A Transcriptional Signature of Fatigue Derived from Patients with Primary Sjögren's Syndrome.

PubMed

James, Katherine; Al-Ali, Shereen; Tarn, Jessica; Cockell, Simon J; Gillespie, Colin S; Hindmarsh, Victoria; Locke, James; Mitchell, Sheryl; Lendrem, Dennis; Bowman, Simon; Price, Elizabeth; Pease, Colin T; Emery, Paul; Lanyon, Peter; Hunter, John A; Gupta, Monica; Bombardieri, Michele; Sutcliffe, Nurhan; Pitzalis, Costantino; McLaren, John; Cooper, Annie; Regan, Marian; Giles, Ian; Isenberg, David; Saravanan, Vadivelu; Coady, David; Dasgupta, Bhaskar; McHugh, Neil; Young-Min, Steven; Moots, Robert; Gendi, Nagui; Akil, Mohammed; Griffiths, Bridget; Wipat, Anil; Newton, Julia; Jones, David E; Isaacs, John; Hallinan, Jennifer; Ng, Wan-Fai

2015-01-01

Fatigue is a debilitating condition with a significant impact on patients' quality of life. Fatigue is frequently reported by patients suffering from primary Sjögren's Syndrome (pSS), a chronic autoimmune condition characterised by dryness of the eyes and the mouth. However, although fatigue is common in pSS, it does not manifest in all sufferers, providing an excellent model with which to explore the potential underpinning biological mechanisms. Whole blood samples from 133 fully-phenotyped pSS patients stratified for the presence of fatigue, collected by the UK primary Sjögren's Syndrome Registry, were used for whole genome microarray. The resulting data were analysed both on a gene by gene basis and using pre-defined groups of genes. Finally, gene set enrichment analysis (GSEA) was used as a feature selection technique for input into a support vector machine (SVM) classifier. Classification was assessed using area under curve (AUC) of receiver operator characteristic and standard error of Wilcoxon statistic, SE(W). Although no genes were individually found to be associated with fatigue, 19 metabolic pathways were enriched in the high fatigue patient group using GSEA. Analysis revealed that these enrichments arose from the presence of a subset of 55 genes. A radial kernel SVM classifier with this subset of genes as input displayed significantly improved performance over classifiers using all pathway genes as input. The classifiers had AUCs of 0.866 (SE(W) 0.002) and 0.525 (SE(W) 0.006), respectively. Systematic analysis of gene expression data from pSS patients discordant for fatigue identified 55 genes which are predictive of fatigue level using SVM classification. This list represents the first step in understanding the underlying pathophysiological mechanisms of fatigue in patients with pSS.

Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data.

PubMed

Lepre, Jorge; Rice, J Jeremy; Tu, Yuhai; Stolovitzky, Gustavo

2004-05-01

Despite the growing literature devoted to finding differentially expressed genes in assays probing different tissues types, little attention has been paid to the combinatorial nature of feature selection inherent to large, high-dimensional gene expression datasets. New flexible data analysis approaches capable of searching relevant subgroups of genes and experiments are needed to understand multivariate associations of gene expression patterns with observed phenotypes. We present in detail a deterministic algorithm to discover patterns of multivariate gene associations in gene expression data. The patterns discovered are differential with respect to a control dataset. The algorithm is exhaustive and efficient, reporting all existent patterns that fit a given input parameter set while avoiding enumeration of the entire pattern space. The value of the pattern discovery approach is demonstrated by finding a set of genes that differentiate between two types of lymphoma. Moreover, these genes are found to behave consistently in an independent dataset produced in a different laboratory using different arrays, thus validating the genes selected using our algorithm. We show that the genes deemed significant in terms of their multivariate statistics will be missed using other methods. Our set of pattern discovery algorithms including a user interface is distributed as a package called Genes@Work. This package is freely available to non-commercial users and can be downloaded from our website (http://www.research.ibm.com/FunGen).

Computing all hybridization networks for multiple binary phylogenetic input trees.

PubMed

Albrecht, Benjamin

2015-07-30

The computation of phylogenetic trees on the same set of species that are based on different orthologous genes can lead to incongruent trees. One possible explanation for this behavior are interspecific hybridization events recombining genes of different species. An important approach to analyze such events is the computation of hybridization networks. This work presents the first algorithm computing the hybridization number as well as a set of representative hybridization networks for multiple binary phylogenetic input trees on the same set of taxa. To improve its practical runtime, we show how this algorithm can be parallelized. Moreover, we demonstrate the efficiency of the software Hybroscale, containing an implementation of our algorithm, by comparing it to PIRNv2.0, which is so far the best available software computing the exact hybridization number for multiple binary phylogenetic trees on the same set of taxa. The algorithm is part of the software Hybroscale, which was developed specifically for the investigation of hybridization networks including their computation and visualization. Hybroscale is freely available(1) and runs on all three major operating systems. Our simulation study indicates that our approach is on average 100 times faster than PIRNv2.0. Moreover, we show how Hybroscale improves the interpretation of the reported hybridization networks by adding certain features to its graphical representation.

The Microbial Genomes Atlas (MiGA) webserver: taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level.

PubMed

Rodriguez-R, Luis M; Gunturu, Santosh; Harvey, William T; Rosselló-Mora, Ramon; Tiedje, James M; Cole, James R; Konstantinidis, Konstantinos T

2018-06-14

The small subunit ribosomal RNA gene (16S rRNA) has been successfully used to catalogue and study the diversity of prokaryotic species and communities but it offers limited resolution at the species and finer levels, and cannot represent the whole-genome diversity and fluidity. To overcome these limitations, we introduced the Microbial Genomes Atlas (MiGA), a webserver that allows the classification of an unknown query genomic sequence, complete or partial, against all taxonomically classified taxa with available genome sequences, as well as comparisons to other related genomes including uncultivated ones, based on the genome-aggregate Average Nucleotide and Amino Acid Identity (ANI/AAI) concepts. MiGA integrates best practices in sequence quality trimming and assembly and allows input to be raw reads or assemblies from isolate genomes, single-cell sequences, and metagenome-assembled genomes (MAGs). Further, MiGA can take as input hundreds of closely related genomes of the same or closely related species (a so-called 'Clade Project') to assess their gene content diversity and evolutionary relationships, and calculate important clade properties such as the pangenome and core gene sets. Therefore, MiGA is expected to facilitate a range of genome-based taxonomic and diversity studies, and quality assessment across environmental and clinical settings. MiGA is available at http://microbial-genomes.org/.

Microgravity and immunity: Changes in lymphocyte gene expression.

NASA Astrophysics Data System (ADS)

Risin, D.; Ward, N. E.; Risin, S. A.; Pellis, N. R.

Earlier studies had shown that modeled and true microgravity MG cause multiple direct effects on human lymphocytes MG inhibits lymphocyte locomotion suppresses polyclonal and antigen-specific activation affects signal transduction mechanisms as well as activation-induced apoptosis In this study we assessed changes in gene expression associated with lymphocyte exposure to microgravity in an attempt to identify microgravity-sensitive genes MGSG in general and specifically those genes that might be responsible for the functional and structural changes observed earlier Two sets of experiments targeting different goals were conducted In the first set T-lymphocytes from normal donors were activated with anti-CD3 and IL2 and then cultured in 1g static and modeled MG MMG conditions Rotating Wall Vessel bioreactor for 24 hours This setting allowed searching for MGSG by comparison of gene expression patterns in zero and 1 g gravity In the second set - activated T-cells after culturing for 24 hours in 1g and MMG were exposed three hours before harvesting to a secondary activation stimulus PHA thus triggering the apoptotic pathway Total RNA was extracted using the RNeasy isolation kit Qiagen Valencia CA Affymetrix Gene Chips U133A allowing testing for 18 400 human genes were used for microarray analysis The experiments were performed in triplicates with T-cells obtained from different blood donors to minimize the possible input of biological variation in gene expression and discriminate changes that are associated with the

Detection of Pathways Affected by Positive Selection in Primate Lineages Ancestral to Humans

PubMed Central

Moretti, S.; Davydov, I.I.; Excoffier, L.

2017-01-01

Abstract Gene set enrichment approaches have been increasingly successful in finding signals of recent polygenic selection in the human genome. In this study, we aim at detecting biological pathways affected by positive selection in more ancient human evolutionary history. Focusing on four branches of the primate tree that lead to modern humans, we tested all available protein coding gene trees of the Primates clade for signals of adaptation in these branches, using the likelihood-based branch site test of positive selection. The results of these locus-specific tests were then used as input for a gene set enrichment test, where whole pathways are globally scored for a signal of positive selection, instead of focusing only on outlier “significant” genes. We identified signals of positive selection in several pathways that are mainly involved in immune response, sensory perception, metabolism, and energy production. These pathway-level results are highly significant, even though there is no functional enrichment when only focusing on top scoring genes. Interestingly, several gene sets are found significant at multiple levels in the phylogeny, but different genes are responsible for the selection signal in the different branches. This suggests that the same function has been optimized in different ways at different times in primate evolution. PMID:28333345

Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

PubMed

Gibson, Scott M; Ficklin, Stephen P; Isaacson, Sven; Luo, Feng; Feltus, Frank A; Smith, Melissa C

2013-01-01

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.

A Transcriptional Signature of Fatigue Derived from Patients with Primary Sjögren’s Syndrome

PubMed Central

James, Katherine; Al-Ali, Shereen; Tarn, Jessica; Cockell, Simon J.; Gillespie, Colin S.; Hindmarsh, Victoria; Locke, James; Mitchell, Sheryl; Lendrem, Dennis; Bowman, Simon; Price, Elizabeth; Pease, Colin T.; Emery, Paul; Lanyon, Peter; Hunter, John A.; Gupta, Monica; Bombardieri, Michele; Sutcliffe, Nurhan; Pitzalis, Costantino; McLaren, John; Cooper, Annie; Regan, Marian; Giles, Ian; Isenberg, David; Saravanan, Vadivelu; Coady, David; Dasgupta, Bhaskar; McHugh, Neil; Young-Min, Steven; Moots, Robert; Gendi, Nagui; Akil, Mohammed; Griffiths, Bridget; Wipat, Anil; Newton, Julia; Jones, David E.; Isaacs, John; Hallinan, Jennifer; Ng, Wan-Fai

2015-01-01

Background Fatigue is a debilitating condition with a significant impact on patients’ quality of life. Fatigue is frequently reported by patients suffering from primary Sjögren’s Syndrome (pSS), a chronic autoimmune condition characterised by dryness of the eyes and the mouth. However, although fatigue is common in pSS, it does not manifest in all sufferers, providing an excellent model with which to explore the potential underpinning biological mechanisms. Methods Whole blood samples from 133 fully-phenotyped pSS patients stratified for the presence of fatigue, collected by the UK primary Sjögren’s Syndrome Registry, were used for whole genome microarray. The resulting data were analysed both on a gene by gene basis and using pre-defined groups of genes. Finally, gene set enrichment analysis (GSEA) was used as a feature selection technique for input into a support vector machine (SVM) classifier. Classification was assessed using area under curve (AUC) of receiver operator characteristic and standard error of Wilcoxon statistic, SE(W). Results Although no genes were individually found to be associated with fatigue, 19 metabolic pathways were enriched in the high fatigue patient group using GSEA. Analysis revealed that these enrichments arose from the presence of a subset of 55 genes. A radial kernel SVM classifier with this subset of genes as input displayed significantly improved performance over classifiers using all pathway genes as input. The classifiers had AUCs of 0.866 (SE(W) 0.002) and 0.525 (SE(W) 0.006), respectively. Conclusions Systematic analysis of gene expression data from pSS patients discordant for fatigue identified 55 genes which are predictive of fatigue level using SVM classification. This list represents the first step in understanding the underlying pathophysiological mechanisms of fatigue in patients with pSS. PMID:26694930

DiRE: identifying distant regulatory elements of co-expressed genes

PubMed Central

Gotea, Valer; Ovcharenko, Ivan

2008-01-01

Regulation of gene expression in eukaryotic genomes is established through a complex cooperative activity of proximal promoters and distant regulatory elements (REs) such as enhancers, repressors and silencers. We have developed a web server named DiRE, based on the Enhancer Identification (EI) method, for predicting distant regulatory elements in higher eukaryotic genomes, namely for determining their chromosomal location and functional characteristics. The server uses gene co-expression data, comparative genomics and profiles of transcription factor binding sites (TFBSs) to determine TFBS-association signatures that can be used for discriminating specific regulatory functions. DiRE's unique feature is its ability to detect REs outside of proximal promoter regions, as it takes advantage of the full gene locus to conduct the search. DiRE can predict common REs for any set of input genes for which the user has prior knowledge of co-expression, co-function or other biologically meaningful grouping. The server predicts function-specific REs consisting of clusters of specifically-associated TFBSs and it also scores the association of individual transcription factors (TFs) with the biological function shared by the group of input genes. Its integration with the Array2BIO server allows users to start their analysis with raw microarray expression data. The DiRE web server is freely available at http://dire.dcode.org. PMID:18487623

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

PubMed Central

Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

2012-01-01

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction

PubMed Central

De Oliveira Martins, Leonardo; Mallo, Diego; Posada, David

2016-01-01

Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models. PMID:25281847

Are genes destiny? Have adenine, cytosine, guanine and thymine replaced Lachesis, Clotho and Atropos as the weavers of our fate?

PubMed

Eisenberg, Leon

2005-02-01

It is as futile to ask how much of the phenotype of an organism is due to nature and how much to its nurture as it is to determine how much of the area of a rectangle is due to its length and how much to its height. Phenotype and area are joint products. The spectacular success of genomics, unfortunately, threatens to re-awaken belief in genes as the principal determinants of human behavior. This paper develops the thesis that gene expression is modified by environmental inputs and that the impact of the environment on a given organism is modified by its genome. Genes set the boundaries of the possible; environments parse out the actual.

Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory

PubMed Central

Isaacson, Sven; Luo, Feng; Feltus, Frank A.; Smith, Melissa C.

2013-01-01

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust. PMID:23409071

Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm

NASA Astrophysics Data System (ADS)

Salameh Shreem, Salam; Abdullah, Salwani; Nazri, Mohd Zakree Ahmad

2016-04-01

Microarray technology can be used as an efficient diagnostic system to recognise diseases such as tumours or to discriminate between different types of cancers in normal tissues. This technology has received increasing attention from the bioinformatics community because of its potential in designing powerful decision-making tools for cancer diagnosis. However, the presence of thousands or tens of thousands of genes affects the predictive accuracy of this technology from the perspective of classification. Thus, a key issue in microarray data is identifying or selecting the smallest possible set of genes from the input data that can achieve good predictive accuracy for classification. In this work, we propose a two-stage selection algorithm for gene selection problems in microarray data-sets called the symmetrical uncertainty filter and harmony search algorithm wrapper (SU-HSA). Experimental results show that the SU-HSA is better than HSA in isolation for all data-sets in terms of the accuracy and achieves a lower number of genes on 6 out of 10 instances. Furthermore, the comparison with state-of-the-art methods shows that our proposed approach is able to obtain 5 (out of 10) new best results in terms of the number of selected genes and competitive results in terms of the classification accuracy.

PINTA: a web server for network-based gene prioritization from expression data

PubMed Central

Nitsch, Daniela; Tranchevent, Léon-Charles; Gonçalves, Joana P.; Vogt, Josef Korbinian; Madeira, Sara C.; Moreau, Yves

2011-01-01

PINTA (available at http://www.esat.kuleuven.be/pinta/; this web site is free and open to all users and there is no login requirement) is a web resource for the prioritization of candidate genes based on the differential expression of their neighborhood in a genome-wide protein–protein interaction network. Our strategy is meant for biological and medical researchers aiming at identifying novel disease genes using disease specific expression data. PINTA supports both candidate gene prioritization (starting from a user defined set of candidate genes) as well as genome-wide gene prioritization and is available for five species (human, mouse, rat, worm and yeast). As input data, PINTA only requires disease specific expression data, whereas various platforms (e.g. Affymetrix) are supported. As a result, PINTA computes a gene ranking and presents the results as a table that can easily be browsed and downloaded by the user. PMID:21602267

A fuzzy neural network for intelligent data processing

NASA Astrophysics Data System (ADS)

Xie, Wei; Chu, Feng; Wang, Lipo; Lim, Eng Thiam

2005-03-01

In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.

MAAMD: a workflow to standardize meta-analyses and comparison of affymetrix microarray data

PubMed Central

2014-01-01

Background Mandatory deposit of raw microarray data files for public access, prior to study publication, provides significant opportunities to conduct new bioinformatics analyses within and across multiple datasets. Analysis of raw microarray data files (e.g. Affymetrix CEL files) can be time consuming, complex, and requires fundamental computational and bioinformatics skills. The development of analytical workflows to automate these tasks simplifies the processing of, improves the efficiency of, and serves to standardize multiple and sequential analyses. Once installed, workflows facilitate the tedious steps required to run rapid intra- and inter-dataset comparisons. Results We developed a workflow to facilitate and standardize Meta-Analysis of Affymetrix Microarray Data analysis (MAAMD) in Kepler. Two freely available stand-alone software tools, R and AltAnalyze were embedded in MAAMD. The inputs of MAAMD are user-editable csv files, which contain sample information and parameters describing the locations of input files and required tools. MAAMD was tested by analyzing 4 different GEO datasets from mice and drosophila. MAAMD automates data downloading, data organization, data quality control assesment, differential gene expression analysis, clustering analysis, pathway visualization, gene-set enrichment analysis, and cross-species orthologous-gene comparisons. MAAMD was utilized to identify gene orthologues responding to hypoxia or hyperoxia in both mice and drosophila. The entire set of analyses for 4 datasets (34 total microarrays) finished in ~ one hour. Conclusions MAAMD saves time, minimizes the required computer skills, and offers a standardized procedure for users to analyze microarray datasets and make new intra- and inter-dataset comparisons. PMID:24621103

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study.

PubMed

Feltus, F Alex; Ficklin, Stephen P; Gibson, Scott M; Smith, Melissa C

2013-06-05

In genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues. When large sets of samples are obtained from public repositories it is often unmanageable to associate samples into condition-specific groups, and combining samples from various conditions has a negative effect on network size. A fixed significance threshold is often applied also limiting the size of the final network. Therefore, we propose pre-clustering of input expression samples to approximate condition-specific grouping of samples and individual network construction of each group as a means for dynamic significance thresholding. The net effect is increase sensitivity thus maximizing the total co-expression relationships in the final co-expression network compendium. A total of 86 Arabidopsis thaliana co-expression networks were constructed after k-means partitioning of 7,105 publicly available ATH1 Affymetrix microarray samples. We term each pre-sorted network a Gene Interaction Layer (GIL). Random Matrix Theory (RMT), an un-supervised thresholding method, was used to threshold each of the 86 networks independently, effectively providing a dynamic (non-global) threshold for the network. The overall gene count across all GILs reached 19,588 genes (94.7% measured gene coverage) and 558,022 unique co-expression relationships. In comparison, network construction without pre-sorting of input samples yielded only 3,297 genes (15.9%) and 129,134 relationships. in the global network. Here we show that pre-clustering of microarray samples helps approximate condition-specific networks and allows for dynamic thresholding using un-supervised methods. Because RMT ensures only highly significant interactions are kept, the GIL compendium consists of 558,022 unique high quality A. thaliana co-expression relationships across almost all of the measurable genes on the ATH1 array. For A. thaliana, these networks represent the largest compendium to date of significant gene co-expression relationships, and are a means to explore complex pathway, polygenic, and pleiotropic relationships for this focal model plant. The networks can be explored at sysbio.genome.clemson.edu. Finally, this method is applicable to any large expression profile collection for any organism and is best suited where a knowledge-independent network construction method is desired.

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study

PubMed Central

2013-01-01

Background In genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues. When large sets of samples are obtained from public repositories it is often unmanageable to associate samples into condition-specific groups, and combining samples from various conditions has a negative effect on network size. A fixed significance threshold is often applied also limiting the size of the final network. Therefore, we propose pre-clustering of input expression samples to approximate condition-specific grouping of samples and individual network construction of each group as a means for dynamic significance thresholding. The net effect is increase sensitivity thus maximizing the total co-expression relationships in the final co-expression network compendium. Results A total of 86 Arabidopsis thaliana co-expression networks were constructed after k-means partitioning of 7,105 publicly available ATH1 Affymetrix microarray samples. We term each pre-sorted network a Gene Interaction Layer (GIL). Random Matrix Theory (RMT), an un-supervised thresholding method, was used to threshold each of the 86 networks independently, effectively providing a dynamic (non-global) threshold for the network. The overall gene count across all GILs reached 19,588 genes (94.7% measured gene coverage) and 558,022 unique co-expression relationships. In comparison, network construction without pre-sorting of input samples yielded only 3,297 genes (15.9%) and 129,134 relationships. in the global network. Conclusions Here we show that pre-clustering of microarray samples helps approximate condition-specific networks and allows for dynamic thresholding using un-supervised methods. Because RMT ensures only highly significant interactions are kept, the GIL compendium consists of 558,022 unique high quality A. thaliana co-expression relationships across almost all of the measurable genes on the ATH1 array. For A. thaliana, these networks represent the largest compendium to date of significant gene co-expression relationships, and are a means to explore complex pathway, polygenic, and pleiotropic relationships for this focal model plant. The networks can be explored at sysbio.genome.clemson.edu. Finally, this method is applicable to any large expression profile collection for any organism and is best suited where a knowledge-independent network construction method is desired. PMID:23738693

Are genes destiny? Have adenine, cytosine, guanine and thymine replaced Lachesis, Clotho and Atropos as the weavers of our fate?

PubMed Central

EISENBERG, LEON

2005-01-01

It is as futile to ask how much of the phenotype of an organism is due to nature and how much to its nurture as it is to determine how much of the area of a rectangle is due to its length and how much to its height. Phenotype and area are joint products. The spectacular success of genomics, unfortunately, threatens to re-awaken belief in genes as the principal determinants of human behavior. This paper develops the thesis that gene expression is modified by environmental inputs and that the impact of the environment on a given organism is modified by its genome. Genes set the boundaries of the possible; environments parse out the actual. PMID:16633494

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

PubMed

Duan, Qiaonan; Flynn, Corey; Niepel, Mario; Hafner, Marc; Muhlich, Jeremy L; Fernandez, Nicolas F; Rouillard, Andrew D; Tan, Christopher M; Chen, Edward Y; Golub, Todd R; Sorger, Peter K; Subramanian, Aravind; Ma'ayan, Avi

2014-07-01

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Statistical assessment of crosstalk enrichment between gene groups in biological networks.

PubMed

McCormack, Theodore; Frings, Oliver; Alexeyenko, Andrey; Sonnhammer, Erik L L

2013-01-01

Analyzing groups of functionally coupled genes or proteins in the context of global interaction networks has become an important aspect of bioinformatic investigations. Assessing the statistical significance of crosstalk enrichment between or within groups of genes can be a valuable tool for functional annotation of experimental gene sets. Here we present CrossTalkZ, a statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA). CrossTalkZ (available at http://sonnhammer.sbc.su.se/download/software/CrossTalkZ/) is implemented in C++, easy to use, fast, accepts various input file formats, and produces a number of statistics. These include z-score, p-value, false discovery rate, and a test of normality for the null distributions.

Graph mining for next generation sequencing: leveraging the assembly graph for biological insights.

PubMed

Warnke-Sommer, Julia; Ali, Hesham

2016-05-06

The assembly of Next Generation Sequencing (NGS) reads remains a challenging task. This is especially true for the assembly of metagenomics data that originate from environmental samples potentially containing hundreds to thousands of unique species. The principle objective of current assembly tools is to assemble NGS reads into contiguous stretches of sequence called contigs while maximizing for both accuracy and contig length. The end goal of this process is to produce longer contigs with the major focus being on assembly only. Sequence read assembly is an aggregative process, during which read overlap relationship information is lost as reads are merged into longer sequences or contigs. The assembly graph is information rich and capable of capturing the genomic architecture of an input read data set. We have developed a novel hybrid graph in which nodes represent sequence regions at different levels of granularity. This model, utilized in the assembly and analysis pipeline Focus, presents a concise yet feature rich view of a given input data set, allowing for the extraction of biologically relevant graph structures for graph mining purposes. Focus was used to create hybrid graphs to model metagenomics data sets obtained from the gut microbiomes of five individuals with Crohn's disease and eight healthy individuals. Repetitive and mobile genetic elements are found to be associated with hybrid graph structure. Using graph mining techniques, a comparative study of the Crohn's disease and healthy data sets was conducted with focus on antibiotics resistance genes associated with transposase genes. Results demonstrated significant differences in the phylogenetic distribution of categories of antibiotics resistance genes in the healthy and diseased patients. Focus was also evaluated as a pure assembly tool and produced excellent results when compared against the Meta-velvet, Omega, and UD-IDBA assemblers. Mining the hybrid graph can reveal biological phenomena captured by its structure. We demonstrate the advantages of considering assembly graphs as data-mining support in addition to their role as frameworks for assembly.

Missing value imputation in DNA microarrays based on conjugate gradient method.

PubMed

Dorri, Fatemeh; Azmi, Paeiz; Dorri, Faezeh

2012-02-01

Analysis of gene expression profiles needs a complete matrix of gene array values; consequently, imputation methods have been suggested. In this paper, an algorithm that is based on conjugate gradient (CG) method is proposed to estimate missing values. k-nearest neighbors of the missed entry are first selected based on absolute values of their Pearson correlation coefficient. Then a subset of genes among the k-nearest neighbors is labeled as the best similar ones. CG algorithm with this subset as its input is then used to estimate the missing values. Our proposed CG based algorithm (CGimpute) is evaluated on different data sets. The results are compared with sequential local least squares (SLLSimpute), Bayesian principle component analysis (BPCAimpute), local least squares imputation (LLSimpute), iterated local least squares imputation (ILLSimpute) and adaptive k-nearest neighbors imputation (KNNKimpute) methods. The average of normalized root mean squares error (NRMSE) and relative NRMSE in different data sets with various missing rates shows CGimpute outperforms other methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

PubMed Central

2013-01-01

Background System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Results Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Conclusions Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr. PMID:23586463

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.

PubMed

Chen, Edward Y; Tan, Christopher M; Kou, Yan; Duan, Qiaonan; Wang, Zichen; Meirelles, Gabriela Vaz; Clark, Neil R; Ma'ayan, Avi

2013-04-15

System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts

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

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2011-02-18

Transcriptional regulatory networks are being determined using “reverse engineering” methods that infer connections based on correlations in gene state. Corroboration of such networks through independent means such as evidence from the biomedical literature is desirable. Here, we explore a novel approach, a bootstrapping version of our previous Cross-Ontological Analytic method (XOA) that can be used for semi-automated annotation and verification of inferred regulatory connections, as well as for discovery of additional functional relationships between the genes. First, we use our annotation and network expansion method on a biological network learned entirely from the literature. We show how new relevant linksmore » between genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. Second, we apply our method to annotation, verification, and expansion of a set of regulatory connections found by the Context Likelihood of Relatedness algorithm.« less

Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs

PubMed Central

Dutta, Noton K.; Bandyopadhyay, Nirmalya; Veeramani, Balaji; Lamichhane, Gyanu; Karakousis, Petros C.; Bader, Joel S.

2014-01-01

ABSTRACT Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes. PMID:24549847

Computerized system for recognition of autism on the basis of gene expression microarray data.

PubMed

Latkowski, Tomasz; Osowski, Stanislaw

2015-01-01

The aim of this paper is to provide a means to recognize a case of autism using gene expression microarrays. The crucial task is to discover the most important genes which are strictly associated with autism. The paper presents an application of different methods of gene selection, to select the most representative input attributes for an ensemble of classifiers. The set of classifiers is responsible for distinguishing autism data from the reference class. Simultaneous application of a few gene selection methods enables analysis of the ill-conditioned gene expression matrix from different points of view. The results of selection combined with a genetic algorithm and SVM classifier have shown increased accuracy of autism recognition. Early recognition of autism is extremely important for treatment of children and increases the probability of their recovery and return to normal social communication. The results of this research can find practical application in early recognition of autism on the basis of gene expression microarray analysis. Copyright © 2014 Elsevier Ltd. All rights reserved.

No Evidence That Schizophrenia Candidate Genes Are More Associated With Schizophrenia Than Noncandidate Genes.

PubMed

Johnson, Emma C; Border, Richard; Melroy-Greif, Whitney E; de Leeuw, Christiaan A; Ehringer, Marissa A; Keller, Matthew C

2017-11-15

A recent analysis of 25 historical candidate gene polymorphisms for schizophrenia in the largest genome-wide association study conducted to date suggested that these commonly studied variants were no more associated with the disorder than would be expected by chance. However, the same study identified other variants within those candidate genes that demonstrated genome-wide significant associations with schizophrenia. As such, it is possible that variants within historic schizophrenia candidate genes are associated with schizophrenia at levels above those expected by chance, even if the most-studied specific polymorphisms are not. The present study used association statistics from the largest schizophrenia genome-wide association study conducted to date as input to a gene set analysis to investigate whether variants within schizophrenia candidate genes are enriched for association with schizophrenia. As a group, variants in the most-studied candidate genes were no more associated with schizophrenia than were variants in control sets of noncandidate genes. While a small subset of candidate genes did appear to be significantly associated with schizophrenia, these genes were not particularly noteworthy given the large number of more strongly associated noncandidate genes. The history of schizophrenia research should serve as a cautionary tale to candidate gene investigators examining other phenotypes: our findings indicate that the most investigated candidate gene hypotheses of schizophrenia are not well supported by genome-wide association studies, and it is likely that this will be the case for other complex traits as well. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Analyzing gene perturbation screens with nested effects models in R and bioconductor.

PubMed

Fröhlich, Holger; Beissbarth, Tim; Tresch, Achim; Kostka, Dennis; Jacob, Juby; Spang, Rainer; Markowetz, F

2008-11-01

Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. NEMs reverse engineer upstream/downstream relations of cellular signaling cascades. NEMs take as input a set of candidate pathway genes and phenotypic profiles of perturbing these genes. NEMs return a pathway structure explaining the observed perturbation effects. Here, we describe the package nem, an open-source software to efficiently infer NEMs from data. Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. The methods we present summarize the current state-of-the-art in NEMs. Our software is written in the R language and freely avail-able via the Bioconductor project at http://www.bioconductor.org.

Genes with minimal phylogenetic information are problematic for coalescent analyses when gene tree estimation is biased.

PubMed

Xi, Zhenxiang; Liu, Liang; Davis, Charles C

2015-11-01

The development and application of coalescent methods are undergoing rapid changes. One little explored area that bears on the application of gene-tree-based coalescent methods to species tree estimation is gene informativeness. Here, we investigate the accuracy of these coalescent methods when genes have minimal phylogenetic information, including the implementation of the multilocus bootstrap approach. Using simulated DNA sequences, we demonstrate that genes with minimal phylogenetic information can produce unreliable gene trees (i.e., high error in gene tree estimation), which may in turn reduce the accuracy of species tree estimation using gene-tree-based coalescent methods. We demonstrate that this problem can be alleviated by sampling more genes, as is commonly done in large-scale phylogenomic analyses. This applies even when these genes are minimally informative. If gene tree estimation is biased, however, gene-tree-based coalescent analyses will produce inconsistent results, which cannot be remedied by increasing the number of genes. In this case, it is not the gene-tree-based coalescent methods that are flawed, but rather the input data (i.e., estimated gene trees). Along these lines, the commonly used program PhyML has a tendency to infer one particular bifurcating topology even though it is best represented as a polytomy. We additionally corroborate these findings by analyzing the 183-locus mammal data set assembled by McCormack et al. (2012) using ultra-conserved elements (UCEs) and flanking DNA. Lastly, we demonstrate that when employing the multilocus bootstrap approach on this 183-locus data set, there is no strong conflict between species trees estimated from concatenation and gene-tree-based coalescent analyses, as has been previously suggested by Gatesy and Springer (2014). Copyright © 2015 Elsevier Inc. All rights reserved.

CORE_TF: a user-friendly interface to identify evolutionary conserved transcription factor binding sites in sets of co-regulated genes

PubMed Central

Hestand, Matthew S; van Galen, Michiel; Villerius, Michel P; van Ommen, Gert-Jan B; den Dunnen, Johan T; 't Hoen, Peter AC

2008-01-01

Background The identification of transcription factor binding sites is difficult since they are only a small number of nucleotides in size, resulting in large numbers of false positives and false negatives in current approaches. Computational methods to reduce false positives are to look for over-representation of transcription factor binding sites in a set of similarly regulated promoters or to look for conservation in orthologous promoter alignments. Results We have developed a novel tool, "CORE_TF" (Conserved and Over-REpresented Transcription Factor binding sites) that identifies common transcription factor binding sites in promoters of co-regulated genes. To improve upon existing binding site predictions, the tool searches for position weight matrices from the TRANSFACR database that are over-represented in an experimental set compared to a random set of promoters and identifies cross-species conservation of the predicted transcription factor binding sites. The algorithm has been evaluated with expression and chromatin-immunoprecipitation on microarray data. We also implement and demonstrate the importance of matching the random set of promoters to the experimental promoters by GC content, which is a unique feature of our tool. Conclusion The program CORE_TF is accessible in a user friendly web interface at . It provides a table of over-represented transcription factor binding sites in the users input genes' promoters and a graphical view of evolutionary conserved transcription factor binding sites. In our test data sets it successfully predicts target transcription factors and their binding sites. PMID:19036135

The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

PubMed Central

Sioson, Allan A; Mane, Shrinivasrao P; Li, Pinghua; Sha, Wei; Heath, Lenwood S; Bohnert, Hans J; Grene, Ruth

2006-01-01

Background Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. Results The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compared to Expresso analysis with IIV data. The typical TM4 normalization and filtering pipeline corrects systematic intensity-specific bias on a per microarray basis. Subsequent statistical analysis with Expresso or a TM4 t-test can effectively identify differentially expressed genes. The best agreement with qRT-PCR data is obtained through the use of Expresso analysis and MIV data. Conclusion The results of this research are of practical value to biologists who analyze microarray data sets. The TM4 normalization and filtering pipeline corrects microarray-specific systematic bias and complements the normalization stage in Expresso analysis. The results of Expresso using MIV data have the best agreement with qRT-PCR results. In one experiment, MIV is a better choice than IIV as input to data normalization and statistical analysis methods, as it yields as greater number of statistically significant differentially expressed genes; TM4 does not support the choice of MIV input data. Overall, the more flexible and extensive statistical models of Expresso achieve more accurate analytical results, when judged by the yardstick of qRT-PCR data, in the context of an experimental design of modest complexity. PMID:16626497

Effects of RNA integrity on transcript quantification by total RNA sequencing of clinically collected human placental samples.

PubMed

Reiman, Mario; Laan, Maris; Rull, Kristiina; Sõber, Siim

2017-08-01

RNA degradation is a ubiquitous process that occurs in living and dead cells, as well as during handling and storage of extracted RNA. Reduced RNA quality caused by degradation is an established source of uncertainty for all RNA-based gene expression quantification techniques. RNA sequencing is an increasingly preferred method for transcriptome analyses, and dependence of its results on input RNA integrity is of significant practical importance. This study aimed to characterize the effects of varying input RNA integrity [estimated as RNA integrity number (RIN)] on transcript level estimates and delineate the characteristic differences between transcripts that differ in degradation rate. The study used ribodepleted total RNA sequencing data from a real-life clinically collected set ( n = 32) of human solid tissue (placenta) samples. RIN-dependent alterations in gene expression profiles were quantified by using DESeq2 software. Our results indicate that small differences in RNA integrity affect gene expression quantification by introducing a moderate and pervasive bias in expression level estimates that significantly affected 8.1% of studied genes. The rapidly degrading transcript pool was enriched in pseudogenes, short noncoding RNAs, and transcripts with extended 3' untranslated regions. Typical slowly degrading transcripts (median length, 2389 nt) represented protein coding genes with 4-10 exons and high guanine-cytosine content.-Reiman, M., Laan, M., Rull, K., Sõber, S. Effects of RNA integrity on transcript quantification by total RNA sequencing of clinically collected human placental samples. © FASEB.

Learning Biological Networks via Bootstrapping with Optimized GO-based Gene Similarity

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

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2010-08-02

Microarray gene expression data provide a unique information resource for learning biological networks using "reverse engineering" methods. However, there are a variety of cases in which we know which genes are involved in a given pathology of interest, but we do not have enough experimental evidence to support the use of fully-supervised/reverse-engineering learning methods. In this paper, we explore a novel semi-supervised approach in which biological networks are learned from a reference list of genes and a partial set of links for these genes extracted automatically from PubMed abstracts, using a knowledge-driven bootstrapping algorithm. We show how new relevant linksmore » across genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. We describe an application of this approach to the TGFB pathway as a case study and show how the ensuing results prove the feasibility of the approach as an alternate or complementary technique to fully supervised methods.« less

EG-05COMBINATION OF GENE COPY GAIN AND EPIGENETIC DEREGULATION ARE ASSOCIATED WITH THE ABERRANT EXPRESSION OF A STEM CELL RELATED HOX-SIGNATURE IN GLIOBLASTOMA

PubMed Central

Kurscheid, Sebastian; Bady, Pierre; Sciuscio, Davide; Samarzija, Ivana; Shay, Tal; Vassallo, Irene; Van Criekinge, Wim; Domany, Eytan; Stupp, Roger; Delorenzi, Mauro; Hegi, Monika

2014-01-01

We previously reported a stem cell related HOX gene signature associated with resistance to chemo-radiotherapy (TMZ/RT- > TMZ) in glioblastoma. However, underlying mechanisms triggering overexpression remain mostly elusive. Interestingly, HOX genes are neither involved in the developing brain, nor expressed in normal brain, suggestive of an acquired gene expression signature during gliomagenesis. HOXA genes are located on CHR 7 that displays trisomy in most glioblastoma which strongly impacts gene expression on this chromosome, modulated by local regulatory elements. Furthermore we observed more pronounced DNA methylation across the HOXA locus as compared to non-tumoral brain (Human methylation 450K BeadChip Illumina; 59 glioblastoma, 5 non-tumoral brain sampes). CpG probes annotated for HOX-signature genes, contributing most to the variability, served as input into the analysis of DNA methylation and expression to identify key regulatory regions. The structural similarity of the observed correlation matrices between DNA methylation and gene expression in our cohort and an independent data-set from TCGA (106 glioblastoma) was remarkable (RV-coefficient, 0.84; p-value < 0.0001). We identified a CpG located in the promoter region of the HOXA10 locus exerting the strongest mean negative correlation between methylation and expression of the whole HOX-signature. Applying this analysis the same CpG emerged in the external set. We then determined the contribution of both, gene copy aberration (CNA) and methylation at the selected probe to explain expression of the HOX-signature using a linear model. Statistically significant results suggested an additive effect between gene dosage and methylation at the key CpG identified. Similarly, such an additive effect was also observed in the external data-set. Taken together, we hypothesize that overexpression of the stem-cell related HOX signature is triggered by gain of trisomy 7 and escape from compensatory DNA methylation at positions controlling the effect of enhanced gene dose on expression.

Scuba: scalable kernel-based gene prioritization.

PubMed

Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio

2018-01-25

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

Pharmaco-miR: linking microRNAs and drug effects

PubMed Central

Rukov, Jakob Lewin; Wilentzik, Roni; Jaffe, Ishai; Vinther, Jeppe; Shomron, Noam

2014-01-01

MicroRNAs (miRNAs) are short regulatory RNAs that down-regulate gene expression. They are essential for cell homeostasis and active in many disease states. A major discovery is the ability of miRNAs to determine the efficacy of drugs, which has given rise to the field of ‘miRNA pharmacogenomics’ through ‘Pharmaco-miRs’. miRNAs play a significant role in pharmacogenomics by down-regulating genes that are important for drug function. These interactions can be described as triplet sets consisting of a miRNA, a target gene and a drug associated with the gene. We have developed a web server which links miRNA expression and drug function by combining data on miRNA targeting and protein–drug interactions. miRNA targeting information derive from both experimental data and computational predictions, and protein–drug interactions are annotated by the Pharmacogenomics Knowledge base (PharmGKB). Pharmaco-miR’s input consists of miRNAs, genes and/or drug names and the output consists of miRNA pharmacogenomic sets or a list of unique associated miRNAs, genes and drugs. We have furthermore built a database, named Pharmaco-miR Verified Sets (VerSe), which contains miRNA pharmacogenomic data manually curated from the literature, can be searched and downloaded via Pharmaco-miR and informs on trends and generalities published in the field. Overall, we present examples of how Pharmaco-miR provides possible explanations for previously published observations, including how the cisplatin and 5-fluorouracil resistance induced by miR-148a may be caused by miR-148a targeting of the gene KIT. The information is available at www.Pharmaco-miR.org. PMID:23376192

Reference Gene Validation for RT-qPCR, a Note on Different Available Software Packages

PubMed Central

De Spiegelaere, Ward; Dern-Wieloch, Jutta; Weigel, Roswitha; Schumacher, Valérie; Schorle, Hubert; Nettersheim, Daniel; Bergmann, Martin; Brehm, Ralph; Kliesch, Sabine; Vandekerckhove, Linos; Fink, Cornelia

2015-01-01

Background An appropriate normalization strategy is crucial for data analysis from real time reverse transcription polymerase chain reactions (RT-qPCR). It is widely supported to identify and validate stable reference genes, since no single biological gene is stably expressed between cell types or within cells under different conditions. Different algorithms exist to validate optimal reference genes for normalization. Applying human cells, we here compare the three main methods to the online available RefFinder tool that integrates these algorithms along with R-based software packages which include the NormFinder and GeNorm algorithms. Results 14 candidate reference genes were assessed by RT-qPCR in two sample sets, i.e. a set of samples of human testicular tissue containing carcinoma in situ (CIS), and a set of samples from the human adult Sertoli cell line (FS1) either cultured alone or in co-culture with the seminoma like cell line (TCam-2) or with equine bone marrow derived mesenchymal stem cells (eBM-MSC). Expression stabilities of the reference genes were evaluated using geNorm, NormFinder, and BestKeeper. Similar results were obtained by the three approaches for the most and least stably expressed genes. The R-based packages NormqPCR, SLqPCR and the NormFinder for R script gave identical gene rankings. Interestingly, different outputs were obtained between the original software packages and the RefFinder tool, which is based on raw Cq values for input. When the raw data were reanalysed assuming 100% efficiency for all genes, then the outputs of the original software packages were similar to the RefFinder software, indicating that RefFinder outputs may be biased because PCR efficiencies are not taken into account. Conclusions This report shows that assay efficiency is an important parameter for reference gene validation. New software tools that incorporate these algorithms should be carefully validated prior to use. PMID:25825906

Reference gene validation for RT-qPCR, a note on different available software packages.

