMT-based structural mapping. FDD discovered novel subunit-specific PTM signatures, including unusual nontop-surface N-glycosylations, that may drive subunit biases in human Cys-loop LGIC assembly and pharmacology, by redefining subunit/ligand interfaces and connecting function domains. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Patterns of DNA barcode variation in Canadian marine molluscs.

PubMed

Layton, Kara K S; Martel, André L; Hebert, Paul D N

2014-01-01

Molluscs are the most diverse marine phylum and this high diversity has resulted in considerable taxonomic problems. Because the number of species in Canadian oceans remains uncertain, there is a need to incorporate molecular methods into species identifications. A 648 base pair segment of the cytochrome c oxidase subunit I gene has proven useful for the identification and discovery of species in many animal lineages. While the utility of DNA barcoding in molluscs has been demonstrated in other studies, this is the first effort to construct a DNA barcode registry for marine molluscs across such a large geographic area. This study examines patterns of DNA barcode variation in 227 species of Canadian marine molluscs. Intraspecific sequence divergences ranged from 0-26.4% and a barcode gap existed for most taxa. Eleven cases of relatively deep (>2%) intraspecific divergence were detected, suggesting the possible presence of overlooked species. Structural variation was detected in COI with indels found in 37 species, mostly bivalves. Some indels were present in divergent lineages, primarily in the region of the first external loop, suggesting certain areas are hotspots for change. Lastly, mean GC content varied substantially among orders (24.5%-46.5%), and showed a significant positive correlation with nearest neighbour distances. DNA barcoding is an effective tool for the identification of Canadian marine molluscs and for revealing possible cases of overlooked species. Some species with deep intraspecific divergence showed a biogeographic partition between lineages on the Atlantic, Arctic and Pacific coasts, suggesting the role of Pleistocene glaciations in the subdivision of their populations. Indels were prevalent in the barcode region of the COI gene in bivalves and gastropods. This study highlights the efficacy of DNA barcoding for providing insights into sequence variation across a broad taxonomic group on a large geographic scale.

Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification.

PubMed

Fu, Shuyue; Liu, Xiang; Luo, Maochao; Xie, Ke; Nice, Edouard C; Zhang, Haiyuan; Huang, Canhua

2017-04-01

Chemoresistance is a major obstacle for current cancer treatment. Proteogenomics is a powerful multi-omics research field that uses customized protein sequence databases generated by genomic and transcriptomic information to identify novel genes (e.g. noncoding, mutation and fusion genes) from mass spectrometry-based proteomic data. By identifying aberrations that are differentially expressed between tumor and normal pairs, this approach can also be applied to validate protein variants in cancer, which may reveal the response to drug treatment. Areas covered: In this review, we will present recent advances in proteogenomic investigations of cancer drug resistance with an emphasis on integrative proteogenomic pipelines and the biomarker discovery which contributes to achieving the goal of using precision/personalized medicine for cancer treatment. Expert commentary: The discovery and comprehensive understanding of potential biomarkers help identify the cohort of patients who may benefit from particular treatments, and will assist real-time clinical decision-making to maximize therapeutic efficacy and minimize adverse effects. With the development of MS-based proteomics and NGS-based sequencing, a growing number of proteogenomic tools are being developed specifically to investigate cancer drug resistance.

Joint deep shape and appearance learning: application to optic pathway glioma segmentation

NASA Astrophysics Data System (ADS)

Mansoor, Awais; Li, Ien; Packer, Roger J.; Avery, Robert A.; Linguraru, Marius George

2017-03-01

Automated tissue characterization is one of the major applications of computer-aided diagnosis systems. Deep learning techniques have recently demonstrated impressive performance for the image patch-based tissue characterization. However, existing patch-based tissue classification techniques struggle to exploit the useful shape information. Local and global shape knowledge such as the regional boundary changes, diameter, and volumetrics can be useful in classifying the tissues especially in scenarios where the appearance signature does not provide significant classification information. In this work, we present a deep neural network-based method for the automated segmentation of the tumors referred to as optic pathway gliomas (OPG) located within the anterior visual pathway (AVP; optic nerve, chiasm or tracts) using joint shape and appearance learning. Voxel intensity values of commonly used MRI sequences are generally not indicative of OPG. To be considered an OPG, current clinical practice dictates that some portion of AVP must demonstrate shape enlargement. The method proposed in this work integrates multiple sequence magnetic resonance image (T1, T2, and FLAIR) along with local boundary changes to train a deep neural network. For training and evaluation purposes, we used a dataset of multiple sequence MRI obtained from 20 subjects (10 controls, 10 NF1+OPG). To our best knowledge, this is the first deep representation learning-based approach designed to merge shape and multi-channel appearance data for the glioma detection. In our experiments, mean misclassification errors of 2:39% and 0:48% were observed respectively for glioma and control patches extracted from the AVP. Moreover, an overall dice similarity coefficient of 0:87+/-0:13 (0:93+/-0:06 for healthy tissue, 0:78+/-0:18 for glioma tissue) demonstrates the potential of the proposed method in the accurate localization and early detection of OPG.

Targeted parallel sequencing of the Musa species: searching for an alternative model system for polyploidy studies

USDA-ARS?s Scientific Manuscript database

Modern day genomics holds the promise of solving the complexities of basic plant sciences, and of catalyzing practical advances in plant breeding. While contiguous, "base perfect" deep sequencing is a key module of any genome project, recent advances in parallel next generation sequencing technologi...

Eric Davidson and deep time.

PubMed

Erwin, Douglas H

2017-10-13

Eric Davidson had a deep and abiding interest in the role developmental mechanisms played in generating evolutionary patterns documented in deep time, from the origin of the euechinoids to the processes responsible for the morphological architectures of major animal clades. Although not an evolutionary biologist, Davidson's interests long preceded the current excitement over comparative evolutionary developmental biology. Here I discuss three aspects at the intersection between his research and evolutionary patterns in deep time: First, understanding the mechanisms of body plan formation, particularly those associated with the early diversification of major metazoan clades. Second, a critique of early claims about ancestral metazoans based on the discoveries of highly conserved genes across bilaterian animals. Third, Davidson's own involvement in paleontology through a collaborative study of the fossil embryos from the Ediacaran Doushantuo Formation in south China.

NOAA Office of Ocean Exploration and Research'sOkeanos Explorer Program 2014 Discoveries - U.S. Atlantic Continental Margin and Gulf of Mexico

NASA Astrophysics Data System (ADS)

Lobecker, E.; McKenna, L.; Sowers, D.; Elliott, K.; Kennedy, B.

2014-12-01

NOAA ShipOkeanos Explorer, the only U.S. federal vessel dedicated to global ocean exploration, made several important discoveries in U.S. waters of the North Atlantic Ocean and Gulf of Mexico during the 2014 field season. Based on input received from a broad group ofmarine scientists and resource managers, over 100,000 square kilometers of seafloor and associated water column were systematically explored using advanced mapping sonars. 39 ROV diveswere conducted, leading to new discoveries that will further ourunderstanding of biologic, geologic, and underwater-cultural heritage secrets hidden withinthe oceans. In the Atlantic, season highlights include completion of a multi-year submarine canyons mapping effort of the continental shelf break from North Carolina to the U.S.-Canada maritime border;new information on the ephemerality of recently discovered and geographically extensive cold water seeps; and continued exploration of the New England Seamount chain; and mapping of two potential historically significant World War II wreck sites. In the Gulf of Mexico, season highlights includecompletion of a multi-year mapping effort of the West Florida Escarpment providing new insight into submarine landslides and detachment zones;the discovery of at least two asphalt volcanoes, or 'tar lilies'; range extensions of deep-sea corals; discovery of two potential new species of crinoids; identification of at least 300 potential cold water seeps; and ROV exploration of three historically significant19th century shipwrecks. In both regions, high-resolution mapping led to new insight into the geological context in which deep sea corals develop,while ROV dives provided valuable observations of deep sea coral habitats and their associated organisms, and chemosynthetic habitats. All mapping and ROV data is freely available to the public in usable data formats and maintained in national geophysical and oceanographic data archives.

BLSSpeller: exhaustive comparative discovery of conserved cis-regulatory elements.