PubMed

De Spiegelaere, Ward; Dern-Wieloch, Jutta; Weigel, Roswitha; Schumacher, Valérie; Schorle, Hubert; Nettersheim, Daniel; Bergmann, Martin; Brehm, Ralph; Kliesch, Sabine; Vandekerckhove, Linos; Fink, Cornelia

2015-01-01

An appropriate normalization strategy is crucial for data analysis from real time reverse transcription polymerase chain reactions (RT-qPCR). It is widely supported to identify and validate stable reference genes, since no single biological gene is stably expressed between cell types or within cells under different conditions. Different algorithms exist to validate optimal reference genes for normalization. Applying human cells, we here compare the three main methods to the online available RefFinder tool that integrates these algorithms along with R-based software packages which include the NormFinder and GeNorm algorithms. 14 candidate reference genes were assessed by RT-qPCR in two sample sets, i.e. a set of samples of human testicular tissue containing carcinoma in situ (CIS), and a set of samples from the human adult Sertoli cell line (FS1) either cultured alone or in co-culture with the seminoma like cell line (TCam-2) or with equine bone marrow derived mesenchymal stem cells (eBM-MSC). Expression stabilities of the reference genes were evaluated using geNorm, NormFinder, and BestKeeper. Similar results were obtained by the three approaches for the most and least stably expressed genes. The R-based packages NormqPCR, SLqPCR and the NormFinder for R script gave identical gene rankings. Interestingly, different outputs were obtained between the original software packages and the RefFinder tool, which is based on raw Cq values for input. When the raw data were reanalysed assuming 100% efficiency for all genes, then the outputs of the original software packages were similar to the RefFinder software, indicating that RefFinder outputs may be biased because PCR efficiencies are not taken into account. This report shows that assay efficiency is an important parameter for reference gene validation. New software tools that incorporate these algorithms should be carefully validated prior to use.

MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation

PubMed Central

Wang, Luman; Mo, Qiaochu; Wang, Jianxin

2015-01-01

Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI and r values, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI, r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches. PMID:26881263

MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation.

PubMed

Wang, Luman; Mo, Qiaochu; Wang, Jianxin

2015-01-01

Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI and r values, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI, r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches.

Exact Algorithms for Duplication-Transfer-Loss Reconciliation with Non-Binary Gene Trees.

PubMed

Kordi, Misagh; Bansal, Mukul S

2017-06-01

Duplication-Transfer-Loss (DTL) reconciliation is a powerful method for studying gene family evolution in the presence of horizontal gene transfer. DTL reconciliation seeks to reconcile gene trees with species trees by postulating speciation, duplication, transfer, and loss events. Efficient algorithms exist for finding optimal DTL reconciliations when the gene tree is binary. In practice, however, gene trees are often non-binary due to uncertainty in the gene tree topologies, and DTL reconciliation with non-binary gene trees is known to be NP-hard. In this paper, we present the first exact algorithms for DTL reconciliation with non-binary gene trees. Specifically, we (i) show that the DTL reconciliation problem for non-binary gene trees is fixed-parameter tractable in the maximum degree of the gene tree, (ii) present an exponential-time, but in-practice efficient, algorithm to track and enumerate all optimal binary resolutions of a non-binary input gene tree, and (iii) apply our algorithms to a large empirical data set of over 4700 gene trees from 100 species to study the impact of gene tree uncertainty on DTL-reconciliation and to demonstrate the applicability and utility of our algorithms. The new techniques and algorithms introduced in this paper will help biologists avoid incorrect evolutionary inferences caused by gene tree uncertainty.

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.

PubMed

Enns, Eva A; Cipriano, Lauren E; Simons, Cyrena T; Kong, Chung Yin

2015-02-01

To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. © The Author(s) 2014.

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach

PubMed Central

Enns, Eva A.; Cipriano, Lauren E.; Simons, Cyrena T.; Kong, Chung Yin

2014-01-01

Background To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single “goodness-of-fit” (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. Methods We demonstrate the Pareto frontier approach in the calibration of two models: a simple, illustrative Markov model and a previously-published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to two possible weighted-sum GOF scoring systems, and compare the health economic conclusions arising from these different definitions of best-fitting. Results For the simple model, outcomes evaluated over the best-fitting input sets according to the two weighted-sum GOF schemes were virtually non-overlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95%CI: 72,500 – 87,600] vs. $139,700 [95%CI: 79,900 - 182,800] per QALY gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95%CI: 64,900 – 156,200] per QALY gained). The TAVR model yielded similar results. Conclusions Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. PMID:24799456

UniPrime2: a web service providing easier Universal Primer design.

PubMed

Boutros, Robin; Stokes, Nicola; Bekaert, Michaël; Teeling, Emma C

2009-07-01

The UniPrime2 web server is a publicly available online resource which automatically designs large sets of universal primers when given a gene reference ID or Fasta sequence input by a user. UniPrime2 works by automatically retrieving and aligning homologous sequences from GenBank, identifying regions of conservation within the alignment, and generating suitable primers that can be used to amplify variable genomic regions. In essence, UniPrime2 is a suite of publicly available software packages (Blastn, T-Coffee, GramAlign, Primer3), which reduces the laborious process of primer design, by integrating these programs into a single software pipeline. Hence, UniPrime2 differs from previous primer design web services in that all steps are automated, linked, saved and phylogenetically delimited, only requiring a single user-defined gene reference ID or input sequence. We provide an overview of the web service and wet-laboratory validation of the primers generated. The system is freely accessible at: http://uniprime.batlab.eu. UniPrime2 is licenced under a Creative Commons Attribution Noncommercial-Share Alike 3.0 Licence.

The role of feeding rhythm, adrenal hormones and neuronal inputs in synchronizing daily clock gene rhythms in the liver.

PubMed

Su, Yan; Cailotto, Cathy; Foppen, Ewout; Jansen, Remi; Zhang, Zhi; Buijs, Ruud; Fliers, Eric; Kalsbeek, Andries

2016-02-15

The master clock in the hypothalamic suprachiasmatic nucleus (SCN) is assumed to distribute rhythmic information to the periphery via neural, humoral and/or behavioral connections. Until now, feeding, corticosterone and neural inputs are considered important signals for synchronizing daily rhythms in the liver. In this study, we investigated the necessity of neural inputs as well as of the feeding and adrenal hormone rhythms for maintaining daily hepatic clock gene rhythms. Clock genes kept their daily rhythm when only one of these three signals was disrupted, or when we disrupted hepatic neuronal inputs together with the adrenal hormone rhythm or with the daily feeding rhythm. However, all clock genes studied lost their daily expression rhythm after simultaneous disruption of the feeding and adrenal hormone rhythm. These data indicate that either a daily rhythm of feeding or adrenal hormones should be present to synchronize clock gene rhythms in the liver with the SCN. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Data identification for improving gene network inference using computational algebra.

PubMed

Dimitrova, Elena; Stigler, Brandilyn

2014-11-01

Identification of models of gene regulatory networks is sensitive to the amount of data used as input. Considering the substantial costs in conducting experiments, it is of value to have an estimate of the amount of data required to infer the network structure. To minimize wasted resources, it is also beneficial to know which data are necessary to identify the network. Knowledge of the data and knowledge of the terms in polynomial models are often required a priori in model identification. In applications, it is unlikely that the structure of a polynomial model will be known, which may force data sets to be unnecessarily large in order to identify a model. Furthermore, none of the known results provides any strategy for constructing data sets to uniquely identify a model. We provide a specialization of an existing criterion for deciding when a set of data points identifies a minimal polynomial model when its monomial terms have been specified. Then, we relax the requirement of the knowledge of the monomials and present results for model identification given only the data. Finally, we present a method for constructing data sets that identify minimal polynomial models.

Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function

PubMed Central

Tian, Weidong; Zhang, Lan V; Taşan, Murat; Gibbons, Francis D; King, Oliver D; Park, Julie; Wunderlich, Zeba; Cherry, J Michael; Roth, Frederick P

2008-01-01

Background: Learning the function of genes is a major goal of computational genomics. Methods for inferring gene function have typically fallen into two categories: 'guilt-by-profiling', which exploits correlation between function and other gene characteristics; and 'guilt-by-association', which transfers function from one gene to another via biological relationships. Results: We have developed a strategy ('Funckenstein') that performs guilt-by-profiling and guilt-by-association and combines the results. Using a benchmark set of functional categories and input data for protein-coding genes in Saccharomyces cerevisiae, Funckenstein was compared with a previous combined strategy. Subsequently, we applied Funckenstein to 2,455 Gene Ontology terms. In the process, we developed 2,455 guilt-by-profiling classifiers based on 8,848 gene characteristics and 12 functional linkage graphs based on 23 biological relationships. Conclusion: Funckenstein outperforms a previous combined strategy using a common benchmark dataset. The combination of 'guilt-by-profiling' and 'guilt-by-association' gave significant improvement over the component classifiers, showing the greatest synergy for the most specific functions. Performance was evaluated by cross-validation and by literature examination of the top-scoring novel predictions. These quantitative predictions should help prioritize experimental study of yeast gene functions. PMID:18613951

DeCoSTAR: Reconstructing the Ancestral Organization of Genes or Genomes Using Reconciled Phylogenies

PubMed Central

Anselmetti, Yoann; Patterson, Murray; Ponty, Yann; B�rard, S�verine; Chauve, Cedric; Scornavacca, Celine; Daubin, Vincent; Tannier, Eric

2017-01-01

DeCoSTAR is a software that aims at reconstructing the organization of ancestral genes or genomes in the form of sets of neighborhood relations (adjacencies) between pairs of ancestral genes or gene domains. It can also improve the assembly of fragmented genomes by proposing evolutionary-induced adjacencies between scaffolding fragments. Ancestral genes or domains are deduced from reconciled phylogenetic trees under an evolutionary model that considers gains, losses, speciations, duplications, and transfers as possible events for gene evolution. Reconciliations are either given as input or computed with the ecceTERA package, into which DeCoSTAR is integrated. DeCoSTAR computes adjacency evolutionary scenarios using a scoring scheme based on a weighted sum of adjacency gains and breakages. Solutions, both optimal and near-optimal, are sampled according to the Boltzmann–Gibbs distribution centered around parsimonious solutions, and statistical supports on ancestral and extant adjacencies are provided. DeCoSTAR supports the features of previously contributed tools that reconstruct ancestral adjacencies, namely DeCo, DeCoLT, ART-DeCo, and DeClone. In a few minutes, DeCoSTAR can reconstruct the evolutionary history of domains inside genes, of gene fusion and fission events, or of gene order along chromosomes, for large data sets including dozens of whole genomes from all kingdoms of life. We illustrate the potential of DeCoSTAR with several applications: ancestral reconstruction of gene orders for Anopheles mosquito genomes, multidomain proteins in Drosophila, and gene fusion and fission detection in Actinobacteria. Availability: http://pbil.univ-lyon1.fr/software/DeCoSTAR (Last accessed April 24, 2017). PMID:28402423

Repressor logic modules assembled by rolling circle amplification platform to construct a set of logic gates

PubMed Central

Wei, Hua; Hu, Bo; Tang, Suming; Zhao, Guojie; Guan, Yifu

2016-01-01

Small molecule metabolites and their allosterically regulated repressors play an important role in many gene expression and metabolic disorder processes. These natural sensors, though valuable as good logic switches, have rarely been employed without transcription machinery in cells. Here, two pairs of repressors, which function in opposite ways, were cloned, purified and used to control DNA replication in rolling circle amplification (RCA) in vitro. By using metabolites and repressors as inputs, RCA signals as outputs, four basic logic modules were constructed successfully. To achieve various logic computations based on these basic modules, we designed series and parallel strategies of circular templates, which can further assemble these repressor modules in an RCA platform to realize twelve two-input Boolean logic gates and a three-input logic gate. The RCA-output and RCA-assembled platform was proved to be easy and flexible for complex logic processes and might have application potential in molecular computing and synthetic biology. PMID:27869177

xGDBvm: A Web GUI-Driven Workflow for Annotating Eukaryotic Genomes in the Cloud[OPEN

PubMed Central

Merchant, Nirav

2016-01-01

Genome-wide annotation of gene structure requires the integration of numerous computational steps. Currently, annotation is arguably best accomplished through collaboration of bioinformatics and domain experts, with broad community involvement. However, such a collaborative approach is not scalable at today’s pace of sequence generation. To address this problem, we developed the xGDBvm software, which uses an intuitive graphical user interface to access a number of common genome analysis and gene structure tools, preconfigured in a self-contained virtual machine image. Once their virtual machine instance is deployed through iPlant’s Atmosphere cloud services, users access the xGDBvm workflow via a unified Web interface to manage inputs, set program parameters, configure links to high-performance computing (HPC) resources, view and manage output, apply analysis and editing tools, or access contextual help. The xGDBvm workflow will mask the genome, compute spliced alignments from transcript and/or protein inputs (locally or on a remote HPC cluster), predict gene structures and gene structure quality, and display output in a public or private genome browser complete with accessory tools. Problematic gene predictions are flagged and can be reannotated using the integrated yrGATE annotation tool. xGDBvm can also be configured to append or replace existing data or load precomputed data. Multiple genomes can be annotated and displayed, and outputs can be archived for sharing or backup. xGDBvm can be adapted to a variety of use cases including de novo genome annotation, reannotation, comparison of different annotations, and training or teaching. PMID:27020957

xGDBvm: A Web GUI-Driven Workflow for Annotating Eukaryotic Genomes in the Cloud.

PubMed

Duvick, Jon; Standage, Daniel S; Merchant, Nirav; Brendel, Volker P

2016-04-01

Genome-wide annotation of gene structure requires the integration of numerous computational steps. Currently, annotation is arguably best accomplished through collaboration of bioinformatics and domain experts, with broad community involvement. However, such a collaborative approach is not scalable at today's pace of sequence generation. To address this problem, we developed the xGDBvm software, which uses an intuitive graphical user interface to access a number of common genome analysis and gene structure tools, preconfigured in a self-contained virtual machine image. Once their virtual machine instance is deployed through iPlant's Atmosphere cloud services, users access the xGDBvm workflow via a unified Web interface to manage inputs, set program parameters, configure links to high-performance computing (HPC) resources, view and manage output, apply analysis and editing tools, or access contextual help. The xGDBvm workflow will mask the genome, compute spliced alignments from transcript and/or protein inputs (locally or on a remote HPC cluster), predict gene structures and gene structure quality, and display output in a public or private genome browser complete with accessory tools. Problematic gene predictions are flagged and can be reannotated using the integrated yrGATE annotation tool. xGDBvm can also be configured to append or replace existing data or load precomputed data. Multiple genomes can be annotated and displayed, and outputs can be archived for sharing or backup. xGDBvm can be adapted to a variety of use cases including de novo genome annotation, reannotation, comparison of different annotations, and training or teaching. © 2016 American Society of Plant Biologists. All rights reserved.

CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets

PubMed Central

Li, Yang; Liu, Jun S.; Mootha, Vamsi K.

2017-01-01

In recent years, there has been a huge rise in the number of publicly available transcriptional profiling datasets. These massive compendia comprise billions of measurements and provide a special opportunity to predict the function of unstudied genes based on co-expression to well-studied pathways. Such analyses can be very challenging, however, since biological pathways are modular and may exhibit co-expression only in specific contexts. To overcome these challenges we introduce CLIC, CLustering by Inferred Co-expression. CLIC accepts as input a pathway consisting of two or more genes. It then uses a Bayesian partition model to simultaneously partition the input gene set into coherent co-expressed modules (CEMs), while assigning the posterior probability for each dataset in support of each CEM. CLIC then expands each CEM by scanning the transcriptome for additional co-expressed genes, quantified by an integrated log-likelihood ratio (LLR) score weighted for each dataset. As a byproduct, CLIC automatically learns the conditions (datasets) within which a CEM is operative. We implemented CLIC using a compendium of 1774 mouse microarray datasets (28628 microarrays) or 1887 human microarray datasets (45158 microarrays). CLIC analysis reveals that of 910 canonical biological pathways, 30% consist of strongly co-expressed gene modules for which new members are predicted. For example, CLIC predicts a functional connection between protein C7orf55 (FMC1) and the mitochondrial ATP synthase complex that we have experimentally validated. CLIC is freely available at www.gene-clic.org. We anticipate that CLIC will be valuable both for revealing new components of biological pathways as well as the conditions in which they are active. PMID:28719601

Gene Ontology Consortium: going forward

PubMed Central

2015-01-01

The Gene Ontology (GO; http://www.geneontology.org) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge. Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community. The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes. We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions. The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data. Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser. We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology. PMID:25428369

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

PubMed

Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

Optimizing information flow in small genetic networks. IV. Spatial coupling

NASA Astrophysics Data System (ADS)

Sokolowski, Thomas R.; Tkačik, Gašper

2015-06-01

We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the input) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively different regulatory strategy emerges where individual cells respond to the input in a nearly steplike fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.

Drosophila Araucan and Caupolican Integrate Intrinsic and Signalling Inputs for the Acquisition by Muscle Progenitors of the Lateral Transverse Fate

PubMed Central

Carrasco-Rando, Marta; Tutor, Antonio S.; Prieto-Sánchez, Silvia; González-Pérez, Esther; Barrios, Natalia; Letizia, Annalisa; Martín, Paloma; Campuzano, Sonsoles; Ruiz-Gómez, Mar

2011-01-01

A central issue of myogenesis is the acquisition of identity by individual muscles. In Drosophila, at the time muscle progenitors are singled out, they already express unique combinations of muscle identity genes. This muscle code results from the integration of positional and temporal signalling inputs. Here we identify, by means of loss-of-function and ectopic expression approaches, the Iroquois Complex homeobox genes araucan and caupolican as novel muscle identity genes that confer lateral transverse muscle identity. The acquisition of this fate requires that Araucan/Caupolican repress other muscle identity genes such as slouch and vestigial. In addition, we show that Caupolican-dependent slouch expression depends on the activation state of the Ras/Mitogen Activated Protein Kinase cascade. This provides a comprehensive insight into the way Iroquois genes integrate in muscle progenitors, signalling inputs that modulate gene expression and protein activity. PMID:21811416

cMapper: gene-centric connectivity mapper for EBI-RDF platform.

PubMed

Shoaib, Muhammad; Ansari, Adnan Ahmad; Ahn, Sung-Min

2017-01-15

In this era of biological big data, data integration has become a common task and a challenge for biologists. The Resource Description Framework (RDF) was developed to enable interoperability of heterogeneous datasets. The EBI-RDF platform enables an efficient data integration of six independent biological databases using RDF technologies and shared ontologies. However, to take advantage of this platform, biologists need to be familiar with RDF technologies and SPARQL query language. To overcome this practical limitation of the EBI-RDF platform, we developed cMapper, a web-based tool that enables biologists to search the EBI-RDF databases in a gene-centric manner without a thorough knowledge of RDF and SPARQL. cMapper allows biologists to search data entities in the EBI-RDF platform that are connected to genes or small molecules of interest in multiple biological contexts. The input to cMapper consists of a set of genes or small molecules, and the output are data entities in six independent EBI-RDF databases connected with the given genes or small molecules in the user's query. cMapper provides output to users in the form of a graph in which nodes represent data entities and the edges represent connections between data entities and inputted set of genes or small molecules. Furthermore, users can apply filters based on database, taxonomy, organ and pathways in order to focus on a core connectivity graph of their interest. Data entities from multiple databases are differentiated based on background colors. cMapper also enables users to investigate shared connections between genes or small molecules of interest. Users can view the output graph on a web browser or download it in either GraphML or JSON formats. cMapper is available as a web application with an integrated MySQL database. The web application was developed using Java and deployed on Tomcat server. We developed the user interface using HTML5, JQuery and the Cytoscape Graph API. cMapper can be accessed at http://cmapper.ewostech.net Readers can download the development manual from the website http://cmapper.ewostech.net/docs/cMapperDocumentation.pdf. Source Code is available at https://github.com/muhammadshoaib/cmapperContact:smahn@gachon.ac.krSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models.

PubMed

Fu, Guifang; Dai, Xiaotian; Symanzik, Jürgen; Bushman, Shaun

2017-01-01

Leaf shape traits have long been a focus of many disciplines, but the complex genetic and environmental interactive mechanisms regulating leaf shape variation have not yet been investigated in detail. The question of the respective roles of genes and environment and how they interact to modulate leaf shape is a thorny evolutionary problem, and sophisticated methodology is needed to address it. In this study, we investigated a framework-level approach that inputs shape image photographs and genetic and environmental data, and then outputs the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed framework was confirmed by simulation and a Populus szechuanica var. tibetica data set. This new methodology resulted in the detection of novel shape characteristics, and also confirmed some previous findings. The quantitative modeling of a combination of polygenetic, plastic, epistatic, and gene-environment interactive effects, as investigated in this study, will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology data sets. Unlike the majority of approaches in the quantitative leaf shape literature, this framework-level approach is data-driven, without assuming any pre-known shape attributes, landmarks, or model structures. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

A MYB/ZML Complex Regulates Wound-Induced Lignin Genes in Maize

PubMed Central

Vélez-Bermúdez, Isabel-Cristina; Salazar-Henao, Jorge E.; Franco-Zorrilla, José-Manuel; Grotewold, Erich; Solano, Roberto

2015-01-01

Lignin is an essential polymer in vascular plants that plays key structural roles in vessels and fibers. Lignification is induced by external inputs such as wounding, but the molecular mechanisms that link this stress to lignification remain largely unknown. In this work, we provide evidence that three maize (Zea mays) lignin repressors, MYB11, MYB31, and MYB42, participate in wound-induced lignification by interacting with ZML2, a protein belonging to the TIFY family. We determined that the three R2R3-MYB factors and ZML2 bind in vivo to AC-rich and GAT(A/C) cis-elements, respectively, present in a set of lignin genes. In particular, we show that MYB11 and ZML2 bind simultaneously to the AC-rich and GAT(A/C) cis-elements present in the promoter of the caffeic acid O-methyl transferase (comt) gene. We show that, like the R2R3-MYB factors, ZML2 also acts as a transcriptional repressor. We found that upon wounding and methyl jasmonate treatments, MYB11 and ZML2 proteins are degraded and comt transcription is induced. Based on these results, we propose a molecular regulatory mechanism involving a MYB/ZML complex in which wound-induced lignification can be achieved by the derepression of a set of lignin genes. PMID:26566917

Inferring gene regression networks with model trees

PubMed Central

2010-01-01

Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REGNET. PMID:20950452

Inferring species trees from incongruent multi-copy gene trees using the Robinson-Foulds distance

PubMed Central

2013-01-01

Background Constructing species trees from multi-copy gene trees remains a challenging problem in phylogenetics. One difficulty is that the underlying genes can be incongruent due to evolutionary processes such as gene duplication and loss, deep coalescence, or lateral gene transfer. Gene tree estimation errors may further exacerbate the difficulties of species tree estimation. Results We present a new approach for inferring species trees from incongruent multi-copy gene trees that is based on a generalization of the Robinson-Foulds (RF) distance measure to multi-labeled trees (mul-trees). We prove that it is NP-hard to compute the RF distance between two mul-trees; however, it is easy to calculate this distance between a mul-tree and a singly-labeled species tree. Motivated by this, we formulate the RF problem for mul-trees (MulRF) as follows: Given a collection of multi-copy gene trees, find a singly-labeled species tree that minimizes the total RF distance from the input mul-trees. We develop and implement a fast SPR-based heuristic algorithm for the NP-hard MulRF problem. We compare the performance of the MulRF method (available at http://genome.cs.iastate.edu/CBL/MulRF/) with several gene tree parsimony approaches using gene tree simulations that incorporate gene tree error, gene duplications and losses, and/or lateral transfer. The MulRF method produces more accurate species trees than gene tree parsimony approaches. We also demonstrate that the MulRF method infers in minutes a credible plant species tree from a collection of nearly 2,000 gene trees. Conclusions Our new phylogenetic inference method, based on a generalized RF distance, makes it possible to quickly estimate species trees from large genomic data sets. Since the MulRF method, unlike gene tree parsimony, is based on a generic tree distance measure, it is appealing for analyses of genomic data sets, in which many processes such as deep coalescence, recombination, gene duplication and losses as well as phylogenetic error may contribute to gene tree discord. In experiments, the MulRF method estimated species trees accurately and quickly, demonstrating MulRF as an efficient alternative approach for phylogenetic inference from large-scale genomic data sets. PMID:24180377

Use of Gene Expression Programming in regionalization of flow duration curve

NASA Astrophysics Data System (ADS)

Hashmi, Muhammad Z.; Shamseldin, Asaad Y.

2014-06-01

In this paper, a recently introduced artificial intelligence technique known as Gene Expression Programming (GEP) has been employed to perform symbolic regression for developing a parametric scheme of flow duration curve (FDC) regionalization, to relate selected FDC characteristics to catchment characteristics. Stream flow records of selected catchments located in the Auckland Region of New Zealand were used. FDCs of the selected catchments were normalised by dividing the ordinates by their median value. Input for the symbolic regression analysis using GEP was (a) selected characteristics of normalised FDCs; and (b) 26 catchment characteristics related to climate, morphology, soil properties and land cover properties obtained using the observed data and GIS analysis. Our study showed that application of this artificial intelligence technique expedites the selection of a set of the most relevant independent variables out of a large set, because these are automatically selected through the GEP process. Values of the FDC characteristics obtained from the developed relationships have high correlations with the observed values.

Epigenetic regulation of female puberty.

PubMed

Lomniczi, Alejandro; Wright, Hollis; Ojeda, Sergio R

2015-01-01

Substantial progress has been made in recent years toward deciphering the molecular and genetic underpinnings of the pubertal process. The availability of powerful new methods to interrogate the human genome has led to the identification of genes that are essential for puberty to occur. Evidence has also emerged suggesting that the initiation of puberty requires the coordinated activity of gene sets organized into functional networks. At a cellular level, it is currently thought that loss of transsynaptic inhibition, accompanied by an increase in excitatory inputs, results in the pubertal activation of GnRH release. This concept notwithstanding, a mechanism of epigenetic repression targeting genes required for the pubertal activation of GnRH neurons was recently identified as a core component of the molecular machinery underlying the central restraint of puberty. In this chapter we will discuss the potential contribution of various mechanisms of epigenetic regulation to the hypothalamic control of female puberty. Copyright © 2014 Elsevier Inc. All rights reserved.

An accelerated training method for back propagation networks

NASA Technical Reports Server (NTRS)

Shelton, Robert O. (Inventor)

1993-01-01

The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.

A novel approach to simulate gene-environment interactions in complex diseases.

PubMed

Amato, Roberto; Pinelli, Michele; D'Andrea, Daniel; Miele, Gennaro; Nicodemi, Mario; Raiconi, Giancarlo; Cocozza, Sergio

2010-01-05

Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study.

Fast ancestral gene order reconstruction of genomes with unequal gene content.

PubMed

Feijão, Pedro; Araujo, Eloi

2016-11-11

During evolution, genomes are modified by large scale structural events, such as rearrangements, deletions or insertions of large blocks of DNA. Of particular interest, in order to better understand how this type of genomic evolution happens, is the reconstruction of ancestral genomes, given a phylogenetic tree with extant genomes at its leaves. One way of solving this problem is to assume a rearrangement model, such as Double Cut and Join (DCJ), and find a set of ancestral genomes that minimizes the number of events on the input tree. Since this problem is NP-hard for most rearrangement models, exact solutions are practical only for small instances, and heuristics have to be used for larger datasets. This type of approach can be called event-based. Another common approach is based on finding conserved structures between the input genomes, such as adjacencies between genes, possibly also assigning weights that indicate a measure of confidence or probability that this particular structure is present on each ancestral genome, and then finding a set of non conflicting adjacencies that optimize some given function, usually trying to maximize total weight and minimizing character changes in the tree. We call this type of methods homology-based. In previous work, we proposed an ancestral reconstruction method that combines homology- and event-based ideas, using the concept of intermediate genomes, that arise in DCJ rearrangement scenarios. This method showed better rate of correctly reconstructed adjacencies than other methods, while also being faster, since the use of intermediate genomes greatly reduces the search space. Here, we generalize the intermediate genome concept to genomes with unequal gene content, extending our method to account for gene insertions and deletions of any length. In many of the simulated datasets, our proposed method had better results than MLGO and MGRA, two state-of-the-art algorithms for ancestral reconstruction with unequal gene content, while running much faster, making it more scalable to larger datasets. Studing ancestral reconstruction problems under a new light, using the concept of intermediate genomes, allows the design of very fast algorithms by greatly reducing the solution search space, while also giving very good results. The algorithms introduced in this paper were implemented in an open-source software called RINGO (ancestral Reconstruction with INtermediate GenOmes), available at https://github.com/pedrofeijao/RINGO .

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks

PubMed Central

Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways. PMID:29049295

Systematic comparison of the response properties of protein and RNA mediated gene regulatory motifs.

PubMed

Iyengar, Bharat Ravi; Pillai, Beena; Venkatesh, K V; Gadgil, Chetan J

2017-05-30

We present a framework enabling the dissection of the effects of motif structure (feedback or feedforward), the nature of the controller (RNA or protein), and the regulation mode (transcriptional, post-transcriptional or translational) on the response to a step change in the input. We have used a common model framework for gene expression where both motif structures have an activating input and repressing regulator, with the same set of parameters, to enable a comparison of the responses. We studied the global sensitivity of the system properties, such as steady-state gain, overshoot, peak time, and peak duration, to parameters. We find that, in all motifs, overshoot correlated negatively whereas peak duration varied concavely with peak time. Differences in the other system properties were found to be mainly dependent on the nature of the controller rather than the motif structure. Protein mediated motifs showed a higher degree of adaptation i.e. a tendency to return to baseline levels; in particular, feedforward motifs exhibited perfect adaptation. RNA mediated motifs had a mild regulatory effect; they also exhibited a lower peaking tendency and mean overshoot. Protein mediated feedforward motifs showed higher overshoot and lower peak time compared to the corresponding feedback motifs.

Programmable single-cell mammalian biocomputers.

PubMed

Ausländer, Simon; Ausländer, David; Müller, Marius; Wieland, Markus; Fussenegger, Martin

2012-07-05

Synthetic biology has advanced the design of standardized control devices that program cellular functions and metabolic activities in living organisms. Rational interconnection of these synthetic switches resulted in increasingly complex designer networks that execute input-triggered genetic instructions with precision, robustness and computational logic reminiscent of electronic circuits. Using trigger-controlled transcription factors, which independently control gene expression, and RNA-binding proteins that inhibit the translation of transcripts harbouring specific RNA target motifs, we have designed a set of synthetic transcription–translation control devices that could be rewired in a plug-and-play manner. Here we show that these combinatorial circuits integrated a two-molecule input and performed digital computations with NOT, AND, NAND and N-IMPLY expression logic in single mammalian cells. Functional interconnection of two N-IMPLY variants resulted in bitwise intracellular XOR operations, and a combinatorial arrangement of three logic gates enabled independent cells to perform programmable half-subtractor and half-adder calculations. Individual mammalian cells capable of executing basic molecular arithmetic functions isolated or coordinated to metabolic activities in a predictable, precise and robust manner may provide new treatment strategies and bio-electronic interfaces in future gene-based and cell-based therapies.

L.U.St: a tool for approximated maximum likelihood supertree reconstruction.

PubMed

Akanni, Wasiu A; Creevey, Christopher J; Wilkinson, Mark; Pisani, Davide

2014-06-12

Supertrees combine disparate, partially overlapping trees to generate a synthesis that provides a high level perspective that cannot be attained from the inspection of individual phylogenies. Supertrees can be seen as meta-analytical tools that can be used to make inferences based on results of previous scientific studies. Their meta-analytical application has increased in popularity since it was realised that the power of statistical tests for the study of evolutionary trends critically depends on the use of taxon-dense phylogenies. Further to that, supertrees have found applications in phylogenomics where they are used to combine gene trees and recover species phylogenies based on genome-scale data sets. Here, we present the L.U.St package, a python tool for approximate maximum likelihood supertree inference and illustrate its application using a genomic data set for the placental mammals. L.U.St allows the calculation of the approximate likelihood of a supertree, given a set of input trees, performs heuristic searches to look for the supertree of highest likelihood, and performs statistical tests of two or more supertrees. To this end, L.U.St implements a winning sites test allowing ranking of a collection of a-priori selected hypotheses, given as a collection of input supertree topologies. It also outputs a file of input-tree-wise likelihood scores that can be used as input to CONSEL for calculation of standard tests of two trees (e.g. Kishino-Hasegawa, Shimidoara-Hasegawa and Approximately Unbiased tests). This is the first fully parametric implementation of a supertree method, it has clearly understood properties, and provides several advantages over currently available supertree approaches. It is easy to implement and works on any platform that has python installed. bitBucket page - https://afro-juju@bitbucket.org/afro-juju/l.u.st.git. Davide.Pisani@bristol.ac.uk.

LiPISC: A Lightweight and Flexible Method for Privacy-Aware Intersection Set Computation

PubMed Central

Huang, Shiyong; Ren, Yi; Choo, Kim-Kwang Raymond

2016-01-01

Privacy-aware intersection set computation (PISC) can be modeled as secure multi-party computation. The basic idea is to compute the intersection of input sets without leaking privacy. Furthermore, PISC should be sufficiently flexible to recommend approximate intersection items. In this paper, we reveal two previously unpublished attacks against PISC, which can be used to reveal and link one input set to another input set, resulting in privacy leakage. We coin these as Set Linkage Attack and Set Reveal Attack. We then present a lightweight and flexible PISC scheme (LiPISC) and prove its security (including against Set Linkage Attack and Set Reveal Attack). PMID:27326763

LiPISC: A Lightweight and Flexible Method for Privacy-Aware Intersection Set Computation.

PubMed

Ren, Wei; Huang, Shiyong; Ren, Yi; Choo, Kim-Kwang Raymond

2016-01-01

Privacy-aware intersection set computation (PISC) can be modeled as secure multi-party computation. The basic idea is to compute the intersection of input sets without leaking privacy. Furthermore, PISC should be sufficiently flexible to recommend approximate intersection items. In this paper, we reveal two previously unpublished attacks against PISC, which can be used to reveal and link one input set to another input set, resulting in privacy leakage. We coin these as Set Linkage Attack and Set Reveal Attack. We then present a lightweight and flexible PISC scheme (LiPISC) and prove its security (including against Set Linkage Attack and Set Reveal Attack).

Gene Ontology Consortium: going forward.

PubMed

2015-01-01

The Gene Ontology (GO; http://www.geneontology.org) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge. Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community. The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes. We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions. The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data. Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser. We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

DOE PAGES

Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; ...

2015-03-27

Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termedmore » as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.« less

Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

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

Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.

Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termedmore » as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.« less

A MYB/ZML Complex Regulates Wound-Induced Lignin Genes in Maize.

PubMed

Vélez-Bermúdez, Isabel-Cristina; Salazar-Henao, Jorge E; Fornalé, Silvia; López-Vidriero, Irene; Franco-Zorrilla, José-Manuel; Grotewold, Erich; Gray, John; Solano, Roberto; Schmidt, Wolfgang; Pagés, Montserrat; Riera, Marta; Caparros-Ruiz, David

2015-11-01

Lignin is an essential polymer in vascular plants that plays key structural roles in vessels and fibers. Lignification is induced by external inputs such as wounding, but the molecular mechanisms that link this stress to lignification remain largely unknown. In this work, we provide evidence that three maize (Zea mays) lignin repressors, MYB11, MYB31, and MYB42, participate in wound-induced lignification by interacting with ZML2, a protein belonging to the TIFY family. We determined that the three R2R3-MYB factors and ZML2 bind in vivo to AC-rich and GAT(A/C) cis-elements, respectively, present in a set of lignin genes. In particular, we show that MYB11 and ZML2 bind simultaneously to the AC-rich and GAT(A/C) cis-elements present in the promoter of the caffeic acid O-methyl transferase (comt) gene. We show that, like the R2R3-MYB factors, ZML2 also acts as a transcriptional repressor. We found that upon wounding and methyl jasmonate treatments, MYB11 and ZML2 proteins are degraded and comt transcription is induced. Based on these results, we propose a molecular regulatory mechanism involving a MYB/ZML complex in which wound-induced lignification can be achieved by the derepression of a set of lignin genes. © 2015 American Society of Plant Biologists. All rights reserved.

Directed Design of Experiments for Validating Probability of Detection Capability of a Testing System

NASA Technical Reports Server (NTRS)

Generazio, Edward R. (Inventor)

2012-01-01

A method of validating a probability of detection (POD) testing system using directed design of experiments (DOE) includes recording an input data set of observed hit and miss or analog data for sample components as a function of size of a flaw in the components. The method also includes processing the input data set to generate an output data set having an optimal class width, assigning a case number to the output data set, and generating validation instructions based on the assigned case number. An apparatus includes a host machine for receiving the input data set from the testing system and an algorithm for executing DOE to validate the test system. The algorithm applies DOE to the input data set to determine a data set having an optimal class width, assigns a case number to that data set, and generates validation instructions based on the case number.

Fuzzy Neuron: Method and Hardware Realization

NASA Technical Reports Server (NTRS)

Krasowski, Michael J.; Prokop, Norman F.

2014-01-01

This innovation represents a method by which single-to-multi-input, single-to-many-output system transfer functions can be estimated from input/output data sets. This innovation can be run in the background while a system is operating under other means (e.g., through human operator effort), or may be utilized offline using data sets created from observations of the estimated system. It utilizes a set of fuzzy membership functions spanning the input space for each input variable. Linear combiners associated with combinations of input membership functions are used to create the output(s) of the estimator. Coefficients are adjusted online through the use of learning algorithms.

Human Splice-Site Prediction with Deep Neural Networks.

PubMed

Naito, Tatsuhiko

2018-04-18

Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNNs) have achieved record-breaking results and often outperformed other supervised learning techniques. In this study, a new method of splice-site prediction using DNNs was proposed. The proposed system receives an input sequence data and returns an answer as to whether it is splice site. The length of input is 140 nucleotides, with the consensus sequence (i.e., "GT" and "AG" for the donor and acceptor sites, respectively) in the middle. Each input sequence model is applied to the pretrained DNN model that determines the probability that an input is a splice site. The model consists of convolutional layers and bidirectional long short-term memory network layers. The pretraining and validation were conducted using the data set tested in previously reported methods. The performance evaluation results showed that the proposed method can outperform the previous methods. In addition, the pattern learned by the DNNs was visualized as position frequency matrices (PFMs). Some of PFMs were very similar to the consensus sequence. The trained DNN model and the brief source code for the prediction system are uploaded. Further improvement will be achieved following the further development of DNNs.

Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study.

PubMed

Ficklin, Stephen P; Dunwoodie, Leland J; Poehlman, William L; Watson, Christopher; Roche, Kimberly E; Feltus, F Alex

2017-08-17

A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.

Optimal inverse functions created via population-based optimization.

PubMed

Jennings, Alan L; Ordóñez, Raúl

2014-06-01

Finding optimal inputs for a multiple-input, single-output system is taxing for a system operator. Population-based optimization is used to create sets of functions that produce a locally optimal input based on a desired output. An operator or higher level planner could use one of the functions in real time. For the optimization, each agent in the population uses the cost and output gradients to take steps lowering the cost while maintaining their current output. When an agent reaches an optimal input for its current output, additional agents are generated in the output gradient directions. The new agents then settle to the local optima for the new output values. The set of associated optimal points forms an inverse function, via spline interpolation, from a desired output to an optimal input. In this manner, multiple locally optimal functions can be created. These functions are naturally clustered in input and output spaces allowing for a continuous inverse function. The operator selects the best cluster over the anticipated range of desired outputs and adjusts the set point (desired output) while maintaining optimality. This reduces the demand from controlling multiple inputs, to controlling a single set point with no loss in performance. Results are demonstrated on a sample set of functions and on a robot control problem.

Rumbling Orchids: How To Assess Divergent Evolution Between Chloroplast Endosymbionts and the Nuclear Host.

PubMed

Pérez-Escobar, Oscar Alejandro; Balbuena, Juan Antonio; Gottschling, Marc

2016-01-01

Phylogenetic relationships inferred from multilocus organellar and nuclear DNA data are often difficult to resolve because of evolutionary conflicts among gene trees. However, conflicting or "outlier" associations (i.e., linked pairs of "operational terminal units" in two phylogenies) among these data sets often provide valuable information on evolutionary processes such as chloroplast capture following hybridization, incomplete lineage sorting, and horizontal gene transfer. Statistical tools that to date have been used in cophylogenetic studies only also have the potential to test for the degree of topological congruence between organellar and nuclear data sets and reliably detect outlier associations. Two distance-based methods, namely ParaFit and Procrustean Approach to Cophylogeny (PACo), were used in conjunction to detect those outliers contributing to conflicting phylogenies independently derived from chloroplast and nuclear sequence data. We explored their efficiency of retrieving outlier associations, and the impact of input data (unit branch length and additive trees) between data sets, by using several simulation approaches. To test their performance using real data sets, we additionally inferred the phylogenetic relationships within Neotropical Catasetinae (Epidendroideae, Orchidaceae), which is a suitable group to investigate phylogenetic incongruence because of hybridization processes between some of its constituent species. A comparison between trees derived from chloroplast and nuclear sequence data reflected strong, well-supported incongruence within Catasetum, Cycnoches, and Mormodes. As a result, outliers among chloroplast and nuclear data sets, and in experimental simulations, were successfully detected by PACo when using patristic distance matrices obtained from phylograms, but not from unit branch length trees. The performance of ParaFit was overall inferior compared to PACo, using either phylograms or unit branch lengths as input data. Because workflows for applying cophylogenetic analyses are not standardized yet, we provide a pipeline for executing PACo and ParaFit as well as displaying outlier associations in plots and trees by using the software R. The pipeline renders a method to identify outliers with high reliability and to assess the combinability of the independently derived data sets by means of statistical analyses. © The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Significant impact of amount of PCR input templates on various PCR-based DNA methylation analysis and countermeasure.

PubMed

Liu, Zhaojun; Zhou, Jing; Gu, Liankun; Deng, Dajun

2016-08-30

Methylation changes of CpG islands can be determined using PCR-based assays. However, the exact impact of the amount of input templates (TAIT) on DNA methylation analysis has not been previously recognized. Using COL2A1 gene as an input reference, TAIT difference between human tissues with methylation-positive and -negative detection was calculated for two representative genes GFRA1 and P16. Results revealed that TAIT in GFRA1 methylation-positive frozen samples (n = 332) was significantly higher than the methylation-negative ones (n = 44) (P < 0.001). Similar difference was found in P16 methylation analysis. The TAIT-related effect was also observed in methylation-specific PCR (MSP) and denatured high performance liquid chromatography (DHPLC) analysis. Further study showed that the minimum TAIT for a successful MethyLight PCR reaction should be ≥ 9.4 ng (CtCOL2A1 ≤ 29.3), when the cutoff value of the methylated-GFRA1 proportion for methylation-positive detection was set at 1.6%. After TAIT of the methylation non-informative frozen samples (n = 94; CtCOL2A1 > 29.3) was increased above the minimum TAIT, the methylation-positive rate increased from 72.3% to 95.7% for GFRA1 and 26.6% to 54.3% for P16, respectively (Ps < 0.001). Similar results were observed in the FFPE samples. In conclusion, TAIT critically affects results of various PCR-based DNA methylation analyses. Characterization of the minimum TAIT for target CpG islands is essential to avoid false-negative results.

An evaluation of open set recognition for FLIR images

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2015-05-01

Typical supervised classification algorithms label inputs according to what was learned in a training phase. Thus, test inputs that were not seen in training are always given incorrect labels. Open set recognition algorithms address this issue by accounting for inputs that are not present in training and providing the classifier with an option to reject" unknown samples. A number of such techniques have been developed in the literature, many of which are based on support vector machines (SVMs). One approach, the 1-vs-set machine, constructs a slab" in feature space using the SVM hyperplane. Inputs falling on one side of the slab or within the slab belong to a training class, while inputs falling on the far side of the slab are rejected. We note that rejection of unknown inputs can be achieved by thresholding class posterior probabilities. Another recently developed approach, the Probabilistic Open Set SVM (POS-SVM), empirically determines good probability thresholds. We apply the 1-vs-set machine, POS-SVM, and closed set SVMs to FLIR images taken from the Comanche SIG dataset. Vehicles in the dataset are divided into three general classes: wheeled, armored personnel carrier (APC), and tank. For each class, a coarse pose estimate (front, rear, left, right) is taken. In a closed set sense, we analyze these algorithms for prediction of vehicle class and pose. To test open set performance, one or more vehicle classes are held out from training. By considering closed and open set performance separately, we may closely analyze both inter-class discrimination and threshold effectiveness.

Systems and methods for predicting materials properties

DOEpatents

Ceder, Gerbrand; Fischer, Chris; Tibbetts, Kevin; Morgan, Dane; Curtarolo, Stefano

2007-11-06

Systems and methods for predicting features of materials of interest. Reference data are analyzed to deduce relationships between the input data sets and output data sets. Reference data includes measured values and/or computed values. The deduced relationships can be specified as equations, correspondences, and/or algorithmic processes that produce appropriate output data when suitable input data is used. In some instances, the output data set is a subset of the input data set, and computational results may be refined by optionally iterating the computational procedure. To deduce features of a new material of interest, a computed or measured input property of the material is provided to an equation, correspondence, or algorithmic procedure previously deduced, and an output is obtained. In some instances, the output is iteratively refined. In some instances, new features deduced for the material of interest are added to a database of input and output data for known materials.

Delimiting Coalescence Genes (C-Genes) in Phylogenomic Data Sets.

PubMed

Springer, Mark S; Gatesy, John

2018-02-26

coalescence methods have emerged as a popular alternative for inferring species trees with large genomic datasets, because these methods explicitly account for incomplete lineage sorting. However, statistical consistency of summary coalescence methods is not guaranteed unless several model assumptions are true, including the critical assumption that recombination occurs freely among but not within coalescence genes (c-genes), which are the fundamental units of analysis for these methods. Each c-gene has a single branching history, and large sets of these independent gene histories should be the input for genome-scale coalescence estimates of phylogeny. By contrast, numerous studies have reported the results of coalescence analyses in which complete protein-coding sequences are treated as c-genes even though exons for these loci can span more than a megabase of DNA. Empirical estimates of recombination breakpoints suggest that c-genes may be much shorter, especially when large clades with many species are the focus of analysis. Although this idea has been challenged recently in the literature, the inverse relationship between c-gene size and increased taxon sampling in a dataset-the 'recombination ratchet'-is a fundamental property of c-genes. For taxonomic groups characterized by genes with long intron sequences, complete protein-coding sequences are likely not valid c-genes and are inappropriate units of analysis for summary coalescence methods unless they occur in recombination deserts that are devoid of incomplete lineage sorting (ILS). Finally, it has been argued that coalescence methods are robust when the no-recombination within loci assumption is violated, but recombination must matter at some scale because ILS, a by-product of recombination, is the raison d'etre for coalescence methods. That is, extensive recombination is required to yield the large number of independently segregating c-genes used to infer a species tree. If coalescent methods are powerful enough to infer the correct species tree for difficult phylogenetic problems in the anomaly zone, where concatenation is expected to fail because of ILS, then there should be a decreasing probability of inferring the correct species tree using longer loci with many intralocus recombination breakpoints (i.e., increased levels of concatenation).

Delimiting Coalescence Genes (C-Genes) in Phylogenomic Data Sets

PubMed Central

Springer, Mark S.; Gatesy, John

2018-01-01

Summary coalescence methods have emerged as a popular alternative for inferring species trees with large genomic datasets, because these methods explicitly account for incomplete lineage sorting. However, statistical consistency of summary coalescence methods is not guaranteed unless several model assumptions are true, including the critical assumption that recombination occurs freely among but not within coalescence genes (c-genes), which are the fundamental units of analysis for these methods. Each c-gene has a single branching history, and large sets of these independent gene histories should be the input for genome-scale coalescence estimates of phylogeny. By contrast, numerous studies have reported the results of coalescence analyses in which complete protein-coding sequences are treated as c-genes even though exons for these loci can span more than a megabase of DNA. Empirical estimates of recombination breakpoints suggest that c-genes may be much shorter, especially when large clades with many species are the focus of analysis. Although this idea has been challenged recently in the literature, the inverse relationship between c-gene size and increased taxon sampling in a dataset—the ‘recombination ratchet’—is a fundamental property of c-genes. For taxonomic groups characterized by genes with long intron sequences, complete protein-coding sequences are likely not valid c-genes and are inappropriate units of analysis for summary coalescence methods unless they occur in recombination deserts that are devoid of incomplete lineage sorting (ILS). Finally, it has been argued that coalescence methods are robust when the no-recombination within loci assumption is violated, but recombination must matter at some scale because ILS, a by-product of recombination, is the raison d’etre for coalescence methods. That is, extensive recombination is required to yield the large number of independently segregating c-genes used to infer a species tree. If coalescent methods are powerful enough to infer the correct species tree for difficult phylogenetic problems in the anomaly zone, where concatenation is expected to fail because of ILS, then there should be a decreasing probability of inferring the correct species tree using longer loci with many intralocus recombination breakpoints (i.e., increased levels of concatenation). PMID:29495400

Processing Device for High-Speed Execution of an Xrisc Computer Program

NASA Technical Reports Server (NTRS)

Ng, Tak-Kwong (Inventor); Mills, Carl S. (Inventor)

2016-01-01

A processing device for high-speed execution of a computer program is provided. A memory module may store one or more computer programs. A sequencer may select one of the computer programs and controls execution of the selected program. A register module may store intermediate values associated with a current calculation set, a set of output values associated with a previous calculation set, and a set of input values associated with a subsequent calculation set. An external interface may receive the set of input values from a computing device and provides the set of output values to the computing device. A computation interface may provide a set of operands for computation during processing of the current calculation set. The set of input values are loaded into the register and the set of output values are unloaded from the register in parallel with processing of the current calculation set.

Interdicting an Adversary’s Economy Viewed As a Trade Sanction Inoperability Input Output Model

DTIC Science & Technology

2017-03-01

set of sectors. The design of an economic sanction, in the context of this thesis, is the selection of the sector or set of sectors to sanction...We propose two optimization models. The first, the Trade Sanction Inoperability Input-output Model (TS-IIM), selects the sector or set of sectors that...Interdependency analysis: Extensions to demand reduction inoperability input-output modeling and portfolio selection . Unpublished doctoral dissertation

Noise facilitates transcriptional control under dynamic inputs.

PubMed

Kellogg, Ryan A; Tay, Savaş

2015-01-29

Cells must respond sensitively to time-varying inputs in complex signaling environments. To understand how signaling networks process dynamic inputs into gene expression outputs and the role of noise in cellular information processing, we studied the immune pathway NF-κB under periodic cytokine inputs using microfluidic single-cell measurements and stochastic modeling. We find that NF-κB dynamics in fibroblasts synchronize with oscillating TNF signal and become entrained, leading to significantly increased NF-κB oscillation amplitude and mRNA output compared to non-entrained response. Simulations show that intrinsic biochemical noise in individual cells improves NF-κB oscillation and entrainment, whereas cell-to-cell variability in NF-κB natural frequency creates population robustness, together enabling entrainment over a wider range of dynamic inputs. This wide range is confirmed by experiments where entrained cells were measured under all input periods. These results indicate that synergy between oscillation and noise allows cells to achieve efficient gene expression in dynamically changing signaling environments. Copyright © 2015 Elsevier Inc. All rights reserved.

How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database

PubMed Central

Mashiach, R.; Cohen, S.; Kedem, A.; Baron, A.; Zajicek, M.; Feldman, I.; Seidman, D.; Soriano, D.

2018-01-01

Endometriosis is a disease characterized by the development of endometrial tissue outside the uterus, but its cause remains largely unknown. Numerous genes have been studied and proposed to help explain its pathogenesis. However, the large number of these candidate genes has made functional validation through experimental methodologies nearly impossible. Computational methods could provide a useful alternative for prioritizing those most likely to be susceptibility genes. Using artificial intelligence applied to text mining, this study analyzed the genes involved in the pathogenesis, development, and progression of endometriosis. The data extraction by text mining of the endometriosis-related genes in the PubMed database was based on natural language processing, and the data were filtered to remove false positives. Using data from the text mining and gene network information as input for the web-based tool, 15,207 endometriosis-related genes were ranked according to their score in the database. Characterization of the filtered gene set through gene ontology, pathway, and network analysis provided information about the numerous mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissue, as well as the migration, implantation, survival, and proliferation of ectopic endometrial cells. Finally, the human genome was scanned through various databases using filtered genes as a seed to determine novel genes that might also be involved in the pathogenesis of endometriosis but which have not yet been characterized. These genes could be promising candidates to serve as useful diagnostic biomarkers and therapeutic targets in the management of endometriosis. PMID:29750165

How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database.

PubMed

Bouaziz, J; Mashiach, R; Cohen, S; Kedem, A; Baron, A; Zajicek, M; Feldman, I; Seidman, D; Soriano, D

2018-01-01

Endometriosis is a disease characterized by the development of endometrial tissue outside the uterus, but its cause remains largely unknown. Numerous genes have been studied and proposed to help explain its pathogenesis. However, the large number of these candidate genes has made functional validation through experimental methodologies nearly impossible. Computational methods could provide a useful alternative for prioritizing those most likely to be susceptibility genes. Using artificial intelligence applied to text mining, this study analyzed the genes involved in the pathogenesis, development, and progression of endometriosis. The data extraction by text mining of the endometriosis-related genes in the PubMed database was based on natural language processing, and the data were filtered to remove false positives. Using data from the text mining and gene network information as input for the web-based tool, 15,207 endometriosis-related genes were ranked according to their score in the database. Characterization of the filtered gene set through gene ontology, pathway, and network analysis provided information about the numerous mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissue, as well as the migration, implantation, survival, and proliferation of ectopic endometrial cells. Finally, the human genome was scanned through various databases using filtered genes as a seed to determine novel genes that might also be involved in the pathogenesis of endometriosis but which have not yet been characterized. These genes could be promising candidates to serve as useful diagnostic biomarkers and therapeutic targets in the management of endometriosis.

Three-input gate logic circuits on chemically assembled single-electron transistors with organic and inorganic hybrid passivation layers

PubMed Central

Majima, Yutaka; Hackenberger, Guillaume; Azuma, Yasuo; Kano, Shinya; Matsuzaki, Kosuke; Susaki, Tomofumi; Sakamoto, Masanori; Teranishi, Toshiharu

2017-01-01

Abstract Single-electron transistors (SETs) are sub-10-nm scale electronic devices based on conductive Coulomb islands sandwiched between double-barrier tunneling barriers. Chemically assembled SETs with alkanethiol-protected Au nanoparticles show highly stable Coulomb diamonds and two-input logic operations. The combination of bottom-up and top-down processes used to form the passivation layer is vital for realizing multi-gate chemically assembled SET circuits, as this combination enables us to connect conventional complementary metal oxide semiconductor (CMOS) technologies via planar processes. Here, three-input gate exclusive-OR (XOR) logic operations are demonstrated in passivated chemically assembled SETs. The passivation layer is a hybrid bilayer of self-assembled monolayers (SAMs) and pulsed laser deposited (PLD) aluminum oxide (AlOx), and top-gate electrodes were prepared on the hybrid passivation layers. Top and two-side-gated SETs showed clear Coulomb oscillation and diamonds for each of the three available gates, and three-input gate XOR logic operation was clearly demonstrated. These results show the potential of chemically assembled SETs to work as logic devices with multi-gate inputs using organic and inorganic hybrid passivation layers. PMID:28634499

Three-input gate logic circuits on chemically assembled single-electron transistors with organic and inorganic hybrid passivation layers.

PubMed

Majima, Yutaka; Hackenberger, Guillaume; Azuma, Yasuo; Kano, Shinya; Matsuzaki, Kosuke; Susaki, Tomofumi; Sakamoto, Masanori; Teranishi, Toshiharu

2017-01-01

Single-electron transistors (SETs) are sub-10-nm scale electronic devices based on conductive Coulomb islands sandwiched between double-barrier tunneling barriers. Chemically assembled SETs with alkanethiol-protected Au nanoparticles show highly stable Coulomb diamonds and two-input logic operations. The combination of bottom-up and top-down processes used to form the passivation layer is vital for realizing multi-gate chemically assembled SET circuits, as this combination enables us to connect conventional complementary metal oxide semiconductor (CMOS) technologies via planar processes. Here, three-input gate exclusive-OR (XOR) logic operations are demonstrated in passivated chemically assembled SETs. The passivation layer is a hybrid bilayer of self-assembled monolayers (SAMs) and pulsed laser deposited (PLD) aluminum oxide (AlO[Formula: see text]), and top-gate electrodes were prepared on the hybrid passivation layers. Top and two-side-gated SETs showed clear Coulomb oscillation and diamonds for each of the three available gates, and three-input gate XOR logic operation was clearly demonstrated. These results show the potential of chemically assembled SETs to work as logic devices with multi-gate inputs using organic and inorganic hybrid passivation layers.

Online learning from input versus offline memory evolution in adult word learning: effects of neighborhood density and phonologically related practice.

PubMed

Storkel, Holly L; Bontempo, Daniel E; Pak, Natalie S

2014-10-01

In this study, the authors investigated adult word learning to determine how neighborhood density and practice across phonologically related training sets influence online learning from input during training versus offline memory evolution during no-training gaps. Sixty-one adults were randomly assigned to learn low- or high-density nonwords. Within each density condition, participants were trained on one set of words and then were trained on a second set of words, consisting of phonological neighbors of the first set. Learning was measured in a picture-naming test. Data were analyzed using multilevel modeling and spline regression. Steep learning during input was observed, with new words from dense neighborhoods and new words that were neighbors of recently learned words (i.e., second-set words) being learned better than other words. In terms of memory evolution, large and significant forgetting was observed during 1-week gaps in training. Effects of density and practice during memory evolution were opposite of those during input. Specifically, forgetting was greater for high-density and second-set words than for low-density and first-set words. High phonological similarity, regardless of source (i.e., known words or recent training), appears to facilitate online learning from input but seems to impede offline memory evolution.

Evolution of Bow-Tie Architectures in Biology

PubMed Central

Friedlander, Tamar; Mayo, Avraham E.; Tlusty, Tsvi; Alon, Uri

2015-01-01

Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved. PMID:25798588

GEsture: an online hand-drawing tool for gene expression pattern search.

PubMed

Wang, Chunyan; Xu, Yiqing; Wang, Xuelin; Zhang, Li; Wei, Suyun; Ye, Qiaolin; Zhu, Youxiang; Yin, Hengfu; Nainwal, Manoj; Tanon-Reyes, Luis; Cheng, Feng; Yin, Tongming; Ye, Ning

2018-01-01

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a 'desirable' or 'user-defined' pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users' requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.

An open-source framework for large-scale, flexible evaluation of biomedical text mining systems.

PubMed

Baumgartner, William A; Cohen, K Bretonnel; Hunter, Lawrence

2008-01-29

Improved evaluation methodologies have been identified as a necessary prerequisite to the improvement of text mining theory and practice. This paper presents a publicly available framework that facilitates thorough, structured, and large-scale evaluations of text mining technologies. The extensibility of this framework and its ability to uncover system-wide characteristics by analyzing component parts as well as its usefulness for facilitating third-party application integration are demonstrated through examples in the biomedical domain. Our evaluation framework was assembled using the Unstructured Information Management Architecture. It was used to analyze a set of gene mention identification systems involving 225 combinations of system, evaluation corpus, and correctness measure. Interactions between all three were found to affect the relative rankings of the systems. A second experiment evaluated gene normalization system performance using as input 4,097 combinations of gene mention systems and gene mention system-combining strategies. Gene mention system recall is shown to affect gene normalization system performance much more than does gene mention system precision, and high gene normalization performance is shown to be achievable with remarkably low levels of gene mention system precision. The software presented in this paper demonstrates the potential for novel discovery resulting from the structured evaluation of biomedical language processing systems, as well as the usefulness of such an evaluation framework for promoting collaboration between developers of biomedical language processing technologies. The code base is available as part of the BioNLP UIMA Component Repository on SourceForge.net.

An open-source framework for large-scale, flexible evaluation of biomedical text mining systems

PubMed Central

Baumgartner, William A; Cohen, K Bretonnel; Hunter, Lawrence

2008-01-01

Background Improved evaluation methodologies have been identified as a necessary prerequisite to the improvement of text mining theory and practice. This paper presents a publicly available framework that facilitates thorough, structured, and large-scale evaluations of text mining technologies. The extensibility of this framework and its ability to uncover system-wide characteristics by analyzing component parts as well as its usefulness for facilitating third-party application integration are demonstrated through examples in the biomedical domain. Results Our evaluation framework was assembled using the Unstructured Information Management Architecture. It was used to analyze a set of gene mention identification systems involving 225 combinations of system, evaluation corpus, and correctness measure. Interactions between all three were found to affect the relative rankings of the systems. A second experiment evaluated gene normalization system performance using as input 4,097 combinations of gene mention systems and gene mention system-combining strategies. Gene mention system recall is shown to affect gene normalization system performance much more than does gene mention system precision, and high gene normalization performance is shown to be achievable with remarkably low levels of gene mention system precision. Conclusion The software presented in this paper demonstrates the potential for novel discovery resulting from the structured evaluation of biomedical language processing systems, as well as the usefulness of such an evaluation framework for promoting collaboration between developers of biomedical language processing technologies. The code base is available as part of the BioNLP UIMA Component Repository on SourceForge.net. PMID:18230184

Signal persistence and amplification in cancer development and possible, related opportunities for novel therapies.

PubMed

Ford, Shea A; Blanck, George

2015-01-01

Research in cancer biology has been largely driven by experimental approaches whereby discreet inputs are used to assess discreet outputs, for example, gene-knockouts to assess cancer occurrence. However, cancer hallmarks are only rarely, if ever, exclusively dependent on discreet regulatory processes. Rather, cancer-related regulatory factors affect multiple cancer hallmarks. Thus, novel approaches and paradigms are needed for further advances. Signal pathway persistence and amplification, rather than signal pathway activation resulting from an on/off switch, represent emerging paradigms for cancer research, closely related to developmental regulatory paradigms. In this review, we address both mechanisms and effects of signal pathway persistence and amplification in cancer settings; and address the possibility that hyper-activation of pro-proliferative signal pathways in certain cancer settings could be exploited for therapy. Copyright © 2014. Published by Elsevier B.V.

Accurate HLA type inference using a weighted similarity graph.

PubMed

Xie, Minzhu; Li, Jing; Jiang, Tao

2010-12-14

The human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true. In this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate, achieving an accuracy of 96% for gene HLA-A, 95% for HLA-B, 97% for HLA-C, 84% for HLA-DRB1, 98% for HLA-DQA1 and 97% for HLA-DQB1 in a leave-one-out test. Our algorithm can infer HLA gene types from neighboring SNP genotype data accurately. Compared with a recent approach on the same input data, our algorithm achieved a higher accuracy. The code of our algorithm is available to the public for free upon request to the corresponding authors.

Construction of trypanosome artificial mini-chromosomes.

PubMed Central

Lee, M G; E, Y; Axelrod, N

1995-01-01

We report the preparation of two linear constructs which, when transformed into the procyclic form of Trypanosoma brucei, become stably inherited artificial mini-chromosomes. Both of the two constructs, one of 10 kb and the other of 13 kb, contain a T.brucei PARP promoter driving a chloramphenicol acetyltransferase (CAT) gene. In the 10 kb construct the CAT gene is followed by one hygromycin phosphotransferase (Hph) gene, and in the 13 kb construct the CAT gene is followed by three tandemly linked Hph genes. At each end of these linear molecules are telomere repeats and subtelomeric sequences. Electroporation of these linear DNA constructs into the procyclic form of T.brucei generated hygromycin-B resistant cell lines. In these cell lines, the input DNA remained linear and bounded by the telomere ends, but it increased in size. In the cell lines generated by the 10 kb construct, the input DNA increased in size to 20-50 kb. In the cell lines generated by the 13 kb constructs, two sizes of linear DNAs containing the input plasmid were detected: one of 40-50 kb and the other of 150 kb. The increase in size was not the result of in vivo tandem repetitions of the input plasmid, but represented the addition of new sequences. These Hph containing linear DNA molecules were maintained stably in cell lines for at least 20 generations in the absence of drug selection and were subsequently referred to as trypanosome artificial mini-chromosomes, or TACs. Images PMID:8532534

A bottom-up characterization of transfer functions for synthetic biology designs: lessons from enzymology

PubMed Central

Carbonell-Ballestero, Max; Duran-Nebreda, Salva; Montañez, Raúl; Solé, Ricard; Macía, Javier; Rodríguez-Caso, Carlos

2014-01-01

Within the field of synthetic biology, a rational design of genetic parts should include a causal understanding of their input-output responses—the so-called transfer function—and how to tune them. However, a commonly adopted strategy is to fit data to Hill-shaped curves without considering the underlying molecular mechanisms. Here we provide a novel mathematical formalization that allows prediction of the global behavior of a synthetic device by considering the actual information from the involved biological parts. This is achieved by adopting an enzymology-like framework, where transfer functions are described in terms of their input affinity constant and maximal response. As a proof of concept, we characterize a set of Lux homoserine-lactone-inducible genetic devices with different levels of Lux receptor and signal molecule. Our model fits the experimental results and predicts the impact of the receptor's ribosome-binding site strength, as a tunable parameter that affects gene expression. The evolutionary implications are outlined. PMID:25404136

Predicting neuroblastoma using developmental signals and a logic-based model.

PubMed

Kasemeier-Kulesa, Jennifer C; Schnell, Santiago; Woolley, Thomas; Spengler, Jennifer A; Morrison, Jason A; McKinney, Mary C; Pushel, Irina; Wolfe, Lauren A; Kulesa, Paul M

2018-07-01

Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression. Copyright © 2018. Published by Elsevier B.V.

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

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

Leung, Elo; Huang, Amy; Cadag, Eithon

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

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

DOE PAGES

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

2016-01-20

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

DLRS: gene tree evolution in light of a species tree.

PubMed

Sjöstrand, Joel; Sennblad, Bengt; Arvestad, Lars; Lagergren, Jens

2012-11-15

PrIME-DLRS (or colloquially: 'Delirious') is a phylogenetic software tool to simultaneously infer and reconcile a gene tree given a species tree. It accounts for duplication and loss events, a relaxed molecular clock and is intended for the study of homologous gene families, for example in a comparative genomics setting involving multiple species. PrIME-DLRS uses a Bayesian MCMC framework, where the input is a known species tree with divergence times and a multiple sequence alignment, and the output is a posterior distribution over gene trees and model parameters. PrIME-DLRS is available for Java SE 6+ under the New BSD License, and JAR files and source code can be downloaded from http://code.google.com/p/jprime/. There is also a slightly older C++ version available as a binary package for Ubuntu, with download instructions at http://prime.sbc.su.se. The C++ source code is available upon request. joel.sjostrand@scilifelab.se or jens.lagergren@scilifelab.se. PrIME-DLRS is based on a sound probabilistic model (Åkerborg et al., 2009) and has been thoroughly validated on synthetic and biological datasets (Supplementary Material online).

Impact of environmental inputs on reverse-engineering approach to network structures.

PubMed

Wu, Jianhua; Sinfield, James L; Buchanan-Wollaston, Vicky; Feng, Jianfeng

2009-12-04

Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach.

PubMed

Liang, Muxuan; Li, Zhizhong; Chen, Ting; Zeng, Jianyang

2015-01-01

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Exploring Plant Co-Expression and Gene-Gene Interactions with CORNET 3.0.

PubMed

Van Bel, Michiel; Coppens, Frederik

2017-01-01

Selecting and filtering a reference expression and interaction dataset when studying specific pathways and regulatory interactions can be a very time-consuming and error-prone task. In order to reduce the duplicated efforts required to amass such datasets, we have created the CORNET (CORrelation NETworks) platform which allows for easy access to a wide variety of data types: coexpression data, protein-protein interactions, regulatory interactions, and functional annotations. The CORNET platform outputs its results in either text format or through the Cytoscape framework, which is automatically launched by the CORNET website.CORNET 3.0 is the third iteration of the web platform designed for the user exploration of the coexpression space of plant genomes, with a focus on the model species Arabidopsis thaliana. Here we describe the platform: the tools, data, and best practices when using the platform. We indicate how the platform can be used to infer networks from a set of input genes, such as upregulated genes from an expression experiment. By exploring the network, new target and regulator genes can be discovered, allowing for follow-up experiments and more in-depth study. We also indicate how to avoid common pitfalls when evaluating the networks and how to avoid over interpretation of the results.All CORNET versions are available at http://bioinformatics.psb.ugent.be/cornet/ .

SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis.

PubMed

Johnson, Benjamin K; Scholz, Matthew B; Teal, Tracy K; Abramovitch, Robert B

2016-02-04

Many tools exist in the analysis of bacterial RNA sequencing (RNA-seq) transcriptional profiling experiments to identify differentially expressed genes between experimental conditions. Generally, the workflow includes quality control of reads, mapping to a reference, counting transcript abundance, and statistical tests for differentially expressed genes. In spite of the numerous tools developed for each component of an RNA-seq analysis workflow, easy-to-use bacterially oriented workflow applications to combine multiple tools and automate the process are lacking. With many tools to choose from for each step, the task of identifying a specific tool, adapting the input/output options to the specific use-case, and integrating the tools into a coherent analysis pipeline is not a trivial endeavor, particularly for microbiologists with limited bioinformatics experience. To make bacterial RNA-seq data analysis more accessible, we developed a Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis (SPARTA). SPARTA is a reference-based bacterial RNA-seq analysis workflow application for single-end Illumina reads. SPARTA is turnkey software that simplifies the process of analyzing RNA-seq data sets, making bacterial RNA-seq analysis a routine process that can be undertaken on a personal computer or in the classroom. The easy-to-install, complete workflow processes whole transcriptome shotgun sequencing data files by trimming reads and removing adapters, mapping reads to a reference, counting gene features, calculating differential gene expression, and, importantly, checking for potential batch effects within the data set. SPARTA outputs quality analysis reports, gene feature counts and differential gene expression tables and scatterplots. SPARTA provides an easy-to-use bacterial RNA-seq transcriptional profiling workflow to identify differentially expressed genes between experimental conditions. This software will enable microbiologists with limited bioinformatics experience to analyze their data and integrate next generation sequencing (NGS) technologies into the classroom. The SPARTA software and tutorial are available at sparta.readthedocs.org.

BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS.

PubMed

Hoff, Katharina J; Lange, Simone; Lomsadze, Alexandre; Borodovsky, Mark; Stanke, Mario

2016-03-01

Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome assembly file and a file in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/ katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.

PubMed

Akimoto, Yuki; Yugi, Katsuyuki; Uda, Shinsuke; Kudo, Takamasa; Komori, Yasunori; Kubota, Hiroyuki; Kuroda, Shinya

2013-01-01

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

Emulating RRTMG Radiation with Deep Neural Networks for the Accelerated Model for Climate and Energy

NASA Astrophysics Data System (ADS)

Pal, A.; Norman, M. R.

2017-12-01

The RRTMG radiation scheme in the Accelerated Model for Climate and Energy Multi-scale Model Framework (ACME-MMF), is a bottleneck and consumes approximately 50% of the computational time. To simulate a case using RRTMG radiation scheme in ACME-MMF with high throughput and high resolution will therefore require a speed-up of this calculation while retaining physical fidelity. In this study, RRTMG radiation is emulated with Deep Neural Networks (DNNs). The first step towards this goal is to run a case with ACME-MMF and generate input data sets for the DNNs. A principal component analysis of these input data sets are carried out. Artificial data sets are created using the previous data sets to cover a wider space. These artificial data sets are used in a standalone RRTMG radiation scheme to generate outputs in a cost effective manner. These input-output pairs are used to train multiple architectures DNNs(1). Another DNN(2) is trained using the inputs to predict the error. A reverse emulation is trained to map the output to input. An error controlled code is developed with the two DNNs (1 and 2) and will determine when/if the original parameterization needs to be used.

No3CoGP: non-conserved and conserved coexpressed gene pairs.

PubMed

Mal, Chittabrata; Aftabuddin, Md; Kundu, Sudip

2014-12-08

Analyzing the microarray data of different conditions, one can identify the conserved and condition-specific genes and gene modules, and thus can infer the underlying cellular activities. All the available tools based on Bioconductor and R packages differ in how they extract differential coexpression and at what level they study. There is a need for a user-friendly, flexible tool which can start analysis using raw or preprocessed microarray data and can report different levels of useful information. We present a GUI software, No3CoGP: Non-Conserved and Conserved Coexpressed Gene Pairs which takes Affymetrix microarray data (.CEL files or log2 normalized.txt files) along with annotation file (.csv file), Chip Definition File (CDF file) and probe file as inputs, utilizes the concept of network density cut-off and Fisher's z-test to extract biologically relevant information. It can identify four possible types of gene pairs based on their coexpression relationships. These are (i) gene pair showing coexpression in one condition but not in the other, (ii) gene pair which is positively coexpressed in one condition but negatively coexpressed in the other condition, (iii) positively and (iv) negatively coexpressed in both the conditions. Further, it can generate modules of coexpressed genes. Easy-to-use GUI interface enables researchers without knowledge in R language to use No3CoGP. Utilization of one or more CPU cores, depending on the availability, speeds up the program. The output files stored in the respective directories under the user-defined project offer the researchers to unravel condition-specific functionalities of gene, gene sets or modules.

The peripheral sensory nervous system in the vertebrate head: a gene regulatory perspective.

PubMed

Grocott, Timothy; Tambalo, Monica; Streit, Andrea

2012-10-01

In the vertebrate head, crucial parts of the sense organs and sensory ganglia develop from special regions, the cranial placodes. Despite their cellular and functional diversity, they arise from a common field of multipotent progenitors and acquire distinct identity later under the influence of local signalling. Here we present the gene regulatory network that summarises our current understanding of how sensory cells are specified, how they become different from other ectodermal derivatives and how they begin to diversify to generate placodes with different identities. This analysis reveals how sequential activation of sets of transcription factors subdivides the ectoderm over time into smaller domains of progenitors for the central nervous system, neural crest, epidermis and sensory placodes. Within this hierarchy the timing of signalling and developmental history of each cell population is of critical importance to determine the ultimate outcome. A reoccurring theme is that local signals set up broad gene expression domains, which are further refined by mutual repression between different transcription factors. The Six and Eya network lies at the heart of sensory progenitor specification. In a positive feedback loop these factors perpetuate their own expression thus stabilising pre-placodal fate, while simultaneously repressing neural and neural crest specific factors. Downstream of the Six and Eya cassette, Pax genes in combination with other factors begin to impart regional identity to placode progenitors. While our review highlights the wealth of information available, it also points to the lack information on the cis-regulatory mechanisms that control placode specification and of how the repeated use of signalling input is integrated. Copyright © 2012. Published by Elsevier Inc.

Gene Expression Biomarkers Provide Sensitive Indicators of in Planta Nitrogen Status in Maize[W][OA

PubMed Central

Yang, Xiaofeng S.; Wu, Jingrui; Ziegler, Todd E.; Yang, Xiao; Zayed, Adel; Rajani, M.S.; Zhou, Dafeng; Basra, Amarjit S.; Schachtman, Daniel P.; Peng, Mingsheng; Armstrong, Charles L.; Caldo, Rico A.; Morrell, James A.; Lacy, Michelle; Staub, Jeffrey M.

2011-01-01

Over the last several decades, increased agricultural production has been driven by improved agronomic practices and a dramatic increase in the use of nitrogen-containing fertilizers to maximize the yield potential of crops. To reduce input costs and to minimize the potential environmental impacts of nitrogen fertilizer that has been used to optimize yield, an increased understanding of the molecular responses to nitrogen under field conditions is critical for our ability to further improve agricultural sustainability. Using maize (Zea mays) as a model, we have characterized the transcriptional response of plants grown under limiting and sufficient nitrogen conditions and during the recovery of nitrogen-starved plants. We show that a large percentage (approximately 7%) of the maize transcriptome is nitrogen responsive, similar to previous observations in other plant species. Furthermore, we have used statistical approaches to identify a small set of genes whose expression profiles can quantitatively assess the response of plants to varying nitrogen conditions. Using a composite gene expression scoring system, this single set of biomarker genes can accurately assess nitrogen responses independently of genotype, developmental stage, tissue type, or environment, including in plants grown under controlled environments or in the field. Importantly, the biomarker composite expression response is much more rapid and quantitative than phenotypic observations. Consequently, we have successfully used these biomarkers to monitor nitrogen status in real-time assays of field-grown maize plants under typical production conditions. Our results suggest that biomarkers have the potential to be used as agronomic tools to monitor and optimize nitrogen fertilizer usage to help achieve maximal crop yields. PMID:21980173

[Outline on the phylogenetic history of Metazoa aging phenomenon (to the study of number of dominating pseudo-scientific concepts in biology of aging)].

PubMed

Boĭko, A G; Labas, Iu A; Gordeeva, A V

2009-01-01

Natural selection is just one of the factors determining genome evolution of Metazoa. But it's not a domineering one along with non-adaptive processes: horizontal gene transfer and input of egoistic genetic elements. That's why in phylogenesis (1) there are more genes of first Metazoa lost than new ones acquired; (2) the appearance of new genes among Metazoa branches is a very rare occasion; (3) genetically Metazoa is a homogeneous group of species with similar set of cellular mechanisms which was established in the course of evolution. The genome of first Metazoa turned to be so successful that evolution connected with organism amplification didn't demand radical changes in the genetic repertoire but it demanded changes in DNA sites regulating genes work. These facts along with the fact of existence of species of Metazoa with negligible aging overturn the core theories of aging biology which consider this or that cellular mechanism to be the initiating factor of organism's aging as sets of cellular mechanisms in aging and non-aging Metazoa forms are practically identical. That's why the basis of aging biology is in essence a collection of theories and dogmas that have never been proved but which are still in use and which have since long ago turned into a dangerous myth standing in the way of progress. If we are interested in progress of biogerontology a number of domineering pseudo scientific dogmas must be revisited. The matter of conservatism in this issue is inappropriate.

Gene expression biomarkers provide sensitive indicators of in planta nitrogen status in maize.

PubMed

Yang, Xiaofeng S; Wu, Jingrui; Ziegler, Todd E; Yang, Xiao; Zayed, Adel; Rajani, M S; Zhou, Dafeng; Basra, Amarjit S; Schachtman, Daniel P; Peng, Mingsheng; Armstrong, Charles L; Caldo, Rico A; Morrell, James A; Lacy, Michelle; Staub, Jeffrey M

2011-12-01

Over the last several decades, increased agricultural production has been driven by improved agronomic practices and a dramatic increase in the use of nitrogen-containing fertilizers to maximize the yield potential of crops. To reduce input costs and to minimize the potential environmental impacts of nitrogen fertilizer that has been used to optimize yield, an increased understanding of the molecular responses to nitrogen under field conditions is critical for our ability to further improve agricultural sustainability. Using maize (Zea mays) as a model, we have characterized the transcriptional response of plants grown under limiting and sufficient nitrogen conditions and during the recovery of nitrogen-starved plants. We show that a large percentage (approximately 7%) of the maize transcriptome is nitrogen responsive, similar to previous observations in other plant species. Furthermore, we have used statistical approaches to identify a small set of genes whose expression profiles can quantitatively assess the response of plants to varying nitrogen conditions. Using a composite gene expression scoring system, this single set of biomarker genes can accurately assess nitrogen responses independently of genotype, developmental stage, tissue type, or environment, including in plants grown under controlled environments or in the field. Importantly, the biomarker composite expression response is much more rapid and quantitative than phenotypic observations. Consequently, we have successfully used these biomarkers to monitor nitrogen status in real-time assays of field-grown maize plants under typical production conditions. Our results suggest that biomarkers have the potential to be used as agronomic tools to monitor and optimize nitrogen fertilizer usage to help achieve maximal crop yields.

Set Theory Applied to Uniquely Define the Inputs to Territorial Systems in Emergy Analyses

EPA Science Inventory

The language of set theory can be utilized to represent the emergy involved in all processes. In this paper we use set theory in an emergy evaluation to ensure an accurate representation of the inputs to territorial systems. We consider a generic territorial system and we describ...

PathogenFinder--distinguishing friend from foe using bacterial whole genome sequence data.

PubMed

Cosentino, Salvatore; Voldby Larsen, Mette; Møller Aarestrup, Frank; Lund, Ole

2013-01-01

Although the majority of bacteria are harmless or even beneficial to their host, others are highly virulent and can cause serious diseases, and even death. Due to the constantly decreasing cost of high-throughput sequencing there are now many completely sequenced genomes available from both human pathogenic and innocuous strains. The data can be used to identify gene families that correlate with pathogenicity and to develop tools to predict the pathogenicity of newly sequenced strains, investigations that previously were mainly done by means of more expensive and time consuming experimental approaches. We describe PathogenFinder (http://cge.cbs.dtu.dk/services/PathogenFinder/), a web-server for the prediction of bacterial pathogenicity by analysing the input proteome, genome, or raw reads provided by the user. The method relies on groups of proteins, created without regard to their annotated function or known involvement in pathogenicity. The method has been built to work with all taxonomic groups of bacteria and using the entire training-set, achieved an accuracy of 88.6% on an independent test-set, by correctly classifying 398 out of 449 completely sequenced bacteria. The approach here proposed is not biased on sets of genes known to be associated with pathogenicity, thus the approach could aid the discovery of novel pathogenicity factors. Furthermore the pathogenicity prediction web-server could be used to isolate the potential pathogenic features of both known and unknown strains.

Regulatory logic of pan-neuronal gene expression in C. elegans

PubMed Central

Stefanakis, Nikolaos; Carrera, Ines; Hobert, Oliver

2015-01-01

While neuronal cell types display an astounding degree of phenotypic diversity, most if not all neuron types share a core panel of terminal features. However, little is known about how pan-neuronal expression patterns are genetically programmed. Through an extensive analysis of the cis-regulatory control regions of a battery of pan-neuronal C.elegans genes, including genes involved in synaptic vesicle biology and neuropeptide signaling, we define a common organizational principle in the regulation of pan-neuronal genes in the form of a surprisingly complex array of seemingly redundant, parallel-acting cis-regulatory modules that direct expression to broad, overlapping domains throughout the nervous system. These parallel-acting cis-regulatory modules are responsive to a multitude of distinct trans-acting factors. Neuronal gene expression programs therefore fall into two fundamentally distinct classes. Neuron type-specific genes are generally controlled by discrete and non-redundantly acting regulatory inputs, while pan-neuronal gene expression is controlled by diverse, coincident and seemingly redundant regulatory inputs. PMID:26291158

Analysis and selection of optimal function implementations in massively parallel computer

DOEpatents

Archer, Charles Jens [Rochester, MN; Peters, Amanda [Rochester, MN; Ratterman, Joseph D [Rochester, MN

2011-05-31

An apparatus, program product and method optimize the operation of a parallel computer system by, in part, collecting performance data for a set of implementations of a function capable of being executed on the parallel computer system based upon the execution of the set of implementations under varying input parameters in a plurality of input dimensions. The collected performance data may be used to generate selection program code that is configured to call selected implementations of the function in response to a call to the function under varying input parameters. The collected performance data may be used to perform more detailed analysis to ascertain the comparative performance of the set of implementations of the function under the varying input parameters.

New control concepts for uncertain water resources systems: 1. Theory

NASA Astrophysics Data System (ADS)

Georgakakos, Aris P.; Yao, Huaming

1993-06-01

A major complicating factor in water resources systems management is handling unknown inputs. Stochastic optimization provides a sound mathematical framework but requires that enough data exist to develop statistical input representations. In cases where data records are insufficient (e.g., extreme events) or atypical of future input realizations, stochastic methods are inadequate. This article presents a control approach where input variables are only expected to belong in certain sets. The objective is to determine sets of admissible control actions guaranteeing that the system will remain within desirable bounds. The solution is based on dynamic programming and derived for the case where all sets are convex polyhedra. A companion paper (Yao and Georgakakos, this issue) addresses specific applications and problems in relation to reservoir system management.