PubMed

De Witte, Dieter; Van de Velde, Jan; Decap, Dries; Van Bel, Michiel; Audenaert, Pieter; Demeester, Piet; Dhoedt, Bart; Vandepoele, Klaas; Fostier, Jan

2015-12-01

The accurate discovery and annotation of regulatory elements remains a challenging problem. The growing number of sequenced genomes creates new opportunities for comparative approaches to motif discovery. Putative binding sites are then considered to be functional if they are conserved in orthologous promoter sequences of multiple related species. Existing methods for comparative motif discovery usually rely on pregenerated multiple sequence alignments, which are difficult to obtain for more diverged species such as plants. As a consequence, misaligned regulatory elements often remain undetected. We present a novel algorithm that supports both alignment-free and alignment-based motif discovery in the promoter sequences of related species. Putative motifs are exhaustively enumerated as words over the IUPAC alphabet and screened for conservation using the branch length score. Additionally, a confidence score is established in a genome-wide fashion. In order to take advantage of a cloud computing infrastructure, the MapReduce programming model is adopted. The method is applied to four monocotyledon plant species and it is shown that high-scoring motifs are significantly enriched for open chromatin regions in Oryza sativa and for transcription factor binding sites inferred through protein-binding microarrays in O.sativa and Zea mays. Furthermore, the method is shown to recover experimentally profiled ga2ox1-like KN1 binding sites in Z.mays. BLSSpeller was written in Java. Source code and manual are available at http://bioinformatics.intec.ugent.be/blsspeller Klaas.Vandepoele@psb.vib-ugent.be or jan.fostier@intec.ugent.be. Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

BLSSpeller: exhaustive comparative discovery of conserved cis-regulatory elements

PubMed Central

De Witte, Dieter; Van de Velde, Jan; Decap, Dries; Van Bel, Michiel; Audenaert, Pieter; Demeester, Piet; Dhoedt, Bart; Vandepoele, Klaas; Fostier, Jan

2015-01-01

Motivation: The accurate discovery and annotation of regulatory elements remains a challenging problem. The growing number of sequenced genomes creates new opportunities for comparative approaches to motif discovery. Putative binding sites are then considered to be functional if they are conserved in orthologous promoter sequences of multiple related species. Existing methods for comparative motif discovery usually rely on pregenerated multiple sequence alignments, which are difficult to obtain for more diverged species such as plants. As a consequence, misaligned regulatory elements often remain undetected. Results: We present a novel algorithm that supports both alignment-free and alignment-based motif discovery in the promoter sequences of related species. Putative motifs are exhaustively enumerated as words over the IUPAC alphabet and screened for conservation using the branch length score. Additionally, a confidence score is established in a genome-wide fashion. In order to take advantage of a cloud computing infrastructure, the MapReduce programming model is adopted. The method is applied to four monocotyledon plant species and it is shown that high-scoring motifs are significantly enriched for open chromatin regions in Oryza sativa and for transcription factor binding sites inferred through protein-binding microarrays in O.sativa and Zea mays. Furthermore, the method is shown to recover experimentally profiled ga2ox1-like KN1 binding sites in Z.mays. Availability and implementation: BLSSpeller was written in Java. Source code and manual are available at http://bioinformatics.intec.ugent.be/blsspeller Contact: Klaas.Vandepoele@psb.vib-ugent.be or jan.fostier@intec.ugent.be Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26254488

Assessing DNA Barcodes for Species Identification in North American Reptiles and Amphibians in Natural History Collections.

PubMed

Chambers, E Anne; Hebert, Paul D N

2016-01-01

High rates of species discovery and loss have led to the urgent need for more rapid assessment of species diversity in the herpetofauna. DNA barcoding allows for the preliminary identification of species based on sequence divergence. Prior DNA barcoding work on reptiles and amphibians has revealed higher biodiversity counts than previously estimated due to cases of cryptic and undiscovered species. Past studies have provided DNA barcodes for just 14% of the North American herpetofauna, revealing the need for expanded coverage. This study extends the DNA barcode reference library for North American herpetofauna, assesses the utility of this approach in aiding species delimitation, and examines the correspondence between current species boundaries and sequence clusters designated by the BIN system. Sequences were obtained from 730 specimens, representing 274 species (43%) from the North American herpetofauna. Mean intraspecific divergences were 1% and 3%, while average congeneric sequence divergences were 16% and 14% in amphibians and reptiles, respectively. BIN assignments corresponded with current species boundaries in 79% of amphibians, 100% of turtles, and 60% of squamates. Deep divergences (>2%) were noted in 35% of squamate and 16% of amphibian species, and low divergences (<2%) occurred in 12% of reptiles and 23% of amphibians, patterns reflected in BIN assignments. Sequence recovery declined with specimen age, and variation in recovery success was noted among collections. Within collections, barcodes effectively flagged seven mislabeled tissues, and barcode fragments were recovered from five formalin-fixed specimens. This study demonstrates that DNA barcodes can effectively flag errors in museum collections, while BIN splits and merges reveal taxa belonging to deeply diverged or hybridizing lineages. This study is the first effort to compile a reference library of DNA barcodes for herpetofauna on a continental scale.

Assessing DNA Barcodes for Species Identification in North American Reptiles and Amphibians in Natural History Collections

PubMed Central

Chambers, E. Anne; Hebert, Paul D. N.

2016-01-01

Background High rates of species discovery and loss have led to the urgent need for more rapid assessment of species diversity in the herpetofauna. DNA barcoding allows for the preliminary identification of species based on sequence divergence. Prior DNA barcoding work on reptiles and amphibians has revealed higher biodiversity counts than previously estimated due to cases of cryptic and undiscovered species. Past studies have provided DNA barcodes for just 14% of the North American herpetofauna, revealing the need for expanded coverage. Methodology/Principal Findings This study extends the DNA barcode reference library for North American herpetofauna, assesses the utility of this approach in aiding species delimitation, and examines the correspondence between current species boundaries and sequence clusters designated by the BIN system. Sequences were obtained from 730 specimens, representing 274 species (43%) from the North American herpetofauna. Mean intraspecific divergences were 1% and 3%, while average congeneric sequence divergences were 16% and 14% in amphibians and reptiles, respectively. BIN assignments corresponded with current species boundaries in 79% of amphibians, 100% of turtles, and 60% of squamates. Deep divergences (>2%) were noted in 35% of squamate and 16% of amphibian species, and low divergences (<2%) occurred in 12% of reptiles and 23% of amphibians, patterns reflected in BIN assignments. Sequence recovery declined with specimen age, and variation in recovery success was noted among collections. Within collections, barcodes effectively flagged seven mislabeled tissues, and barcode fragments were recovered from five formalin-fixed specimens. Conclusions/Significance This study demonstrates that DNA barcodes can effectively flag errors in museum collections, while BIN splits and merges reveal taxa belonging to deeply diverged or hybridizing lineages. This study is the first effort to compile a reference library of DNA barcodes for herpetofauna on a continental scale. PMID:27116180

The chronostratigraphy of the Haua Fteah cave (Cyrenaica, northeast Libya).

PubMed

Douka, Katerina; Jacobs, Zenobia; Lane, Christine; Grün, Rainer; Farr, Lucy; Hunt, Chris; Inglis, Robyn H; Reynolds, Tim; Albert, Paul; Aubert, Maxime; Cullen, Victoria; Hill, Evan; Kinsley, Leslie; Roberts, Richard G; Tomlinson, Emma L; Wulf, Sabine; Barker, Graeme

2014-01-01

The 1950s excavations by Charles McBurney in the Haua Fteah, a large karstic cave on the coast of northeast Libya, revealed a deep sequence of human occupation. Most subsequent research on North African prehistory refers to his discoveries and interpretations, but the chronology of its archaeological and geological sequences has been based on very early age determinations. This paper reports on the initial results of a comprehensive multi-method dating program undertaken as part of new work at the site, involving radiocarbon dating of charcoal, land snails and marine shell, cryptotephra investigations, optically stimulated luminescence (OSL) dating of sediments, and electron spin resonance (ESR) dating of tooth enamel. The dating samples were collected from the newly exposed and cleaned faces of the upper 7.5 m of the ∼14.0 m-deep McBurney trench, which contain six of the seven major cultural phases that he identified. Despite problems of sediment transport and reworking, using a Bayesian statistical model the new dating program establishes a robust framework for the five major lithostratigraphic units identified in the stratigraphic succession, and for the major cultural units. The age of two anatomically modern human mandibles found by McBurney in Layer XXXIII near the base of his Levalloiso-Mousterian phase can now be estimated to between 73 and 65 ka (thousands of years ago) at the 95.4% confidence level, within Marine Isotope Stage (MIS) 4. McBurney's Layer XXV, associated with Upper Palaeolithic Dabban blade industries, has a clear stratigraphic relationship with Campanian Ignimbrite tephra. Microlithic Oranian technologies developed following the climax of the Last Glacial Maximum and the more microlithic Capsian in the Younger Dryas. Neolithic pottery and perhaps domestic livestock were used in the cave from the mid Holocene but there is no certain evidence for plant cultivation until the Graeco-Roman period. Copyright © 2013 Elsevier Ltd. All rights reserved.

Patterns of homoeologous gene expression shown by RNA sequencing in hexaploid bread wheat

PubMed Central

2014-01-01

Background Bread wheat (Triticum aestivum) has a large, complex and hexaploid genome consisting of A, B and D homoeologous chromosome sets. Therefore each wheat gene potentially exists as a trio of A, B and D homoeoloci, each of which may contribute differentially to wheat phenotypes. We describe a novel approach combining wheat cytogenetic resources (chromosome substitution ‘nullisomic-tetrasomic’ lines) with next generation deep sequencing of gene transcripts (RNA-Seq), to directly and accurately identify homoeologue-specific single nucleotide variants and quantify the relative contribution of individual homoeoloci to gene expression. Results We discover, based on a sample comprising ~5-10% of the total wheat gene content, that at least 45% of wheat genes are expressed from all three distinct homoeoloci. Most of these genes show strikingly biased expression patterns in which expression is dominated by a single homoeolocus. The remaining ~55% of wheat genes are expressed from either one or two homoeoloci only, through a combination of extensive transcriptional silencing and homoeolocus loss. Conclusions We conclude that wheat is tending towards functional diploidy, through a variety of mechanisms causing single homoeoloci to become the predominant source of gene transcripts. This discovery has profound consequences for wheat breeding and our understanding of wheat evolution. PMID:24726045

deepTools2: a next generation web server for deep-sequencing data analysis.