Random forests-based differential analysis of gene sets for gene expression data.

PubMed

Hsueh, Huey-Miin; Zhou, Da-Wei; Tsai, Chen-An

2013-04-10

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients. In this study, we propose a method of gene set analysis, in which gene sets are used to develop classifications of patients based on the Random Forest (RF) algorithm. The corresponding empirical p-value of an observed out-of-bag (OOB) error rate of the classifier is introduced to identify differentially expressed gene sets using an adequate resampling method. In addition, we discuss the impacts and correlations of genes within each gene set based on the measures of variable importance in the RF algorithm. Significant classifications are reported and visualized together with the underlying gene sets and their contribution to the phenotypes of interest. Numerical studies using both synthesized data and a series of publicly available gene expression data sets are conducted to evaluate the performance of the proposed methods. Compared with other hypothesis testing approaches, our proposed methods are reliable and successful in identifying enriched gene sets and in discovering the contributions of genes within a gene set. The classification results of identified gene sets can provide an valuable alternative to gene set testing to reveal the unknown, biologically relevant classes of samples or patients. In summary, our proposed method allows one to simultaneously assess the discriminatory ability of gene sets and the importance of genes for interpretation of data in complex biological systems. The classifications of biologically defined gene sets can reveal the underlying interactions of gene sets associated with the phenotypes, and provide an insightful complement to conventional gene set analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources

PubMed Central

2011-01-01

Background Several tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input. Results TRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified. Conclusions TRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at http://apollo11.isto.unibo.it/software/, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes. PMID:21333005

Mechanisms of gap gene expression canalization in the Drosophila blastoderm.

PubMed

Gursky, Vitaly V; Panok, Lena; Myasnikova, Ekaterina M; Manu; Samsonova, Maria G; Reinitz, John; Samsonov, Alexander M

2011-01-01

Extensive variation in early gap gene expression in the Drosophila blastoderm is reduced over time because of gap gene cross regulation. This phenomenon is a manifestation of canalization, the ability of an organism to produce a consistent phenotype despite variations in genotype or environment. The canalization of gap gene expression can be understood as arising from the actions of attractors in the gap gene dynamical system. In order to better understand the processes of developmental robustness and canalization in the early Drosophila embryo, we investigated the dynamical effects of varying spatial profiles of Bicoid protein concentration on the formation of the expression border of the gap gene hunchback. At several positions on the anterior-posterior axis of the embryo, we analyzed attractors and their basins of attraction in a dynamical model describing expression of four gap genes with the Bicoid concentration profile accounted as a given input in the model equations. This model was tested against a family of Bicoid gradients obtained from individual embryos. These gradients were normalized by two independent methods, which are based on distinct biological hypotheses and provide different magnitudes for Bicoid spatial variability. We showed how the border formation is dictated by the biological initial conditions (the concentration gradient of maternal Hunchback protein) being attracted to specific attracting sets in a local vicinity of the border. Different types of these attracting sets (point attractors or one dimensional attracting manifolds) define several possible mechanisms of border formation. The hunchback border formation is associated with intersection of the spatial gradient of the maternal Hunchback protein and a boundary between the attraction basins of two different point attractors. We demonstrated how the positional variability for hunchback is related to the corresponding variability of the basin boundaries. The observed reduction in variability of the hunchback gene expression can be accounted for by specific geometrical properties of the basin boundaries. We clarified the mechanisms of gap gene expression canalization in early Drosophila embryos. These mechanisms were specified in the case of hunchback in well defined terms of the dynamical system theory.

Discovering time-lagged rules from microarray data using gene profile classifiers

PubMed Central

2011-01-01

Background Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes. Results This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations. Conclusions A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation. PMID:21524308

An Independent Filter for Gene Set Testing Based on Spectral Enrichment.

PubMed

Frost, H Robert; Li, Zhigang; Asselbergs, Folkert W; Moore, Jason H

2015-01-01

Gene set testing has become an indispensable tool for the analysis of high-dimensional genomic data. An important motivation for testing gene sets, rather than individual genomic variables, is to improve statistical power by reducing the number of tested hypotheses. Given the dramatic growth in common gene set collections, however, testing is often performed with nearly as many gene sets as underlying genomic variables. To address the challenge to statistical power posed by large gene set collections, we have developed spectral gene set filtering (SGSF), a novel technique for independent filtering of gene set collections prior to gene set testing. The SGSF method uses as a filter statistic the p-value measuring the statistical significance of the association between each gene set and the sample principal components (PCs), taking into account the significance of the associated eigenvalues. Because this filter statistic is independent of standard gene set test statistics under the null hypothesis but dependent under the alternative, the proportion of enriched gene sets is increased without impacting the type I error rate. As shown using simulated and real gene expression data, the SGSF algorithm accurately filters gene sets unrelated to the experimental outcome resulting in significantly increased gene set testing power.

Down-weighting overlapping genes improves gene set analysis

PubMed Central

2012-01-01

Background The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set. Results In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method Pathway Analysis with Down-weighting of Overlapping Genes (PADOG). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results. Conclusions PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: http://bioinformaticsprb.med.wayne.edu/PADOG/or http://www.bioconductor.org. PMID:22713124

Novel gene sets improve set-level classification of prokaryotic gene expression data.

PubMed

Holec, Matěj; Kuželka, Ondřej; Železný, Filip

2015-10-28

Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

GeneImp: Fast Imputation to Large Reference Panels Using Genotype Likelihoods from Ultralow Coverage Sequencing

PubMed Central

Spiliopoulou, Athina; Colombo, Marco; Orchard, Peter; Agakov, Felix; McKeigue, Paul

2017-01-01

We address the task of genotype imputation to a dense reference panel given genotype likelihoods computed from ultralow coverage sequencing as inputs. In this setting, the data have a high-level of missingness or uncertainty, and are thus more amenable to a probabilistic representation. Most existing imputation algorithms are not well suited for this situation, as they rely on prephasing for computational efficiency, and, without definite genotype calls, the prephasing task becomes computationally expensive. We describe GeneImp, a program for genotype imputation that does not require prephasing and is computationally tractable for whole-genome imputation. GeneImp does not explicitly model recombination, instead it capitalizes on the existence of large reference panels—comprising thousands of reference haplotypes—and assumes that the reference haplotypes can adequately represent the target haplotypes over short regions unaltered. We validate GeneImp based on data from ultralow coverage sequencing (0.5×), and compare its performance to the most recent version of BEAGLE that can perform this task. We show that GeneImp achieves imputation quality very close to that of BEAGLE, using one to two orders of magnitude less time, without an increase in memory complexity. Therefore, GeneImp is the first practical choice for whole-genome imputation to a dense reference panel when prephasing cannot be applied, for instance, in datasets produced via ultralow coverage sequencing. A related future application for GeneImp is whole-genome imputation based on the off-target reads from deep whole-exome sequencing. PMID:28348060

Synthetic dual-input mammalian genetic circuits enable tunable and stringent transcription control by chemical and light.

PubMed

Chen, Xianjun; Li, Ting; Wang, Xue; Du, Zengmin; Liu, Renmei; Yang, Yi

2016-04-07

Programmable transcription factors can enable precise control of gene expression triggered by a chemical inducer or light. To obtain versatile transgene system with combined benefits of a chemical inducer and light inducer, we created various chimeric promoters through the assembly of different copies of the tet operator and Gal4 operator module, which simultaneously responded to a tetracycline-responsive transcription factor and a light-switchable transactivator. The activities of these chimeric promoters can be regulated by tetracycline and blue light synergistically or antagonistically. Further studies of the antagonistic genetic circuit exhibited high spatiotemporal resolution and extremely low leaky expression, which therefore could be used to spatially and stringently control the expression of highly toxic protein Diphtheria toxin A for light regulated gene therapy. When transferring plasmids engineered for the gene switch-driven expression of a firefly luciferase (Fluc) into mice, the Fluc expression levels of the treated animals directly correlated with the tetracycline and light input program. We suggest that dual-input genetic circuits using TET and light that serve as triggers to achieve expression profiles may enable the design of robust therapeutic gene circuits for gene- and cell-based therapies. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Reconfigurable data path processor

NASA Technical Reports Server (NTRS)

Donohoe, Gregory (Inventor)

2005-01-01

A reconfigurable data path processor comprises a plurality of independent processing elements. Each of the processing elements advantageously comprising an identical architecture. Each processing element comprises a plurality of data processing means for generating a potential output. Each processor is also capable of through-putting an input as a potential output with little or no processing. Each processing element comprises a conditional multiplexer having a first conditional multiplexer input, a second conditional multiplexer input and a conditional multiplexer output. A first potential output value is transmitted to the first conditional multiplexer input, and a second potential output value is transmitted to the second conditional multiplexer output. The conditional multiplexer couples either the first conditional multiplexer input or the second conditional multiplexer input to the conditional multiplexer output, according to an output control command. The output control command is generated by processing a set of arithmetic status-bits through a logical mask. The conditional multiplexer output is coupled to a first processing element output. A first set of arithmetic bits are generated according to the processing of the first processable value. A second set of arithmetic bits may be generated from a second processing operation. The selection of the arithmetic status-bits is performed by an arithmetic-status bit multiplexer selects the desired set of arithmetic status bits from among the first and second set of arithmetic status bits. The conditional multiplexer evaluates the select arithmetic status bits according to logical mask defining an algorithm for evaluating the arithmetic status bits.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data.

PubMed

Hettne, Kristina M; Boorsma, André; van Dartel, Dorien A M; Goeman, Jelle J; de Jong, Esther; Piersma, Aldert H; Stierum, Rob H; Kleinjans, Jos C; Kors, Jan A

2013-01-29

Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals. Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals. Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

PubMed Central

2013-01-01

Background Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. Methods We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals. Results Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals. Conclusions Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect. PMID:23356878

Generalized compliant motion primitive

NASA Technical Reports Server (NTRS)

Backes, Paul G. (Inventor)

1994-01-01

This invention relates to a general primitive for controlling a telerobot with a set of input parameters. The primitive includes a trajectory generator; a teleoperation sensor; a joint limit generator; a force setpoint generator; a dither function generator, which produces telerobot motion inputs in a common coordinate frame for simultaneous combination in sensor summers. Virtual return spring motion input is provided by a restoration spring subsystem. The novel features of this invention include use of a single general motion primitive at a remote site to permit the shared and supervisory control of the robot manipulator to perform tasks via a remotely transferred input parameter set.

Digital signaling decouples activation probability and population heterogeneity.

PubMed

Kellogg, Ryan A; Tian, Chengzhe; Lipniacki, Tomasz; Quake, Stephen R; Tay, Savaş

2015-10-21

Digital signaling enhances robustness of cellular decisions in noisy environments, but it is unclear how digital systems transmit temporal information about a stimulus. To understand how temporal input information is encoded and decoded by the NF-κB system, we studied transcription factor dynamics and gene regulation under dose- and duration-modulated inflammatory inputs. Mathematical modeling predicted and microfluidic single-cell experiments confirmed that integral of the stimulus (or area, concentration × duration) controls the fraction of cells that activate NF-κB in the population. However, stimulus temporal profile determined NF-κB dynamics, cell-to-cell variability, and gene expression phenotype. A sustained, weak stimulation lead to heterogeneous activation and delayed timing that is transmitted to gene expression. In contrast, a transient, strong stimulus with the same area caused rapid and uniform dynamics. These results show that digital NF-κB signaling enables multidimensional control of cellular phenotype via input profile, allowing parallel and independent control of single-cell activation probability and population heterogeneity.

The basic circuit of the IC: tectothalamic neurons with different patterns of synaptic organization send different messages to the thalamus

PubMed Central

Ito, Tetsufumi; Oliver, Douglas L.

2012-01-01

The inferior colliculus (IC) in the midbrain of the auditory system uses a unique basic circuit to organize the inputs from virtually all of the lower auditory brainstem and transmit this information to the medial geniculate body (MGB) in the thalamus. Here, we review the basic circuit of the IC, the neuronal types, the organization of their inputs and outputs. We specifically discuss the large GABAergic (LG) neurons and how they differ from the small GABAergic (SG) and the more numerous glutamatergic neurons. The somata and dendrites of LG neurons are identified by axosomatic glutamatergic synapses that are lacking in the other cell types and exclusively contain the glutamate transporter VGLUT2. Although LG neurons are most numerous in the central nucleus of the IC (ICC), an analysis of their distribution suggests that they are not specifically associated with one set of ascending inputs. The inputs to ICC may be organized into functional zones with different subsets of brainstem inputs, but each zone may contain the same three neuron types. However, the sources of VGLUT2 axosomatic terminals on the LG neuron are not known. Neurons in the dorsal cochlear nucleus, superior olivary complex, intermediate nucleus of the lateral lemniscus, and IC itself that express the gene for VGLUT2 only are the likely origin of the dense VGLUT2 axosomatic terminals on LG tectothalamic neurons. The IC is unique since LG neurons are GABAergic tectothalamic neurons in addition to the numerous glutamatergic tectothalamic neurons. SG neurons evidently target other auditory structures. The basic circuit of the IC and the LG neurons in particular, has implications for the transmission of information about sound through the midbrain to the MGB. PMID:22855671

Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies.

PubMed

Schaid, Daniel J; Sinnwell, Jason P; Jenkins, Gregory D; McDonnell, Shannon K; Ingle, James N; Kubo, Michiaki; Goss, Paul E; Costantino, Joseph P; Wickerham, D Lawrence; Weinshilboum, Richard M

2012-01-01

Gene-set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene-set, have plagued current approaches, often leading to ad hoc "fixes." To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene-level and gene-set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene-sets. However, other types of gene-set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene-sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene-sets, we use an efficient computational strategy that accounts for LD and provides accurate step-down adjusted P-values for each gene-set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene-set analyses. © 2011 Wiley Periodicals, Inc.

Distinct findings from the steady-state analysis of a microbial model with time-invariant or seasonal driving forces

NASA Astrophysics Data System (ADS)

Wang, G.; Mayes, M. A.

2017-12-01

Microbially-explicit soil organic matter (SOM) decomposition models are thought to be more biologically realistic than conventional models. Current testing or evaluation of microbial models majorly uses steady-state analysis with time-invariant forces (i.e., soil temperature, moisture and litter input). The findings from such simplified analyses are assumed to be capable of representing the model responses in field soil conditions with seasonal driving forces. Here we show that the steady-state modeling results with seasonal forces may result in distinct findings from the simulations with time-invariant forcing data. We evaluate the response of soil organic C (SOC) to litter addition (L+) in a subtropical pine forest using the calibrated Microbial-ENzyme Decomposition (MEND) model. We implemented two sets of modeling analyses, with each set including two scenarios, i.e., control (CR) vs. litter-addition (L+). The first set (Set1) uses fixed soil temperature and moisture, and constant litter input under Scenario CR vs. increased constant litter input under Scenario L+. The second set (Set2) employs hourly soil temperature and moisture and monthly litter input under Scenario CR. Under Scenario L+ of Set2, A logistic function with an upper plateau represents the increasing trend of litter input to SOM. We conduct long-term simulations to ensure that the models reach steady-states for Set1 or dynamic equilibrium for Set2. Litter addition of Set2 causes an increase of SOC by 29%. However, the steady-state SOC pool sizes of Set1 would not respond to L+ as long as the chemical composition of litter remained the same. Our results indicate the necessity to implement dynamic model simulations with seasonal forcing data, which could lead to modeling results qualitatively different from the steady-state analysis with time-invariant forcing data.

BioVLAB-mCpG-SNP-EXPRESS: A system for multi-level and multi-perspective analysis and exploration of DNA methylation, sequence variation (SNPs), and gene expression from multi-omics data.

PubMed

Chae, Heejoon; Lee, Sangseon; Seo, Seokjun; Jung, Daekyoung; Chang, Hyeonsook; Nephew, Kenneth P; Kim, Sun

2016-12-01

Measuring gene expression, DNA sequence variation, and DNA methylation status is routinely done using high throughput sequencing technologies. To analyze such multi-omics data and explore relationships, reliable bioinformatics systems are much needed. Existing systems are either for exploring curated data or for processing omics data in the form of a library such as R. Thus scientists have much difficulty in investigating relationships among gene expression, DNA sequence variation, and DNA methylation using multi-omics data. In this study, we report a system called BioVLAB-mCpG-SNP-EXPRESS for the integrated analysis of DNA methylation, sequence variation (SNPs), and gene expression for distinguishing cellular phenotypes at the pairwise and multiple phenotype levels. The system can be deployed on either the Amazon cloud or a publicly available high-performance computing node, and the data analysis and exploration of the analysis result can be conveniently done using a web-based interface. In order to alleviate analysis complexity, all the process are fully automated, and graphical workflow system is integrated to represent real-time analysis progression. The BioVLAB-mCpG-SNP-EXPRESS system works in three stages. First, it processes and analyzes multi-omics data as input in the form of the raw data, i.e., FastQ files. Second, various integrated analyses such as methylation vs. gene expression and mutation vs. methylation are performed. Finally, the analysis result can be explored in a number of ways through a web interface for the multi-level, multi-perspective exploration. Multi-level interpretation can be done by either gene, gene set, pathway or network level and multi-perspective exploration can be explored from either gene expression, DNA methylation, sequence variation, or their relationship perspective. The utility of the system is demonstrated by performing analysis of phenotypically distinct 30 breast cancer cell line data set. BioVLAB-mCpG-SNP-EXPRESS is available at http://biohealth.snu.ac.kr/software/biovlab_mcpg_snp_express/. Copyright © 2016 Elsevier Inc. All rights reserved.

Informed walks: whispering hints to gene hunters inside networks' jungle.

PubMed

Bourdakou, Marilena M; Spyrou, George M

2017-10-11

Systemic approaches offer a different point of view on the analysis of several types of molecular associations as well as on the identification of specific gene communities in several cancer types. However, due to lack of sufficient data needed to construct networks based on experimental evidence, statistical gene co-expression networks are widely used instead. Many efforts have been made to exploit the information hidden in these networks. However, these approaches still need to capitalize comprehensively the prior knowledge encrypted into molecular pathway associations and improve their efficiency regarding the discovery of both exclusive subnetworks as candidate biomarkers and conserved subnetworks that may uncover common origins of several cancer types. In this study we present the development of the Informed Walks model based on random walks that incorporate information from molecular pathways to mine candidate genes and gene-gene links. The proposed model has been applied to TCGA (The Cancer Genome Atlas) datasets from seven different cancer types, exploring the reconstructed co-expression networks of the whole set of genes and driving to highlighted sub-networks for each cancer type. In the sequel, we elucidated the impact of each subnetwork on the indication of underlying exclusive and common molecular mechanisms as well as on the short-listing of drugs that have the potential to suppress the corresponding cancer type through a drug-repurposing pipeline. We have developed a method of gene subnetwork highlighting based on prior knowledge, capable to give fruitful insights regarding the underlying molecular mechanisms and valuable input to drug-repurposing pipelines for a variety of cancer types.

Continuous-Time Bilinear System Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan

2003-01-01

The objective of this paper is to describe a new method for identification of a continuous-time multi-input and multi-output bilinear system. The approach is to make judicious use of the linear-model properties of the bilinear system when subjected to a constant input. Two steps are required in the identification process. The first step is to use a set of pulse responses resulting from a constant input of one sample period to identify the state matrix, the output matrix, and the direct transmission matrix. The second step is to use another set of pulse responses with the same constant input over multiple sample periods to identify the input matrix and the coefficient matrices associated with the coupling terms between the state and the inputs. Numerical examples are given to illustrate the concept and the computational algorithm for the identification method.

Systematic evaluation of RNA quality, microarray data reliability and pathway analysis in fresh, fresh frozen and formalin-fixed paraffin-embedded tissue samples.

PubMed

Wimmer, Isabella; Tröscher, Anna R; Brunner, Florian; Rubino, Stephen J; Bien, Christian G; Weiner, Howard L; Lassmann, Hans; Bauer, Jan

2018-04-20

Formalin-fixed paraffin-embedded (FFPE) tissues are valuable resources commonly used in pathology. However, formalin fixation modifies nucleic acids challenging the isolation of high-quality RNA for genetic profiling. Here, we assessed feasibility and reliability of microarray studies analysing transcriptome data from fresh, fresh-frozen (FF) and FFPE tissues. We show that reproducible microarray data can be generated from only 2 ng FFPE-derived RNA. For RNA quality assessment, fragment size distribution (DV200) and qPCR proved most suitable. During RNA isolation, extending tissue lysis time to 10 hours reduced high-molecular-weight species, while additional incubation at 70 °C markedly increased RNA yields. Since FF- and FFPE-derived microarrays constitute different data entities, we used indirect measures to investigate gene signal variation and relative gene expression. Whole-genome analyses revealed high concordance rates, while reviewing on single-genes basis showed higher data variation in FFPE than FF arrays. Using an experimental model, gene set enrichment analysis (GSEA) of FFPE-derived microarrays and fresh tissue-derived RNA-Seq datasets yielded similarly affected pathways confirming the applicability of FFPE tissue in global gene expression analysis. Our study provides a workflow comprising RNA isolation, quality assessment and microarray profiling using minimal RNA input, thus enabling hypothesis-generating pathway analyses from limited amounts of precious, pathologically significant FFPE tissues.

Molecular biology of myopia.

PubMed

Schaeffel, Frank; Simon, Perikles; Feldkaemper, Marita; Ohngemach, Sibylle; Williams, Robert W

2003-09-01

Experiments in animal models of myopia have emphasised the importance of visual input in emmetropisation but it is also evident that the development of human myopia is influenced to some degree by genetic factors. Molecular genetic approaches can help to identify both the genes involved in the control of ocular development and the potential targets for pharmacological intervention. This review covers a variety of techniques that are being used to study the molecular biology of myopia. In the first part, we describe techniques used to analyse visually induced changes in gene expression: Northern Blot, polymerase chain reaction (PCR) and real-time PCR to obtain semi-quantitative and quantitative measures of changes in transcription level of a known gene, differential display reverse transcription PCR (DD-RT-PCR) to search for new genes that are controlled by visual input, rapid amplification of 5' cDNA (5'-RACE) to extend the 5' end of sequences that are regulated by visual input, in situ hybridisation to localise the expression of a given gene in a tissue and oligonucleotide microarray assays to simultaneously test visually induced changes in thousands of transcripts in single experiments. In the second part, we describe techniques that are used to localise regions in the genome that contain genes that are involved in the control of eye growth and refractive errors in mice and humans. These include quantitative trait loci (QTL) mapping, exploiting experimental test crosses of mice and transmission disequilibrium tests (TDT) in humans to find chromosomal intervals that harbour genes involved in myopia development. We review several successful applications of this battery of techniques in myopia research.

R & D GTDS SST: Code Flowcharts and Input

DTIC Science & Technology

1995-01-01

trajectory from a given set of initial conditions Typical output is in the form of a printer le of Cartesian coordinates and Keplerian orbital ... orbiting the Earth The input data specied for an EPHEM run are i Initial elements and epoch ii Orbit generator selection iii Conversion of osculating...discussed ELEMENT sets coordinate system reference central body and rst components of initial state ELEMENT sets the second

Mining functionally relevant gene sets for analyzing physiologically novel clinical expression data.

PubMed

Turcan, Sevin; Vetter, Douglas E; Maron, Jill L; Wei, Xintao; Slonim, Donna K

2011-01-01

Gene set analyses have become a standard approach for increasing the sensitivity of transcriptomic studies. However, analytical methods incorporating gene sets require the availability of pre-defined gene sets relevant to the underlying physiology being studied. For novel physiological problems, relevant gene sets may be unavailable or existing gene set databases may bias the results towards only the best-studied of the relevant biological processes. We describe a successful attempt to mine novel functional gene sets for translational projects where the underlying physiology is not necessarily well characterized in existing annotation databases. We choose targeted training data from public expression data repositories and define new criteria for selecting biclusters to serve as candidate gene sets. Many of the discovered gene sets show little or no enrichment for informative Gene Ontology terms or other functional annotation. However, we observe that such gene sets show coherent differential expression in new clinical test data sets, even if derived from different species, tissues, and disease states. We demonstrate the efficacy of this method on a human metabolic data set, where we discover novel, uncharacterized gene sets that are diagnostic of diabetes, and on additional data sets related to neuronal processes and human development. Our results suggest that our approach may be an efficient way to generate a collection of gene sets relevant to the analysis of data for novel clinical applications where existing functional annotation is relatively incomplete.

Microarray analysis of the rat lacrimal gland following the loss of parasympathetic control of secretion

PubMed Central

Nguyen, Doan H.; Toshida, Hiroshi; Schurr, Jill; Beuerman, Roger W.

2010-01-01

Previous studies showed that loss of muscarinic parasympathetic input to the lacrimal gland (LG) leads to a dramatic reduction in tear secretion and profound changes to LG structure. In this study, we used DNA microarrays to examine the regulation of the gene expression of the genes for secretory function and organization of the LG. Long-Evans rats anesthetized with a mixture of ketamine/xylazine (80:10 mg/kg) underwent unilateral sectioning of the greater superficial petrosal nerve, the input to the pterygopalatine ganglion. After 7 days, tear secretion was measured, the animals were killed, and structural changes in the LG were examined by light microscopy. Total RNA from control and experimental LGs (n = 5) was used for DNA microarray analysis employing the U34A GeneChip. Three statistical algorithms (detection, change call, and signal log ratio) were used to determine differential gene expression using the Microarray Suite (5.0) and Data Mining Tools (3.0). Tear secretion was significantly reduced and corneal ulcers developed in all experimental eyes. Light microscopy showed breakdown of the acinar structure of the LG. DNA microarray analysis showed downregulation of genes associated with the endoplasmic reticulum and Golgi, including genes involved in protein folding and processing. Conversely, transcripts for cytoskeleton and extracellular matrix components, inflammation, and apoptosis were upregulated. The number of significantly upregulated genes (116) was substantially greater than the number of downregulated genes (49). Removal of the main secretory input to the rat LG resulted in clinical symptoms associated with severe dry eye. Components of the secretory pathway were negatively affected, and the increase in cell proliferation and inflammation may lead to loss of organization in the parasympathectomized lacrimal gland. PMID:15084711

Developmental gene regulatory network architecture across 500 million years of echinoderm evolution

NASA Technical Reports Server (NTRS)

Hinman, Veronica F.; Nguyen, Albert T.; Cameron, R. Andrew; Davidson, Eric H.

2003-01-01

Evolutionary change in morphological features must depend on architectural reorganization of developmental gene regulatory networks (GRNs), just as true conservation of morphological features must imply retention of ancestral developmental GRN features. Key elements of the provisional GRN for embryonic endomesoderm development in the sea urchin are here compared with those operating in embryos of a distantly related echinoderm, a starfish. These animals diverged from their common ancestor 520-480 million years ago. Their endomesodermal fate maps are similar, except that sea urchins generate a skeletogenic cell lineage that produces a prominent skeleton lacking entirely in starfish larvae. A relevant set of regulatory genes was isolated from the starfish Asterina miniata, their expression patterns determined, and effects on the other genes of perturbing the expression of each were demonstrated. A three-gene feedback loop that is a fundamental feature of the sea urchin GRN for endoderm specification is found in almost identical form in the starfish: a detailed element of GRN architecture has been retained since the Cambrian Period in both echinoderm lineages. The significance of this retention is highlighted by the observation of numerous specific differences in the GRN connections as well. A regulatory gene used to drive skeletogenesis in the sea urchin is used entirely differently in the starfish, where it responds to endomesodermal inputs that do not affect it in the sea urchin embryo. Evolutionary changes in the GRNs since divergence are limited sharply to certain cis-regulatory elements, whereas others have persisted unaltered.

Detection and characterization of gene-gene and gene-environment interactions in common human diseases and complex clinical endpoints

EPA Science Inventory

Biological organisms are complex systems that dynamically integrate inputs from a multitude of physiological and environmental factors. Therefore, in addressing questions concerning the etiology of complex health outcomes, it is essential that the systemic nature of biology be ta...

Biclustering sparse binary genomic data.

PubMed

van Uitert, Miranda; Meuleman, Wouter; Wessels, Lodewyk

2008-12-01

Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two algorithms have been proposed that specifically deal with binary matrices. None of the gene expression biclustering algorithms can handle the large number of zeros in sparse binary matrices. The two proposed binary algorithms failed to produce meaningful results. In this article, we present a new algorithm that is able to extract biclusters from sparse, binary datasets. A powerful feature is that biclusters with different numbers of rows and columns can be detected, varying from many rows to few columns and few rows to many columns. It allows the user to guide the search towards biclusters of specific dimensions. When applying our algorithm to an input matrix derived from TRANSFAC, we find transcription factors with distinctly dissimilar binding motifs, but a clear set of common targets that are significantly enriched for GO categories.

Detection of non-coding RNA in bacteria and archaea using the DETR'PROK Galaxy pipeline.

PubMed

Toffano-Nioche, Claire; Luo, Yufei; Kuchly, Claire; Wallon, Claire; Steinbach, Delphine; Zytnicki, Matthias; Jacq, Annick; Gautheret, Daniel

2013-09-01

RNA-seq experiments are now routinely used for the large scale sequencing of transcripts. In bacteria or archaea, such deep sequencing experiments typically produce 10-50 million fragments that cover most of the genome, including intergenic regions. In this context, the precise delineation of the non-coding elements is challenging. Non-coding elements include untranslated regions (UTRs) of mRNAs, independent small RNA genes (sRNAs) and transcripts produced from the antisense strand of genes (asRNA). Here we present a computational pipeline (DETR'PROK: detection of ncRNAs in prokaryotes) based on the Galaxy framework that takes as input a mapping of deep sequencing reads and performs successive steps of clustering, comparison with existing annotation and identification of transcribed non-coding fragments classified into putative 5' UTRs, sRNAs and asRNAs. We provide a step-by-step description of the protocol using real-life example data sets from Vibrio splendidus and Escherichia coli. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

The dusp1 Immediate Early Gene is Regulated by Natural Stimuli Predominantly in Sensory Input Neurons

PubMed Central

Horita, Haruhito; Wada, Kazuhiro; Rivas, Miriam V.; Hara, Erina; Jarvis, Erich D.

2010-01-01

Many immediate early genes (IEGs) have activity-dependent induction in a subset of brain subdivisions or neuron types. However, none have been reported yet with regulation specific to thalamic-recipient sensory neurons of the telencephalon or in the thalamic sensory input neurons themselves. Here, we report the first such gene, dual specificity phosphatase 1 (dusp1). Dusp1 is an inactivator of mitogen-activated protein kinase (MAPK), and MAPK activates expression of egr1, one of the most commonly studied IEGs, as determined in cultured cells. We found that in the brain of naturally behaving songbirds and other avian species, hearing song, seeing visual stimuli, or performing motor behavior caused high dusp1 upregulation, respectively, in auditory, visual, and somatosensory input cell populations of the thalamus and thalamic-recipient sensory neurons of the telencephalic pallium, whereas high egr1 upregulation occurred only in subsequently connected secondary and tertiary sensory neuronal populations of these same pathways. Motor behavior did not induce high levels of dusp1 expression in the motor-associated areas adjacent to song nuclei, where egr1 is upregulated in response to movement. Our analysis of dusp1 expression in mouse brain suggests similar regulation in the sensory input neurons of the thalamus and thalamic-recipient layer IV and VI neurons of the cortex. These findings suggest that dusp1 has specialized regulation to sensory input neurons of the thalamus and telencephalon; they further suggest that this regulation may serve to attenuate stimulus-induced expression of egr1 and other IEGs, leading to unique molecular properties of forebrain sensory input neurons. PMID:20506480

Using SCOPE to identify potential regulatory motifs in coregulated genes.

PubMed

Martyanov, Viktor; Gross, Robert H

2011-05-31

SCOPE is an ensemble motif finder that uses three component algorithms in parallel to identify potential regulatory motifs by over-representation and motif position preference. Each component algorithm is optimized to find a different kind of motif. By taking the best of these three approaches, SCOPE performs better than any single algorithm, even in the presence of noisy data. In this article, we utilize a web version of SCOPE to examine genes that are involved in telomere maintenance. SCOPE has been incorporated into at least two other motif finding programs and has been used in other studies. The three algorithms that comprise SCOPE are BEAM, which finds non-degenerate motifs (ACCGGT), PRISM, which finds degenerate motifs (ASCGWT), and SPACER, which finds longer bipartite motifs (ACCnnnnnnnnGGT). These three algorithms have been optimized to find their corresponding type of motif. Together, they allow SCOPE to perform extremely well. Once a gene set has been analyzed and candidate motifs identified, SCOPE can look for other genes that contain the motif which, when added to the original set, will improve the motif score. This can occur through over-representation or motif position preference. Working with partial gene sets that have biologically verified transcription factor binding sites, SCOPE was able to identify most of the rest of the genes also regulated by the given transcription factor. Output from SCOPE shows candidate motifs, their significance, and other information both as a table and as a graphical motif map. FAQs and video tutorials are available at the SCOPE web site which also includes a "Sample Search" button that allows the user to perform a trial run. Scope has a very friendly user interface that enables novice users to access the algorithm's full power without having to become an expert in the bioinformatics of motif finding. As input, SCOPE can take a list of genes, or FASTA sequences. These can be entered in browser text fields, or read from a file. The output from SCOPE contains a list of all identified motifs with their scores, number of occurrences, fraction of genes containing the motif, and the algorithm used to identify the motif. For each motif, result details include a consensus representation of the motif, a sequence logo, a position weight matrix, and a list of instances for every motif occurrence (with exact positions and "strand" indicated). Results are returned in a browser window and also optionally by email. Previous papers describe the SCOPE algorithms in detail.

A method for quantitative analysis of standard and high-throughput qPCR expression data based on input sample quantity.

PubMed

Adamski, Mateusz G; Gumann, Patryk; Baird, Alison E

2014-01-01

Over the past decade rapid advances have occurred in the understanding of RNA expression and its regulation. Quantitative polymerase chain reactions (qPCR) have become the gold standard for quantifying gene expression. Microfluidic next generation, high throughput qPCR now permits the detection of transcript copy number in thousands of reactions simultaneously, dramatically increasing the sensitivity over standard qPCR. Here we present a gene expression analysis method applicable to both standard polymerase chain reactions (qPCR) and high throughput qPCR. This technique is adjusted to the input sample quantity (e.g., the number of cells) and is independent of control gene expression. It is efficiency-corrected and with the use of a universal reference sample (commercial complementary DNA (cDNA)) permits the normalization of results between different batches and between different instruments--regardless of potential differences in transcript amplification efficiency. Modifications of the input quantity method include (1) the achievement of absolute quantification and (2) a non-efficiency corrected analysis. When compared to other commonly used algorithms the input quantity method proved to be valid. This method is of particular value for clinical studies of whole blood and circulating leukocytes where cell counts are readily available.

A bottom-up characterization of transfer functions for synthetic biology designs: lessons from enzymology.

PubMed

Carbonell-Ballestero, Max; Duran-Nebreda, Salva; Montañez, Raúl; Solé, Ricard; Macía, Javier; Rodríguez-Caso, Carlos

2014-12-16

Within the field of synthetic biology, a rational design of genetic parts should include a causal understanding of their input-output responses-the so-called transfer function-and how to tune them. However, a commonly adopted strategy is to fit data to Hill-shaped curves without considering the underlying molecular mechanisms. Here we provide a novel mathematical formalization that allows prediction of the global behavior of a synthetic device by considering the actual information from the involved biological parts. This is achieved by adopting an enzymology-like framework, where transfer functions are described in terms of their input affinity constant and maximal response. As a proof of concept, we characterize a set of Lux homoserine-lactone-inducible genetic devices with different levels of Lux receptor and signal molecule. Our model fits the experimental results and predicts the impact of the receptor's ribosome-binding site strength, as a tunable parameter that affects gene expression. The evolutionary implications are outlined. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data.

PubMed

Tintle, Nathan L; Sitarik, Alexandra; Boerema, Benjamin; Young, Kylie; Best, Aaron A; Dejongh, Matthew

2012-08-08

Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed. We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix® gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size. Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.

A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

PubMed

Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong

2015-01-01

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data

PubMed Central

Seok, Junhee; Davis, Ronald W.; Xiao, Wenzhong

2015-01-01

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge. PMID:25933378

Mathematical models of the simplest fuzzy PI/PD controllers with skewed input and output fuzzy sets.

PubMed

Mohan, B M; Sinha, Arpita

2008-07-01

This paper unveils mathematical models for fuzzy PI/PD controllers which employ two skewed fuzzy sets for each of the two-input variables and three skewed fuzzy sets for the output variable. The basic constituents of these models are Gamma-type and L-type membership functions for each input, trapezoidal/triangular membership functions for output, intersection/algebraic product triangular norm, maximum/drastic sum triangular conorm, Mamdani minimum/Larsen product/drastic product inference method, and center of sums defuzzification method. The existing simplest fuzzy PI/PD controller structures derived via symmetrical fuzzy sets become special cases of the mathematical models revealed in this paper. Finally, a numerical example along with its simulation results are included to demonstrate the effectiveness of the simplest fuzzy PI controllers.

Multi-pass amplifier architecture for high power laser systems

DOEpatents

Manes, Kenneth R; Spaeth, Mary L; Erlandson, Alvin C

2014-04-01

A main amplifier system includes a first reflector operable to receive input light through a first aperture and direct the input light along an optical path. The input light is characterized by a first polarization. The main amplifier system also includes a first polarizer operable to reflect light characterized by the first polarization state. The main amplifier system further includes a first and second set of amplifier modules. Each of the first and second set of amplifier modules includes an entrance window, a quarter wave plate, a plurality of amplifier slablets arrayed substantially parallel to each other, and an exit window. The main amplifier system additionally includes a set of mirrors operable to reflect light exiting the first set of amplifier modules to enter the second set of amplifier modules and a second polarizer operable to reflect light characterized by a second polarization state.

Gene set analysis of purine and pyrimidine antimetabolites cancer therapies.

PubMed

Fridley, Brooke L; Batzler, Anthony; Li, Liang; Li, Fang; Matimba, Alice; Jenkins, Gregory D; Ji, Yuan; Wang, Liewei; Weinshilboum, Richard M

2011-11-01

Responses to therapies, either with regard to toxicities or efficacy, are expected to involve complex relationships of gene products within the same molecular pathway or functional gene set. Therefore, pathways or gene sets, as opposed to single genes, may better reflect the true underlying biology and may be more appropriate units for analysis of pharmacogenomic studies. Application of such methods to pharmacogenomic studies may enable the detection of more subtle effects of multiple genes in the same pathway that may be missed by assessing each gene individually. A gene set analysis of 3821 gene sets is presented assessing the association between basal messenger RNA expression and drug cytotoxicity using ethnically defined human lymphoblastoid cell lines for two classes of drugs: pyrimidines [gemcitabine (dFdC) and arabinoside] and purines [6-thioguanine and 6-mercaptopurine]. The gene set nucleoside-diphosphatase activity was found to be significantly associated with both dFdC and arabinoside, whereas gene set γ-aminobutyric acid catabolic process was associated with dFdC and 6-thioguanine. These gene sets were significantly associated with the phenotype even after adjusting for multiple testing. In addition, five associated gene sets were found in common between the pyrimidines and two gene sets for the purines (3',5'-cyclic-AMP phosphodiesterase activity and γ-aminobutyric acid catabolic process) with a P value of less than 0.0001. Functional validation was attempted with four genes each in gene sets for thiopurine and pyrimidine antimetabolites. All four genes selected from the pyrimidine gene sets (PSME3, CANT1, ENTPD6, ADRM1) were validated, but only one (PDE4D) was validated for the thiopurine gene sets. In summary, results from the gene set analysis of pyrimidine and purine therapies, used often in the treatment of various cancers, provide novel insight into the relationship between genomic variation and drug response.

ColorTree: a batch customization tool for phylogenic trees

PubMed Central

Chen, Wei-Hua; Lercher, Martin J

2009-01-01

Background Genome sequencing projects and comparative genomics studies typically aim to trace the evolutionary history of large gene sets, often requiring human inspection of hundreds of phylogenetic trees. If trees are checked for compatibility with an explicit null hypothesis (e.g., the monophyly of certain groups), this daunting task is greatly facilitated by an appropriate coloring scheme. Findings In this note, we introduce ColorTree, a simple yet powerful batch customization tool for phylogenic trees. Based on pattern matching rules, ColorTree applies a set of customizations to an input tree file, e.g., coloring labels or branches. The customized trees are saved to an output file, which can then be viewed and further edited by Dendroscope (a freely available tree viewer). ColorTree runs on any Perl installation as a stand-alone command line tool, and its application can thus be easily automated. This way, hundreds of phylogenic trees can be customized for easy visual inspection in a matter of minutes. Conclusion ColorTree allows efficient and flexible visual customization of large tree sets through the application of a user-supplied configuration file to multiple tree files. PMID:19646243

ColorTree: a batch customization tool for phylogenic trees.

PubMed

Chen, Wei-Hua; Lercher, Martin J

2009-07-31

Genome sequencing projects and comparative genomics studies typically aim to trace the evolutionary history of large gene sets, often requiring human inspection of hundreds of phylogenetic trees. If trees are checked for compatibility with an explicit null hypothesis (e.g., the monophyly of certain groups), this daunting task is greatly facilitated by an appropriate coloring scheme. In this note, we introduce ColorTree, a simple yet powerful batch customization tool for phylogenic trees. Based on pattern matching rules, ColorTree applies a set of customizations to an input tree file, e.g., coloring labels or branches. The customized trees are saved to an output file, which can then be viewed and further edited by Dendroscope (a freely available tree viewer). ColorTree runs on any Perl installation as a stand-alone command line tool, and its application can thus be easily automated. This way, hundreds of phylogenic trees can be customized for easy visual inspection in a matter of minutes. ColorTree allows efficient and flexible visual customization of large tree sets through the application of a user-supplied configuration file to multiple tree files.

MAGMA: Generalized Gene-Set Analysis of GWAS Data

PubMed Central

de Leeuw, Christiaan A.; Mooij, Joris M.; Heskes, Tom; Posthuma, Danielle

2015-01-01

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well. PMID:25885710

MAGMA: generalized gene-set analysis of GWAS data.

PubMed

de Leeuw, Christiaan A; Mooij, Joris M; Heskes, Tom; Posthuma, Danielle

2015-04-01

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well.