PubMed

Ramírez, Fidel; Ryan, Devon P; Grüning, Björn; Bhardwaj, Vivek; Kilpert, Fabian; Richter, Andreas S; Heyne, Steffen; Dündar, Friederike; Manke, Thomas

2016-07-08

We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Active subsurface cellular function in the Baltic Sea Basin, IODP Exp 347

NASA Astrophysics Data System (ADS)

Reese, B. K.; Zinke, L. A.; Bird, J. T.; Lloyd, K. G.; Marshall, I.; Amend, J.; Jørgensen, B. B.

2016-12-01

The Baltic Sea Basin is a unique depositional setting that has experienced periods of glaciation and deglaciation as a result of global temperature fluctuations over the course of several hundred thousand years. This has resulted in laminated sediments formed during periods with strong permanent salinity stratification. The high sedimentation rates (100-500 cm/1000 y) make this an ideal setting to understand the microbial structure of a deep biosphere community in a high-organic matter environment. The responses of deep sediment microbial communities to variations in conditions during and after deposition are poorly understood. Samples were collected through scientific drilling during the International Ocean Discovery Program (IODP) Expedition 347 on board the Greatship Manisha, September-November 2013. We examined the active microbial community structure using the 16S rRNA gene transcript and active functional genes through metatranscriptome sequencing. Major biogeochemical shifts have been observed in response to the depositional history between the limnic, brackish, and marine phases. The microbial community structure in the BSB is diverse and reflective of the unique changes in the geochemical profile. These data further define the existence life in the deep subsurface and the survival mechanisms required for this extreme environment.

Anonymization of electronic medical records for validating genome-wide association studies

PubMed Central

Loukides, Grigorios; Gkoulalas-Divanis, Aris; Malin, Bradley

2010-01-01

Genome-wide association studies (GWAS) facilitate the discovery of genotype–phenotype relations from population-based sequence databases, which is an integral facet of personalized medicine. The increasing adoption of electronic medical records allows large amounts of patients’ standardized clinical features to be combined with the genomic sequences of these patients and shared to support validation of GWAS findings and to enable novel discoveries. However, disseminating these data “as is” may lead to patient reidentification when genomic sequences are linked to resources that contain the corresponding patients’ identity information based on standardized clinical features. This work proposes an approach that provably prevents this type of data linkage and furnishes a result that helps support GWAS. Our approach automatically extracts potentially linkable clinical features and modifies them in a way that they can no longer be used to link a genomic sequence to a small number of patients, while preserving the associations between genomic sequences and specific sets of clinical features corresponding to GWAS-related diseases. Extensive experiments with real patient data derived from the Vanderbilt's University Medical Center verify that our approach generates data that eliminate the threat of individual reidentification, while supporting GWAS validation and clinical case analysis tasks. PMID:20385806

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

PubMed

Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

2018-03-01

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

Overview of Petroleum Settings in Deep Waters of the Brazilian South Atlantic Margin

NASA Astrophysics Data System (ADS)

Anjos, Sylvia; Penteado, Henrique; Oliveira, Carlos M. M.

2015-04-01

The objective of this work is to present an overall view of the tectonic and stratigraphic evolution of the western South Atlantic with focus on the Brazilian marginal basins. It includes the structural evolution, stratigraphic sequences, depositional environments and petroleum systems model along the Brazilian marginal basins. In addition, a description of the main petroleum provinces and selected plays including the pre-salt carbonates and post-salt turbidite reservoirs is presented. Source-rock ages and types, trap styles, main reservoir characteristics, petroleum compositions, and recent exploration results are discussed. Finally, an outlook and general assessment of the impact of the large pre-salt discoveries on the present-day and future production curves are given.

Exploring DNA variant segregation types in pooled genome sequencing enables effective mapping of weeping trait in Malus

USDA-ARS?s Scientific Manuscript database

In recent years, next generation sequencing (NGS) based bulked segregant analysis (BSA) has become a powerful approach for allele discovery in non-model plant species. However, challenges remain, particular for out-crossing species with complex genomes. Here, the genetic control of a weeping bran...

Scaling up discovery of hidden diversity in fungi: impacts of barcoding approaches.

PubMed

Yahr, Rebecca; Schoch, Conrad L; Dentinger, Bryn T M

2016-09-05

The fungal kingdom is a hyperdiverse group of multicellular eukaryotes with profound impacts on human society and ecosystem function. The challenge of documenting and describing fungal diversity is exacerbated by their typically cryptic nature, their ability to produce seemingly unrelated morphologies from a single individual and their similarity in appearance to distantly related taxa. This multiplicity of hurdles resulted in the early adoption of DNA-based comparisons to study fungal diversity, including linking curated DNA sequence data to expertly identified voucher specimens. DNA-barcoding approaches in fungi were first applied in specimen-based studies for identification and discovery of taxonomic diversity, but are now widely deployed for community characterization based on sequencing of environmental samples. Collectively, fungal barcoding approaches have yielded important advances across biological scales and research applications, from taxonomic, ecological, industrial and health perspectives. A major outstanding issue is the growing problem of 'sequences without names' that are somewhat uncoupled from the traditional framework of fungal classification based on morphology and preserved specimens. This review summarizes some of the most significant impacts of fungal barcoding, its limitations, and progress towards the challenge of effective utilization of the exponentially growing volume of data gathered from high-throughput sequencing technologies.This article is part of the themed issue 'From DNA barcodes to biomes'. © 2016 The Authors.

Overview Article: Identifying transcriptional cis-regulatory modules in animal genomes

PubMed Central

Suryamohan, Kushal; Halfon, Marc S.

2014-01-01

Gene expression is regulated through the activity of transcription factors and chromatin modifying proteins acting on specific DNA sequences, referred to as cis-regulatory elements. These include promoters, located at the transcription initiation sites of genes, and a variety of distal cis-regulatory modules (CRMs), the most common of which are transcriptional enhancers. Because regulated gene expression is fundamental to cell differentiation and acquisition of new cell fates, identifying, characterizing, and understanding the mechanisms of action of CRMs is critical for understanding development. CRM discovery has historically been challenging, as CRMs can be located far from the genes they regulate, have few readily-identifiable sequence characteristics, and for many years were not amenable to high-throughput discovery methods. However, the recent availability of complete genome sequences and the development of next-generation sequencing methods has led to an explosion of both computational and empirical methods for CRM discovery in model and non-model organisms alike. Experimentally, CRMs can be identified through chromatin immunoprecipitation directed against transcription factors or histone post-translational modifications, identification of nucleosome-depleted “open” chromatin regions, or sequencing-based high-throughput functional screening. Computational methods include comparative genomics, clustering of known or predicted transcription factor binding sites, and supervised machine-learning approaches trained on known CRMs. All of these methods have proven effective for CRM discovery, but each has its own considerations and limitations, and each is subject to a greater or lesser number of false-positive identifications. Experimental confirmation of predictions is essential, although shortcomings in current methods suggest that additional means of validation need to be developed. PMID:25704908

Sequence-Based Discovery Demonstrates That Fixed Light Chain Human Transgenic Rats Produce a Diverse Repertoire of Antigen-Specific Antibodies.

PubMed

Harris, Katherine E; Aldred, Shelley Force; Davison, Laura M; Ogana, Heather Anne N; Boudreau, Andrew; Brüggemann, Marianne; Osborn, Michael; Ma, Biao; Buelow, Benjamin; Clarke, Starlynn C; Dang, Kevin H; Iyer, Suhasini; Jorgensen, Brett; Pham, Duy T; Pratap, Payal P; Rangaswamy, Udaya S; Schellenberger, Ute; van Schooten, Wim C; Ugamraj, Harshad S; Vafa, Omid; Buelow, Roland; Trinklein, Nathan D

2018-01-01

We created a novel transgenic rat that expresses human antibodies comprising a diverse repertoire of heavy chains with a single common rearranged kappa light chain (IgKV3-15-JK1). This fixed light chain animal, called OmniFlic, presents a unique system for human therapeutic antibody discovery and a model to study heavy chain repertoire diversity in the context of a constant light chain. The purpose of this study was to analyze heavy chain variable gene usage, clonotype diversity, and to describe the sequence characteristics of antigen-specific monoclonal antibodies (mAbs) isolated from immunized OmniFlic animals. Using next-generation sequencing antibody repertoire analysis, we measured heavy chain variable gene usage and the diversity of clonotypes present in the lymph node germinal centers of 75 OmniFlic rats immunized with 9 different protein antigens. Furthermore, we expressed 2,560 unique heavy chain sequences sampled from a diverse set of clonotypes as fixed light chain antibody proteins and measured their binding to antigen by ELISA. Finally, we measured patterns and overall levels of somatic hypermutation in the full B-cell repertoire and in the 2,560 mAbs tested for binding. The results demonstrate that OmniFlic animals produce an abundance of antigen-specific antibodies with heavy chain clonotype diversity that is similar to what has been described with unrestricted light chain use in mammals. In addition, we show that sequence-based discovery is a highly effective and efficient way to identify a large number of diverse monoclonal antibodies to a protein target of interest.

Deep sequencing reveals exceptional diversity and modes of transmission for bacterial sponge symbionts.