Poplar Interactome: Project Final Report

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

Jaiswal, Pankaj

The feedstock plant Poplar has many advantages over traditional crop plants. Not only Poplar needs low energy input and off season storage as compared to feedstocks such as corn, in the winter season Poplar biomass is stored on the stem/trunk, and Poplar plantations serve as large carbon sink. A key constraint to the expansion of cellulosic bioenergy sources such as in Poplar however, is the negative consequence of converting land use from food crops to energy crops. Therefore in order for Poplar to become a viable energy crop it needs to be grown mostly on marginal land unsuitable agricultural crops.more » For this we need a better understanding of abiotic stress and adaptation response in poplar. In the process we expected to find new and existing poplar genes and their function that respond to sustain abiotic stress. We carried out an extensive gene expression study on the control untreated and stress (drought, salinity, cold and heat) treated poplar plants. The samples were collected from the stem, leaf and root tissues. The RNA of protein coding genes and regulatory smallRNA genes were sequenced generating more than a billion reads. This is the first such known study in Poplar plants. These were used for quantification and genomic analysis to identify stress responsive genes in poplar. Based on the quantification and genomic analysis, a select set of genes were studied for gene-gene interactions to find their association to stress response. The data was also used to find novel stress responsive genes in poplar that were previously not identified in the Poplar reference genome. The data is made available to the public through the national and international genomic data archives.« less

JAva GUi for Applied Research (JAGUAR) v 3.0

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

JAGUAR is a Java software tool for automatically rendering a graphical user interface (GUI) from a structured input specification. It is designed as a plug-in to the Eclipse workbench to enable users to create, edit, and externally execute analysis application input decks and then view the results. JAGUAR serves as a GUI for Sandia's DAKOTA software toolkit for optimization and uncertainty quantification. It will include problem (input deck)set-up, option specification, analysis execution, and results visualization. Through the use of wizards, templates, and views, JAGUAR helps uses navigate the complexity of DAKOTA's complete input specification. JAGUAR is implemented in Java, leveragingmore » Eclipse extension points and Eclipse user interface. JAGUAR parses a DAKOTA NIDR input specification and presents the user with linked graphical and plain text representations of problem set-up and option specification for DAKOTA studies. After the data has been input by the user, JAGUAR generates one or more input files for DAKOTA, executes DAKOTA, and captures and interprets the results« less

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

Earth observing system. Output data products and input requirements, version 2.0. Volume 3: Algorithm summary tables and non-EOS data products

NASA Technical Reports Server (NTRS)

Lu, Yun-Chi; Chang, Hyo Duck; Krupp, Brian; Kumar, Ravindar; Swaroop, Anand

1992-01-01

Volume 3 assists Earth Observing System (EOS) investigators in locating required non-EOS data products by identifying their non-EOS input requirements and providing the information on data sets available at various Distributed Active Archive Centers (DAAC's), including those from Pathfinder Activities and Earth Probes. Volume 3 is intended to complement, not to duplicate, the the EOSDIS Science Data Plan (SDP) by providing detailed data set information which was not presented in the SDP. Section 9 of this volume discusses the algorithm summary tables containing information on retrieval algorithms, expected outputs and required input data. Section 10 describes the non-EOS input requirements of instrument teams and IDS investigators. Also described are the current and future data holdings of the original seven DAACS and data products planned from the future missions and projects including Earth Probes and Pathfinder Activities. Section 11 describes source of information used in compiling data set information presented in this volume. A list of data set attributes used to describe various data sets is presented in section 12 along with their descriptions. Finally, Section 13 presents the SPSO's future plan to improve this report .

Complexity and non-commutativity of learning operations on graphs.

PubMed

Atmanspacher, Harald; Filk, Thomas

2006-07-01

We present results from numerical studies of supervised learning operations in small recurrent networks considered as graphs, leading from a given set of input conditions to predetermined outputs. Graphs that have optimized their output for particular inputs with respect to predetermined outputs are asymptotically stable and can be characterized by attractors, which form a representation space for an associative multiplicative structure of input operations. As the mapping from a series of inputs onto a series of such attractors generally depends on the sequence of inputs, this structure is generally non-commutative. Moreover, the size of the set of attractors, indicating the complexity of learning, is found to behave non-monotonically as learning proceeds. A tentative relation between this complexity and the notion of pragmatic information is indicated.

Timing discriminator using leading-edge extrapolation

DOEpatents

Gottschalk, Bernard

1983-01-01

A discriminator circuit to recover timing information from slow-rising pulses by means of an output trailing edge, a fixed time after the starting corner of the input pulse, which is nearly independent of risetime and threshold setting. This apparatus comprises means for comparing pulses with a threshold voltage; a capacitor to be charged at a certain rate when the input signal is one-third threshold voltage, and at a lower rate when the input signal is two-thirds threshold voltage; current-generating means for charging the capacitor; means for comparing voltage capacitor with a bias voltage; a flip-flop to be set when the input pulse reaches threshold voltage and reset when capacitor voltage reaches the bias voltage; and a clamping means for discharging the capacitor when the input signal returns below one-third threshold voltage.

Machine Learning Classification of Heterogeneous Fields to Estimate Physical Responses

NASA Astrophysics Data System (ADS)

McKenna, S. A.; Akhriev, A.; Alzate, C.; Zhuk, S.

2017-12-01

The promise of machine learning to enhance physics-based simulation is examined here using the transient pressure response to a pumping well in a heterogeneous aquifer. 10,000 random fields of log10 hydraulic conductivity (K) are created and conditioned on a single K measurement at the pumping well. Each K-field is used as input to a forward simulation of drawdown (pressure decline). The differential equations governing groundwater flow to the well serve as a non-linear transform of the input K-field to an output drawdown field. The results are stored and the data set is split into training and testing sets for classification. A Euclidean distance measure between any two fields is calculated and the resulting distances between all pairs of fields define a similarity matrix. Similarity matrices are calculated for both input K-fields and the resulting drawdown fields at the end of the simulation. The similarity matrices are then used as input to spectral clustering to determine groupings of similar input and output fields. Additionally, the similarity matrix is used as input to multi-dimensional scaling to visualize the clustering of fields in lower dimensional spaces. We examine the ability to cluster both input K-fields and output drawdown fields separately with the goal of identifying K-fields that create similar drawdowns and, conversely, given a set of simulated drawdown fields, identify meaningful clusters of input K-fields. Feature extraction based on statistical parametric mapping provides insight into what features of the fields drive the classification results. The final goal is to successfully classify input K-fields into the correct output class, and also, given an output drawdown field, be able to infer the correct class of input field that created it.

Gene set analysis using variance component tests.

PubMed

Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

GARNET--gene set analysis with exploration of annotation relations.

PubMed

Rho, Kyoohyoung; Kim, Bumjin; Jang, Youngjun; Lee, Sanghyun; Bae, Taejeong; Seo, Jihae; Seo, Chaehwa; Lee, Jihyun; Kang, Hyunjung; Yu, Ungsik; Kim, Sunghoon; Lee, Sanghyuk; Kim, Wan Kyu

2011-02-15

Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information. GARNET (Gene Annotation Relationship NEtwork Tools) is an integrative platform for gene set analysis with many novel features. It includes tools for retrieval of genes from annotation database, statistical analysis & visualization of annotation relationships, and managing gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, we have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules--gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations. GARNET (gene annotation relationship network tools) is an integrative platform for diverse types of gene set analysis, where complex relationships among gene annotations can be easily explored with an intuitive network visualization tool (http://garnet.isysbio.org/ or http://ercsb.ewha.ac.kr/garnet/).

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Estimation of gene induction enables a relevance-based ranking of gene sets.

PubMed

Bartholomé, Kilian; Kreutz, Clemens; Timmer, Jens

2009-07-01

In order to handle and interpret the vast amounts of data produced by microarray experiments, the analysis of sets of genes with a common biological functionality has been shown to be advantageous compared to single gene analyses. Some statistical methods have been proposed to analyse the differential gene expression of gene sets in microarray experiments. However, most of these methods either require threshhold values to be chosen for the analysis, or they need some reference set for the determination of significance. We present a method that estimates the number of differentially expressed genes in a gene set without requiring a threshold value for significance of genes. The method is self-contained (i.e., it does not require a reference set for comparison). In contrast to other methods which are focused on significance, our approach emphasizes the relevance of the regulation of gene sets. The presented method measures the degree of regulation of a gene set and is a useful tool to compare the induction of different gene sets and place the results of microarray experiments into the biological context. An R-package is available.

Parametric analysis of parameters for electrical-load forecasting using artificial neural networks

NASA Astrophysics Data System (ADS)

Gerber, William J.; Gonzalez, Avelino J.; Georgiopoulos, Michael

1997-04-01

Accurate total system electrical load forecasting is a necessary part of resource management for power generation companies. The better the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Automating this process is a profitable goal and neural networks should provide an excellent means of doing the automation. However, prior to developing such a system, the optimal set of input parameters must be determined. The approach of this research was to determine what those inputs should be through a parametric study of potentially good inputs. Input parameters tested were ambient temperature, total electrical load, the day of the week, humidity, dew point temperature, daylight savings time, length of daylight, season, forecast light index and forecast wind velocity. For testing, a limited number of temperatures and total electrical loads were used as a basic reference input parameter set. Most parameters showed some forecasting improvement when added individually to the basic parameter set. Significantly, major improvements were exhibited with the day of the week, dew point temperatures, additional temperatures and loads, forecast light index and forecast wind velocity.

Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts.

PubMed

Xu, Lijing; Furlotte, Nicholas; Lin, Yunyue; Heinrich, Kevin; Berry, Michael W; George, Ebenezer O; Homayouni, Ramin

2011-04-14

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. GCAT is freely available at http://binf1.memphis.edu/gcat.

Quantitative system drift compensates for altered maternal inputs to the gap gene network of the scuttle fly Megaselia abdita

PubMed Central

Wotton, Karl R; Jiménez-Guri, Eva; Crombach, Anton; Janssens, Hilde; Alcaine-Colet, Anna; Lemke, Steffen; Schmidt-Ott, Urs; Jaeger, Johannes

2015-01-01

The segmentation gene network in insects can produce equivalent phenotypic outputs despite differences in upstream regulatory inputs between species. We investigate the mechanistic basis of this phenomenon through a systems-level analysis of the gap gene network in the scuttle fly Megaselia abdita (Phoridae). It combines quantification of gene expression at high spatio-temporal resolution with systematic knock-downs by RNA interference (RNAi). Initiation and dynamics of gap gene expression differ markedly between M. abdita and Drosophila melanogaster, while the output of the system converges to equivalent patterns at the end of the blastoderm stage. Although the qualitative structure of the gap gene network is conserved, there are differences in the strength of regulatory interactions between species. We term such network rewiring ‘quantitative system drift’. It provides a mechanistic explanation for the developmental hourglass model in the dipteran lineage. Quantitative system drift is likely to be a widespread mechanism for developmental evolution. DOI: http://dx.doi.org/10.7554/eLife.04785.001 PMID:25560971

Processing Oscillatory Signals by Incoherent Feedforward Loops

PubMed Central

Zhang, Carolyn; You, Lingchong

2016-01-01

From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop (IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input. This property serves as the foundation for distinguishing input signals with varying temporal profiles. Here, we use quantitative modeling to examine another property of IFFLs—the ability to process oscillatory signals. Our results indicate that the system’s ability to translate pulsatile dynamics is limited by two constraints. The kinetics of the IFFL components dictate the input range for which the network is able to decode pulsatile dynamics. In addition, a match between the network parameters and input signal characteristics is required for optimal “counting”. We elucidate one potential mechanism by which information processing occurs in natural networks, and our work has implications in the design of synthetic gene circuits for this purpose. PMID:27623175

Testing an Instructional Model in a University Educational Setting from the Student's Perspective

ERIC Educational Resources Information Center

Betoret, Fernando Domenech

2006-01-01

We tested a theoretical model that hypothesized relationships between several variables from input, process and product in an educational setting, from the university student's perspective, using structural equation modeling. In order to carry out the analysis, we measured in sequential order the input (referring to students' personal…

Integrating Public Input into Healthcare Priority-Setting Decisions

ERIC Educational Resources Information Center

Mitton, Craig; Smith, Neale; Peacock, Stuart; Evoy, Brian; Abelson, Julia

2011-01-01

Decision makers are pressed to involve the public in priority setting. However, public input is only one form of evidence. So, how can information from the public be combined with other knowledge? The authors qualitatively analysed articles that explicitly address this question. We identified the other forms of information that tend to be used in…

Profiling of potential driver mutations in sarcomas by targeted next generation sequencing.

PubMed

Andersson, Carola; Fagman, Henrik; Hansson, Magnus; Enlund, Fredrik

2016-04-01

Comprehensive genetic profiling by massively parallel sequencing, commonly known as next generation sequencing (NGS), is becoming the foundation of personalized oncology. For sarcomas very few targeted treatments are currently in routine use. In clinical practice the preoperative diagnostic workup of soft tissue tumours largely relies on core needle biopsies. Although mostly sufficient for histopathological diagnosis, only very limited amounts of formalin fixated paraffin embedded tissue are often available for predictive mutation analysis. Targeted NGS may thus open up new possibilities for comprehensive characterization of scarce biopsies. We therefore set out to search for driver mutations by NGS in a cohort of 55 clinically and morphologically well characterized sarcomas using low input of DNA from formalin fixated paraffin embedded tissues. The aim was to investigate if there are any recurrent or targetable aberrations in cancer driver genes in addition to known chromosome translocations in different types of sarcomas. We employed a panel covering 207 mutation hotspots in 50 cancer-associated genes to analyse DNA from nine gastrointestinal stromal tumours, 14 synovial sarcomas, seven myxoid liposarcomas, 22 Ewing sarcomas and three Ewing-like small round cell tumours at a large sequencing depth to detect also mutations that are subclonal or occur at low allele frequencies. We found nine mutations in eight different potential driver genes, some of which are potentially actionable by currently existing targeted therapies. Even though no recurrent mutations in driver genes were found in the different sarcoma groups, we show that targeted NGS-based sequencing is clearly feasible in a diagnostic setting with very limited amounts of paraffin embedded tissue and may provide novel insights into mesenchymal cell signalling and potentially druggable targets. Interestingly, we also identify five non-synonymous sequence variants in 4 established cancer driver genes in DNA from normal tissue from sarcoma patients that may possibly predispose or contribute to neoplastic development. Copyright © 2016 Elsevier Inc. All rights reserved.

Comparing fixed and variable-width Gaussian networks.

PubMed

Kůrková, Věra; Kainen, Paul C

2014-09-01

The role of width of Gaussians in two types of computational models is investigated: Gaussian radial-basis-functions (RBFs) where both widths and centers vary and Gaussian kernel networks which have fixed widths but varying centers. The effect of width on functional equivalence, universal approximation property, and form of norms in reproducing kernel Hilbert spaces (RKHS) is explored. It is proven that if two Gaussian RBF networks have the same input-output functions, then they must have the same numbers of units with the same centers and widths. Further, it is shown that while sets of input-output functions of Gaussian kernel networks with two different widths are disjoint, each such set is large enough to be a universal approximator. Embedding of RKHSs induced by "flatter" Gaussians into RKHSs induced by "sharper" Gaussians is described and growth of the ratios of norms on these spaces with increasing input dimension is estimated. Finally, large sets of argminima of error functionals in sets of input-output functions of Gaussian RBFs are described. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Gene Set Builder: collation, curation, and distribution of sets of genes

PubMed Central

Yusuf, Dimas; Lim, Jonathan S; Wasserman, Wyeth W

2005-01-01

Background In bioinformatics and genomics, there are many applications designed to investigate the common properties for a set of genes. Often, these multi-gene analysis tools attempt to reveal sequential, functional, and expressional ties. However, while tremendous effort has been invested in developing tools that can analyze a set of genes, minimal effort has been invested in developing tools that can help researchers compile, store, and annotate gene sets in the first place. As a result, the process of making or accessing a set often involves tedious and time consuming steps such as finding identifiers for each individual gene. These steps are often repeated extensively to shift from one identifier type to another; or to recreate a published set. In this paper, we present a simple online tool which – with the help of the gene catalogs Ensembl and GeneLynx – can help researchers build and annotate sets of genes quickly and easily. Description The Gene Set Builder is a database-driven, web-based tool designed to help researchers compile, store, export, and share sets of genes. This application supports the 17 eukaryotic genomes found in version 32 of the Ensembl database, which includes species from yeast to human. User-created information such as sets and customized annotations are stored to facilitate easy access. Gene sets stored in the system can be "exported" in a variety of output formats – as lists of identifiers, in tables, or as sequences. In addition, gene sets can be "shared" with specific users to facilitate collaborations or fully released to provide access to published results. The application also features a Perl API (Application Programming Interface) for direct connectivity to custom analysis tools. A downloadable Quick Reference guide and an online tutorial are available to help new users learn its functionalities. Conclusion The Gene Set Builder is an Ensembl-facilitated online tool designed to help researchers compile and manage sets of genes in a user-friendly environment. The application can be accessed via . PMID:16371163

Current Source Logic Gate

NASA Technical Reports Server (NTRS)

Krasowski, Michael J. (Inventor); Prokop, Norman F. (Inventor)

2017-01-01

A current source logic gate with depletion mode field effect transistor ("FET") transistors and resistors may include a current source, a current steering switch input stage, and a resistor divider level shifting output stage. The current source may include a transistor and a current source resistor. The current steering switch input stage may include a transistor to steer current to set an output stage bias point depending on an input logic signal state. The resistor divider level shifting output stage may include a first resistor and a second resistor to set the output stage point and produce valid output logic signal states. The transistor of the current steering switch input stage may function as a switch to provide at least two operating points.

Spectral gene set enrichment (SGSE).

PubMed

Frost, H Robert; Li, Zhigang; Moore, Jason H

2015-03-03

Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracy-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Unsupervised gene set testing can provide important information about the biological signal held in high-dimensional genomic data sets. Because it uses the association between gene sets and samples PCs to generate a measure of unsupervised enrichment, the SGSE method is independent of cluster or network creation algorithms and, most importantly, is able to utilize the statistical significance of PC eigenvalues to ignore elements of the data most likely to represent noise.

SiBIC: a web server for generating gene set networks based on biclusters obtained by maximal frequent itemset mining.

PubMed

Takahashi, Kei-ichiro; Takigawa, Ichigaku; Mamitsuka, Hiroshi

2013-01-01

Detecting biclusters from expression data is useful, since biclusters are coexpressed genes under only part of all given experimental conditions. We present a software called SiBIC, which from a given expression dataset, first exhaustively enumerates biclusters, which are then merged into rather independent biclusters, which finally are used to generate gene set networks, in which a gene set assigned to one node has coexpressed genes. We evaluated each step of this procedure: 1) significance of the generated biclusters biologically and statistically, 2) biological quality of merged biclusters, and 3) biological significance of gene set networks. We emphasize that gene set networks, in which nodes are not genes but gene sets, can be more compact than usual gene networks, meaning that gene set networks are more comprehensible. SiBIC is available at http://utrecht.kuicr.kyoto-u.ac.jp:8080/miami/faces/index.jsp.

40 CFR 96.76 - Additional requirements to provide heat input data for allocations purposes.

Code of Federal Regulations, 2011 CFR

2011-07-01

... heat input data for allocations purposes. 96.76 Section 96.76 Protection of Environment ENVIRONMENTAL... to provide heat input data for allocations purposes. (a) The owner or operator of a unit that elects... also monitor and report heat input at the unit level using the procedures set forth in part 75 of this...

40 CFR 96.76 - Additional requirements to provide heat input data for allocations purposes.

Code of Federal Regulations, 2013 CFR

2013-07-01

... heat input data for allocations purposes. 96.76 Section 96.76 Protection of Environment ENVIRONMENTAL... to provide heat input data for allocations purposes. (a) The owner or operator of a unit that elects... also monitor and report heat input at the unit level using the procedures set forth in part 75 of this...

40 CFR 96.76 - Additional requirements to provide heat input data for allocations purposes.

Code of Federal Regulations, 2012 CFR

2012-07-01

... heat input data for allocations purposes. 96.76 Section 96.76 Protection of Environment ENVIRONMENTAL... to provide heat input data for allocations purposes. (a) The owner or operator of a unit that elects... also monitor and report heat input at the unit level using the procedures set forth in part 75 of this...

40 CFR 96.76 - Additional requirements to provide heat input data for allocations purposes.

Code of Federal Regulations, 2014 CFR

2014-07-01

... heat input data for allocations purposes. 96.76 Section 96.76 Protection of Environment ENVIRONMENTAL... to provide heat input data for allocations purposes. (a) The owner or operator of a unit that elects... also monitor and report heat input at the unit level using the procedures set forth in part 75 of this...

Why does parental language input style predict child language development? A twin study of gene-environment correlation.

PubMed

Dale, Philip S; Tosto, Maria Grazia; Hayiou-Thomas, Marianna E; Plomin, Robert

2015-01-01

There are well-established correlations between parental input style and child language development, which have typically been interpreted as evidence that the input style causes, or influences the rate of, changes in child language. We present evidence from a large twin study (TEDS; 8395 pairs for this report) that there are also likely to be both child-to-parent effects and shared genetic effects on parent and child. Self-reported parental language style at child age 3 and age 4 was aggregated into an 'informal language stimulation' factor and a 'corrective feedback' factor at each age; the former was positively correlated with child language concurrently and longitudinally at 3, 4, and 4.5 years, whereas the latter was weakly and negatively correlated. Both parental input factors were moderately heritable, as was child language. Longitudinal bivariate analysis showed that the correlation between the language stimulation factor and child language was significantly and moderately due to shared genes. There is some suggestive evidence from longitudinal phenotypic analysis that the prediction from parental language stimulation to child language includes both evocative and passive gene-environment correlation, with the latter playing a larger role. The reader will understand why correlations between parental language and rate of child language are by themselves ambiguous, and how twin studies can clarify the relationship. The reader will also understand that, based on the present study, at least two aspects of parental language style - informal language stimulation and corrective feedback - have substantial genetic influence, and that for informal language stimulation, a substantial portion of the prediction to child language represents the effect of shared genes on both parent and child. It will also be appreciated that these basic research findings do not imply that parental language input style is unimportant or that interventions cannot be effective. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

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

Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu

Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT,more » status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.« less

Effect of the absolute statistic on gene-sampling gene-set analysis methods.

PubMed

Nam, Dougu

2017-06-01

Gene-set enrichment analysis and its modified versions have commonly been used for identifying altered functions or pathways in disease from microarray data. In particular, the simple gene-sampling gene-set analysis methods have been heavily used for datasets with only a few sample replicates. The biggest problem with this approach is the highly inflated false-positive rate. In this paper, the effect of absolute gene statistic on gene-sampling gene-set analysis methods is systematically investigated. Thus far, the absolute gene statistic has merely been regarded as a supplementary method for capturing the bidirectional changes in each gene set. Here, it is shown that incorporating the absolute gene statistic in gene-sampling gene-set analysis substantially reduces the false-positive rate and improves the overall discriminatory ability. Its effect was investigated by power, false-positive rate, and receiver operating curve for a number of simulated and real datasets. The performances of gene-set analysis methods in one-tailed (genome-wide association study) and two-tailed (gene expression data) tests were also compared and discussed.

Timing discriminator using leading-edge extrapolation

DOEpatents

Gottschalk, B.

1981-07-30

A discriminator circuit to recover timing information from slow-rising pulses by means of an output trailing edge, a fixed time after the starting corner of the input pulse, which is nearly independent of risetime and threshold setting is described. This apparatus comprises means for comparing pulses with a threshold voltage; a capacitor to be charged at a certain rate when the input signal is one-third threshold voltage, and at a lower rate when the input signal is two-thirds threshold voltage; current-generating means for charging the capacitor; means for comparing voltage capacitor with a bias voltage; a flip-flop to be set when the input pulse reaches threshold voltage and reset when capacitor voltage reaches the bias voltage; and a clamping means for discharging the capacitor when the input signal returns below one-third threshold voltage.

Preferences in Data Production Planning

NASA Technical Reports Server (NTRS)

Golden, Keith; Brafman, Ronen; Pang, Wanlin

2005-01-01

This paper discusses the data production problem, which consists of transforming a set of (initial) input data into a set of (goal) output data. There are typically many choices among input data and processing algorithms, each leading to significantly different end products. To discriminate among these choices, the planner supports an input language that provides a number of constructs for specifying user preferences over data (and plan) properties. We discuss these preference constructs, how we handle them to guide search, and additional challenges in the area of preference management that this important application domain offers.

The limitations of simple gene set enrichment analysis assuming gene independence.

PubMed

Tamayo, Pablo; Steinhardt, George; Liberzon, Arthur; Mesirov, Jill P

2016-02-01

Since its first publication in 2003, the Gene Set Enrichment Analysis method, based on the Kolmogorov-Smirnov statistic, has been heavily used, modified, and also questioned. Recently a simplified approach using a one-sample t-test score to assess enrichment and ignoring gene-gene correlations was proposed by Irizarry et al. 2009 as a serious contender. The argument criticizes Gene Set Enrichment Analysis's nonparametric nature and its use of an empirical null distribution as unnecessary and hard to compute. We refute these claims by careful consideration of the assumptions of the simplified method and its results, including a comparison with Gene Set Enrichment Analysis's on a large benchmark set of 50 datasets. Our results provide strong empirical evidence that gene-gene correlations cannot be ignored due to the significant variance inflation they produced on the enrichment scores and should be taken into account when estimating gene set enrichment significance. In addition, we discuss the challenges that the complex correlation structure and multi-modality of gene sets pose more generally for gene set enrichment methods. © The Author(s) 2012.

Regularizing Unpredictable Variation: Evidence from a Natural Language Setting

ERIC Educational Resources Information Center

Hendricks, Alison Eisel; Miller, Karen; Jackson, Carrie N.

2018-01-01

While previous sociolinguistic research has demonstrated that children faithfully acquire probabilistic input constrained by sociolinguistic and linguistic factors (e.g., gender and socioeconomic status), research suggests children regularize inconsistent input-probabilistic input that is not sociolinguistically constrained (e.g., Hudson Kam &…

scEpath: Energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data.

PubMed

Jin, Suoqin; MacLean, Adam L; Peng, Tao; Nie, Qing

2018-02-05

Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data. Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using "single-cell energy" and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are - in combination - more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates. A MATLAB package of scEpath is available at https://github.com/sqjin/scEpath. qnie@uci.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2018. Published by Oxford University Press.

Tissue Non-Specific Genes and Pathways Associated with Diabetes: An Expression Meta-Analysis.

PubMed

Mei, Hao; Li, Lianna; Liu, Shijian; Jiang, Fan; Griswold, Michael; Mosley, Thomas

2017-01-21

We performed expression studies to identify tissue non-specific genes and pathways of diabetes by meta-analysis. We searched curated datasets of the Gene Expression Omnibus (GEO) database and identified 13 and five expression studies of diabetes and insulin responses at various tissues, respectively. We tested differential gene expression by empirical Bayes-based linear method and investigated gene set expression association by knowledge-based enrichment analysis. Meta-analysis by different methods was applied to identify tissue non-specific genes and gene sets. We also proposed pathway mapping analysis to infer functions of the identified gene sets, and correlation and independent analysis to evaluate expression association profile of genes and gene sets between studies and tissues. Our analysis showed that PGRMC1 and HADH genes were significant over diabetes studies, while IRS1 and MPST genes were significant over insulin response studies, and joint analysis showed that HADH and MPST genes were significant over all combined data sets. The pathway analysis identified six significant gene sets over all studies. The KEGG pathway mapping indicated that the significant gene sets are related to diabetes pathogenesis. The results also presented that 12.8% and 59.0% pairwise studies had significantly correlated expression association for genes and gene sets, respectively; moreover, 12.8% pairwise studies had independent expression association for genes, but no studies were observed significantly different for expression association of gene sets. Our analysis indicated that there are both tissue specific and non-specific genes and pathways associated with diabetes pathogenesis. Compared to the gene expression, pathway association tends to be tissue non-specific, and a common pathway influencing diabetes development is activated through different genes at different tissues.

Reverse engineering gene regulatory networks from measurement with missing values.

PubMed

Ogundijo, Oyetunji E; Elmas, Abdulkadir; Wang, Xiaodong

2016-12-01

Gene expression time series data are usually in the form of high-dimensional arrays. Unfortunately, the data may sometimes contain missing values: for either the expression values of some genes at some time points or the entire expression values of a single time point or some sets of consecutive time points. This significantly affects the performance of many algorithms for gene expression analysis that take as an input, the complete matrix of gene expression measurement. For instance, previous works have shown that gene regulatory interactions can be estimated from the complete matrix of gene expression measurement. Yet, till date, few algorithms have been proposed for the inference of gene regulatory network from gene expression data with missing values. We describe a nonlinear dynamic stochastic model for the evolution of gene expression. The model captures the structural, dynamical, and the nonlinear natures of the underlying biomolecular systems. We present point-based Gaussian approximation (PBGA) filters for joint state and parameter estimation of the system with one-step or two-step missing measurements . The PBGA filters use Gaussian approximation and various quadrature rules, such as the unscented transform (UT), the third-degree cubature rule and the central difference rule for computing the related posteriors. The proposed algorithm is evaluated with satisfying results for synthetic networks, in silico networks released as a part of the DREAM project, and the real biological network, the in vivo reverse engineering and modeling assessment (IRMA) network of yeast Saccharomyces cerevisiae . PBGA filters are proposed to elucidate the underlying gene regulatory network (GRN) from time series gene expression data that contain missing values. In our state-space model, we proposed a measurement model that incorporates the effect of the missing data points into the sequential algorithm. This approach produces a better inference of the model parameters and hence, more accurate prediction of the underlying GRN compared to when using the conventional Gaussian approximation (GA) filters ignoring the missing data points.

Polynomial-Time Algorithms for Building a Consensus MUL-Tree

PubMed Central

Cui, Yun; Jansson, Jesper

2012-01-01

Abstract A multi-labeled phylogenetic tree, or MUL-tree, is a generalization of a phylogenetic tree that allows each leaf label to be used many times. MUL-trees have applications in biogeography, the study of host–parasite cospeciation, gene evolution studies, and computer science. Here, we consider the problem of inferring a consensus MUL-tree that summarizes a given set of conflicting MUL-trees, and present the first polynomial-time algorithms for solving it. In particular, we give a straightforward, fast algorithm for building a strict consensus MUL-tree for any input set of MUL-trees with identical leaf label multisets, as well as a polynomial-time algorithm for building a majority rule consensus MUL-tree for the special case where every leaf label occurs at most twice. We also show that, although it is NP-hard to find a majority rule consensus MUL-tree in general, the variant that we call the singular majority rule consensus MUL-tree can be constructed efficiently whenever it exists. PMID:22963134

Polynomial-time algorithms for building a consensus MUL-tree.

PubMed

Cui, Yun; Jansson, Jesper; Sung, Wing-Kin

2012-09-01

A multi-labeled phylogenetic tree, or MUL-tree, is a generalization of a phylogenetic tree that allows each leaf label to be used many times. MUL-trees have applications in biogeography, the study of host-parasite cospeciation, gene evolution studies, and computer science. Here, we consider the problem of inferring a consensus MUL-tree that summarizes a given set of conflicting MUL-trees, and present the first polynomial-time algorithms for solving it. In particular, we give a straightforward, fast algorithm for building a strict consensus MUL-tree for any input set of MUL-trees with identical leaf label multisets, as well as a polynomial-time algorithm for building a majority rule consensus MUL-tree for the special case where every leaf label occurs at most twice. We also show that, although it is NP-hard to find a majority rule consensus MUL-tree in general, the variant that we call the singular majority rule consensus MUL-tree can be constructed efficiently whenever it exists.

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

Kapuscinski, A.R.; Hallerman, E.M.

Among the many methodologies encompassing biotechnology in aquaculture, this report addresses: the production of genetically modified aquatic organisms (aquatic GMOs) by gene transfer, chromosome set manipulation, or hybridization or protoplast fusion between species; new health management tools, including DNA-Based diagnostics and recombinant DNA vaccines; Marker-assisted selection; cryopreservation; and stock marking. These methodologies pose a wide range of potential economic benefits for aquaculture by providing improved or new means to affect the mix of necessary material inputs, enhance production efficiency, or improve product quality. Advances in aquaculture through biotechnology could simulate growth of the aquaculture industry to provide a larger proportionmore » of consummer demand, and thereby reduce pressure and natural stocks from over-harvest. Judicious application of gamete cryopreservation and chromosome set manipulations to achieve sterilization could reduce environmental risks of some aquaculture operations. Given the significant losses to disease in many aquaculture enterprises, potential benefits of DNA-based health management tools are very high and appear to pose no major environmental risks or social concerns.« less

WebMOTIFS: automated discovery, filtering and scoring of DNA sequence motifs using multiple programs and Bayesian approaches

PubMed Central

Romer, Katherine A.; Kayombya, Guy-Richard; Fraenkel, Ernest

2007-01-01

WebMOTIFS provides a web interface that facilitates the discovery and analysis of DNA-sequence motifs. Several studies have shown that the accuracy of motif discovery can be significantly improved by using multiple de novo motif discovery programs and using randomized control calculations to identify the most significant motifs or by using Bayesian approaches. WebMOTIFS makes it easy to apply these strategies. Using a single submission form, users can run several motif discovery programs and score, cluster and visualize the results. In addition, the Bayesian motif discovery program THEME can be used to determine the class of transcription factors that is most likely to regulate a set of sequences. Input can be provided as a list of gene or probe identifiers. Used with the default settings, WebMOTIFS accurately identifies biologically relevant motifs from diverse data in several species. WebMOTIFS is freely available at http://fraenkel.mit.edu/webmotifs. PMID:17584794

Open set recognition of aircraft in aerial imagery using synthetic template models

NASA Astrophysics Data System (ADS)

Bapst, Aleksander B.; Tran, Jonathan; Koch, Mark W.; Moya, Mary M.; Swahn, Robert

2017-05-01

Fast, accurate and robust automatic target recognition (ATR) in optical aerial imagery can provide game-changing advantages to military commanders and personnel. ATR algorithms must reject non-targets with a high degree of confidence in a world with an infinite number of possible input images. Furthermore, they must learn to recognize new targets without requiring massive data collections. Whereas most machine learning algorithms classify data in a closed set manner by mapping inputs to a fixed set of training classes, open set recognizers incorporate constraints that allow for inputs to be labelled as unknown. We have adapted two template-based open set recognizers to use computer generated synthetic images of military aircraft as training data, to provide a baseline for military-grade ATR: (1) a frequentist approach based on probabilistic fusion of extracted image features, and (2) an open set extension to the one-class support vector machine (SVM). These algorithms both use histograms of oriented gradients (HOG) as features as well as artificial augmentation of both real and synthetic image chips to take advantage of minimal training data. Our results show that open set recognizers trained with synthetic data and tested with real data can successfully discriminate real target inputs from non-targets. However, there is still a requirement for some knowledge of the real target in order to calibrate the relationship between synthetic template and target score distributions. We conclude by proposing algorithm modifications that may improve the ability of synthetic data to represent real data.

Quantifying the Effect of DNA Packaging on Gene Expression Level

NASA Astrophysics Data System (ADS)

Kim, Harold

2010-10-01

Gene expression, the process by which the genetic code comes alive in the form of proteins, is one of the most important biological processes in living cells, and begins when transcription factors bind to specific DNA sequences in the promoter region upstream of a gene. The relationship between gene expression output and transcription factor input which is termed the gene regulation function is specific to each promoter, and predicting this gene regulation function from the locations of transcription factor binding sites is one of the challenges in biology. In eukaryotic organisms (for example, animals, plants, fungi etc), DNA is highly compacted into nucleosomes, 147-bp segments of DNA tightly wrapped around histone protein core, and therefore, the accessibility of transcription factor binding sites depends on their locations with respect to nucleosomes - sites inside nucleosomes are less accessible than those outside nucleosomes. To understand how transcription factor binding sites contribute to gene expression in a quantitative manner, we obtain gene regulation functions of promoters with various configurations of transcription factor binding sites by using fluorescent protein reporters to measure transcription factor input and gene expression output in single yeast cells. In this talk, I will show that the affinity of a transcription factor binding site inside and outside the nucleosome controls different aspects of the gene regulation function, and explain this finding based on a mass-action kinetic model that includes competition between nucleosomes and transcription factors.

Imer-product array processor for retrieval of stored images represented by bipolar binary (+1,-1) pixels using partial input trinary pixels represented by (+1,-1)

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Awwal, Abdul A. S. (Inventor); Karim, Mohammad A. (Inventor)

1993-01-01

An inner-product array processor is provided with thresholding of the inner product during each iteration to make more significant the inner product employed in estimating a vector to be used as the input vector for the next iteration. While stored vectors and estimated vectors are represented in bipolar binary (1,-1), only those elements of an initial partial input vector that are believed to be common with those of a stored vector are represented in bipolar binary; the remaining elements of a partial input vector are set to 0. This mode of representation, in which the known elements of a partial input vector are in bipolar binary form and the remaining elements are set equal to 0, is referred to as trinary representation. The initial inner products corresponding to the partial input vector will then be equal to the number of known elements. Inner-product thresholding is applied to accelerate convergence and to avoid convergence to a negative input product.

Multiplexed color-coded probe-based gene expression assessment for clinical molecular diagnostics in formalin-fixed paraffin-embedded human renal allograft tissue.

PubMed

Adam, Benjamin; Afzali, Bahman; Dominy, Katherine M; Chapman, Erin; Gill, Reeda; Hidalgo, Luis G; Roufosse, Candice; Sis, Banu; Mengel, Michael

2016-03-01

Histopathologic diagnoses in transplantation can be improved with molecular testing. Preferably, molecular diagnostics should fit into standard-of-care workflows for transplant biopsies, that is, formalin-fixed paraffin-embedded (FFPE) processing. The NanoString(®) gene expression platform has recently been shown to work with FFPE samples. We aimed to evaluate its methodological robustness and feasibility for gene expression studies in human FFPE renal allograft samples. A literature-derived antibody-mediated rejection (ABMR) 34-gene set, comprised of endothelial, NK cell, and inflammation transcripts, was analyzed in different retrospective biopsy cohorts and showed potential to molecularly discriminate ABMR cases, including FFPE samples. NanoString(®) results were reproducible across a range of RNA input quantities (r = 0.998), with different operators (r = 0.998), and between different reagent lots (r = 0.983). There was moderate correlation between NanoString(®) with FFPE tissue and quantitative reverse transcription polymerase chain reaction (qRT-PCR) with corresponding dedicated fresh-stabilized tissue (r = 0.487). Better overall correlation with histology was observed with NanoString(®) (r = 0.354) than with qRT-PCR (r = 0.146). Our results demonstrate the feasibility of multiplexed gene expression quantification from FFPE renal allograft tissue. This represents a method for prospective and retrospective validation of molecular diagnostics and its adoption in clinical transplantation pathology. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Integration of RAM-SCB into the Space Weather Modeling Framework

DOE PAGES

Welling, Daniel; Toth, Gabor; Jordanova, Vania Koleva; ...

2018-02-07

We present that numerical simulations of the ring current are a challenging endeavor. They require a large set of inputs, including electric and magnetic fields and plasma sheet fluxes. Because the ring current broadly affects the magnetosphere-ionosphere system, the input set is dependent on the ring current region itself. This makes obtaining a set of inputs that are self-consistent with the ring current difficult. To overcome this challenge, researchers have begun coupling ring current models to global models of the magnetosphere-ionosphere system. This paper describes the coupling between the Ring current Atmosphere interaction Model with Self-Consistent Magnetic field (RAM-SCB) tomore » the models within the Space Weather Modeling Framework. Full details on both previously introduced and new coupling mechanisms are defined. Finally, the impact of self-consistently including the ring current on the magnetosphere-ionosphere system is illustrated via a set of example simulations.« less

Integration of RAM-SCB into the Space Weather Modeling Framework

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

Welling, Daniel; Toth, Gabor; Jordanova, Vania Koleva

We present that numerical simulations of the ring current are a challenging endeavor. They require a large set of inputs, including electric and magnetic fields and plasma sheet fluxes. Because the ring current broadly affects the magnetosphere-ionosphere system, the input set is dependent on the ring current region itself. This makes obtaining a set of inputs that are self-consistent with the ring current difficult. To overcome this challenge, researchers have begun coupling ring current models to global models of the magnetosphere-ionosphere system. This paper describes the coupling between the Ring current Atmosphere interaction Model with Self-Consistent Magnetic field (RAM-SCB) tomore » the models within the Space Weather Modeling Framework. Full details on both previously introduced and new coupling mechanisms are defined. Finally, the impact of self-consistently including the ring current on the magnetosphere-ionosphere system is illustrated via a set of example simulations.« less

Influential input classification in probabilistic multimedia models

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

Maddalena, Randy L.; McKone, Thomas E.; Hsieh, Dennis P.H.

1999-05-01

Monte Carlo analysis is a statistical simulation method that is often used to assess and quantify the outcome variance in complex environmental fate and effects models. Total outcome variance of these models is a function of (1) the uncertainty and/or variability associated with each model input and (2) the sensitivity of the model outcome to changes in the inputs. To propagate variance through a model using Monte Carlo techniques, each variable must be assigned a probability distribution. The validity of these distributions directly influences the accuracy and reliability of the model outcome. To efficiently allocate resources for constructing distributions onemore » should first identify the most influential set of variables in the model. Although existing sensitivity and uncertainty analysis methods can provide a relative ranking of the importance of model inputs, they fail to identify the minimum set of stochastic inputs necessary to sufficiently characterize the outcome variance. In this paper, we describe and demonstrate a novel sensitivity/uncertainty analysis method for assessing the importance of each variable in a multimedia environmental fate model. Our analyses show that for a given scenario, a relatively small number of input variables influence the central tendency of the model and an even smaller set determines the shape of the outcome distribution. For each input, the level of influence depends on the scenario under consideration. This information is useful for developing site specific models and improving our understanding of the processes that have the greatest influence on the variance in outcomes from multimedia models.« less

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

PubMed Central

Hejblum, Boris P.; Skinner, Jason; Thiébaut, Rodolphe

2015-01-01

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package. PMID:26111374

Puerto Rico water resources planning model program description

USGS Publications Warehouse

Moody, D.W.; Maddock, Thomas; Karlinger, M.R.; Lloyd, J.J.