PubMed

Webster, Nicole S; Taylor, Michael W; Behnam, Faris; Lücker, Sebastian; Rattei, Thomas; Whalan, Stephen; Horn, Matthias; Wagner, Michael

2010-08-01

Marine sponges contain complex bacterial communities of considerable ecological and biotechnological importance, with many of these organisms postulated to be specific to sponge hosts. Testing this hypothesis in light of the recent discovery of the rare microbial biosphere, we investigated three Australian sponges by massively parallel 16S rRNA gene tag pyrosequencing. Here we show bacterial diversity that is unparalleled in an invertebrate host, with more than 250,000 sponge-derived sequence tags being assigned to 23 bacterial phyla and revealing up to 2996 operational taxonomic units (95% sequence similarity) per sponge species. Of the 33 previously described 'sponge-specific' clusters that were detected in this study, 48% were found exclusively in adults and larvae - implying vertical transmission of these groups. The remaining taxa, including 'Poribacteria', were also found at very low abundance among the 135,000 tags retrieved from surrounding seawater. Thus, members of the rare seawater biosphere may serve as seed organisms for widely occurring symbiont populations in sponges and their host association might have evolved much more recently than previously thought. © 2009 Society for Applied Microbiology and Blackwell Publishing Ltd.

Deep sequencing reveals exceptional diversity and modes of transmission for bacterial sponge symbionts

PubMed Central

Webster, Nicole S; Taylor, Michael W; Behnam, Faris; Lücker, Sebastian; Rattei, Thomas; Whalan, Stephen; Horn, Matthias; Wagner, Michael

2010-01-01

Marine sponges contain complex bacterial communities of considerable ecological and biotechnological importance, with many of these organisms postulated to be specific to sponge hosts. Testing this hypothesis in light of the recent discovery of the rare microbial biosphere, we investigated three Australian sponges by massively parallel 16S rRNA gene tag pyrosequencing. Here we show bacterial diversity that is unparalleled in an invertebrate host, with more than 250 000 sponge-derived sequence tags being assigned to 23 bacterial phyla and revealing up to 2996 operational taxonomic units (95% sequence similarity) per sponge species. Of the 33 previously described ‘sponge-specific’ clusters that were detected in this study, 48% were found exclusively in adults and larvae – implying vertical transmission of these groups. The remaining taxa, including ‘Poribacteria’, were also found at very low abundance among the 135 000 tags retrieved from surrounding seawater. Thus, members of the rare seawater biosphere may serve as seed organisms for widely occurring symbiont populations in sponges and their host association might have evolved much more recently than previously thought. PMID:21966903

SACCHARIS: an automated pipeline to streamline discovery of carbohydrate active enzyme activities within polyspecific families and de novo sequence datasets.

PubMed

Jones, Darryl R; Thomas, Dallas; Alger, Nicholas; Ghavidel, Ata; Inglis, G Douglas; Abbott, D Wade

2018-01-01

Deposition of new genetic sequences in online databases is expanding at an unprecedented rate. As a result, sequence identification continues to outpace functional characterization of carbohydrate active enzymes (CAZymes). In this paradigm, the discovery of enzymes with novel functions is often hindered by high volumes of uncharacterized sequences particularly when the enzyme sequence belongs to a family that exhibits diverse functional specificities (i.e., polyspecificity). Therefore, to direct sequence-based discovery and characterization of new enzyme activities we have developed an automated in silico pipeline entitled: Sequence Analysis and Clustering of CarboHydrate Active enzymes for Rapid Informed prediction of Specificity (SACCHARIS). This pipeline streamlines the selection of uncharacterized sequences for discovery of new CAZyme or CBM specificity from families currently maintained on the CAZy website or within user-defined datasets. SACCHARIS was used to generate a phylogenetic tree of a GH43, a CAZyme family with defined subfamily designations. This analysis confirmed that large datasets can be organized into sequence clusters of manageable sizes that possess related functions. Seeding this tree with a GH43 sequence from Bacteroides dorei DSM 17855 (BdGH43b, revealed it partitioned as a single sequence within the tree. This pattern was consistent with it possessing a unique enzyme activity for GH43 as BdGH43b is the first described α-glucanase described for this family. The capacity of SACCHARIS to extract and cluster characterized carbohydrate binding module sequences was demonstrated using family 6 CBMs (i.e., CBM6s). This CBM family displays a polyspecific ligand binding profile and contains many structurally determined members. Using SACCHARIS to identify a cluster of divergent sequences, a CBM6 sequence from a unique clade was demonstrated to bind yeast mannan, which represents the first description of an α-mannan binding CBM. Additionally, we have performed a CAZome analysis of an in-house sequenced bacterial genome and a comparative analysis of B. thetaiotaomicron VPI-5482 and B. thetaiotaomicron 7330, to demonstrate that SACCHARIS can generate "CAZome fingerprints", which differentiate between the saccharolytic potential of two related strains in silico. Establishing sequence-function and sequence-structure relationships in polyspecific CAZyme families are promising approaches for streamlining enzyme discovery. SACCHARIS facilitates this process by embedding CAZyme and CBM family trees generated from biochemically to structurally characterized sequences, with protein sequences that have unknown functions. In addition, these trees can be integrated with user-defined datasets (e.g., genomics, metagenomics, and transcriptomics) to inform experimental characterization of new CAZymes or CBMs not currently curated, and for researchers to compare differential sequence patterns between entire CAZomes. In this light, SACCHARIS provides an in silico tool that can be tailored for enzyme bioprospecting in datasets of increasing complexity and for diverse applications in glycobiotechnology.

Large-scale identification and comparative analysis of miRNA expression profile in the respiratory tree of the sea cucumber Apostichopus japonicus during aestivation.

PubMed

Chen, Muyan; Storey, Kenneth B

2014-02-01

The sea cucumber Apostichopus japonicus withstands high water temperatures in the summer by suppressing its metabolic rate and entering a state of aestivation. We hypothesized that changes in the expression of miRNAs could provide important post-transcriptional regulation of gene expression during hypometabolism via control over mRNA translation. The present study analyzed profiles of miRNA expression in the sea cucumber respiratory tree using Solexa deep sequencing technology. We identified 279 sea cucumber miRNAs, including 15 novel miRNAs specific to sea cucumber. Animals sampled during deep aestivation (DA; after at least 15 days of continuous torpor) were compared with animals from a non-aestivation (NA) state (animals that had passed through aestivation and returned to an active state). We identified 30 differentially expressed miRNAs ([RPM (reads per million) >10, |FC| (|fold change|)≥1, FDR (false discovery rate)<0.01]) during aestivation, which were validated by two other miRNA profiling methods: miRNA microarray and real-time PCR. Among the most prominent miRNA species, miR-124, miR-124-3p, miR-79, miR-9 and miR-2010 were significantly over-expressed during deep aestivation compared with non-aestivation animals, suggesting that these miRNAs may play important roles in metabolic rate suppression during aestivation. High-throughput sequencing data and microarray data have been submitted to the GEO database with accession number: 16902695. Copyright © 2014 Elsevier B.V. All rights reserved.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge.

PubMed

Zhan, Tianming; Chen, Yi; Hong, Xunning; Lu, Zhenyu; Chen, Yunjie

2017-01-01

In this paper, we propose an automatic brain tumor segmentation method based on Deep Belief Networks (DBNs) and pathological knowledge. The proposed method is targeted against gliomas (both low and high grade) obtained in multi-sequence magnetic resonance images (MRIs). Firstly, a novel deep architecture is proposed to combine the multi-sequences intensities feature extraction with classification to get the classification probabilities of each voxel. Then, graph cut based optimization is executed on the classification probabilities to strengthen the spatial relationships of voxels. At last, pathological knowledge of gliomas is applied to remove some false positives. Our method was validated in the Brain Tumor Segmentation Challenge 2012 and 2013 databases (BRATS 2012, 2013). The performance of segmentation results demonstrates our proposal providing a competitive solution with stateof- the-art methods. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Ancient orphan crop joins modern era: gene-based SNP discovery and mapping in lentil.

PubMed

Sharpe, Andrew G; Ramsay, Larissa; Sanderson, Lacey-Anne; Fedoruk, Michael J; Clarke, Wayne E; Li, Rong; Kagale, Sateesh; Vijayan, Perumal; Vandenberg, Albert; Bett, Kirstin E

2013-03-18

The genus Lens comprises a range of closely related species within the galegoid clade of the Papilionoideae family. The clade includes other important crops (e.g. chickpea and pea) as well as a sequenced model legume (Medicago truncatula). Lentil is a global food crop increasing in importance in the Indian sub-continent and elsewhere due to its nutritional value and quick cooking time. Despite this importance there has been a dearth of genetic and genomic resources for the crop and this has limited the application of marker-assisted selection strategies in breeding. We describe here the development of a deep and diverse transcriptome resource for lentil using next generation sequencing technology. The generation of data in multiple cultivated (L. culinaris) and wild (L. ervoides) genotypes together with the utilization of a bioinformatics workflow enabled the identification of a large collection of SNPs and the subsequent development of a genotyping platform that was used to establish the first comprehensive genetic map of the L. culinaris genome. Extensive collinearity with M. truncatula was evident on the basis of sequence homology between mapped markers and the model genome and large translocations and inversions relative to M. truncatula were identified. An estimate for the time divergence of L. culinaris from L. ervoides and of both from M. truncatula was also calculated. The availability of the genomic and derived molecular marker resources presented here will help change lentil breeding strategies and lead to increased genetic gain in the future.

MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.