1973-01-01

Because the use of the Mathematical Programming System -Extended (MPSX) to solve large linear and mixed integer programs requires the preparation of many input data cards, a matrix generator program to produce the MPSX input data from a much more limited set of data may expedite the use of the mixed integer programming optimization technique. The Model Definition and Control Program (MODCQP) is intended to assist a planner in preparing MPSX input data for the Puerto Rico Water Resources Planning Model. The model utilizes a mixed-integer mathematical program to identify a minimum present cost set of water resources projects (diversions, reservoirs, ground-water fields, desalinization plants, water treatment plants, and inter-basin transfers of water) which will meet a set of future water demands and to determine their sequence of construction. While MODCOP was specifically written to generate MPSX input data for the planning model described in this report, the program can be easily modified to reflect changes in the model's mathematical structure.

Generalization of some hidden subgroup algorithms for input sets of arbitrary size

NASA Astrophysics Data System (ADS)

Poslu, Damla; Say, A. C. Cem

2006-05-01

We consider the problem of generalizing some quantum algorithms so that they will work on input domains whose cardinalities are not necessarily powers of two. When analyzing the algorithms we assume that generating superpositions of arbitrary subsets of basis states whose cardinalities are not necessarily powers of two perfectly is possible. We have taken Ballhysa's model as a template and have extended it to Chi, Kim and Lee's generalizations of the Deutsch-Jozsa algorithm and to Simon's algorithm. With perfectly equal superpositions of input sets of arbitrary size, Chi, Kim and Lee's generalized Deutsch-Jozsa algorithms, both for evenly-distributed and evenly-balanced functions, worked with one-sided error property. For Simon's algorithm the success probability of the generalized algorithm is the same as that of the original for input sets of arbitrary cardinalities with equiprobable superpositions, since the property that the measured strings are all those which have dot product zero with the string we search, for the case where the function is 2-to-1, is not lost.

Effect of UVA/LED/TiO2 photocatalysis treated sulfamethoxazole and trimethoprim containing wastewater on antibiotic resistance development in sequencing batch reactors.

PubMed

Cai, Qinqing; Hu, Jiangyong

2018-04-24

Controlling of antibiotics is the crucial step for preventing antibiotic resistance genes (ARGs) dissemination; UV photocatalysis has been identified as a promising pre-treatment technology for antibiotics removal. However, information about the effects of intermediates present in the treated antibiotics wastewater on the downstream biological treatment processes or ARGs development is very limited. In the present study, continuous UVA/LED/TiO 2 photocatalysis removed more than 90% of 100 ppb sulfamethoxazole (SMX)/trimethoprim (TMP), the treated wastewater was fed into SBR systems for over one year monitoring. Residual SMX/TMP (2-3 ppb) and intermediates present in the treated wastewater did not adversely affect SBR performance in terms of TOC and TN removal. SMX and TMP resistance genes (sulI, sulII, sulIII, dfrII, dfrV and dfr13) were also quantified in SBRs microbial consortia. Results suggested that continuous feeding of treated SMX/TMP containing wastewaters did not trigger any ARGs promotion during the one year operation. By stopping the input of 100 ppb SMX/TMP, abundance of sulII and dfrV genes were reduced by 83% and 100%, respectively. sulI gene was identified as the most persistence ARG, and controlling of 100 ppb SMX input did not achieve significant removal of sulI gene. A significant correlation between sulI gene and class 1 integrons was found at the level of p = 1.4E-10 (r = 0.94), and sulII gene positively correlated with the plasmid transfer efficiency (r = 2.442E-10, r = 0.87). Continuous input of 100 ppb SMX enhanced plasmid transfer efficiency in the SBR system, resulting in sulII gene abundance increasing more than 40 times. Copyright © 2018 Elsevier Ltd. All rights reserved.

Building accurate historic and future climate MEPDG input files for Louisiana DOTD : tech summary.

DOT National Transportation Integrated Search

2017-02-01

The new pavement design process (originally MEPDG, then DARWin-ME, and now Pavement ME Design) requires two types : of inputs to infl uence the prediction of pavement distress for a selected set of pavement materials and structure. One input is : tra...

snpGeneSets: An R Package for Genome-Wide Study Annotation

PubMed Central

Mei, Hao; Li, Lianna; Jiang, Fan; Simino, Jeannette; Griswold, Michael; Mosley, Thomas; Liu, Shijian

2016-01-01

Genome-wide studies (GWS) of SNP associations and differential gene expressions have generated abundant results; next-generation sequencing technology has further boosted the number of variants and genes identified. Effective interpretation requires massive annotation and downstream analysis of these genome-wide results, a computationally challenging task. We developed the snpGeneSets package to simplify annotation and analysis of GWS results. Our package integrates local copies of knowledge bases for SNPs, genes, and gene sets, and implements wrapper functions in the R language to enable transparent access to low-level databases for efficient annotation of large genomic data. The package contains functions that execute three types of annotations: (1) genomic mapping annotation for SNPs and genes and functional annotation for gene sets; (2) bidirectional mapping between SNPs and genes, and genes and gene sets; and (3) calculation of gene effect measures from SNP associations and performance of gene set enrichment analyses to identify functional pathways. We applied snpGeneSets to type 2 diabetes (T2D) results from the NHGRI genome-wide association study (GWAS) catalog, a Finnish GWAS, and a genome-wide expression study (GWES). These studies demonstrate the usefulness of snpGeneSets for annotating and performing enrichment analysis of GWS results. The package is open-source, free, and can be downloaded at: https://www.umc.edu/biostats_software/. PMID:27807048

Synthetic Biology Platform for Sensing and Integrating Endogenous Transcriptional Inputs in Mammalian Cells.

PubMed

Angelici, Bartolomeo; Mailand, Erik; Haefliger, Benjamin; Benenson, Yaakov

2016-08-30

One of the goals of synthetic biology is to develop programmable artificial gene networks that can transduce multiple endogenous molecular cues to precisely control cell behavior. Realizing this vision requires interfacing natural molecular inputs with synthetic components that generate functional molecular outputs. Interfacing synthetic circuits with endogenous mammalian transcription factors has been particularly difficult. Here, we describe a systematic approach that enables integration and transduction of multiple mammalian transcription factor inputs by a synthetic network. The approach is facilitated by a proportional amplifier sensor based on synergistic positive autoregulation. The circuits efficiently transduce endogenous transcription factor levels into RNAi, transcriptional transactivation, and site-specific recombination. They also enable AND logic between pairs of arbitrary transcription factors. The results establish a framework for developing synthetic gene networks that interface with cellular processes through transcriptional regulators. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Genome-wide expression profiling of maize in response to individual and combined water and nitrogen stresses

PubMed Central

2013-01-01

Background Water and nitrogen are two of the most critical inputs required to achieve the high yield potential of modern corn varieties. Under most agricultural settings however they are often scarce and costly. Fortunately, tremendous progress has been made in the past decades in terms of modeling to assist growers in the decision making process and many tools are now available to achieve more sustainable practices both environmentally and economically. Nevertheless large gaps remain between our empirical knowledge of the physiological changes observed in the field in response to nitrogen and water stresses, and our limited understanding of the molecular processes leading to those changes. Results This work examines in particular the impact of simultaneous stresses on the transcriptome. In a greenhouse setting, corn plants were grown under tightly controlled nitrogen and water conditions, allowing sampling of various tissues and stress combinations. A microarray profiling experiment was performed using this material and showed that the concomitant presence of nitrogen and water limitation affects gene expression to an extent much larger than anticipated. A clustering analysis also revealed how the interaction between the two stresses shapes the patterns of gene expression over various levels of water stresses and recovery. Conclusions Overall, this study suggests that the molecular signature of a specific combination of stresses on the transcriptome might be as unique as the impact of individual stresses, and hence underlines the difficulty to extrapolate conclusions obtained from the study of individual stress responses to more complex settings. PMID:23324127

MAVTgsa: An R Package for Gene Set (Enrichment) Analysis

DOE PAGES

Chien, Chih-Yi; Chang, Ching-Wei; Tsai, Chen-An; ...

2014-01-01

Gene semore » t analysis methods aim to determine whether an a priori defined set of genes shows statistically significant difference in expression on either categorical or continuous outcomes. Although many methods for gene set analysis have been proposed, a systematic analysis tool for identification of different types of gene set significance modules has not been developed previously. This work presents an R package, called MAVTgsa, which includes three different methods for integrated gene set enrichment analysis. (1) The one-sided OLS (ordinary least squares) test detects coordinated changes of genes in gene set in one direction, either up- or downregulation. (2) The two-sided MANOVA (multivariate analysis variance) detects changes both up- and downregulation for studying two or more experimental conditions. (3) A random forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes. MAVTgsa computes the P values and FDR (false discovery rate) q -value for all gene sets in the study. Furthermore, MAVTgsa provides several visualization outputs to support and interpret the enrichment results. This package is available online.« less

Contrasting Effects of Starting Age and Input on the Oral Performance of Foreign Language Learners

ERIC Educational Resources Information Center

Muñoz, Carmen

2014-01-01

The present study focuses on the influence of starting age and input on foreign language learning. In relation to starting age, the study investigates whether early starters in instructional settings achieve the same kind of long-term advantage as learners in naturalistic settings and it complements previous research by using data from oral…

StereoGene: rapid estimation of genome-wide correlation of continuous or interval feature data.

PubMed

Stavrovskaya, Elena D; Niranjan, Tejasvi; Fertig, Elana J; Wheelan, Sarah J; Favorov, Alexander V; Mironov, Andrey A

2017-10-15

Genomics features with similar genome-wide distributions are generally hypothesized to be functionally related, for example, colocalization of histones and transcription start sites indicate chromatin regulation of transcription factor activity. Therefore, statistical algorithms to perform spatial, genome-wide correlation among genomic features are required. Here, we propose a method, StereoGene, that rapidly estimates genome-wide correlation among pairs of genomic features. These features may represent high-throughput data mapped to reference genome or sets of genomic annotations in that reference genome. StereoGene enables correlation of continuous data directly, avoiding the data binarization and subsequent data loss. Correlations are computed among neighboring genomic positions using kernel correlation. Representing the correlation as a function of the genome position, StereoGene outputs the local correlation track as part of the analysis. StereoGene also accounts for confounders such as input DNA by partial correlation. We apply our method to numerous comparisons of ChIP-Seq datasets from the Human Epigenome Atlas and FANTOM CAGE to demonstrate its wide applicability. We observe the changes in the correlation between epigenomic features across developmental trajectories of several tissue types consistent with known biology and find a novel spatial correlation of CAGE clusters with donor splice sites and with poly(A) sites. These analyses provide examples for the broad applicability of StereoGene for regulatory genomics. The StereoGene C ++ source code, program documentation, Galaxy integration scripts and examples are available from the project homepage http://stereogene.bioinf.fbb.msu.ru/. favorov@sensi.org. 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

Stochastic modular analysis for gene circuits: interplay among retroactivity, nonlinearity, and stochasticity.

PubMed

Kim, Kyung Hyuk; Sauro, Herbert M

2015-01-01

This chapter introduces a computational analysis method for analyzing gene circuit dynamics in terms of modules while taking into account stochasticity, system nonlinearity, and retroactivity. (1) ANALOG ELECTRICAL CIRCUIT REPRESENTATION FOR GENE CIRCUITS: A connection between two gene circuit components is often mediated by a transcription factor (TF) and the connection signal is described by the TF concentration. The TF is sequestered to its specific binding site (promoter region) and regulates downstream transcription. This sequestration has been known to affect the dynamics of the TF by increasing its response time. The downstream effect-retroactivity-has been shown to be explicitly described in an electrical circuit representation, as an input capacitance increase. We provide a brief review on this topic. (2) MODULAR DESCRIPTION OF NOISE PROPAGATION: Gene circuit signals are noisy due to the random nature of biological reactions. The noisy fluctuations in TF concentrations affect downstream regulation. Thus, noise can propagate throughout the connected system components. This can cause different circuit components to behave in a statistically dependent manner, hampering a modular analysis. Here, we show that the modular analysis is still possible at the linear noise approximation level. (3) NOISE EFFECT ON MODULE INPUT-OUTPUT RESPONSE: We investigate how to deal with a module input-output response and its noise dependency. Noise-induced phenotypes are described as an interplay between system nonlinearity and signal noise. Lastly, we provide the comprehensive approach incorporating the above three analysis methods, which we call "stochastic modular analysis." This method can provide an analysis framework for gene circuit dynamics when the nontrivial effects of retroactivity, stochasticity, and nonlinearity need to be taken into account.

Labor and Capital in the Soviet Union by Republics

DTIC Science & Technology

1977-08-01

under the title ’Input-Output Analysis and the Soviet Economy. An Annotated Bibliotraphy.’ 934 entries. 180 pp. I 2. Jaees UT. Cillula The Structure ...Input-Output in the Soviet Union.’* April 1974, 94 pp. S. eneD. Guill, "Interteporal Comparison of the Structure of the Soviet Economy.- February...49 pp. I *10. Daniel L. Bond, "Input-Output Structure of a Soviet Republic, the Latvian SSR, August 1975." (with an appendix by Gene Guill and Per

Initial description of primate-specific cystine-knot Prometheus genes and differential gene expansions of D-dopachrome tautomerase genes

PubMed Central

Premzl, Marko

2015-01-01

Using eutherian comparative genomic analysis protocol and public genomic sequence data sets, the present work attempted to update and revise two gene data sets. The most comprehensive third party annotation gene data sets of eutherian adenohypophysis cystine-knot genes (128 complete coding sequences), and d-dopachrome tautomerases and macrophage migration inhibitory factor genes (30 complete coding sequences) were annotated. For example, the present study first described primate-specific cystine-knot Prometheus genes, as well as differential gene expansions of D-dopachrome tautomerase genes. Furthermore, new frameworks of future experiments of two eutherian gene data sets were proposed. PMID:25941635

19 CFR 351.523 - Upstream subsidies.

Code of Federal Regulations, 2010 CFR

2010-04-01

... the input product and the producer of the subject merchandise are affiliated; (B) The price for the subsidized input product is lower than the price that the producer of the subject merchandise otherwise would... government sets the price of the input product so as to guarantee that the benefit provided with respect to...

Device-independent tests of quantum channels

NASA Astrophysics Data System (ADS)

Dall'Arno, Michele; Brandsen, Sarah; Buscemi, Francesco

2017-03-01

We develop a device-independent framework for testing quantum channels. That is, we falsify a hypothesis about a quantum channel based only on an observed set of input-output correlations. Formally, the problem consists of characterizing the set of input-output correlations compatible with any arbitrary given quantum channel. For binary (i.e. two input symbols, two output symbols) correlations, we show that extremal correlations are always achieved by orthogonal encodings and measurements, irrespective of whether or not the channel preserves commutativity. We further provide a full, closed-form characterization of the sets of binary correlations in the case of: (i) any dihedrally covariant qubit channel (such as any Pauli and amplitude-damping channels) and (ii) any universally-covariant commutativity-preserving channel in an arbitrary dimension (such as any erasure, depolarizing, universal cloning and universal transposition channels).

Device-independent tests of quantum channels.

PubMed

Dall'Arno, Michele; Brandsen, Sarah; Buscemi, Francesco

2017-03-01

We develop a device-independent framework for testing quantum channels. That is, we falsify a hypothesis about a quantum channel based only on an observed set of input-output correlations. Formally, the problem consists of characterizing the set of input-output correlations compatible with any arbitrary given quantum channel. For binary (i.e. two input symbols, two output symbols) correlations, we show that extremal correlations are always achieved by orthogonal encodings and measurements, irrespective of whether or not the channel preserves commutativity. We further provide a full, closed-form characterization of the sets of binary correlations in the case of: (i) any dihedrally covariant qubit channel (such as any Pauli and amplitude-damping channels) and (ii) any universally-covariant commutativity-preserving channel in an arbitrary dimension (such as any erasure, depolarizing, universal cloning and universal transposition channels).

Complex cellular logic computation using ribocomputing devices.

PubMed

Green, Alexander A; Kim, Jongmin; Ma, Duo; Silver, Pamela A; Collins, James J; Yin, Peng

2017-08-03

Synthetic biology aims to develop engineering-driven approaches to the programming of cellular functions that could yield transformative technologies. Synthetic gene circuits that combine DNA, protein, and RNA components have demonstrated a range of functions such as bistability, oscillation, feedback, and logic capabilities. However, it remains challenging to scale up these circuits owing to the limited number of designable, orthogonal, high-performance parts, the empirical and often tedious composition rules, and the requirements for substantial resources for encoding and operation. Here, we report a strategy for constructing RNA-only nanodevices to evaluate complex logic in living cells. Our 'ribocomputing' systems are composed of de-novo-designed parts and operate through predictable and designable base-pairing rules, allowing the effective in silico design of computing devices with prescribed configurations and functions in complex cellular environments. These devices operate at the post-transcriptional level and use an extended RNA transcript to co-localize all circuit sensing, computation, signal transduction, and output elements in the same self-assembled molecular complex, which reduces diffusion-mediated signal losses, lowers metabolic cost, and improves circuit reliability. We demonstrate that ribocomputing devices in Escherichia coli can evaluate two-input logic with a dynamic range up to 900-fold and scale them to four-input AND, six-input OR, and a complex 12-input expression (A1 AND A2 AND NOT A1*) OR (B1 AND B2 AND NOT B2*) OR (C1 AND C2) OR (D1 AND D2) OR (E1 AND E2). Successful operation of ribocomputing devices based on programmable RNA interactions suggests that systems employing the same design principles could be implemented in other host organisms or in extracellular settings.

Training set selection for the prediction of essential genes.

PubMed

Cheng, Jian; Xu, Zhao; Wu, Wenwu; Zhao, Li; Li, Xiangchen; Liu, Yanlin; Tao, Shiheng

2014-01-01

Various computational models have been developed to transfer annotations of gene essentiality between organisms. However, despite the increasing number of microorganisms with well-characterized sets of essential genes, selection of appropriate training sets for predicting the essential genes of poorly-studied or newly sequenced organisms remains challenging. In this study, a machine learning approach was applied reciprocally to predict the essential genes in 21 microorganisms. Results showed that training set selection greatly influenced predictive accuracy. We determined four criteria for training set selection: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. We then analyzed the performance of an incomplete training set and an integrated training set with multiple organisms. We found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Additionally, the integrated training sets exhibited remarkable increase in stability and accuracy compared with single sets. Finally, we compared the performance of the integrated training sets with the four criteria and with random selection. The results revealed that a rational selection of training sets based on our criteria yields better performance than random selection. Thus, our results provide empirical guidance on training set selection for the identification of essential genes on a genome-wide scale.

[Neuroendocrine mechanisms of puberty onset].

PubMed

Teinturier, C

2002-10-01

An increase in pulsatile release of GnRH is essential for the onset of puberty. However, the mechanism controlling the pubertal increase in GnRH release is still unclear. The GnRH neurosecretory system is already active during the neonatal period but subsequently enters a dormant state by central inhibition in the juvenile period. When this central inhibition is removed or diminished, an increase in GnRH release occurs with increase in synthesis and release of gonadotropins and gonadal steroids, followed by the appearance of secondary sexual characteristics. Recent studies suggest that disinhibition of GnRH neurons from GABA (gamma-aminobutyric acid) appears to be a critical factor in female rhesus monkey. After central inhibition is removed, increases in stimulatory input from glutamatergic neurons as well as new stimulatory input from norepinephrine and NPY neurons and inhibitory input from beta endorphin neurons appear to control pulsatile GnRH release as well as gonadal steroids. Nonetheless, the most important question still remains: what determines the timing to remove central inhibition? Because many genes are turned on or turned off to establish a complex series of events occurring during puberty, the timing of puberty must be regulated by a master gene or genes, as a part of developmental events.

In silico study of protein to protein interaction analysis of AMP-activated protein kinase and mitochondrial activity in three different farm animal species

NASA Astrophysics Data System (ADS)

Prastowo, S.; Widyas, N.

2018-03-01

AMP-activated protein kinase (AMPK) is cellular energy censor which works based on ATP and AMP concentration. This protein interacts with mitochondria in determine its activity to generate energy for cell metabolism purposes. For that, this paper aims to compare the protein to protein interaction of AMPK and mitochondrial activity genes in the metabolism of known animal farm (domesticated) that are cattle (Bos taurus), pig (Sus scrofa) and chicken (Gallus gallus). In silico study was done using STRING V.10 as prominent protein interaction database, followed with biological function comparison in KEGG PATHWAY database. Set of genes (12 in total) were used as input analysis that are PRKAA1, PRKAA2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PRKAG3, PPARGC1, ACC, CPT1B, NRF2 and SOD. The first 7 genes belong to gene in AMPK family, while the last 5 belong to mitochondrial activity genes. The protein interaction result shows 11, 8 and 5 metabolism pathways in Bos taurus, Sus scrofa and Gallus gallus, respectively. The top pathway in Bos taurus is AMPK signaling pathway (10 genes), Sus scrofa is Adipocytokine signaling pathway (8 genes) and Gallus gallus is FoxO signaling pathway (5 genes). Moreover, the common pathways found in those 3 species are Adipocytokine signaling pathway, Insulin signaling pathway and FoxO signaling pathway. Genes clustered in Adipocytokine and Insulin signaling pathway are PRKAA2, PPARGC1A, PRKAB1 and PRKAG2. While, in FoxO signaling pathway are PRKAA2, PRKAB1, PRKAG2. According to that, we found PRKAA2, PRKAB1 and PRKAG2 are the common genes. Based on the bioinformatics analysis, we can demonstrate that protein to protein interaction shows distinct different of metabolism in different species. However, further validation is needed to give a clear explanation.

Regression with Small Data Sets: A Case Study using Code Surrogates in Additive Manufacturing

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

Kamath, C.; Fan, Y. J.

There has been an increasing interest in recent years in the mining of massive data sets whose sizes are measured in terabytes. While it is easy to collect such large data sets in some application domains, there are others where collecting even a single data point can be very expensive, so the resulting data sets have only tens or hundreds of samples. For example, when complex computer simulations are used to understand a scientific phenomenon, we want to run the simulation for many different values of the input parameters and analyze the resulting output. The data set relating the simulationmore » inputs and outputs is typically quite small, especially when each run of the simulation is expensive. However, regression techniques can still be used on such data sets to build an inexpensive \\surrogate" that could provide an approximate output for a given set of inputs. A good surrogate can be very useful in sensitivity analysis, uncertainty analysis, and in designing experiments. In this paper, we compare different regression techniques to determine how well they predict melt-pool characteristics in the problem domain of additive manufacturing. Our analysis indicates that some of the commonly used regression methods do perform quite well even on small data sets.« less

ChIP-chip.

PubMed

Kim, Tae Hoon; Dekker, Job

2018-05-01

ChIP-chip can be used to analyze protein-DNA interactions in a region-wide and genome-wide manner. DNA microarrays contain PCR products or oligonucleotide probes that are designed to represent genomic sequences. Identification of genomic sites that interact with a specific protein is based on competitive hybridization of the ChIP-enriched DNA and the input DNA to DNA microarrays. The ChIP-chip protocol can be divided into two main sections: Amplification of ChIP DNA and hybridization of ChIP DNA to arrays. A large amount of DNA is required to hybridize to DNA arrays, and hybridization to a set of multiple commercial arrays that represent the entire human genome requires two rounds of PCR amplifications. The relative hybridization intensity of ChIP DNA and that of the input DNA is used to determine whether the probe sequence is a potential site of protein-DNA interaction. Resolution of actual genomic sites bound by the protein is dependent on the size of the chromatin and on the genomic distance between the probes on the array. As with expression profiling using gene chips, ChIP-chip experiments require multiple replicates for reliable statistical measure of protein-DNA interactions. © 2018 Cold Spring Harbor Laboratory Press.

Next Generation MUT-MAP, a High-Sensitivity High-Throughput Microfluidics Chip-Based Mutation Analysis Panel

PubMed Central

Patel, Rajesh; Tsan, Alison; Sumiyoshi, Teiko; Fu, Ling; Desai, Rupal; Schoenbrunner, Nancy; Myers, Thomas W.; Bauer, Keith; Smith, Edward; Raja, Rajiv

2014-01-01

Molecular profiling of tumor tissue to detect alterations, such as oncogenic mutations, plays a vital role in determining treatment options in oncology. Hence, there is an increasing need for a robust and high-throughput technology to detect oncogenic hotspot mutations. Although commercial assays are available to detect genetic alterations in single genes, only a limited amount of tissue is often available from patients, requiring multiplexing to allow for simultaneous detection of mutations in many genes using low DNA input. Even though next-generation sequencing (NGS) platforms provide powerful tools for this purpose, they face challenges such as high cost, large DNA input requirement, complex data analysis, and long turnaround times, limiting their use in clinical settings. We report the development of the next generation mutation multi-analyte panel (MUT-MAP), a high-throughput microfluidic, panel for detecting 120 somatic mutations across eleven genes of therapeutic interest (AKT1, BRAF, EGFR, FGFR3, FLT3, HRAS, KIT, KRAS, MET, NRAS, and PIK3CA) using allele-specific PCR (AS-PCR) and Taqman technology. This mutation panel requires as little as 2 ng of high quality DNA from fresh frozen or 100 ng of DNA from formalin-fixed paraffin-embedded (FFPE) tissues. Mutation calls, including an automated data analysis process, have been implemented to run 88 samples per day. Validation of this platform using plasmids showed robust signal and low cross-reactivity in all of the newly added assays and mutation calls in cell line samples were found to be consistent with the Catalogue of Somatic Mutations in Cancer (COSMIC) database allowing for direct comparison of our platform to Sanger sequencing. High correlation with NGS when compared to the SuraSeq500 panel run on the Ion Torrent platform in a FFPE dilution experiment showed assay sensitivity down to 0.45%. This multiplexed mutation panel is a valuable tool for high-throughput biomarker discovery in personalized medicine and cancer drug development. PMID:24658394

Positive-unlabeled learning for disease gene identification

PubMed Central

Yang, Peng; Li, Xiao-Li; Mei, Jian-Ping; Kwoh, Chee-Keong; Ng, See-Kiong

2012-01-01

Background: Identifying disease genes from human genome is an important but challenging task in biomedical research. Machine learning methods can be applied to discover new disease genes based on the known ones. Existing machine learning methods typically use the known disease genes as the positive training set P and the unknown genes as the negative training set N (non-disease gene set does not exist) to build classifiers to identify new disease genes from the unknown genes. However, such kind of classifiers is actually built from a noisy negative set N as there can be unknown disease genes in N itself. As a result, the classifiers do not perform as well as they could be. Result: Instead of treating the unknown genes as negative examples in N, we treat them as an unlabeled set U. We design a novel positive-unlabeled (PU) learning algorithm PUDI (PU learning for disease gene identification) to build a classifier using P and U. We first partition U into four sets, namely, reliable negative set RN, likely positive set LP, likely negative set LN and weak negative set WN. The weighted support vector machines are then used to build a multi-level classifier based on the four training sets and positive training set P to identify disease genes. Our experimental results demonstrate that our proposed PUDI algorithm outperformed the existing methods significantly. Conclusion: The proposed PUDI algorithm is able to identify disease genes more accurately by treating the unknown data more appropriately as unlabeled set U instead of negative set N. Given that many machine learning problems in biomedical research do involve positive and unlabeled data instead of negative data, it is possible that the machine learning methods for these problems can be further improved by adopting PU learning methods, as we have done here for disease gene identification. Availability and implementation: The executable program and data are available at http://www1.i2r.a-star.edu.sg/∼xlli/PUDI/PUDI.html. Contact: xlli@i2r.a-star.edu.sg or yang0293@e.ntu.edu.sg Supplementary information: Supplementary Data are available at Bioinformatics online. PMID:22923290

Functional Gene Differences in Soil Microbial Communities from Conventional, Low-Input, and Organic Farmlands

PubMed Central

Xue, Kai; Wu, Liyou; Deng, Ye; He, Zhili; Van Nostrand, Joy; Robertson, Philip G.; Schmidt, Thomas M.

2013-01-01

Various agriculture management practices may have distinct influences on soil microbial communities and their ecological functions. In this study, we utilized GeoChip, a high-throughput microarray-based technique containing approximately 28,000 probes for genes involved in nitrogen (N)/carbon (C)/sulfur (S)/phosphorus (P) cycles and other processes, to evaluate the potential functions of soil microbial communities under conventional (CT), low-input (LI), and organic (ORG) management systems at an agricultural research site in Michigan. Compared to CT, a high diversity of functional genes was observed in LI. The functional gene diversity in ORG did not differ significantly from that of either CT or LI. Abundances of genes encoding enzymes involved in C/N/P/S cycles were generally lower in CT than in LI or ORG, with the exceptions of genes in pathways for lignin degradation, methane generation/oxidation, and assimilatory N reduction, which all remained unchanged. Canonical correlation analysis showed that selected soil (bulk density, pH, cation exchange capacity, total C, C/N ratio, NO3−, NH4+, available phosphorus content, and available potassium content) and crop (seed and whole biomass) variables could explain 69.5% of the variation of soil microbial community composition. Also, significant correlations were observed between NO3− concentration and denitrification genes, NH4+ concentration and ammonification genes, and N2O flux and denitrification genes, indicating a close linkage between soil N availability or process and associated functional genes. PMID:23241975

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool

PubMed Central

Clark, Neil R.; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D.; Jones, Matthew R.; Ma’ayan, Avi

2016-01-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community. PMID:26848405

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

PubMed

Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

2015-11-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

Applications of information theory, genetic algorithms, and neural models to predict oil flow

NASA Astrophysics Data System (ADS)

Ludwig, Oswaldo; Nunes, Urbano; Araújo, Rui; Schnitman, Leizer; Lepikson, Herman Augusto

2009-07-01

This work introduces a new information-theoretic methodology for choosing variables and their time lags in a prediction setting, particularly when neural networks are used in non-linear modeling. The first contribution of this work is the Cross Entropy Function (XEF) proposed to select input variables and their lags in order to compose the input vector of black-box prediction models. The proposed XEF method is more appropriate than the usually applied Cross Correlation Function (XCF) when the relationship among the input and output signals comes from a non-linear dynamic system. The second contribution is a method that minimizes the Joint Conditional Entropy (JCE) between the input and output variables by means of a Genetic Algorithm (GA). The aim is to take into account the dependence among the input variables when selecting the most appropriate set of inputs for a prediction problem. In short, theses methods can be used to assist the selection of input training data that have the necessary information to predict the target data. The proposed methods are applied to a petroleum engineering problem; predicting oil production. Experimental results obtained with a real-world dataset are presented demonstrating the feasibility and effectiveness of the method.

Computation and application of tissue-specific gene set weights.

PubMed

Frost, H Robert

2018-04-06

Gene set testing, or pathway analysis, has become a critical tool for the analysis of highdimensional genomic data. Although the function and activity of many genes and higher-level processes is tissue-specific, gene set testing is typically performed in a tissue agnostic fashion, which impacts statistical power and the interpretation and replication of results. To address this challenge, we have developed a bioinformatics approach to compute tissuespecific weights for individual gene sets using information on tissue-specific gene activity from the Human Protein Atlas (HPA). We used this approach to create a public repository of tissue-specific gene set weights for 37 different human tissue types from the HPA and all collections in the Molecular Signatures Database (MSigDB). To demonstrate the validity and utility of these weights, we explored three different applications: the functional characterization of human tissues, multi-tissue analysis for systemic diseases and tissue-specific gene set testing. All data used in the reported analyses is publicly available. An R implementation of the method and tissue-specific weights for MSigDB gene set collections can be downloaded at http://www.dartmouth.edu/∼hrfrost/TissueSpecificGeneSets. rob.frost@dartmouth.edu.

Feathermoss and epiphytic Nostoc cooperate differently: expanding the spectrum of plant-cyanobacteria symbiosis.

PubMed

Warshan, Denis; Espinoza, Josh L; Stuart, Rhona K; Richter, R Alexander; Kim, Sea-Yong; Shapiro, Nicole; Woyke, Tanja; C Kyrpides, Nikos; Barry, Kerrie; Singan, Vasanth; Lindquist, Erika; Ansong, Charles; Purvine, Samuel O; M Brewer, Heather; Weyman, Philip D; Dupont, Christopher L; Rasmussen, Ulla

2017-12-01

Dinitrogen (N 2 )-fixation by cyanobacteria in symbiosis with feathermosses is the primary pathway of biological nitrogen (N) input into boreal forests. Despite its significance, little is known about the cyanobacterial gene repertoire and regulatory rewiring needed for the establishment and maintenance of the symbiosis. To determine gene acquisitions and regulatory changes allowing cyanobacteria to form and maintain this symbiosis, we compared genomically closely related symbiotic-competent and -incompetent Nostoc strains using a proteogenomics approach and an experimental set up allowing for controlled chemical and physical contact between partners. Thirty-two gene families were found only in the genomes of symbiotic strains, including some never before associated with cyanobacterial symbiosis. We identified conserved orthologs that were differentially expressed in symbiotic strains, including protein families involved in chemotaxis and motility, NO regulation, sulfate/phosphate transport, and glycosyl-modifying and oxidative stress-mediating exoenzymes. The physical moss-cyanobacteria epiphytic symbiosis is distinct from other cyanobacteria-plant symbioses, with Nostoc retaining motility, and lacking modulation of N 2 -fixation, photosynthesis, GS-GOGAT cycle and heterocyst formation. The results expand our knowledge base of plant-cyanobacterial symbioses, provide a model of information and material exchange in this ecologically significant symbiosis, and suggest new currencies, namely nitric oxide and aliphatic sulfonates, may be involved in establishing and maintaining the cyanobacteria-feathermoss symbiosis.

A non-linear dimension reduction methodology for generating data-driven stochastic input models

NASA Astrophysics Data System (ADS)

Ganapathysubramanian, Baskar; Zabaras, Nicholas

2008-06-01

Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space Rn. An isometric mapping F from M to a low-dimensional, compact, connected set A⊂Rd(d≪n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology by constructing low-dimensional input stochastic models to represent thermal diffusivity in two-phase microstructures. This model is used in analyzing the effect of topological variations of two-phase microstructures on the evolution of temperature in heat conduction processes.

Recommendations and settings of word prediction software by health-related professionals for patients with spinal cord injury: a prospective observational study.

PubMed

Pouplin, Samuel; Roche, Nicolas; Hugeron, Caroline; Vaugier, Isabelle; Bensmail, Djamel

2016-02-01

For people with cervical spinal cord injury (SCI), access to computers can be difficult, thus several devices have been developed to facilitate their use. However, text input speed remains very slow compared to users who do not have a disability, even with these devices. Several methods have been developed to increase text input speed, such as word prediction software (WPS). Health-related professionals (HRP) often recommend this type of software to people with cervical SCI. WPS can be customized using different settings. It is likely that the settings used will influence the effectiveness of the software on text input speed. However, there is currently a lack of literature regarding professional practices for the setting of WPS as well as the impact for users. To analyze word prediction software settings used by HRP for people with cervical SCI. Prospective observational study. Garches, France; health-related professionals who recommend Word Prediction Software. A questionnaire was submitted to HRP who advise tetraplegic people regarding the use of communication devices. A total of 93 professionals responded to the survey. The most frequently recommended software was Skippy, a commercially available software. HRP rated the importance of the possibility to customise the settings as high. Moreover, they rated some settings as more important than others (P<0.001). However, except for the number of words displayed, each setting was configured by less than 50% of HRP. The results showed that there was a difference between the perception of the importance of some settings and data in the literature regarding the optimization of settings. Moreover, although some parameters were considered as very important, they were rarely specifically configured. Confidence in default settings and lack of information regarding optimal settings seem to be the main reasons for this discordance. This could also explain the disparate results of studies which evaluated the impact of WPS on text input speed in people with cervical SCI. The results showed that there was a difference between the perception of the importance of some settings and data in the literature regarding the optimization of settings. Moreover, although some parameters were considered as very important, they were rarely specifically configured. Confidence in default settings and lack of information regarding optimal settings seem to be the main reasons for this discordance. This could also explain the disparate results of studies which evaluated the impact of WPS on text input speed in people with cervical SCI. Professionals tend to have confidence in default settings, despite the fact they are not always appropriate for users. It thus seems essential to develop information networks and training to disseminate the results of studies and in consequence possibly improve communication for people with cervical SCI who use such devices.

GO-based functional dissimilarity of gene sets.

PubMed

Díaz-Díaz, Norberto; Aguilar-Ruiz, Jesús S

2011-09-01

The Gene Ontology (GO) provides a controlled vocabulary for describing the functions of genes and can be used to evaluate the functional coherence of gene sets. Many functional coherence measures consider each pair of gene functions in a set and produce an output based on all pairwise distances. A single gene can encode multiple proteins that may differ in function. For each functionality, other proteins that exhibit the same activity may also participate. Therefore, an identification of the most common function for all of the genes involved in a biological process is important in evaluating the functional similarity of groups of genes and a quantification of functional coherence can helps to clarify the role of a group of genes working together. To implement this approach to functional assessment, we present GFD (GO-based Functional Dissimilarity), a novel dissimilarity measure for evaluating groups of genes based on the most relevant functions of the whole set. The measure assigns a numerical value to the gene set for each of the three GO sub-ontologies. Results show that GFD performs robustly when applied to gene set of known functionality (extracted from KEGG). It performs particularly well on randomly generated gene sets. An ROC analysis reveals that the performance of GFD in evaluating the functional dissimilarity of gene sets is very satisfactory. A comparative analysis against other functional measures, such as GS2 and those presented by Resnik and Wang, also demonstrates the robustness of GFD.

Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features.

PubMed

Stember, Joseph N; Deng, Fang-Ming; Taneja, Samir S; Rosenkrantz, Andrew B

2014-08-01

To present results of a pilot study to develop software that identifies regions suspicious for prostate transition zone (TZ) tumor, free of user input. Eight patients with TZ tumors were used to develop the model by training a Naïve Bayes classifier to detect tumors based on selection of most accurate predictors among various signal and textural features on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps. Features tested as inputs were: average signal, signal standard deviation, energy, contrast, correlation, homogeneity and entropy (all defined on T2WI); and average ADC. A forward selection scheme was used on the remaining 20% of training set supervoxels to identify important inputs. The trained model was tested on a different set of ten patients, half with TZ tumors. In training cases, the software tiled the TZ with 4 × 4-voxel "supervoxels," 80% of which were used to train the classifier. Each of 100 iterations selected T2WI energy and average ADC, which therefore were deemed the optimal model input. The two-feature model was applied blindly to the separate set of test patients, again without operator input of suspicious foci. The software correctly predicted presence or absence of TZ tumor in all test patients. Furthermore, locations of predicted tumors corresponded spatially with locations of biopsies that had confirmed their presence. Preliminary findings suggest that this tool has potential to accurately predict TZ tumor presence and location, without operator input. © 2013 Wiley Periodicals, Inc.

System and method for pre-cooling of buildings

DOEpatents

Springer, David A.; Rainer, Leo I.

2011-08-09

A method for nighttime pre-cooling of a building comprising inputting one or more user settings, lowering the indoor temperature reading of the building during nighttime by operating an outside air ventilation system followed, if necessary, by a vapor compression cooling system. The method provides for nighttime pre-cooling of a building that maintains indoor temperatures within a comfort range based on the user input settings, calculated operational settings, and predictions of indoor and outdoor temperature trends for a future period of time such as the next day.

Application of Artificial Neural Network to Optical Fluid Analyzer

NASA Astrophysics Data System (ADS)

Kimura, Makoto; Nishida, Katsuhiko

1994-04-01

A three-layer artificial neural network has been applied to the presentation of optical fluid analyzer (OFA) raw data, and the accuracy of oil fraction determination has been significantly improved compared to previous approaches. To apply the artificial neural network approach to solving a problem, the first step is training to determine the appropriate weight set for calculating the target values. This involves using a series of data sets (each comprising a set of input values and an associated set of output values that the artificial neural network is required to determine) to tune artificial neural network weighting parameters so that the output of the neural network to the given set of input values is as close as possible to the required output. The physical model used to generate the series of learning data sets was the effective flow stream model, developed for OFA data presentation. The effectiveness of the training was verified by reprocessing the same input data as were used to determine the weighting parameters and then by comparing the results of the artificial neural network to the expected output values. The standard deviation of the expected and obtained values was approximately 10% (two sigma).

Investigating the different mechanisms of genotoxic and non-genotoxic carcinogens by a gene set analysis.

PubMed

Lee, Won Jun; Kim, Sang Cheol; Lee, Seul Ji; Lee, Jeongmi; Park, Jeong Hill; Yu, Kyung-Sang; Lim, Johan; Kwon, Sung Won

2014-01-01

Based on the process of carcinogenesis, carcinogens are classified as either genotoxic or non-genotoxic. In contrast to non-genotoxic carcinogens, many genotoxic carcinogens have been reported to cause tumor in carcinogenic bioassays in animals. Thus evaluating the genotoxicity potential of chemicals is important to discriminate genotoxic from non-genotoxic carcinogens for health care and pharmaceutical industry safety. Additionally, investigating the difference between the mechanisms of genotoxic and non-genotoxic carcinogens could provide the foundation for a mechanism-based classification for unknown compounds. In this study, we investigated the gene expression of HepG2 cells treated with genotoxic or non-genotoxic carcinogens and compared their mechanisms of action. To enhance our understanding of the differences in the mechanisms of genotoxic and non-genotoxic carcinogens, we implemented a gene set analysis using 12 compounds for the training set (12, 24, 48 h) and validated significant gene sets using 22 compounds for the test set (24, 48 h). For a direct biological translation, we conducted a gene set analysis using Globaltest and selected significant gene sets. To validate the results, training and test compounds were predicted by the significant gene sets using a prediction analysis for microarrays (PAM). Finally, we obtained 6 gene sets, including sets enriched for genes involved in the adherens junction, bladder cancer, p53 signaling pathway, pathways in cancer, peroxisome and RNA degradation. Among the 6 gene sets, the bladder cancer and p53 signaling pathway sets were significant at 12, 24 and 48 h. We also found that the DDB2, RRM2B and GADD45A, genes related to the repair and damage prevention of DNA, were consistently up-regulated for genotoxic carcinogens. Our results suggest that a gene set analysis could provide a robust tool in the investigation of the different mechanisms of genotoxic and non-genotoxic carcinogens and construct a more detailed understanding of the perturbation of significant pathways.

Investigating the Different Mechanisms of Genotoxic and Non-Genotoxic Carcinogens by a Gene Set Analysis

PubMed Central

Lee, Won Jun; Kim, Sang Cheol; Lee, Seul Ji; Lee, Jeongmi; Park, Jeong Hill; Yu, Kyung-Sang; Lim, Johan; Kwon, Sung Won

2014-01-01

Based on the process of carcinogenesis, carcinogens are classified as either genotoxic or non-genotoxic. In contrast to non-genotoxic carcinogens, many genotoxic carcinogens have been reported to cause tumor in carcinogenic bioassays in animals. Thus evaluating the genotoxicity potential of chemicals is important to discriminate genotoxic from non-genotoxic carcinogens for health care and pharmaceutical industry safety. Additionally, investigating the difference between the mechanisms of genotoxic and non-genotoxic carcinogens could provide the foundation for a mechanism-based classification for unknown compounds. In this study, we investigated the gene expression of HepG2 cells treated with genotoxic or non-genotoxic carcinogens and compared their mechanisms of action. To enhance our understanding of the differences in the mechanisms of genotoxic and non-genotoxic carcinogens, we implemented a gene set analysis using 12 compounds for the training set (12, 24, 48 h) and validated significant gene sets using 22 compounds for the test set (24, 48 h). For a direct biological translation, we conducted a gene set analysis using Globaltest and selected significant gene sets. To validate the results, training and test compounds were predicted by the significant gene sets using a prediction analysis for microarrays (PAM). Finally, we obtained 6 gene sets, including sets enriched for genes involved in the adherens junction, bladder cancer, p53 signaling pathway, pathways in cancer, peroxisome and RNA degradation. Among the 6 gene sets, the bladder cancer and p53 signaling pathway sets were significant at 12, 24 and 48 h. We also found that the DDB2, RRM2B and GADD45A, genes related to the repair and damage prevention of DNA, were consistently up-regulated for genotoxic carcinogens. Our results suggest that a gene set analysis could provide a robust tool in the investigation of the different mechanisms of genotoxic and non-genotoxic carcinogens and construct a more detailed understanding of the perturbation of significant pathways. PMID:24497971

Method for guessing the response of a physical system to an arbitrary input

DOEpatents

Wolpert, David H.

1996-01-01

Stacked generalization is used to minimize the generalization errors of one or more generalizers acting on a known set of input values and output values representing a physical manifestation and a transformation of that manifestation, e.g., hand-written characters to ASCII characters, spoken speech to computer command, etc. Stacked generalization acts to deduce the biases of the generalizer(s) with respect to a known learning set and then correct for those biases. This deduction proceeds by generalizing in a second space whose inputs are the guesses of the original generalizers when taught with part of the learning set and trying to guess the rest of it, and whose output is the correct guess. Stacked generalization can be used to combine multiple generalizers or to provide a correction to a guess from a single generalizer.

System and method for motor speed estimation of an electric motor

DOEpatents

Lu, Bin [Kenosha, WI; Yan, Ting [Brookfield, WI; Luebke, Charles John [Sussex, WI; Sharma, Santosh Kumar [Viman Nagar, IN

2012-06-19

A system and method for a motor management system includes a computer readable storage medium and a processing unit. The processing unit configured to determine a voltage value of a voltage input to an alternating current (AC) motor, determine a frequency value of at least one of a voltage input and a current input to the AC motor, determine a load value from the AC motor, and access a set of motor nameplate data, where the set of motor nameplate data includes a rated power, a rated speed, a rated frequency, and a rated voltage of the AC motor. The processing unit is also configured to estimate a motor speed based on the voltage value, the frequency value, the load value, and the set of nameplate data and also store the motor speed on the computer readable storage medium.

Single-Event Transient Response of Comparator Pre-Amplifiers in a Complementary SiGe Technology

NASA Astrophysics Data System (ADS)

Ildefonso, Adrian; Lourenco, Nelson E.; Fleetwood, Zachary E.; Wachter, Mason T.; Tzintzarov, George N.; Cardoso, Adilson S.; Roche, Nicolas J.-H.; Khachatrian, Ani; McMorrow, Dale; Buchner, Stephen P.; Warner, Jeffrey H.; Paki, Pauline; Kaynak, Mehmet; Tillack, Bernd; Cressler, John D.

2017-01-01

The single-event transient (SET) response of the pre-amplification stage of two latched comparators designed using either npn or pnp silicon-germanium heterojunction bipolar transistors (SiGe HBTs) is investigated via two-photon absorption (TPA) carrier injection and mixed-mode TCAD simulations. Experimental data and TCAD simulations showed an improved SET response for the pnp comparator circuit. 2-D raster scans revealed that the devices in the pnp circuit exhibit a reduction in sensitive area of up to 80% compared to their npn counterparts. In addition, by sweeping the input voltage, the sensitive operating region with respect to SETs was determined. By establishing a figure-of-merit, relating the transient peaks and input voltage polarities, the pnp device was determined to have a 21.4% improved response with respect to input voltage. This study has shown that using pnp devices is an effective way to mitigate SETs, and could enable further radiation-hardening-by-design techniques.

NEURAL NETWORK INTERACTIONS AND INGESTIVE BEHAVIOR CONTROL DURING ANOREXIA

PubMed Central

Watts, Alan G.; Salter, Dawna S.; Neuner, Christina M.

2007-01-01

Many models have been proposed over the years to explain how motivated feeding behavior is controlled. One of the most compelling is based on the original concepts of Eliot Stellar whereby sets of interosensory and exterosensory inputs converge on a hypothalamic control network that can either stimulate or inhibit feeding. These inputs arise from information originating in the blood, the viscera, and the telencephalon. In this manner the relative strengths of the hypothalamic stimulatory and inhibitory networks at a particular time dictates how an animal feeds. Anorexia occurs when the balance within the networks consistently favors the restraint of feeding. This article discusses experimental evidence supporting a model whereby the increases in plasma osmolality that result from drinking hypertonic saline activate pathways projecting to neurons in the paraventricular nucleus of the hypothalamus (PVH) and lateral hypothalamic area (LHA). These neurons constitute the hypothalamic controller for ingestive behavior, and receive a set of afferent inputs from regions of the brain that process sensory information that is critical for different aspects of feeding. Important sets of inputs arise in the arcuate nucleus, the hindbrain, and in the telencephalon. Anorexia is generated in dehydrated animals by way of osmosensitive projections to the behavior control neurons in the PVH and LHA, rather than by actions on their afferent inputs. PMID:17531275

L1000CDS2: LINCS L1000 characteristic direction signatures search engine.

PubMed

Duan, Qiaonan; Reid, St Patrick; Clark, Neil R; Wang, Zichen; Fernandez, Nicolas F; Rouillard, Andrew D; Readhead, Ben; Tritsch, Sarah R; Hodos, Rachel; Hafner, Marc; Niepel, Mario; Sorger, Peter K; Dudley, Joel T; Bavari, Sina; Panchal, Rekha G; Ma'ayan, Avi

2016-01-01

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS 2 . The L1000CDS 2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS 2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS 2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS 2 , we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS 2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS 2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource.

Analytical performance of the ThyroSeq v3 genomic classifier for cancer diagnosis in thyroid nodules.

PubMed

Nikiforova, Marina N; Mercurio, Stephanie; Wald, Abigail I; Barbi de Moura, Michelle; Callenberg, Keith; Santana-Santos, Lucas; Gooding, William E; Yip, Linwah; Ferris, Robert L; Nikiforov, Yuri E

2018-04-15

Molecular tests have clinical utility for thyroid nodules with indeterminate fine-needle aspiration (FNA) cytology, although their performance requires further improvement. This study evaluated the analytical performance of the newly created ThyroSeq v3 test. ThyroSeq v3 is a DNA- and RNA-based next-generation sequencing assay that analyzes 112 genes for a variety of genetic alterations, including point mutations, insertions/deletions, gene fusions, copy number alterations, and abnormal gene expression, and it uses a genomic classifier (GC) to separate malignant lesions from benign lesions. It was validated in 238 tissue samples and 175 FNA samples with known surgical follow-up. Analytical performance studies were conducted. In the training tissue set of samples, ThyroSeq GC detected more than 100 genetic alterations, including BRAF, RAS, TERT, and DICER1 mutations, NTRK1/3, BRAF, and RET fusions, 22q loss, and gene expression alterations. GC cutoffs were established to distinguish cancer from benign nodules with 93.9% sensitivity, 89.4% specificity, and 92.1% accuracy. This correctly classified most papillary, follicular, and Hurthle cell lesions, medullary thyroid carcinomas, and parathyroid lesions. In the FNA validation set, the GC sensitivity was 98.0%, the specificity was 81.8%, and the accuracy was 90.9%. Analytical accuracy studies demonstrated a minimal required nucleic acid input of 2.5 ng, a 12% minimal acceptable tumor content, and reproducible test results under variable stress conditions. The ThyroSeq v3 GC analyzes 5 different classes of molecular alterations and provides high accuracy for detecting all common types of thyroid cancer and parathyroid lesions. The analytical sensitivity, specificity, and robustness of the test have been successfully validated and indicate its suitability for clinical use. Cancer 2018;124:1682-90. © 2018 American Cancer Society. © 2018 American Cancer Society.

Gene Selection and Cancer Classification: A Rough Sets Based Approach

NASA Astrophysics Data System (ADS)

Sun, Lijun; Miao, Duoqian; Zhang, Hongyun

Indentification of informative gene subsets responsible for discerning between available samples of gene expression data is an important task in bioinformatics. Reducts, from rough sets theory, corresponding to a minimal set of essential genes for discerning samples, is an efficient tool for gene selection. Due to the compuational complexty of the existing reduct algoritms, feature ranking is usually used to narrow down gene space as the first step and top ranked genes are selected . In this paper,we define a novel certierion based on the expression level difference btween classes and contribution to classification of the gene for scoring genes and present a algorithm for generating all possible reduct from informative genes.The algorithm takes the whole attribute sets into account and find short reduct with a significant reduction in computational complexity. An exploration of this approach on benchmark gene expression data sets demonstrates that this approach is successful for selecting high discriminative genes and the classification accuracy is impressive.

Hearing AIDS and music.

PubMed

Chasin, Marshall; Russo, Frank A

2004-01-01

Historically, the primary concern for hearing aid design and fitting is optimization for speech inputs. However, increasingly other types of inputs are being investigated and this is certainly the case for music. Whether the hearing aid wearer is a musician or merely someone who likes to listen to music, the electronic and electro-acoustic parameters described can be optimized for music as well as for speech. That is, a hearing aid optimally set for music can be optimally set for speech, even though the converse is not necessarily true. Similarities and differences between speech and music as inputs to a hearing aid are described. Many of these lead to the specification of a set of optimal electro-acoustic characteristics. Parameters such as the peak input-limiting level, compression issues-both compression ratio and knee-points-and number of channels all can deleteriously affect music perception through hearing aids. In other cases, it is not clear how to set other parameters such as noise reduction and feedback control mechanisms. Regardless of the existence of a "music program,'' unless the various electro-acoustic parameters are available in a hearing aid, music fidelity will almost always be less than optimal. There are many unanswered questions and hypotheses in this area. Future research by engineers, researchers, clinicians, and musicians will aid in the clarification of these questions and their ultimate solutions.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

PubMed Central

Kuleshov, Maxim V.; Jones, Matthew R.; Rouillard, Andrew D.; Fernandez, Nicolas F.; Duan, Qiaonan; Wang, Zichen; Koplev, Simon; Jenkins, Sherry L.; Jagodnik, Kathleen M.; Lachmann, Alexander; McDermott, Michael G.; Monteiro, Caroline D.; Gundersen, Gregory W.; Ma'ayan, Avi

2016-01-01

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr. PMID:27141961

SET oncoprotein accumulation regulates transcription through DNA demethylation and histone hypoacetylation.

PubMed

Almeida, Luciana O; Neto, Marinaldo P C; Sousa, Lucas O; Tannous, Maryna A; Curti, Carlos; Leopoldino, Andreia M

2017-04-18

Epigenetic modifications are essential in the control of normal cellular processes and cancer development. DNA methylation and histone acetylation are major epigenetic modifications involved in gene transcription and abnormal events driving the oncogenic process. SET protein accumulates in many cancer types, including head and neck squamous cell carcinoma (HNSCC); SET is a member of the INHAT complex that inhibits gene transcription associating with histones and preventing their acetylation. We explored how SET protein accumulation impacts on the regulation of gene expression, focusing on DNA methylation and histone acetylation. DNA methylation profile of 24 tumour suppressors evidenced that SET accumulation decreased DNA methylation in association with loss of 5-methylcytidine, formation of 5-hydroxymethylcytosine and increased TET1 levels, indicating an active DNA demethylation mechanism. However, the expression of some suppressor genes was lowered in cells with high SET levels, suggesting that loss of methylation is not the main mechanism modulating gene expression. SET accumulation also downregulated the expression of 32 genes of a panel of 84 transcription factors, and SET directly interacted with chromatin at the promoter of the downregulated genes, decreasing histone acetylation. Gene expression analysis after cell treatment with 5-aza-2'-deoxycytidine (5-AZA) and Trichostatin A (TSA) revealed that histone acetylation reversed transcription repression promoted by SET. These results suggest a new function for SET in the regulation of chromatin dynamics. In addition, TSA diminished both SET protein levels and SET capability to bind to gene promoter, suggesting that administration of epigenetic modifier agents could be efficient to reverse SET phenotype in cancer.

Combining multiple tools outperforms individual methods in gene set enrichment analyses.

PubMed

Alhamdoosh, Monther; Ng, Milica; Wilson, Nicholas J; Sheridan, Julie M; Huynh, Huy; Wilson, Michael J; Ritchie, Matthew E

2017-02-01

Gene set enrichment (GSE) analysis allows researchers to efficiently extract biological insight from long lists of differentially expressed genes by interrogating them at a systems level. In recent years, there has been a proliferation of GSE analysis methods and hence it has become increasingly difficult for researchers to select an optimal GSE tool based on their particular dataset. Moreover, the majority of GSE analysis methods do not allow researchers to simultaneously compare gene set level results between multiple experimental conditions. The ensemble of genes set enrichment analyses (EGSEA) is a method developed for RNA-sequencing data that combines results from twelve algorithms and calculates collective gene set scores to improve the biological relevance of the highest ranked gene sets. EGSEA's gene set database contains around 25 000 gene sets from sixteen collections. It has multiple visualization capabilities that allow researchers to view gene sets at various levels of granularity. EGSEA has been tested on simulated data and on a number of human and mouse datasets and, based on biologists' feedback, consistently outperforms the individual tools that have been combined. Our evaluation demonstrates the superiority of the ensemble approach for GSE analysis, and its utility to effectively and efficiently extrapolate biological functions and potential involvement in disease processes from lists of differentially regulated genes. EGSEA is available as an R package at http://www.bioconductor.org/packages/EGSEA/ . The gene sets collections are available in the R package EGSEAdata from http://www.bioconductor.org/packages/EGSEAdata/ . monther.alhamdoosh@csl.com.au mritchie@wehi.edu.au. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Capacity of the generalized PPM channel

NASA Technical Reports Server (NTRS)

Hamkins, Jon; Klimesh, Matt; McEliece, Bob; Moision, Bruce

2004-01-01

We show the capacity of a generalized pulse-position-modulation (PPM) channel, where the input vectors may be any set that allows a transitive group of coordinate permutations, is achieved by a uniform input distribution.

Rumen papillae morphology of beef steers relative to gain and feed intake and the association of volatile fatty acids with kallikrein gene expression

USDA-ARS?s Scientific Manuscript database

Feed costs are the most expensive input in beef production. Improvement in the feed efficiency of beef cattle would lower feed inputs and reduce the cost of production. The rumen epithelium is responsible for absorption and metabolism of nutrients and microbial by-products, and may play a significan...

Automatic Control of Gene Expression in Mammalian Cells.

PubMed

Fracassi, Chiara; Postiglione, Lorena; Fiore, Gianfranco; di Bernardo, Diego

2016-04-15

Automatic control of gene expression in living cells is paramount importance to characterize both endogenous gene regulatory networks and synthetic circuits. In addition, such a technology can be used to maintain the expression of synthetic circuit components in an optimal range in order to ensure reliable performance. Here we present a microfluidics-based method to automatically control gene expression from the tetracycline-inducible promoter in mammalian cells in real time. Our approach is based on the negative-feedback control engineering paradigm. We validated our method in a monoclonal population of cells constitutively expressing a fluorescent reporter protein (d2EYFP) downstream of a minimal CMV promoter with seven tet-responsive operator motifs (CMV-TET). These cells also constitutively express the tetracycline transactivator protein (tTA). In cells grown in standard growth medium, tTA is able to bind the CMV-TET promoter, causing d2EYFP to be maximally expressed. Upon addition of tetracycline to the culture medium, tTA detaches from the CMV-TET promoter, thus preventing d2EYFP expression. We tested two different model-independent control algorithms (relay and proportional-integral (PI)) to force a monoclonal population of cells to express an intermediate level of d2EYFP equal to 50% of its maximum expression level for up to 3500 min. The control input is either tetracycline-rich or standard growth medium. We demonstrated that both the relay and PI controllers can regulate gene expression at the desired level, despite oscillations (dampened in the case of the PI controller) around the chosen set point.

Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks.

PubMed

Blatti, Charles; Sinha, Saurabh

2016-07-15

Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or 'properties' such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene-gene or gene-property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. DRaWR was implemented as an R package available at veda.cs.illinois.edu/DRaWR. blatti@illinois.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

GSCALite: A Web Server for Gene Set Cancer Analysis.

PubMed

Liu, Chun-Jie; Hu, Fei-Fei; Xia, Mengxuan; Han, Leng; Zhang, Qiong; Guo, An-Yuan

2018-05-22

The availability of cancer genomic data makes it possible to analyze genes related to cancer. Cancer is usually the result of a set of genes and the signal of a single gene could be covered by background noise. Here, we present a web server named Gene Set Cancer Analysis (GSCALite) to analyze a set of genes in cancers with the following functional modules. (i) Differential expression in tumor vs normal, and the survival analysis; (ii) Genomic variations and their survival analysis; (iii) Gene expression associated cancer pathway activity; (iv) miRNA regulatory network for genes; (v) Drug sensitivity for genes; (vi) Normal tissue expression and eQTL for genes. GSCALite is a user-friendly web server for dynamic analysis and visualization of gene set in cancer and drug sensitivity correlation, which will be of broad utilities to cancer researchers. GSCALite is available on http://bioinfo.life.hust.edu.cn/web/GSCALite/. guoay@hust.edu.cn or zhangqiong@hust.edu.cn. Supplementary data are available at Bioinformatics online.

Synthetic biology: applying biological circuits beyond novel therapies.

PubMed

Dobrin, Anton; Saxena, Pratik; Fussenegger, Martin

2016-04-18

Synthetic biology, an engineering, circuit-driven approach to biology, has developed whole new classes of therapeutics. Unfortunately, these advances have thus far been undercapitalized upon by basic researchers. As discussed herein, using synthetic circuits, one can undertake exhaustive investigations of the endogenous circuitry found in nature, develop novel detectors and better temporally and spatially controlled inducers. One could detect changes in DNA, RNA, protein or even transient signaling events, in cell-based systems, in live mice, and in humans. Synthetic biology has also developed inducible systems that can be induced chemically, optically or using radio waves. This induction has been re-wired to lead to changes in gene expression, RNA stability and splicing, protein stability and splicing, and signaling via endogenous pathways. Beyond simple detectors and inducible systems, one can combine these modalities and develop novel signal integration circuits that can react to a very precise pre-programmed set of conditions or even to multiple sets of precise conditions. In this review, we highlight some tools that were developed in which these circuits were combined such that the detection of a particular event automatically triggered a specific output. Furthermore, using novel circuit-design strategies, circuits have been developed that can integrate multiple inputs together in Boolean logic gates composed of up to 6 inputs. We highlight the tools available and what has been developed thus far, and highlight how some clinical tools can be very useful in basic science. Most of the systems that are presented can be integrated together; and the possibilities far exceed the number of currently developed strategies.

Statistical Test of Expression Pattern (STEPath): a new strategy to integrate gene expression data with genomic information in individual and meta-analysis studies.

PubMed

Martini, Paolo; Risso, Davide; Sales, Gabriele; Romualdi, Chiara; Lanfranchi, Gerolamo; Cagnin, Stefano

2011-04-11

In the last decades, microarray technology has spread, leading to a dramatic increase of publicly available datasets. The first statistical tools developed were focused on the identification of significant differentially expressed genes. Later, researchers moved toward the systematic integration of gene expression profiles with additional biological information, such as chromosomal location, ontological annotations or sequence features. The analysis of gene expression linked to physical location of genes on chromosomes allows the identification of transcriptionally imbalanced regions, while, Gene Set Analysis focuses on the detection of coordinated changes in transcriptional levels among sets of biologically related genes. In this field, meta-analysis offers the possibility to compare different studies, addressing the same biological question to fully exploit public gene expression datasets. We describe STEPath, a method that starts from gene expression profiles and integrates the analysis of imbalanced region as an a priori step before performing gene set analysis. The application of STEPath in individual studies produced gene set scores weighted by chromosomal activation. As a final step, we propose a way to compare these scores across different studies (meta-analysis) on related biological issues. One complication with meta-analysis is batch effects, which occur because molecular measurements are affected by laboratory conditions, reagent lots and personnel differences. Major problems occur when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. We evaluated the power of combining chromosome mapping and gene set enrichment analysis, performing the analysis on a dataset of leukaemia (example of individual study) and on a dataset of skeletal muscle diseases (meta-analysis approach). In leukaemia, we identified the Hox gene set, a gene set closely related to the pathology that other algorithms of gene set analysis do not identify, while the meta-analysis approach on muscular disease discriminates between related pathologies and correlates similar ones from different studies. STEPath is a new method that integrates gene expression profiles, genomic co-expressed regions and the information about the biological function of genes. The usage of the STEPath-computed gene set scores overcomes batch effects in the meta-analysis approaches allowing the direct comparison of different pathologies and different studies on a gene set activation level.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.

PubMed

Kuleshov, Maxim V; Jones, Matthew R; Rouillard, Andrew D; Fernandez, Nicolas F; Duan, Qiaonan; Wang, Zichen; Koplev, Simon; Jenkins, Sherry L; Jagodnik, Kathleen M; Lachmann, Alexander; McDermott, Michael G; Monteiro, Caroline D; Gundersen, Gregory W; Ma'ayan, Avi

2016-07-08

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Deciphering the combinatorial architecture of a Drosophila homeotic gene enhancer

PubMed Central

Drewell, Robert A.; Nevarez, Michael J.; Kurata, Jessica S.; Winkler, Lauren N.; Li, Lily; Dresch, Jacqueline M.

2013-01-01

Summary In Drosophila, the 330 kb bithorax complex regulates cellular differentiation along the anterio-posterior axis during development in the thorax and abdomen and is comprised of three homeotic genes: Ultrabithorax, abdominal-A, and Abdominal-B. The expression of each of these genes is in turn controlled through interactions between transcription factors and a number of cis-regulatory modules in the neighboring intergenic regions. In this study, we examine how the sequence architecture of transcription factor binding sites mediates the functional activity of one of these cis-regulatory modules. Using computational, mathematical modeling and experimental molecular genetic approaches we investigate the IAB7b enhancer, which regulates Abdominal-B expression specifically in the presumptive seventh and ninth abdominal segments of the early embryo. A cross-species comparison of the IAB7b enhancer reveals an evolutionarily conserved signature motif containing two FUSHI-TARAZU activator transcription factor binding sites. We find that the transcriptional repressors KNIRPS, KRUPPEL and GIANT are able to restrict reporter gene expression to the posterior abdominal segments, using different molecular mechanisms including short-range repression and competitive binding. Additionally, we show the functional importance of the spacing between the two FUSHI-TARAZU binding sites and discuss the potential importance of cooperativity for transcriptional activation. Our results demonstrate that the transcriptional output of the IAB7b cis-regulatory module relies on a complex set of combinatorial inputs mediated by specific transcription factor binding and that the sequence architecture at this enhancer is critical to maintain robust regulatory function. PMID:24514265

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

Wong, Melissa; Bolovan-Fritts, Cynthia; Dar, Roy D.

Signal transduction circuits have long been known to differentiate between signals by amplifying inputs to different levels. Here, we describe a novel transcriptional circuitry that dynamically converts greater input levels into faster rates, without increasing the final equilibrium level (i.e. a rate amplifier). We utilize time-lapse microscopy to study human herpesvirus (cytomegalovirus) infection of live cells in real time. Strikingly, our results show that transcriptional activators accelerate viral gene expression in single cells without amplifying the steady-state levels of gene products in these cells. Experiment and modeling show that rate amplification operates by dynamically manipulating the traditional gain-bandwidth feedback relationshipmore » from electrical circuit theory to convert greater input levels into faster rates, and is driven by highly self-cooperative transcriptional feedback encoded by the virus s essential transactivator, IE2. This transcriptional rate-amplifier provides a significant fitness advantage for the virus and for minimal synthetic circuits. In general, rate-amplifiers may provide a mechanism for signal-transduction circuits to respond quickly to external signals without increasing steady-state levels of potentially cytotoxic molecules.« less

Phylogenetics and evolution of Su(var)3-9 SET genes in land plants: rapid diversification in structure and function.

PubMed

Zhu, Xinyu; Ma, Hong; Chen, Zhiduan

2011-03-09

Plants contain numerous Su(var)3-9 homologues (SUVH) and related (SUVR) genes, some of which await functional characterization. Although there have been studies on the evolution of plant Su(var)3-9 SET genes, a systematic evolutionary study including major land plant groups has not been reported. Large-scale phylogenetic and evolutionary analyses can help to elucidate the underlying molecular mechanisms and contribute to improve genome annotation. Putative orthologs of plant Su(var)3-9 SET protein sequences were retrieved from major representatives of land plants. A novel clustering that included most members analyzed, henceforth referred to as core Su(var)3-9 homologues and related (cSUVHR) gene clade, was identified as well as all orthologous groups previously identified. Our analysis showed that plant Su(var)3-9 SET proteins possessed a variety of domain organizations, and can be classified into five types and ten subtypes. Plant Su(var)3-9 SET genes also exhibit a wide range of gene structures among different paralogs within a family, even in the regions encoding conserved PreSET and SET domains. We also found that the majority of SUVH members were intronless and formed three subclades within the SUVH clade. A detailed phylogenetic analysis of the plant Su(var)3-9 SET genes was performed. A novel deep phylogenetic relationship including most plant Su(var)3-9 SET genes was identified. Additional domains such as SAR, ZnF_C2H2 and WIYLD were early integrated into primordial PreSET/SET/PostSET domain organization. At least three classes of gene structures had been formed before the divergence of Physcomitrella patens (moss) from other land plants. One or multiple retroposition events might have occurred among SUVH genes with the donor genes leading to the V-2 orthologous group. The structural differences among evolutionary groups of plant Su(var)3-9 SET genes with different functions were described, contributing to the design of further experimental studies.

Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets.

PubMed

Park, Inho; Lee, Kwang H; Lee, Doheon

2010-06-15

Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently. We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology. Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/~ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material. Supplementary data are available at Bioinformatics online.

Statistical plant set estimation using Schroeder-phased multisinusoidal input design

NASA Technical Reports Server (NTRS)

Bayard, D. S.

1992-01-01

A frequency domain method is developed for plant set estimation. The estimation of a plant 'set' rather than a point estimate is required to support many methods of modern robust control design. The approach here is based on using a Schroeder-phased multisinusoid input design which has the special property of placing input energy only at the discrete frequency points used in the computation. A detailed analysis of the statistical properties of the frequency domain estimator is given, leading to exact expressions for the probability distribution of the estimation error, and many important properties. It is shown that, for any nominal parametric plant estimate, one can use these results to construct an overbound on the additive uncertainty to any prescribed statistical confidence. The 'soft' bound thus obtained can be used to replace 'hard' bounds presently used in many robust control analysis and synthesis methods.

Phylogenetics and evolution of Trx SET genes in fully sequenced land plants.

PubMed

Zhu, Xinyu; Chen, Caoyi; Wang, Baohua

2012-04-01

Plant Trx SET proteins are involved in H3K4 methylation and play a key role in plant floral development. Genes encoding Trx SET proteins constitute a multigene family in which the copy number varies among plant species and functional divergence appears to have occurred repeatedly. To investigate the evolutionary history of the Trx SET gene family, we made a comprehensive evolutionary analysis on this gene family from 13 major representatives of green plants. A novel clustering (here named as cpTrx clade), which included the III-1, III-2, and III-4 orthologous groups, previously resolved was identified. Our analysis showed that plant Trx proteins possessed a variety of domain organizations and gene structures among paralogs. Additional domains such as PHD, PWWP, and FYR were early integrated into primordial SET-PostSET domain organization of cpTrx clade. We suggested that the PostSET domain was lost in some members of III-4 orthologous group during the evolution of land plants. At least four classes of gene structures had been formed at the early evolutionary stage of land plants. Three intronless orphan Trx SET genes from the Physcomitrella patens (moss) were identified, and supposedly, their parental genes have been eliminated from the genome. The structural differences among evolutionary groups of plant Trx SET genes with different functions were described, contributing to the design of further experimental studies.

An autonomous molecular computer for logical control of gene expression.

PubMed

Benenson, Yaakov; Gil, Binyamin; Ben-Dor, Uri; Adar, Rivka; Shapiro, Ehud

2004-05-27

Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems. Recently, simple molecular-scale autonomous programmable computers were demonstrated allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for 'logical' control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug.

Large scale cardiac modeling on the Blue Gene supercomputer.

PubMed

Reumann, Matthias; Fitch, Blake G; Rayshubskiy, Aleksandr; Keller, David U; Weiss, Daniel L; Seemann, Gunnar; Dössel, Olaf; Pitman, Michael C; Rice, John J

2008-01-01

Multi-scale, multi-physical heart models have not yet been able to include a high degree of accuracy and resolution with respect to model detail and spatial resolution due to computational limitations of current systems. We propose a framework to compute large scale cardiac models. Decomposition of anatomical data in segments to be distributed on a parallel computer is carried out by optimal recursive bisection (ORB). The algorithm takes into account a computational load parameter which has to be adjusted according to the cell models used. The diffusion term is realized by the monodomain equations. The anatomical data-set was given by both ventricles of the Visible Female data-set in a 0.2 mm resolution. Heterogeneous anisotropy was included in the computation. Model weights as input for the decomposition and load balancing were set to (a) 1 for tissue and 0 for non-tissue elements; (b) 10 for tissue and 1 for non-tissue elements. Scaling results for 512, 1024, 2048, 4096 and 8192 computational nodes were obtained for 10 ms simulation time. The simulations were carried out on an IBM Blue Gene/L parallel computer. A 1 s simulation was then carried out on 2048 nodes for the optimal model load. Load balances did not differ significantly across computational nodes even if the number of data elements distributed to each node differed greatly. Since the ORB algorithm did not take into account computational load due to communication cycles, the speedup is close to optimal for the computation time but not optimal overall due to the communication overhead. However, the simulation times were reduced form 87 minutes on 512 to 11 minutes on 8192 nodes. This work demonstrates that it is possible to run simulations of the presented detailed cardiac model within hours for the simulation of a heart beat.

ADGO: analysis of differentially expressed gene sets using composite GO annotation.

PubMed

Nam, Dougu; Kim, Sang-Bae; Kim, Seon-Kyu; Yang, Sungjin; Kim, Seon-Young; Chu, In-Sun

2006-09-15

Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached approximately 34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data. We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast. chu@kribb.re.kr http://array.kobic.re.kr/ADGO.

Maximize, minimize or target - optimization for a fitted response from a designed experiment

DOE PAGES

Anderson-Cook, Christine Michaela; Cao, Yongtao; Lu, Lu

2016-04-01

One of the common goals of running and analyzing a designed experiment is to find a location in the design space that optimizes the response of interest. Depending on the goal of the experiment, we may seek to maximize or minimize the response, or set the process to hit a particular target value. After the designed experiment, a response model is fitted and the optimal settings of the input factors are obtained based on the estimated response model. Furthermore, the suggested optimal settings of the input factors are then used in the production environment.

Pathway-based analysis of GWAs data identifies association of sex determination genes with susceptibility to testicular germ cell tumors.

PubMed

Koster, Roelof; Mitra, Nandita; D'Andrea, Kurt; Vardhanabhuti, Saran; Chung, Charles C; Wang, Zhaoming; Loren Erickson, R; Vaughn, David J; Litchfield, Kevin; Rahman, Nazneen; Greene, Mark H; McGlynn, Katherine A; Turnbull, Clare; Chanock, Stephen J; Nathanson, Katherine L; Kanetsky, Peter A

2014-11-15

Genome-wide association (GWA) studies of testicular germ cell tumor (TGCT) have identified 18 susceptibility loci, some containing genes encoding proteins important in male germ cell development. Deletions of one of these genes, DMRT1, lead to male-to-female sex reversal and are associated with development of gonadoblastoma. To further explore genetic association with TGCT, we undertook a pathway-based analysis of SNP marker associations in the Penn GWAs (349 TGCT cases and 919 controls). We analyzed a custom-built sex determination gene set consisting of 32 genes using three different methods of pathway-based analysis. The sex determination gene set ranked highly compared with canonical gene sets, and it was associated with TGCT (FDRG = 2.28 × 10(-5), FDRM = 0.014 and FDRI = 0.008 for Gene Set Analysis-SNP (GSA-SNP), Meta-Analysis Gene Set Enrichment of Variant Associations (MAGENTA) and Improved Gene Set Enrichment Analysis for Genome-wide Association Study (i-GSEA4GWAS) analysis, respectively). The association remained after removal of DMRT1 from the gene set (FDRG = 0.0002, FDRM = 0.055 and FDRI = 0.009). Using data from the NCI GWA scan (582 TGCT cases and 1056 controls) and UK scan (986 TGCT cases and 4946 controls), we replicated these findings (NCI: FDRG = 0.006, FDRM = 0.014, FDRI = 0.033, and UK: FDRG = 1.04 × 10(-6), FDRM = 0.016, FDRI = 0.025). After removal of DMRT1 from the gene set, the sex determination gene set remains associated with TGCT in the NCI (FDRG = 0.039, FDRM = 0.050 and FDRI = 0.055) and UK scans (FDRG = 3.00 × 10(-5), FDRM = 0.056 and FDRI = 0.044). With the exception of DMRT1, genes in the sex determination gene set have not previously been identified as TGCT susceptibility loci in these GWA scans, demonstrating the complementary nature of a pathway-based approach for genome-wide analysis of TGCT. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

TimeXNet Web: Identifying cellular response networks from diverse omics time-course data.

PubMed

Tan, Phit Ling; López, Yosvany; Nakai, Kenta; Patil, Ashwini

2018-05-14

Condition-specific time-course omics profiles are frequently used to study cellular response to stimuli and identify associated signaling pathways. However, few online tools allow users to analyze multiple types of high-throughput time-course data. TimeXNet Web is a web server that extracts a time-dependent gene/protein response network from time-course transcriptomic, proteomic or phospho-proteomic data, and an input interaction network. It classifies the given genes/proteins into time-dependent groups based on the time of their highest activity and identifies the most probable paths connecting genes/proteins in consecutive groups. The response sub-network is enriched in activated genes/proteins and contains novel regulators that do not show any observable change in the input data. Users can view the resultant response network and analyze it for functional enrichment. TimeXNet Web supports the analysis of high-throughput data from multiple species by providing high quality, weighted protein-protein interaction networks for 12 model organisms. http://txnet.hgc.jp/. ashwini@hgc.jp. Supplementary data are available at Bioinformatics online.

TRIAC/SCR proportional control circuit

DOEpatents

Hughes, Wallace J.

1999-01-01

A power controller device which uses a voltage-to-frequency converter in conjunction with a zero crossing detector to linearly and proportionally control AC power being supplied to a load. The output of the voltage-to frequency converter controls the "reset" input of a R-S flip flop, while an "0" crossing detector controls the "set" input. The output of the flip flop triggers a monostable multivibrator controlling the SCR or TRIAC firing circuit connected to the load. Logic gates prevent the direct triggering of the multivibrator in the rare instance where the "reset" and "set" inputs of the flip flop are in coincidence. The control circuit can be supplemented with a control loop, providing compensation for line voltage variations.

40 CFR 60.4176 - Additional requirements to provide heat input data.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 6 2011-07-01 2011-07-01 false Additional requirements to provide heat... requirements to provide heat input data. The owner or operator of a Hg Budget unit that monitors and reports Hg... monitor and report heat input rate at the unit level using the procedures set forth in part 75 of this...

Identification and characterization of finger millet OPAQUE2 transcription factor gene under different nitrogen inputs for understanding their role during accumulation of prolamin seed storage protein.

PubMed

Gaur, Vikram Singh; Kumar, Lallan; Gupta, Supriya; Jaiswal, J P; Pandey, Dinesh; Kumar, Anil

2018-03-01

In this study, we report the isolation and characterization of the mRNA encoding OPAQUE2 (O2) like TF of finger millet (FM) ( Eleusine coracana) ( EcO2 ). Full-length EcO2 mRNA was isolated using conserved primers designed by aligning O2 mRNAs of different cereals followed by 3' and 5' RACE (Rapid Amplification of cDNA Ends). The assembled full-length EcO2 mRNA was found to contain an ORF of 1248-nt coding the 416 amino acids O2 protein. Domain analysis revealed the presence of the BLZ and bZIP-C domains which is a characteristic feature of O2 proteins. Phylogenetic analysis of EcO2 protein with other bZIP proteins identified using finger millet transcriptome data and O2 proteins of other cereals showed that EcO2 shared high sequence similarity with barley BLZ1 protein. Transcripts of EcO2 were detected in root, stem, leaves, and seed development stages. Furthermore, to investigate nitrogen responsiveness and the role of EcO2 in regulating seed storage protein gene expression, the expression profiles of EcO2 along with an α-prolamin gene were studied during the seed development stages of two FM genotypes (GE-3885 and GE-1437) differing in grain protein content (13.8 and 6.2%, respectively) grown under increasing nitrogen inputs. Compared to GE-1437, the EcO2 was relatively highly expressed during the S2 stage of seed development which further increased as nitrogen input was increased. The Ecα - prolamin gene was strongly induced in the high protein genotype (GE-3885) at all nitrogen inputs. These results indicate the presence of nitrogen responsiveness regulatory elements which might play an important role in accumulating protein in FM genotypes through modulating EcO2 expression by sensing plant nitrogen status.

Skin Transcriptomes of common bottlenose dolphins (Tursiops truncatus) from the northern Gulf of Mexico and southeastern U.S. Atlantic coasts.

PubMed

Neely, Marion G; Morey, Jeanine S; Anderson, Paul; Balmer, Brian C; Ylitalo, Gina M; Zolman, Eric S; Speakman, Todd R; Sinclair, Carrie; Bachman, Melannie J; Huncik, Kevin; Kucklick, John; Rosel, Patricia E; Mullin, Keith D; Rowles, Teri K; Schwacke, Lori H; Van Dolah, Frances M

2018-04-01

Common bottlenose dolphins serve as sentinels for the health of their coastal environments as they are susceptible to health impacts from anthropogenic inputs through both direct exposure and food web magnification. Remote biopsy samples have been widely used to reveal contaminant burdens in free-ranging bottlenose dolphins, but do not address the health consequences of this exposure. To gain insight into whether remote biopsies can also identify health impacts associated with contaminant burdens, we employed RNA sequencing (RNA-seq) to interrogate the transcriptomes of remote skin biopsies from 116 bottlenose dolphins from the northern Gulf of Mexico and southeastern U.S. Atlantic coasts. Gene expression was analyzed using principal component analysis, differential expression testing, and gene co-expression networks, and the results correlated to season, location, and contaminant burden. Season had a significant impact, with over 60% of genes differentially expressed between spring/summer and winter months. Geographic location exhibited lesser effects on the transcriptome, with 23.5% of genes differentially expressed between the northern Gulf of Mexico and the southeastern U.S. Atlantic locations. Despite a large overlap between the seasonal and geographical gene sets, the pathways altered in the observed gene expression profiles were somewhat distinct. Co-regulated gene modules and differential expression analysis both identified epidermal development and cellular architecture pathways to be expressed at lower levels in animals from the northern Gulf of Mexico. Although contaminant burdens measured were not significantly different between regions, some correlation with contaminant loads in individuals was observed among co-expressed gene modules, but these did not include classical detoxification pathways. Instead, this study identified other, possibly downstream pathways, including those involved in cellular architecture, immune response, and oxidative stress, that may prove to be contaminant responsive markers in bottlenose dolphin skin. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

User-centered design of multi-gene sequencing panel reports for clinicians.

PubMed

Cutting, Elizabeth; Banchero, Meghan; Beitelshees, Amber L; Cimino, James J; Fiol, Guilherme Del; Gurses, Ayse P; Hoffman, Mark A; Jeng, Linda Jo Bone; Kawamoto, Kensaku; Kelemen, Mark; Pincus, Harold Alan; Shuldiner, Alan R; Williams, Marc S; Pollin, Toni I; Overby, Casey Lynnette

2016-10-01

The objective of this study was to develop a high-fidelity prototype for delivering multi-gene sequencing panel (GS) reports to clinicians that simulates the user experience of a final application. The delivery and use of GS reports can occur within complex and high-paced healthcare environments. We employ a user-centered software design approach in a focus group setting in order to facilitate gathering rich contextual information from a diverse group of stakeholders potentially impacted by the delivery of GS reports relevant to two precision medicine programs at the University of Maryland Medical Center. Responses from focus group sessions were transcribed, coded and analyzed by two team members. Notification mechanisms and information resources preferred by participants from our first phase of focus groups were incorporated into scenarios and the design of a software prototype for delivering GS reports. The goal of our second phase of focus group, to gain input on the prototype software design, was accomplished through conducting task walkthroughs with GS reporting scenarios. Preferences for notification, content and consultation from genetics specialists appeared to depend upon familiarity with scenarios for ordering and delivering GS reports. Despite familiarity with some aspects of the scenarios we proposed, many of our participants agreed that they would likely seek consultation from a genetics specialist after viewing the test reports. In addition, participants offered design and content recommendations. Findings illustrated a need to support customized notification approaches, user-specific information, and access to genetics specialists with GS reports. These design principles can be incorporated into software applications that deliver GS reports. Our user-centered approach to conduct this assessment and the specific input we received from clinicians may also be relevant to others working on similar projects. Copyright © 2016 Elsevier Inc. All rights reserved.