PubMed

Wang, Duolin; Zeng, Shuai; Xu, Chunhui; Qiu, Wangren; Liang, Yanchun; Joshi, Trupti; Xu, Dong

2017-12-15

Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction. We present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data. MusiteDeep is provided as an open-source tool available at https://github.com/duolinwang/MusiteDeep. xudong@missouri.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Deep Sequencing to Identify the Causes of Viral Encephalitis

PubMed Central

Chan, Benjamin K.; Wilson, Theodore; Fischer, Kael F.; Kriesel, John D.

2014-01-01

Deep sequencing allows for a rapid, accurate characterization of microbial DNA and RNA sequences in many types of samples. Deep sequencing (also called next generation sequencing or NGS) is being developed to assist with the diagnosis of a wide variety of infectious diseases. In this study, seven frozen brain samples from deceased subjects with recent encephalitis were investigated. RNA from each sample was extracted, randomly reverse transcribed and sequenced. The sequence analysis was performed in a blinded fashion and confirmed with pathogen-specific PCR. This analysis successfully identified measles virus sequences in two brain samples and herpes simplex virus type-1 sequences in three brain samples. No pathogen was identified in the other two brain specimens. These results were concordant with pathogen-specific PCR and partially concordant with prior neuropathological examinations, demonstrating that deep sequencing can accurately identify viral infections in frozen brain tissue. PMID:24699691

Benthic protists and fungi of Mediterranean deep hypsersaline anoxic basin redoxcline sediments.

PubMed

Bernhard, Joan M; Kormas, Konstantinos; Pachiadaki, Maria G; Rocke, Emma; Beaudoin, David J; Morrison, Colin; Visscher, Pieter T; Cobban, Alec; Starczak, Victoria R; Edgcomb, Virginia P

2014-01-01

Some of the most extreme marine habitats known are the Mediterranean deep hypersaline anoxic basins (DHABs; water depth ∼3500 m). Brines of DHABs are nearly saturated with salt, leading many to suspect they are uninhabitable for eukaryotes. While diverse bacterial and protistan communities are reported from some DHAB water-column haloclines and brines, the existence and activity of benthic DHAB protists have rarely been explored. Here, we report findings regarding protists and fungi recovered from sediments of three DHAB (Discovery, Urania, L' Atalante) haloclines, and compare these to communities from sediments underlying normoxic waters of typical Mediterranean salinity. Halocline sediments, where the redoxcline impinges the seafloor, were studied from all three DHABs. Microscopic cell counts suggested that halocline sediments supported denser protist populations than those in adjacent control sediments. Pyrosequencing analysis based on ribosomal RNA detected eukaryotic ribotypes in the halocline sediments from each of the three DHABs, most of which were fungi. Sequences affiliated with Ustilaginomycotina Basidiomycota were the most abundant eukaryotic signatures detected. Benthic communities in these DHABs appeared to differ, as expected, due to differing brine chemistries. Microscopy indicated that only a low proportion of protists appeared to bear associated putative symbionts. In a considerable number of cases, when prokaryotes were associated with a protist, DAPI staining did not reveal presence of any nuclei, suggesting that at least some protists were carcasses inhabited by prokaryotic scavengers.

Benthic protists and fungi of Mediterranean deep hypsersaline anoxic basin redoxcline sediments

PubMed Central

Bernhard, Joan M.; Kormas, Konstantinos; Pachiadaki, Maria G.; Rocke, Emma; Beaudoin, David J.; Morrison, Colin; Visscher, Pieter T.; Cobban, Alec; Starczak, Victoria R.; Edgcomb, Virginia P.

2014-01-01

Some of the most extreme marine habitats known are the Mediterranean deep hypersaline anoxic basins (DHABs; water depth ∼3500 m). Brines of DHABs are nearly saturated with salt, leading many to suspect they are uninhabitable for eukaryotes. While diverse bacterial and protistan communities are reported from some DHAB water-column haloclines and brines, the existence and activity of benthic DHAB protists have rarely been explored. Here, we report findings regarding protists and fungi recovered from sediments of three DHAB (Discovery, Urania, L’ Atalante) haloclines, and compare these to communities from sediments underlying normoxic waters of typical Mediterranean salinity. Halocline sediments, where the redoxcline impinges the seafloor, were studied from all three DHABs. Microscopic cell counts suggested that halocline sediments supported denser protist populations than those in adjacent control sediments. Pyrosequencing analysis based on ribosomal RNA detected eukaryotic ribotypes in the halocline sediments from each of the three DHABs, most of which were fungi. Sequences affiliated with Ustilaginomycotina Basidiomycota were the most abundant eukaryotic signatures detected. Benthic communities in these DHABs appeared to differ, as expected, due to differing brine chemistries. Microscopy indicated that only a low proportion of protists appeared to bear associated putative symbionts. In a considerable number of cases, when prokaryotes were associated with a protist, DAPI staining did not reveal presence of any nuclei, suggesting that at least some protists were carcasses inhabited by prokaryotic scavengers. PMID:25452749

Space Biology Model Organism Research on the Deep Space Gateway to Pioneer Discovery and Advance Human Space Exploration

NASA Astrophysics Data System (ADS)

Sato, K. Y.; Tomko, D. L.; Levine, H. G.; Quincy, C. D.; Rayl, N. A.; Sowa, M. B.; Taylor, E. M.; Sun, S. C.; Kundrot, C. E.

2018-02-01

Model organisms are foundational for conducting physiological and systems biology research to define how life responds to the deep space environment. The organisms, areas of research, and Deep Space Gateway capabilities needed will be presented.

Natural Products from Deep-Sea-Derived Fungi ̶ A New Source of Novel Bioactive Compounds?

PubMed

Daletos, Georgios; Ebrahim, Weaam; Ancheeva, Elena; El-Neketi, Mona; Song, Weiguo; Lin, Wenhan; Proksch, Peter

2018-01-01

Over the last two decades, deep-sea-derived fungi are considered to be a new source of pharmacologically active secondary metabolites for drug discovery mainly based on the underlying assumption that the uniqueness of the deep sea will give rise to equally unprecedented natural products. Indeed, up to now over 200 new metabolites have been identified from deep-sea fungi, which is in support of the statement made above. This review summarizes the new and/or bioactive compounds reported from deepsea- derived fungi in the last six years (2010 - October 2016) and critically evaluates whether the data published so far really support the notion that these fungi are a promising source of new bioactive chemical entities. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Exploring Dance Movement Data Using Sequence Alignment Methods

PubMed Central

Chavoshi, Seyed Hossein; De Baets, Bernard; Neutens, Tijs; De Tré, Guy; Van de Weghe, Nico

2015-01-01

Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers. PMID:26181435

Genomic variation in macrophage-cultured European porcine reproductive and respiratory syndrome virus Olot/91 revealed using ultra-deep next generation sequencing.

PubMed

Lu, Zen H; Brown, Alexander; Wilson, Alison D; Calvert, Jay G; Balasch, Monica; Fuentes-Utrilla, Pablo; Loecherbach, Julia; Turner, Frances; Talbot, Richard; Archibald, Alan L; Ait-Ali, Tahar

2014-03-04

Porcine Reproductive and Respiratory Syndrome (PRRS) is a disease of major economic impact worldwide. The etiologic agent of this disease is the PRRS virus (PRRSV). Increasing evidence suggest that microevolution within a coexisting quasispecies population can give rise to high sequence heterogeneity in PRRSV. We developed a pipeline based on the ultra-deep next generation sequencing approach to first construct the complete genome of a European PRRSV, strain Olot/9, cultured on macrophages and then capture the rare variants representative of the mixed quasispecies population. Olot/91 differs from the reference Lelystad strain by about 5% and a total of 88 variants, with frequencies as low as 1%, were detected in the mixed population. These variants included 16 non-synonymous variants concentrated in the genes encoding structural and nonstructural proteins; including Glycoprotein 2a and 5. Using an ultra-deep sequencing methodology, the complete genome of Olot/91 was constructed without any prior knowledge of the sequence. Rare variants that constitute minor fractions of the heterogeneous PRRSV population could successfully be detected to allow further exploration of microevolutionary events.

Computational methods in drug discovery

PubMed Central

Leelananda, Sumudu P

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed. PMID:28144341

Computational methods in drug discovery.

PubMed

Leelananda, Sumudu P; Lindert, Steffen

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

A non-parametric peak calling algorithm for DamID-Seq.

PubMed

Li, Renhua; Hempel, Leonie U; Jiang, Tingbo

2015-01-01

Protein-DNA interactions play a significant role in gene regulation and expression. In order to identify transcription factor binding sites (TFBS) of double sex (DSX)-an important transcription factor in sex determination, we applied the DNA adenine methylation identification (DamID) technology to the fat body tissue of Drosophila, followed by deep sequencing (DamID-Seq). One feature of DamID-Seq data is that induced adenine methylation signals are not assured to be symmetrically distributed at TFBS, which renders the existing peak calling algorithms for ChIP-Seq, including SPP and MACS, inappropriate for DamID-Seq data. This challenged us to develop a new algorithm for peak calling. A challenge in peaking calling based on sequence data is estimating the averaged behavior of background signals. We applied a bootstrap resampling method to short sequence reads in the control (Dam only). After data quality check and mapping reads to a reference genome, the peaking calling procedure compromises the following steps: 1) reads resampling; 2) reads scaling (normalization) and computing signal-to-noise fold changes; 3) filtering; 4) Calling peaks based on a statistically significant threshold. This is a non-parametric method for peak calling (NPPC). We also used irreproducible discovery rate (IDR) analysis, as well as ChIP-Seq data to compare the peaks called by the NPPC. We identified approximately 6,000 peaks for DSX, which point to 1,225 genes related to the fat body tissue difference between female and male Drosophila. Statistical evidence from IDR analysis indicated that these peaks are reproducible across biological replicates. In addition, these peaks are comparable to those identified by use of ChIP-Seq on S2 cells, in terms of peak number, location, and peaks width.

EHR Big Data Deep Phenotyping

PubMed Central

Lenert, L.; Lopez-Campos, G.

2014-01-01

Summary Objectives Given the quickening speed of discovery of variant disease drivers from combined patient genotype and phenotype data, the objective is to provide methodology using big data technology to support the definition of deep phenotypes in medical records. Methods As the vast stores of genomic information increase with next generation sequencing, the importance of deep phenotyping increases. The growth of genomic data and adoption of Electronic Health Records (EHR) in medicine provides a unique opportunity to integrate phenotype and genotype data into medical records. The method by which collections of clinical findings and other health related data are leveraged to form meaningful phenotypes is an active area of research. Longitudinal data stored in EHRs provide a wealth of information that can be used to construct phenotypes of patients. We focus on a practical problem around data integration for deep phenotype identification within EHR data. The use of big data approaches are described that enable scalable markup of EHR events that can be used for semantic and temporal similarity analysis to support the identification of phenotype and genotype relationships. Conclusions Stead and colleagues’ 2005 concept of using light standards to increase the productivity of software systems by riding on the wave of hardware/processing power is described as a harbinger for designing future healthcare systems. The big data solution, using flexible markup, provides a route to improved utilization of processing power for organizing patient records in genotype and phenotype research. PMID:25123744

Biomarker Discovery and Mechanistic Studies of Prostate Cancer using Targeted Proteomic Approaches

DTIC Science & Technology

2012-07-01

basigin in Drosophila ) tightly regulates cytoskeleton rearrangement in Drosophila melanogaster [23]. Based on the present results and the existing...from OligoEngine according to the manufac- turer’s instruction. Plasmids were amplified in DH5a cell and confirmed by sequencing . Subconfluent cell...electrophoresis and the results are shown in Figure 1 (Panel C). The RT-PCR products were cloned and subjected to DNA sequenc - ing. The sequencing

A Diverse Repertoire of Human Immunoglobulin Variable Genes in a Chicken B Cell Line is Generated by Both Gene Conversion and Somatic Hypermutation.

PubMed

Leighton, Philip A; Schusser, Benjamin; Yi, Henry; Glanville, Jacob; Harriman, William

2015-01-01

Chicken immune responses to human proteins are often more robust than rodent responses because of the phylogenetic relationship between the different species. For discovery of a diverse panel of unique therapeutic antibody candidates, chickens therefore represent an attractive host for human-derived targets. Recent advances in monoclonal antibody technology, specifically new methods for the molecular cloning of antibody genes directly from primary B cells, has ushered in a new era of generating monoclonal antibodies from non-traditional host animals that were previously inaccessible through hybridoma technology. However, such monoclonals still require post-discovery humanization in order to be developed as therapeutics. To obviate the need for humanization, a modified strain of chickens could be engineered to express a human-sequence immunoglobulin variable region repertoire. Here, human variable genes introduced into the chicken immunoglobulin loci through gene targeting were evaluated for their ability to be recognized and diversified by the native chicken recombination machinery that is present in the B-lineage cell line DT40. After expansion in culture the DT40 population accumulated genetic mutants that were detected via deep sequencing. Bioinformatic analysis revealed that the human targeted constructs are performing as expected in the cell culture system, and provide a measure of confidence that they will be functional in transgenic animals.

Drilling the Messinian Salinity Crisis as a Model Analogue for Dolomite Deposition at the End of Massive Salt Deposition Events

NASA Astrophysics Data System (ADS)

McKenzie, Judith A.; Aloisi, Giovanni; Anjos, Sylvia; Latgé, Ricardo; Matsuda, Nilo; Bontognali, Tomaso; Vasconcelos, Crisogono

2015-04-01

Sedimentologic and stratigraphic studies of the Lower Cretaceous sequence, deposited in the economically important Campos Basin, southeast Brazil, document the occurrence of ~20-m-thick dolomite intervals overlying the "massive salt" megasequences of the Lagoa Feia Formation. This stratigaphic succession marks the Aptian/Albian transition from extreme evaporitic conditions of the Lagoa Feia Formation to shallow marine conditions of the Macaé Formation, related to the early opening of the South Atlantic. The facies change from evaporites to dolomite is interpreted as a product of dolomitization resulting from the refuxing of hypersaline fluids from shallow embayments with intense evaporation (Latgé, 2001). Although the reflux model provides a mechanism to produce fluids with geochemical composition favorable for dolomite precipitation, it cannot account for all of the factors required to promote dolomite precipitation. In this study, we propose a different model to explain the post-evaporite deposition of massive dolomite based on the study of sequences deposited at the end Messinian Salinity Crisis, which were recovered from the deep basins of the Mediterranean Sea during DSDP/ODP drilling campaigns. At most of these deep-water sites, the cored interval contained unusual dolomite deposits overlying the uppermost evaporite sections. For example, the upper Messinian sedimentary sequence at DSDP Site 374 comprises non-fossiliferous dolomitic mudstone overlying dolomitic mudstone/gypsum cycles, which in turn overlie anhydrite and halite (Hsü, Montadert et al., 1978). We postulate that the end Messinian dolomite is a product of microbial activity under extreme hypersaline conditions. In the last 20 years, research into the factors controlling dolomite precipitation under Earth surface conditions has led to the development of new models involving the metabolism of microorganisms and associated biofilms to overcome the kinetic inhibitions associated with primary dolomite precipitation. Furthermore, based on the limited pore-water geochemical data obtained during drilling at DSDP Site 374: Messina Abyssal Plain, the dolomitic mudstones of the uppermost Messinian evaporite complex represent an ideal candidate for such an extensive study in a "natural laboratory". In fact, the data suggest that microbial diagenesis and perhaps dolomite precipitation may still be occurring. Thus, to increase our understanding of the biogeochemical processes associated with ancient massive dolomite formation, a major new drilling campaign to study the sub-seafloor Messinian evaporite complex in the deep Mediterranean basins, using greatly enhanced drilling technology currently available within the new International Ocean Discovery Program (IODP), would be timely. Hsü, K., Montadert, L. et al., 1978. Initial Reports of the Deep Sea Drilling Project, Volume 42, Part 1: Washington (U.S. Government Printing Office). Latgé, M. A. R., 2001. O Albiano no Atlântico Sul: estratigrafia, Paleoceanografia e Relações Globais. PhD thesis, Universidade Federal do Rio Grande do Sul, pp. 257.

A biological compression model and its applications.

PubMed

Cao, Minh Duc; Dix, Trevor I; Allison, Lloyd

2011-01-01

A biological compression model, expert model, is presented which is superior to existing compression algorithms in both compression performance and speed. The model is able to compress whole eukaryotic genomes. Most importantly, the model provides a framework for knowledge discovery from biological data. It can be used for repeat element discovery, sequence alignment and phylogenetic analysis. We demonstrate that the model can handle statistically biased sequences and distantly related sequences where conventional knowledge discovery tools often fail.

Alzheimer's Disease Sequencing Project discovery and replication criteria for cases and controls: Data from a community-based prospective cohort study with autopsy follow-up.

PubMed

Crane, Paul K; Foroud, Tatiana; Montine, Thomas J; Larson, Eric B

2017-12-01

The Alzheimer's Disease Sequencing Project (ADSP) used different criteria for assigning case and control status from the discovery and replication phases of the project. We considered data from a community-based prospective cohort study with autopsy follow-up where participants could be categorized as case, control, or neither by both definitions and compared the two sets of criteria. We used data from the Adult Changes in Thought (ACT) study including Diagnostic and Statistical Manual-IV criteria for dementia status, McKhann et al. criteria for clinical Alzheimer's disease, and Braak and Consortium to Establish a Registry for AD findings on neurofibrillary tangles and neuritic plaques to categorize the 621 ACT participants of European ancestry who died and came to autopsy. We applied ADSP discovery and replication definitions to identify controls, cases, and people who were neither controls nor cases. There was some agreement between the discovery and replication definitions. Major areas of discrepancy included the finding that only 40% of the discovery sample controls had sufficiently low levels of neurofibrillary tangles and neuritic plaques to be considered controls by the replication criteria and the finding that 16% of the replication phase cases were diagnosed with non-AD dementia during life and thus were excluded as cases for the discovery phase. These findings should inform interpretation of genetic association findings from the ADSP. Differences in genetic association findings between the two phases of the study may reflect these different phenotype definitions from the discovery and replication phase of the ADSP. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Revealing the unexplored fungal communities in deep groundwater of crystalline bedrock fracture zones in Olkiluoto, Finland.

PubMed

Sohlberg, Elina; Bomberg, Malin; Miettinen, Hanna; Nyyssönen, Mari; Salavirta, Heikki; Vikman, Minna; Itävaara, Merja

2015-01-01

The diversity and functional role of fungi, one of the ecologically most important groups of eukaryotic microorganisms, remains largely unknown in deep biosphere environments. In this study we investigated fungal communities in packer-isolated bedrock fractures in Olkiluoto, Finland at depths ranging from 296 to 798 m below surface level. DNA- and cDNA-based high-throughput amplicon sequencing analysis of the fungal internal transcribed spacer (ITS) gene markers was used to examine the total fungal diversity and to identify the active members in deep fracture zones at different depths. Results showed that fungi were present in fracture zones at all depths and fungal diversity was higher than expected. Most of the observed fungal sequences belonged to the phylum Ascomycota. Phyla Basidiomycota and Chytridiomycota were only represented as a minor part of the fungal community. Dominating fungal classes in the deep bedrock aquifers were Sordariomycetes, Eurotiomycetes, and Dothideomycetes from the Ascomycota phylum and classes Microbotryomycetes and Tremellomycetes from the Basidiomycota phylum, which are the most frequently detected fungal taxa reported also from deep sea environments. In addition some fungal sequences represented potentially novel fungal species. Active fungi were detected in most of the fracture zones, which proves that fungi are able to maintain cellular activity in these oligotrophic conditions. Possible roles of fungi and their origin in deep bedrock groundwater can only be speculated in the light of current knowledge but some species may be specifically adapted to deep subsurface environment and may play important roles in the utilization and recycling of nutrients and thus sustaining the deep subsurface microbial community.

Discovery and profiling of novel and conserved microRNAs during flower development in Carya cathayensis via deep sequencing.

PubMed

Wang, Zheng Jia; Huang, Jian Qin; Huang, You Jun; Li, Zheng; Zheng, Bing Song

2012-08-01

Hickory (Carya cathayensis Sarg.) is an economically important woody plant in China, but its long juvenile phase delays yield. MicroRNAs (miRNAs) are critical regulators of genes and important for normal plant development and physiology, including flower development. We used Solexa technology to sequence two small RNA libraries from two floral differentiation stages in hickory to identify miRNAs related to flower development. We identified 39 conserved miRNA sequences from 114 loci belonging to 23 families as well as two novel and ten potential novel miRNAs belonging to nine families. Moreover, 35 conserved miRNA*s and two novel miRNA*s were detected. Twenty miRNA sequences from 49 loci belonging to 11 families were differentially expressed; all were up-regulated at the later stage of flower development in hickory. Quantitative real-time PCR of 12 conserved miRNA sequences, five novel miRNA families, and two novel miRNA*s validated that all were expressed during hickory flower development, and the expression patterns were similar to those detected with Solexa sequencing. Finally, a total of 146 targets of the novel and conserved miRNAs were predicted. This study identified a diverse set of miRNAs that were closely related to hickory flower development and that could help in plant floral induction.

Reducing false-positive incidental findings with ensemble genotyping and logistic regression based variant filtering methods.

PubMed

Hwang, Kyu-Baek; Lee, In-Hee; Park, Jin-Ho; Hambuch, Tina; Choe, Yongjoon; Kim, MinHyeok; Lee, Kyungjoon; Song, Taemin; Neu, Matthew B; Gupta, Neha; Kohane, Isaac S; Green, Robert C; Kong, Sek Won

2014-08-01

As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false-positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here, we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false-negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous single nucleotide variants (SNVs); 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery in NA12878, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and an ensemble genotyping would be essential to minimize false-positive DNM candidates. © 2014 WILEY PERIODICALS, INC.

Reducing false positive incidental findings with ensemble genotyping and logistic regression-based variant filtering methods

PubMed Central

Hwang, Kyu-Baek; Lee, In-Hee; Park, Jin-Ho; Hambuch, Tina; Choi, Yongjoon; Kim, MinHyeok; Lee, Kyungjoon; Song, Taemin; Neu, Matthew B.; Gupta, Neha; Kohane, Isaac S.; Green, Robert C.; Kong, Sek Won

2014-01-01

As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous SNVs; 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and ensemble genotyping would be essential to minimize false positive DNM candidates. PMID:24829188

Prediction of the Hydrogen Peroxide-Induced Methionine Oxidation Propensity in Monoclonal Antibodies.

PubMed

Agrawal, Neeraj J; Dykstra, Andrew; Yang, Jane; Yue, Hai; Nguyen, Xichdao; Kolvenbach, Carl; Angell, Nicolas

2018-05-01

Methionine oxidation in therapeutic antibodies can impact the product's stability, clinical efficacy, and safety and hence it is desirable to address the methionine oxidation liability during antibody discovery and development phase. Although the current experimental approaches can identify the oxidation-labile methionine residues, their application is limited mostly to the development phase. We demonstrate an in silico method that can be used to predict oxidation-labile residues based solely on the antibody sequence and structure information. Since antibody sequence information is available in the discovery phase, the in silico method can be applied very early on to identify the oxidation-labile methionine residues and subsequently address the oxidation liability. We believe that the in silico method for methionine oxidation liability assessment can aid in antibody discovery and development phase to address the liability in a more rational way. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Motif discovery with data mining in 3D protein structure databases: discovery, validation and prediction of the U-shape zinc binding ("Huf-Zinc") motif.

PubMed

Maurer-Stroh, Sebastian; Gao, He; Han, Hao; Baeten, Lies; Schymkowitz, Joost; Rousseau, Frederic; Zhang, Louxin; Eisenhaber, Frank

2013-02-01

Data mining in protein databases, derivatives from more fundamental protein 3D structure and sequence databases, has considerable unearthed potential for the discovery of sequence motif--structural motif--function relationships as the finding of the U-shape (Huf-Zinc) motif, originally a small student's project, exemplifies. The metal ion zinc is critically involved in universal biological processes, ranging from protein-DNA complexes and transcription regulation to enzymatic catalysis and metabolic pathways. Proteins have evolved a series of motifs to specifically recognize and bind zinc ions. Many of these, so called zinc fingers, are structurally independent globular domains with discontinuous binding motifs made up of residues mostly far apart in sequence. Through a systematic approach starting from the BRIX structure fragment database, we discovered that there exists another predictable subset of zinc-binding motifs that not only have a conserved continuous sequence pattern but also share a characteristic local conformation, despite being included in totally different overall folds. While this does not allow general prediction of all Zn binding motifs, a HMM-based web server, Huf-Zinc, is available for prediction of these novel, as well as conventional, zinc finger motifs in protein sequences. The Huf-Zinc webserver can be freely accessed through this URL (http://mendel.bii.a-star.edu.sg/METHODS/hufzinc/).

The epistellar body and what followed from its discovery.

PubMed

Young, J Z

1990-07-01

The sequence of discoveries that has followed the investigation of this small yellow spot shows the value of studies begun out of "mere curiosity". The spot occurs on the stellate ganglion of octopods. It proved to be an enclosed sac, perhaps a gland. The search for it in squids and cuttlefishes led to the discovery of the giant nerve fibres. At first they were thought to be veins but we soon showed that they were nerve fibres concerned with jet propulsion. Their action potentials, membranes and synapses have been used for thousand of studies, including those that led to the Hodkin Huxley equations. They have been the basis of much of modern neuroscience. The epistellar body itself proved not to be a gland but a photoreceptor. Comparable photosensitive vesicles are especially large in the heads of deep-sea squids. In the mesopelagic ones they allow the squid to conceal itself by counterillumination, matching its own light output to the light coming from above. In bathypelagic squids the vesicles are enormous and probably keep the animals in the dark, where they breed. The function of the epistellar body, lying within the mantle of octopods is still unknown. It may act in the transparent larval stage to trigger the ejection of luminous plankton, which would be a hazard.

Optimization of whole-transcriptome amplification from low cell density deep-sea microbial samples for metatranscriptomic analysis.

PubMed

Wu, Jieying; Gao, Weimin; Zhang, Weiwen; Meldrum, Deirdre R

2011-01-01

Limitation in sample quality and quantity is one of the big obstacles for applying metatranscriptomic technologies to explore gene expression and functionality of microbial communities in natural environments. In this study, several amplification methods were evaluated for whole-transcriptome amplification of deep-sea microbial samples, which are of low cell density and high impurity. The best amplification method was identified and incorporated into a complete protocol to isolate and amplify deep-sea microbial samples. In the protocol, total RNA was first isolated by a modified method combining Trizol (Invitrogen, CA) and RNeasy (QIAGEN, CA) method, amplified with a WT-Ovation™ Pico RNA Amplification System (NuGEN, CA), and then converted to double-strand DNA from single-strand cDNA with a WT-Ovation™ Exon Module (NuGEN, CA). The products from the whole-transcriptome amplification of deep-sea microbial samples were assessed first through random clone library sequencing. The BLAST search results showed that marine-based sequences are dominant in the libraries, consistent with the ecological source of the samples. The products were then used for next-generation Roche GS FLX Titanium sequencing to obtain metatranscriptome data. Preliminary analysis of the metatranscriptomic data showed good sequencing quality. Although the protocol was designed and demonstrated to be effective for deep-sea microbial samples, it should be applicable to similar samples from other extreme environments in exploring community structure and functionality of microbial communities. Copyright © 2010 Elsevier B.V. All rights reserved.

Less is More: Membrane Protein Digestion Beyond Urea-Trypsin Solution for Next-level Proteomics.

PubMed

Zhang, Xi

2015-09-01

The goal of next-level bottom-up membrane proteomics is protein function investigation, via high-coverage high-throughput peptide-centric quantitation of expression, modifications and dynamic structures at systems scale. Yet efficient digestion of mammalian membrane proteins presents a daunting barrier, and prevalent day-long urea-trypsin in-solution digestion proved insufficient to reach this goal. Many efforts contributed incremental advances over past years, but involved protein denaturation that disconnected measurement from functional states. Beyond denaturation, the recent discovery of structure/proteomics omni-compatible detergent n-dodecyl-β-d-maltopyranoside, combined with pepsin and PNGase F columns, enabled breakthroughs in membrane protein digestion: a 2010 DDM-low-TCEP (DLT) method for H/D-exchange (HDX) using human G protein-coupled receptor, and a 2015 flow/detergent-facilitated protease and de-PTM digestions (FDD) for integrative deep sequencing and quantitation using full-length human ion channel complex. Distinguishing protein solubilization from denaturation, protease digestion reliability from theoretical specificity, and reduction from alkylation, these methods shifted day(s)-long paradigms into minutes, and afforded fully automatable (HDX)-protein-peptide-(tandem mass tag)-HPLC pipelines to instantly measure functional proteins at deep coverage, high peptide reproducibility, low artifacts and minimal leakage. Promoting-not destroying-structures and activities harnessed membrane proteins for the next-level streamlined functional proteomics. This review analyzes recent advances in membrane protein digestion methods and highlights critical discoveries for future proteomics. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

ADEPT, a dynamic next generation sequencing data error-detection program with trimming

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

Feng, Shihai; Lo, Chien-Chi; Li, Po-E

Illumina is the most widely used next generation sequencing technology and produces millions of short reads that contain errors. These sequencing errors constitute a major problem in applications such as de novo genome assembly, metagenomics analysis and single nucleotide polymorphism discovery. In this study, we present ADEPT, a dynamic error detection method, based on the quality scores of each nucleotide and its neighboring nucleotides, together with their positions within the read and compares this to the position-specific quality score distribution of all bases within the sequencing run. This method greatly improves upon other available methods in terms of the truemore » positive rate of error discovery without affecting the false positive rate, particularly within the middle of reads. We conclude that ADEPT is the only tool to date that dynamically assesses errors within reads by comparing position-specific and neighboring base quality scores with the distribution of quality scores for the dataset being analyzed. The result is a method that is less prone to position-dependent under-prediction, which is one of the most prominent issues in error prediction. The outcome is that ADEPT improves upon prior efforts in identifying true errors, primarily within the middle of reads, while reducing the false positive rate.« less

ADEPT, a dynamic next generation sequencing data error-detection program with trimming

DOE PAGES

Feng, Shihai; Lo, Chien-Chi; Li, Po-E; ...

2016-02-29

Illumina is the most widely used next generation sequencing technology and produces millions of short reads that contain errors. These sequencing errors constitute a major problem in applications such as de novo genome assembly, metagenomics analysis and single nucleotide polymorphism discovery. In this study, we present ADEPT, a dynamic error detection method, based on the quality scores of each nucleotide and its neighboring nucleotides, together with their positions within the read and compares this to the position-specific quality score distribution of all bases within the sequencing run. This method greatly improves upon other available methods in terms of the truemore » positive rate of error discovery without affecting the false positive rate, particularly within the middle of reads. We conclude that ADEPT is the only tool to date that dynamically assesses errors within reads by comparing position-specific and neighboring base quality scores with the distribution of quality scores for the dataset being analyzed. The result is a method that is less prone to position-dependent under-prediction, which is one of the most prominent issues in error prediction. The outcome is that ADEPT improves upon prior efforts in identifying true errors, primarily within the middle of reads, while reducing the false positive rate.« less

The rise of deep learning in drug discovery.

PubMed

Chen, Hongming; Engkvist, Ola; Wang, Yinhai; Olivecrona, Marcus; Blaschke, Thomas

2018-06-01

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

The sequence of sequencers: The history of sequencing DNA

PubMed Central

Heather, James M.; Chain, Benjamin

2016-01-01

Determining the order of nucleic acid residues in biological samples is an integral component of a wide variety of research applications. Over the last fifty years large numbers of researchers have applied themselves to the production of techniques and technologies to facilitate this feat, sequencing DNA and RNA molecules. This time-scale has witnessed tremendous changes, moving from sequencing short oligonucleotides to millions of bases, from struggling towards the deduction of the coding sequence of a single gene to rapid and widely available whole genome sequencing. This article traverses those years, iterating through the different generations of sequencing technology, highlighting some of the key discoveries, researchers, and sequences along the way. PMID:26554401

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.

Enhanced arbovirus surveillance with deep sequencing: Identification of novel rhabdoviruses and bunyaviruses in Australian mosquitoes.

PubMed

Coffey, Lark L; Page, Brady L; Greninger, Alexander L; Herring, Belinda L; Russell, Richard C; Doggett, Stephen L; Haniotis, John; Wang, Chunlin; Deng, Xutao; Delwart, Eric L

2014-01-05

Viral metagenomics characterizes known and identifies unknown viruses based on sequence similarities to any previously sequenced viral genomes. A metagenomics approach was used to identify virus sequences in Australian mosquitoes causing cytopathic effects in inoculated mammalian cell cultures. Sequence comparisons revealed strains of Liao Ning virus (Reovirus, Seadornavirus), previously detected only in China, livestock-infecting Stretch Lagoon virus (Reovirus, Orbivirus), two novel dimarhabdoviruses, named Beaumont and North Creek viruses, and two novel orthobunyaviruses, named Murrumbidgee and Salt Ash viruses. The novel virus proteomes diverged by ≥ 50% relative to their closest previously genetically characterized viral relatives. Deep sequencing also generated genomes of Warrego and Wallal viruses, orbiviruses linked to kangaroo blindness, whose genomes had not been fully characterized. This study highlights viral metagenomics in concert with traditional arbovirus surveillance to characterize known and new arboviruses in field-collected mosquitoes. Follow-up epidemiological studies are required to determine whether the novel viruses infect humans. © 2013 Elsevier Inc. All rights reserved.

A streamlined collecting and preparation protocol for DNA barcoding of Lepidoptera as part of large-scale rapid biodiversity assessment projects, exemplified by the Indonesian Biodiversity Discovery and Information System (IndoBioSys).

PubMed

Schmidt, Olga; Hausmann, Axel; Cancian de Araujo, Bruno; Sutrisno, Hari; Peggie, Djunijanti; Schmidt, Stefan

2017-01-01

Here we present a general collecting and preparation protocol for DNA barcoding of Lepidoptera as part of large-scale rapid biodiversity assessment projects, and a comparison with alternative preserving and vouchering methods. About 98% of the sequenced specimens processed using the present collecting and preparation protocol yielded sequences with more than 500 base pairs. The study is based on the first outcomes of the Indonesian Biodiversity Discovery and Information System (IndoBioSys). IndoBioSys is a German-Indonesian research project that is conducted by the Museum für Naturkunde in Berlin and the Zoologische Staatssammlung München, in close cooperation with the Research Center for Biology - Indonesian Institute of Sciences (RCB-LIPI, Bogor).

Transcriptomic sequencing reveals a set of unique genes activated by butyrate-induced histone modification

USDA-ARS?s Scientific Manuscript database

Butyrate is a nutritional element with strong epigenetic regulatory activity as an inhibitor of histone deacetylases (HDACs). Based on the analysis of differentially expressed genes induced by butyrate in the bovine epithelial cell using deep RNA-sequencing technology (RNA-seq), a set of unique gen...

Evidence of a modern deep water magmatic hydrothermal system in the Canary Basin (eastern central Atlantic Ocean)

NASA Astrophysics Data System (ADS)

Medialdea, T.; Somoza, L.; González, F. J.; Vázquez, J. T.; de Ignacio, C.; Sumino, H.; Sánchez-Guillamón, O.; Orihashi, Y.; León, R.; Palomino, D.

2017-08-01

New seismic profiles, bathymetric data, and sediment-rock sampling document for the first time the discovery of hydrothermal vent complexes and volcanic cones at 4800-5200 m depth related to recent volcanic and intrusive activity in an unexplored area of the Canary Basin (Eastern Atlantic Ocean, 500 km west of the Canary Islands). A complex of sill intrusions is imaged on seismic profiles showing saucer-shaped, parallel, or inclined geometries. Three main types of structures are related to these intrusions. Type I consists of cone-shaped depressions developed above inclined sills interpreted as hydrothermal vents. Type II is the most abundant and is represented by isolated or clustered hydrothermal domes bounded by faults rooted at the tips of saucer-shaped sills. Domes are interpreted as seabed expressions of reservoirs of CH4 and CO2-rich fluids formed by degassing and contact metamorphism of organic-rich sediments around sill intrusions. Type III are hydrothermal-volcanic complexes originated above stratified or branched inclined sills connected by a chimney to the seabed volcanic edifice. Parallel sills sourced from the magmatic chimney formed also domes surrounding the volcanic cones. Core and dredges revealed that these volcanoes, which must be among the deepest in the world, are constituted by OIB-type, basanites with an outer ring of blue-green hydrothermal Al-rich smectite muds. Magmatic activity is dated, based on lava samples, at 0.78 ± 0.05 and 1.61 ± 0.09 Ma (K/Ar methods) and on tephra layers within cores at 25-237 ky. The Subvent hydrothermal-volcanic complex constitutes the first modern system reported in deep water oceanic basins related to intraplate hotspot activity.