SRB Data and Information

Atmospheric Science Data Center

2017-01-13

... grid. Model inputs of cloud amounts and other atmospheric state parameters are also available in some of the data sets. Primary inputs to ... Analysis (SMOBA), an assimilation product from NOAA's Climate Prediction Center. SRB products are reformatted for the use of ...

Input Decimated Ensembles

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)

2001-01-01

Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.

Impact of enhanced sensory input on treadmill step frequency: infants born with myelomeningocele.

PubMed

Pantall, Annette; Teulier, Caroline; Smith, Beth A; Moerchen, Victoria; Ulrich, Beverly D

2011-01-01

To determine the effect of enhanced sensory input on the step frequency of infants with myelomeningocele (MMC) when supported on a motorized treadmill. Twenty-seven infants aged 2 to 10 months with MMC lesions at, or caudal to, L1 participated. We supported infants upright on the treadmill for 2 sets of 6 trials, each 30 seconds long. Enhanced sensory inputs within each set were presented in random order and included baseline, visual flow, unloading, weights, Velcro, and friction. Overall friction and visual flow significantly increased step rate, particularly for the older subjects. Friction and Velcro increased stance-phase duration. Enhanced sensory input had minimal effect on leg activity when infants were not stepping. : Increased friction via Dycem and enhancing visual flow via a checkerboard pattern on the treadmill belt appear to be more effective than the traditional smooth black belt surface for eliciting stepping patterns in infants with MMC.

Impact of Enhanced Sensory Input on Treadmill Step Frequency: Infants Born With Myelomeningocele

PubMed Central

Pantall, Annette; Teulier, Caroline; Smith, Beth A; Moerchen, Victoria; Ulrich, Beverly D.

2012-01-01

Purpose To determine the effect of enhanced sensory input on the step frequency of infants with myelomeningocele (MMC) when supported on a motorized treadmill. Methods Twenty seven infants aged 2 to 10 months with MMC lesions at or caudal to L1 participated. We supported infants upright on the treadmill for 2 sets of 6 trials, each 30s long. Enhanced sensory inputs within each set were presented in random order and included: baseline, visual flow, unloading, weights, Velcro and friction. Results Overall friction and visual flow significantly increased step rate, particularly for the older group. Friction and Velcro increased stance phase duration. Enhanced sensory input had minimal effect on leg activity when infants were not stepping. Conclusions Increased friction via Dycem and enhancing visual flow via a checkerboard pattern on the treadmill belt appear more effective than the traditional smooth black belt surface for eliciting stepping patterns in infants with MMC. PMID:21266940

Cycle accurate and cycle reproducible memory for an FPGA based hardware accelerator

DOEpatents

Asaad, Sameh W.; Kapur, Mohit

2016-03-15

A method, system and computer program product are disclosed for using a Field Programmable Gate Array (FPGA) to simulate operations of a device under test (DUT). The DUT includes a device memory having a number of input ports, and the FPGA is associated with a target memory having a second number of input ports, the second number being less than the first number. In one embodiment, a given set of inputs is applied to the device memory at a frequency Fd and in a defined cycle of time, and the given set of inputs is applied to the target memory at a frequency Ft. Ft is greater than Fd and cycle accuracy is maintained between the device memory and the target memory. In an embodiment, a cycle accurate model of the DUT memory is created by separating the DUT memory interface protocol from the target memory storage array.

Integration: valuing stakeholder input in setting priorities for socially sustainable egg production.

PubMed

Swanson, J C; Lee, Y; Thompson, P B; Bawden, R; Mench, J A

2011-09-01

Setting directions and goals for animal production systems requires the integration of information achieved through internal and external processes. The importance of stakeholder input in setting goals for sustainable animal production systems should not be overlooked by the agricultural animal industries. Stakeholders play an integral role in setting the course for many aspects of animal production, from influencing consumer preferences to setting public policy. The Socially Sustainable Egg Production Project (SSEP) involved the development of white papers on various aspects of egg production, followed by a stakeholder workshop to help frame the issues for the future of sustainable egg production. Representatives from the environmental, food safety, food retail, consumer, animal welfare, and the general farm and egg production sectors participated with members of the SSEP coordination team in a 1.5-d workshop to explore socially sustainable egg production. This paper reviews the published literature on values integration methodologies and the lessons learned from animal welfare assessment models. The integration method used for the SSEP stakeholder workshop and its outcome are then summarized. The method used for the SSEP stakeholder workshop can be used to obtain stakeholder input on sustainable production in other farm animal industries.

Training a molecular automaton to play a game

NASA Astrophysics Data System (ADS)

Pei, Renjun; Matamoros, Elizabeth; Liu, Manhong; Stefanovic, Darko; Stojanovic, Milan N.

2010-11-01

Research at the interface between chemistry and cybernetics has led to reports of `programmable molecules', but what does it mean to say `we programmed a set of solution-phase molecules to do X'? A survey of recently implemented solution-phase circuitry indicates that this statement could be replaced with `we pre-mixed a set of molecules to do X and functional subsets of X'. These hard-wired mixtures are then exposed to a set of molecular inputs, which can be interpreted as being keyed to human moves in a game, or as assertions of logical propositions. In nucleic acids-based systems, stemming from DNA computation, these inputs can be seen as generic oligonucleotides. Here, we report using reconfigurable nucleic acid catalyst-based units to build a multipurpose reprogrammable molecular automaton that goes beyond single-purpose `hard-wired' molecular automata. The automaton covers all possible responses to two consecutive sets of four inputs (such as four first and four second moves for a generic set of trivial two-player two-move games). This is a model system for more general molecular field programmable gate array (FPGA)-like devices that can be programmed by example, which means that the operator need not have any knowledge of molecular computing methods.

Training a molecular automaton to play a game.

PubMed

Pei, Renjun; Matamoros, Elizabeth; Liu, Manhong; Stefanovic, Darko; Stojanovic, Milan N

2010-11-01

Research at the interface between chemistry and cybernetics has led to reports of 'programmable molecules', but what does it mean to say 'we programmed a set of solution-phase molecules to do X'? A survey of recently implemented solution-phase circuitry indicates that this statement could be replaced with 'we pre-mixed a set of molecules to do X and functional subsets of X'. These hard-wired mixtures are then exposed to a set of molecular inputs, which can be interpreted as being keyed to human moves in a game, or as assertions of logical propositions. In nucleic acids-based systems, stemming from DNA computation, these inputs can be seen as generic oligonucleotides. Here, we report using reconfigurable nucleic acid catalyst-based units to build a multipurpose reprogrammable molecular automaton that goes beyond single-purpose 'hard-wired' molecular automata. The automaton covers all possible responses to two consecutive sets of four inputs (such as four first and four second moves for a generic set of trivial two-player two-move games). This is a model system for more general molecular field programmable gate array (FPGA)-like devices that can be programmed by example, which means that the operator need not have any knowledge of molecular computing methods.

Wastewater and Saltwater: Studying the Biogeochemistry and Microbial Activity Associated with Wastewater Inputs to San Francisco Bay

NASA Astrophysics Data System (ADS)

Challenor, T.; Menendez, A. D.; Damashek, J.; Francis, C. A.; Casciotti, K. L.

2014-12-01

Nitrification is the process of converting ammonium (NH­­4+) into nitrate (NO3-), and is a crucial step in removing nitrogen (N) from aquatic ecosystems. This process is governed by ammonia-oxidizing bacteria (AOB) and archaea (AOA) that utilize the ammonia monooxygenase gene (amoA). Studying the rates of nitrification and the abundances of ammonia-oxidizing microorganisms in south San Francisco Bay's Artesian Slough, which receives treated effluent from the massive San Jose-Santa Clara Regional Wastewater Facility, are important for understanding the cycling of nutrients in this small but complex estuary. Wastewater inputs can have negative environmental impacts, such as the release of nitrous oxide, a byproduct of nitrification and a powerful greenhouse gas. Nutrient inputs can also increase productivity and sometimes lead to oxygen depletion. Assessing the relative abundance and diversity of AOA and AOB, along with measuring nitrification rates gives vital information about the biology and biogeochemistry of this important N-cycling process. To calculate nitrification rates, water samples were spiked with 15N-labeled ammonium and incubated in triplicate for 24 hours. Four time-points were extracted across the incubation and the "denitrifier" method was used to measure the isotopic ratio of nitrate in the samples over time. In order to determine relative ratios of AOB to AOA, DNA was extracted from water samples and used in clade-specific amoA PCR assays. Nitrification rates were detectable in all locations sampled and were higher than in other regions of the bay, as were concentrations of nitrate and ammonium. Rates were highest in the regions of Artesian Slough most directly affected by wastewater effluent. AOB vastly outnumbered AOA, which is consistent with other studies showing that AOB prefer high nutrient environments. AOB diversity includes clades of Nitrosospira and Nitrosomonas prevalent in estuarine settings. Many of the sequenced genes are related to estuarine sediment found at other sites in the San Francisco Bay as well as the Chesapeake Bay, China East Sea, and Pearl River Estuary. Our data provide evidence for the path that N takes once entering the estuary and also further characterize the behavior of nitrifying microorganisms in extremely high-nutrient aquatic environments.

FARO server: Meta-analysis of gene expression by matching gene expression signatures to a compendium of public gene expression data.

PubMed

Manijak, Mieszko P; Nielsen, Henrik B

2011-06-11

Although, systematic analysis of gene annotation is a powerful tool for interpreting gene expression data, it sometimes is blurred by incomplete gene annotation, missing expression response of key genes and secondary gene expression responses. These shortcomings may be partially circumvented by instead matching gene expression signatures to signatures of other experiments. To facilitate this we present the Functional Association Response by Overlap (FARO) server, that match input signatures to a compendium of 242 gene expression signatures, extracted from more than 1700 Arabidopsis microarray experiments. Hereby we present a publicly available tool for robust characterization of Arabidopsis gene expression experiments which can point to similar experimental factors in other experiments. The server is available at http://www.cbs.dtu.dk/services/faro/.

Ways to suppress click and pop for class D amplifiers

NASA Astrophysics Data System (ADS)

Haishi, Wang; Bo, Zhang; Jiang, Sun

2012-08-01

Undesirable audio click and pop may be generated in a speaker or headphone. Compared to linear (class A/B/AB) amplifiers, class D amplifiers that comprise of an input stage and a modulation stage are more prone to producing click and pop. This article analyzes sources that generate click and pop in class D amplifiers, and corresponding ways to suppress them. For a class D amplifier with a single-ended input, click and pop is likely to be due to two factors. One is from a voltage difference (VDIF) between the voltage of an input capacitance (VCIN) and a reference voltage (VREF) of the input stage, and the other one is from the non-linear switching during the setting up of the bias and feedback voltages/currents (BFVC) of the modulation stage. In this article, a fast charging loop is introduced into the input stage to charge VCIN to roughly near VREF. Then a correction loop further charges or discharges VCIN, substantially equalizing it with VREF. Dummy switches are introduced into the modulation stage to provide switching signals for setting up BFVC, and the power switches are disabled until the BFVC are set up successfully. A two channel single-ended class D amplifier with the above features is fabricated with 0.5 μm Bi-CMOS process. Road test and fast Fourier transform analysis indicate that there is no noticeable click and pop.

Childhood socioeconomic status amplifies genetic effects on adult intelligence.

PubMed

Bates, Timothy C; Lewis, Gary J; Weiss, Alexander

2013-10-01

Studies of intelligence in children reveal significantly higher heritability among groups with high socioeconomic status (SES) than among groups with low SES. These interaction effects, however, have not been examined in adults, when between-families environmental effects are reduced. Using 1,702 adult twins (aged 24-84) for whom intelligence assessment data were available, we tested for interactions between childhood SES and genetic effects, between-families environmental effects, and unique environmental effects. Higher SES was associated with higher mean intelligence scores. Moreover, the magnitude of genetic influences on intelligence was proportional to SES. By contrast, environmental influences were constant. These results suggest that rather than setting lower and upper bounds on intelligence, genes multiply environmental inputs that support intellectual growth. This mechanism implies that increasing SES may raise average intelligence but also magnifies individual differences in intelligence.

An analysis of geothermal and carbonic springs in the western United States sustained by deep fluid inputs.

PubMed

Colman, D R; Garcia, J R; Crossey, L J; Karlstrom, K; Jackson-Weaver, O; Takacs-Vesbach, C

2014-01-01

Hydrothermal springs harbor unique microbial communities that have provided insight into the early evolution of life, expanded known microbial diversity, and documented a deep Earth biosphere. Mesothermal (cool but above ambient temperature) continental springs, however, have largely been ignored although they may also harbor unique populations of micro-organisms influenced by deep subsurface fluid mixing with near surface fluids. We investigated the microbial communities of 28 mesothermal springs in diverse geologic provinces of the western United States that demonstrate differential mixing of deeply and shallowly circulated water. Culture-independent analysis of the communities yielded 1966 bacterial and 283 archaeal 16S rRNA gene sequences. The springs harbored diverse taxa and shared few operational taxonomic units (OTUs) across sites. The Proteobacteria phylum accounted for most of the dataset (81.2% of all 16S rRNA genes), with 31 other phyla/candidate divisions comprising the remainder. A small percentage (~6%) of bacterial 16S rRNA genes could not be classified at the phylum level, but were mostly distributed in those springs with greatest inputs of deeply sourced fluids. Archaeal diversity was limited to only four springs and was primarily composed of well-characterized Thaumarchaeota. Geochemistry across the dataset was varied, but statistical analyses suggested that greater input of deeply sourced fluids was correlated with community structure. Those with lesser input contained genera typical of surficial waters, while some of the springs with greater input may contain putatively chemolithotrophic communities. The results reported here expand our understanding of microbial diversity of continental geothermal systems and suggest that these communities are influenced by the geochemical and hydrologic characteristics arising from deeply sourced (mantle-derived) fluid mixing. The springs and communities we report here provide evidence for opportunities to understand new dimensions of continental geobiological processes where warm, highly reduced fluids are mixing with more oxidized surficial waters. © 2013 John Wiley & Sons Ltd.

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights

PubMed Central

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-01

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher’s exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO’s usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher. PMID:26750448

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

PubMed

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-11

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

A Prototype Two-Level Multicomputer Study

DTIC Science & Technology

1999-01-01

Wakeup : When this input is asserted, the wakeJnt bit in the special register ISR (Interrupt Status Register) is set. 43 MYRINET SAN INTERFACE Pin...including the double-word pointed to by RMC, the headJnt bit of ISR is set (page 10). RMW Receive-Message Header Wakeup : This is the same physical register...Register) are both equal to 1. WAKE synch. I Wakeup : When this input is asserted, the wakeJnt bit in the special register ISR (Interrupt Status

A non-linear dimension reduction methodology for generating data-driven stochastic input models

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

Ganapathysubramanian, Baskar; Zabaras, Nicholas

Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem ofmore » manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space R{sup n}. An isometric mapping F from M to a low-dimensional, compact, connected set A is contained in R{sup d}(d<

Hearing Aids and Music

PubMed Central

Chasin, Marshall; Russo, Frank A.

2004-01-01

Historically, the primary concern for hearing aid design and fitting is optimization for speech inputs. However, increasingly other types of inputs are being investigated and this is certainly the case for music. Whether the hearing aid wearer is a musician or merely someone who likes to listen to music, the electronic and electro-acoustic parameters described can be optimized for music as well as for speech. That is, a hearing aid optimally set for music can be optimally set for speech, even though the converse is not necessarily true. Similarities and differences between speech and music as inputs to a hearing aid are described. Many of these lead to the specification of a set of optimal electro-acoustic characteristics. Parameters such as the peak input-limiting level, compression issues—both compression ratio and knee-points—and number of channels all can deleteriously affect music perception through hearing aids. In other cases, it is not clear how to set other parameters such as noise reduction and feedback control mechanisms. Regardless of the existence of a “music program,” unless the various electro-acoustic parameters are available in a hearing aid, music fidelity will almost always be less than optimal. There are many unanswered questions and hypotheses in this area. Future research by engineers, researchers, clinicians, and musicians will aid in the clarification of these questions and their ultimate solutions. PMID:15497032

Program for User-Friendly Management of Input and Output Data Sets

NASA Technical Reports Server (NTRS)

Klimeck, Gerhard

2003-01-01

A computer program manages large, hierarchical sets of input and output (I/O) parameters (typically, sequences of alphanumeric data) involved in computational simulations in a variety of technological disciplines. This program represents sets of parameters as structures coded in object-oriented but otherwise standard American National Standards Institute C language. Each structure contains a group of I/O parameters that make sense as a unit in the simulation program with which this program is used. The addition of options and/or elements to sets of parameters amounts to the addition of new elements to data structures. By association of child data generated in response to a particular user input, a hierarchical ordering of input parameters can be achieved. Associated with child data structures are the creation and description mechanisms within the parent data structures. Child data structures can spawn further child data structures. In this program, the creation and representation of a sequence of data structures is effected by one line of code that looks for children of a sequence of structures until there are no more children to be found. A linked list of structures is created dynamically and is completely represented in the data structures themselves. Such hierarchical data presentation can guide users through otherwise complex setup procedures and it can be integrated within a variety of graphical representations.

A support vector machine based test for incongruence between sets of trees in tree space

PubMed Central

2012-01-01

Background The increased use of multi-locus data sets for phylogenetic reconstruction has increased the need to determine whether a set of gene trees significantly deviate from the phylogenetic patterns of other genes. Such unusual gene trees may have been influenced by other evolutionary processes such as selection, gene duplication, or horizontal gene transfer. Results Motivated by this problem we propose a nonparametric goodness-of-fit test for two empirical distributions of gene trees, and we developed the software GeneOut to estimate a p-value for the test. Our approach maps trees into a multi-dimensional vector space and then applies support vector machines (SVMs) to measure the separation between two sets of pre-defined trees. We use a permutation test to assess the significance of the SVM separation. To demonstrate the performance of GeneOut, we applied it to the comparison of gene trees simulated within different species trees across a range of species tree depths. Applied directly to sets of simulated gene trees with large sample sizes, GeneOut was able to detect very small differences between two set of gene trees generated under different species trees. Our statistical test can also include tree reconstruction into its test framework through a variety of phylogenetic optimality criteria. When applied to DNA sequence data simulated from different sets of gene trees, results in the form of receiver operating characteristic (ROC) curves indicated that GeneOut performed well in the detection of differences between sets of trees with different distributions in a multi-dimensional space. Furthermore, it controlled false positive and false negative rates very well, indicating a high degree of accuracy. Conclusions The non-parametric nature of our statistical test provides fast and efficient analyses, and makes it an applicable test for any scenario where evolutionary or other factors can lead to trees with different multi-dimensional distributions. The software GeneOut is freely available under the GNU public license. PMID:22909268

Method for analyzing the chemical composition of liquid effluent from a direct contact condenser

DOEpatents

Bharathan, Desikan; Parent, Yves; Hassani, A. Vahab

2001-01-01

A computational modeling method for predicting the chemical, physical, and thermodynamic performance of a condenser using calculations based on equations of physics for heat, momentum and mass transfer and equations of equilibrium thermodynamics to determine steady state profiles of parameters throughout the condenser. The method includes providing a set of input values relating to a condenser including liquid loading, vapor loading, and geometric characteristics of the contact medium in the condenser. The geometric and packing characteristics of the contact medium include the dimensions and orientation of a channel in the contact medium. The method further includes simulating performance of the condenser using the set of input values to determine a related set of output values such as outlet liquid temperature, outlet flow rates, pressures, and the concentration(s) of one or more dissolved noncondensable gas species in the outlet liquid. The method may also include iteratively performing the above computation steps using a plurality of sets of input values and then determining whether each of the resulting output values and performance profiles satisfies acceptance criteria.

Family medicine outpatient encounters are more complex than those of cardiology and psychiatry.

PubMed

Katerndahl, David; Wood, Robert; Jaén, Carlos Roberto

2011-01-01

comparison studies suggest that the guideline-concordant care provided for specific medical conditions is less optimal in primary care compared with cardiology and psychiatry settings. The purpose of this study is to estimate the relative complexity of patient encounters in general/family practice, cardiology, and psychiatry settings. secondary analysis of the 2000 National Ambulatory Medical Care Survey data for ambulatory patients seen in general/family practice, cardiology, and psychiatry settings was performed. The complexity for each variable was estimated as the quantity weighted by variability and diversity. there is minimal difference in the unadjusted input and total encounter complexity of general/family practice and cardiology; psychiatry's input is less complex. Cardiology encounters involved more input quantitatively, but the diversity of general/family practice input eliminated the difference. Cardiology also involved more complex output. However, when the duration of visit is factored in, the complexity of care provided per hour in general/family practice is 33% more relative to cardiology and 5 times more relative to psychiatry. care during family physician visits is more complex per hour than the care during visits to cardiologists or psychiatrists. This may account for a lower rate of completion of process items measured for quality of care.

Automated method for the systematic interpretation of resonance peaks in spectrum data

DOEpatents

Damiano, B.; Wood, R.T.

1997-04-22

A method is described for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical model. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system. 1 fig.

Automated method for the systematic interpretation of resonance peaks in spectrum data

DOEpatents

Damiano, Brian; Wood, Richard T.

1997-01-01

A method for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system.

Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

PubMed Central

Boonjing, Veera; Intakosum, Sarun

2016-01-01

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span. PMID:27974883

Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend.

PubMed

Inthachot, Montri; Boonjing, Veera; Intakosum, Sarun

2016-01-01

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

Coarse-coded higher-order neural networks for PSRI object recognition. [position, scale, and rotation invariant

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly; Reid, Max B.

1993-01-01

A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.

Fuzzy rule based estimation of agricultural diffuse pollution concentration in streams.

PubMed

Singh, Raj Mohan

2008-04-01

Outflow from the agricultural fields carries diffuse pollutants like nutrients, pesticides, herbicides etc. and transports the pollutants into the nearby streams. It is a matter of serious concern for water managers and environmental researchers. The application of chemicals in the agricultural fields, and transport of these chemicals into streams are uncertain that cause complexity in reliable stream quality predictions. The chemical characteristics of applied chemical, percentage of area under the chemical application etc. are some of the main inputs that cause pollution concentration as output in streams. Each of these inputs and outputs may contain measurement errors. Fuzzy rule based model based on fuzzy sets suits to address uncertainties in inputs by incorporating overlapping membership functions for each of inputs even for limited data availability situations. In this study, the property of fuzzy sets to address the uncertainty in input-output relationship is utilized to obtain the estimate of concentrations of a herbicide, atrazine, in a stream. The data of White river basin, a part of the Mississippi river system, is used for developing the fuzzy rule based models. The performance of the developed methodology is found encouraging.

Virulotyping of Shigella spp. isolated from pediatric patients in Tehran, Iran.

PubMed

Ranjbar, Reza; Bolandian, Masomeh; Behzadi, Payam

2017-03-01

Shigellosis is a considerable infectious disease with high morbidity and mortality among children worldwide. In this survey the prevalence of four important virulence genes including ial, ipaH, set1A, and set1B were investigated among Shigella strains and the related gene profiles identified in the present investigation, stool specimens were collected from children who were referred to two hospitals in Tehran, Iran. The samples were collected during 3 years (2008-2010) from children who were suspected to shigellosis. Shigella spp. were identified throughout microbiological and serological tests and then subjected to PCR for virulotyping. Shigella sonnei was ranking first (65.5%) followed by Shigella flexneri (25.9%), Shigella boydii (6.9%), and Shigella dysenteriae (1.7%). The ial gene was the most frequent virulence gene among isolated bacterial strains and was followed by ipaH, set1B, and set1A. S. flexneri possessed all of the studied virulence genes (ial 65.51%, ipaH 58.62%, set1A 12.07%, and set1B 22.41%). Moreover, the pattern of virulence gene profiles including ial, ial-ipaH, ial-ipaH-set1B, and ial-ipaH-set1B-set1A was identified for isolated Shigella spp. strains. The pattern of virulence genes is changed in isolated strains of Shigella in this study. So, the ial gene is placed first and the ipaH in second.

A method of searching for related literature on protein structure analysis by considering a user's intention

PubMed Central

2015-01-01

Background In recent years, with advances in techniques for protein structure analysis, the knowledge about protein structure and function has been published in a vast number of articles. A method to search for specific publications from such a large pool of articles is needed. In this paper, we propose a method to search for related articles on protein structure analysis by using an article itself as a query. Results Each article is represented as a set of concepts in the proposed method. Then, by using similarities among concepts formulated from databases such as Gene Ontology, similarities between articles are evaluated. In this framework, the desired search results vary depending on the user's search intention because a variety of information is included in a single article. Therefore, the proposed method provides not only one input article (primary article) but also additional articles related to it as an input query to determine the search intention of the user, based on the relationship between two query articles. In other words, based on the concepts contained in the input article and additional articles, we actualize a relevant literature search that considers user intention by varying the degree of attention given to each concept and modifying the concept hierarchy graph. Conclusions We performed an experiment to retrieve relevant papers from articles on protein structure analysis registered in the Protein Data Bank by using three query datasets. The experimental results yielded search results with better accuracy than when user intention was not considered, confirming the effectiveness of the proposed method. PMID:25952498

WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation

PubMed Central

2013-01-01

Background SNPs&GO is a method for the prediction of deleterious Single Amino acid Polymorphisms (SAPs) using protein functional annotation. In this work, we present the web server implementation of SNPs&GO (WS-SNPs&GO). The server is based on Support Vector Machines (SVM) and for a given protein, its input comprises: the sequence and/or its three-dimensional structure (when available), a set of target variations and its functional Gene Ontology (GO) terms. The output of the server provides, for each protein variation, the probabilities to be associated to human diseases. Results The server consists of two main components, including updated versions of the sequence-based SNPs&GO (recently scored as one of the best algorithms for predicting deleterious SAPs) and of the structure-based SNPs&GO3d programs. Sequence and structure based algorithms are extensively tested on a large set of annotated variations extracted from the SwissVar database. Selecting a balanced dataset with more than 38,000 SAPs, the sequence-based approach achieves 81% overall accuracy, 0.61 correlation coefficient and an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve of 0.88. For the subset of ~6,600 variations mapped on protein structures available at the Protein Data Bank (PDB), the structure-based method scores with 84% overall accuracy, 0.68 correlation coefficient, and 0.91 AUC. When tested on a new blind set of variations, the results of the server are 79% and 83% overall accuracy for the sequence-based and structure-based inputs, respectively. Conclusions WS-SNPs&GO is a valuable tool that includes in a unique framework information derived from protein sequence, structure, evolutionary profile, and protein function. WS-SNPs&GO is freely available at http://snps.biofold.org/snps-and-go. PMID:23819482

Integrating genome-wide association study and expression quantitative trait loci data identifies multiple genes and gene set associated with neuroticism.

PubMed

Fan, Qianrui; Wang, Wenyu; Hao, Jingcan; He, Awen; Wen, Yan; Guo, Xiong; Wu, Cuiyan; Ning, Yujie; Wang, Xi; Wang, Sen; Zhang, Feng

2017-08-01

Neuroticism is a fundamental personality trait with significant genetic determinant. To identify novel susceptibility genes for neuroticism, we conducted an integrative analysis of genomic and transcriptomic data of genome wide association study (GWAS) and expression quantitative trait locus (eQTL) study. GWAS summary data was driven from published studies of neuroticism, totally involving 170,906 subjects. eQTL dataset containing 927,753 eQTLs were obtained from an eQTL meta-analysis of 5311 samples. Integrative analysis of GWAS and eQTL data was conducted by summary data-based Mendelian randomization (SMR) analysis software. To identify neuroticism associated gene sets, the SMR analysis results were further subjected to gene set enrichment analysis (GSEA). The gene set annotation dataset (containing 13,311 annotated gene sets) of GSEA Molecular Signatures Database was used. SMR single gene analysis identified 6 significant genes for neuroticism, including MSRA (p value=2.27×10 -10 ), MGC57346 (p value=6.92×10 -7 ), BLK (p value=1.01×10 -6 ), XKR6 (p value=1.11×10 -6 ), C17ORF69 (p value=1.12×10 -6 ) and KIAA1267 (p value=4.00×10 -6 ). Gene set enrichment analysis observed significant association for Chr8p23 gene set (false discovery rate=0.033). Our results provide novel clues for the genetic mechanism studies of neuroticism. Copyright © 2017. Published by Elsevier Inc.

ExAtlas: An interactive online tool for meta-analysis of gene expression data.

PubMed

Sharov, Alexei A; Schlessinger, David; Ko, Minoru S H

2015-12-01

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein-protein interaction) are pre-loaded and can be used for functional annotations.

Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set and Network Analysis

PubMed Central

Lee, Won Jun; Kim, Sang Cheol; Yoon, Jung-Ho; Yoon, Sang Jun; Lim, Johan; Kim, You-Sun; Kwon, Sung Won; Park, Jeong Hill

2016-01-01

Generally, cancer stem cells have epithelial-to-mesenchymal-transition characteristics and other aggressive properties that cause metastasis. However, there have been no confident markers for the identification of cancer stem cells and comparative methods examining adherent and sphere cells are widely used to investigate mechanism underlying cancer stem cells, because sphere cells have been known to maintain cancer stem cell characteristics. In this study, we conducted a meta-analysis that combined gene expression profiles from several studies that utilized tumorsphere technology to investigate tumor stem-like breast cancer cells. We used our own gene expression profiles along with the three different gene expression profiles from the Gene Expression Omnibus, which we combined using the ComBat method, and obtained significant gene sets using the gene set analysis of our datasets and the combined dataset. This experiment focused on four gene sets such as cytokine-cytokine receptor interaction that demonstrated significance in both datasets. Our observations demonstrated that among the genes of four significant gene sets, six genes were consistently up-regulated and satisfied the p-value of < 0.05, and our network analysis showed high connectivity in five genes. From these results, we established CXCR4, CXCL1 and HMGCS1, the intersecting genes of the datasets with high connectivity and p-value of < 0.05, as significant genes in the identification of cancer stem cells. Additional experiment using quantitative reverse transcription-polymerase chain reaction showed significant up-regulation in MCF-7 derived sphere cells and confirmed the importance of these three genes. Taken together, using meta-analysis that combines gene set and network analysis, we suggested CXCR4, CXCL1 and HMGCS1 as candidates involved in tumor stem-like breast cancer cells. Distinct from other meta-analysis, by using gene set analysis, we selected possible markers which can explain the biological mechanisms and suggested network analysis as an additional criterion for selecting candidates. PMID:26870956

Feathermoss and epiphytic Nostoc cooperate differently: expanding the spectrum of plant–cyanobacteria symbiosis

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

Warshan, Denis; Espinoza, Josh L.; Stuart, Rhona K.

Dinitrogen (N 2)-fixation by cyanobacteria in symbiosis with feathermosses is the primary pathway of biological nitrogen (N) input into boreal forests. Despite its significance, little is known about the cyanobacterial gene repertoire and regulatory rewiring needed for the establishment and maintenance of the symbiosis. To determine gene acquisitions and regulatory changes allowing cyanobacteria to form and maintain this symbiosis, we compared genomically closely related symbiotic-competent and -incompetent Nostoc strains using a proteogenomics approach and an experimental set up allowing for controlled chemical and physical contact between partners. Thirty-two gene families were found only in the genomes of symbiotic strains, includingmore » some never before associated with cyanobacterial symbiosis. We identified conserved orthologs that were differentially expressed in symbiotic strains, including protein families involved in chemotaxis and motility, NO regulation, sulfate/phosphate transport, and glycosyl-modifying and oxidative stress-mediating exoenzymes. The physical moss–cyanobacteria epiphytic symbiosis is distinct from other cyanobacteria–plant symbioses, with Nostoc retaining motility, and lacking modulation of N 2-fixation, photosynthesis, GS-GOGAT cycle and heterocyst formation. The results expand our knowledge base of plant–cyanobacterial symbioses, provide a model of information and material exchange in this ecologically significant symbiosis, and suggest new currencies, namely nitric oxide and aliphatic sulfonates, may be involved in establishing and maintaining the cyanobacteria–feathermoss symbiosis.« less

The PhenX Toolkit: Get the Most From Your Measures

PubMed Central

Hamilton, Carol M.; Strader, Lisa C.; Pratt, Joseph G.; Maiese, Deborah; Hendershot, Tabitha; Kwok, Richard K.; Hammond, Jane A.; Huggins, Wayne; Jackman, Dean; Pan, Huaqin; Nettles, Destiney S.; Beaty, Terri H.; Farrer, Lindsay A.; Kraft, Peter; Marazita, Mary L.; Ordovas, Jose M.; Pato, Carlos N.; Spitz, Margaret R.; Wagener, Diane; Williams, Michelle; Junkins, Heather A.; Harlan, William R.; Ramos, Erin M.; Haines, Jonathan

2011-01-01

The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies. To facilitate replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project. The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains. PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The selected measures are then made freely available to the scientific community via the PhenX Toolkit. Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies. PhenX measures will have the most impact when included at the experimental design stage. The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data. Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions. PMID:21749974

Feathermoss and epiphytic Nostoc cooperate differently: expanding the spectrum of plant–cyanobacteria symbiosis

PubMed Central

Warshan, Denis; Espinoza, Josh L; Stuart, Rhona K; Richter, R Alexander; Kim, Sea-Yong; Shapiro, Nicole; Woyke, Tanja; C Kyrpides, Nikos; Barry, Kerrie; Singan, Vasanth; Lindquist, Erika; Ansong, Charles; Purvine, Samuel O; M Brewer, Heather; Weyman, Philip D; Dupont, Christopher L; Rasmussen, Ulla

2017-01-01

Dinitrogen (N2)-fixation by cyanobacteria in symbiosis with feathermosses is the primary pathway of biological nitrogen (N) input into boreal forests. Despite its significance, little is known about the cyanobacterial gene repertoire and regulatory rewiring needed for the establishment and maintenance of the symbiosis. To determine gene acquisitions and regulatory changes allowing cyanobacteria to form and maintain this symbiosis, we compared genomically closely related symbiotic-competent and -incompetent Nostoc strains using a proteogenomics approach and an experimental set up allowing for controlled chemical and physical contact between partners. Thirty-two gene families were found only in the genomes of symbiotic strains, including some never before associated with cyanobacterial symbiosis. We identified conserved orthologs that were differentially expressed in symbiotic strains, including protein families involved in chemotaxis and motility, NO regulation, sulfate/phosphate transport, and glycosyl-modifying and oxidative stress-mediating exoenzymes. The physical moss–cyanobacteria epiphytic symbiosis is distinct from other cyanobacteria–plant symbioses, with Nostoc retaining motility, and lacking modulation of N2-fixation, photosynthesis, GS-GOGAT cycle and heterocyst formation. The results expand our knowledge base of plant–cyanobacterial symbioses, provide a model of information and material exchange in this ecologically significant symbiosis, and suggest new currencies, namely nitric oxide and aliphatic sulfonates, may be involved in establishing and maintaining the cyanobacteria–feathermoss symbiosis. PMID:28800136

Feathermoss and epiphytic Nostoc cooperate differently: expanding the spectrum of plant–cyanobacteria symbiosis

DOE PAGES

Warshan, Denis; Espinoza, Josh L.; Stuart, Rhona K.; ...

2017-08-11

Dinitrogen (N 2)-fixation by cyanobacteria in symbiosis with feathermosses is the primary pathway of biological nitrogen (N) input into boreal forests. Despite its significance, little is known about the cyanobacterial gene repertoire and regulatory rewiring needed for the establishment and maintenance of the symbiosis. To determine gene acquisitions and regulatory changes allowing cyanobacteria to form and maintain this symbiosis, we compared genomically closely related symbiotic-competent and -incompetent Nostoc strains using a proteogenomics approach and an experimental set up allowing for controlled chemical and physical contact between partners. Thirty-two gene families were found only in the genomes of symbiotic strains, includingmore » some never before associated with cyanobacterial symbiosis. We identified conserved orthologs that were differentially expressed in symbiotic strains, including protein families involved in chemotaxis and motility, NO regulation, sulfate/phosphate transport, and glycosyl-modifying and oxidative stress-mediating exoenzymes. The physical moss–cyanobacteria epiphytic symbiosis is distinct from other cyanobacteria–plant symbioses, with Nostoc retaining motility, and lacking modulation of N 2-fixation, photosynthesis, GS-GOGAT cycle and heterocyst formation. The results expand our knowledge base of plant–cyanobacterial symbioses, provide a model of information and material exchange in this ecologically significant symbiosis, and suggest new currencies, namely nitric oxide and aliphatic sulfonates, may be involved in establishing and maintaining the cyanobacteria–feathermoss symbiosis.« less

An experimental approach to identify dynamical models of transcriptional regulation in living cells

NASA Astrophysics Data System (ADS)

Fiore, G.; Menolascina, F.; di Bernardo, M.; di Bernardo, D.

2013-06-01

We describe an innovative experimental approach, and a proof of principle investigation, for the application of System Identification techniques to derive quantitative dynamical models of transcriptional regulation in living cells. Specifically, we constructed an experimental platform for System Identification based on a microfluidic device, a time-lapse microscope, and a set of automated syringes all controlled by a computer. The platform allows delivering a time-varying concentration of any molecule of interest to the cells trapped in the microfluidics device (input) and real-time monitoring of a fluorescent reporter protein (output) at a high sampling rate. We tested this platform on the GAL1 promoter in the yeast Saccharomyces cerevisiae driving expression of a green fluorescent protein (Gfp) fused to the GAL1 gene. We demonstrated that the System Identification platform enables accurate measurements of the input (sugars concentrations in the medium) and output (Gfp fluorescence intensity) signals, thus making it possible to apply System Identification techniques to obtain a quantitative dynamical model of the promoter. We explored and compared linear and nonlinear model structures in order to select the most appropriate to derive a quantitative model of the promoter dynamics. Our platform can be used to quickly obtain quantitative models of eukaryotic promoters, currently a complex and time-consuming process.

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Sand, Olivier; Janky, Rekin's; Vanderstocken, Gilles; Deville, Yves; van Helden, Jacques

2008-07-01

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.

DiffPy-CMI-Python libraries for Complex Modeling Initiative

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

Billinge, Simon; Juhas, Pavol; Farrow, Christopher

2014-02-01

Software to manipulate and describe crystal and molecular structures and set up structural refinements from multiple experimental inputs. Calculation and simulation of structure derived physical quantities. Library for creating customized refinements of atomic structures from available experimental and theoretical inputs.

Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.

PubMed

Lai, Yinglei; Zhang, Fanni; Nayak, Tapan K; Modarres, Reza; Lee, Norman H; McCaffrey, Timothy A

2014-01-01

Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment. We categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets. We used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method. This study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

Non-blocking crossbar permutation engine with constant routing latency

NASA Technical Reports Server (NTRS)

Monacos, Steve P. (Inventor)

1994-01-01

The invention is embodied in an N x N crossbar for routing packets from a set of N input ports to a set of N output ports, each packet having a header identifying one of the output ports as its destination, including a plurality of individual links which carry individual packets. Each link has a link input end and a link output end, a plurality of switches. Each of the switches has at least top and bottom switch inputs connected to a corresponding pair of the link output ends and top and bottom switch outputs connected to a corresponding pair of link input ends, whereby each switch is connected to four different links. Each of the switches has an exchange state which routes packets from the top and bottom switch inputs to the bottom and top switch outputs, respectively, and a bypass state which routes packets from the top and bottom switch inputs to the top and bottom switch outputs, respectively. A plurality of individual controller devices governing respective switches for sensing from a header of a packet at each switch input for the identity of the destination output port of the packet and selecting one of the exchange and bypass states in accordance with the identity of the destination output port and with the location of the corresponding switch relative to the destination output port.

shinyGISPA: A web application for characterizing phenotype by gene sets using multiple omics data combinations.

PubMed

Dwivedi, Bhakti; Kowalski, Jeanne

2018-01-01

While many methods exist for integrating multi-omics data or defining gene sets, there is no one single tool that defines gene sets based on merging of multiple omics data sets. We present shinyGISPA, an open-source application with a user-friendly web-based interface to define genes according to their similarity in several molecular changes that are driving a disease phenotype. This tool was developed to help facilitate the usability of a previously published method, Gene Integrated Set Profile Analysis (GISPA), among researchers with limited computer-programming skills. The GISPA method allows the identification of multiple gene sets that may play a role in the characterization, clinical application, or functional relevance of a disease phenotype. The tool provides an automated workflow that is highly scalable and adaptable to applications that go beyond genomic data merging analysis. It is available at http://shinygispa.winship.emory.edu/shinyGISPA/.

shinyGISPA: A web application for characterizing phenotype by gene sets using multiple omics data combinations

PubMed Central

Dwivedi, Bhakti

2018-01-01

While many methods exist for integrating multi-omics data or defining gene sets, there is no one single tool that defines gene sets based on merging of multiple omics data sets. We present shinyGISPA, an open-source application with a user-friendly web-based interface to define genes according to their similarity in several molecular changes that are driving a disease phenotype. This tool was developed to help facilitate the usability of a previously published method, Gene Integrated Set Profile Analysis (GISPA), among researchers with limited computer-programming skills. The GISPA method allows the identification of multiple gene sets that may play a role in the characterization, clinical application, or functional relevance of a disease phenotype. The tool provides an automated workflow that is highly scalable and adaptable to applications that go beyond genomic data merging analysis. It is available at http://shinygispa.winship.emory.edu/shinyGISPA/. PMID:29415010

Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets.

PubMed

Demartines, P; Herault, J

1997-01-01

We present a new strategy called "curvilinear component analysis" (CCA) for dimensionality reduction and representation of multidimensional data sets. The principle of CCA is a self-organized neural network performing two tasks: vector quantization (VQ) of the submanifold in the data set (input space); and nonlinear projection (P) of these quantizing vectors toward an output space, providing a revealing unfolding of the submanifold. After learning, the network has the ability to continuously map any new point from one space into another: forward mapping of new points in the input space, or backward mapping of an arbitrary position in the output space.

TRIAC/SCR proportional control circuit

DOEpatents

Hughes, W.J.

1999-04-06

A power controller device is disclosed which uses a voltage-to-frequency converter in conjunction with a zero crossing detector to linearly and proportionally control AC power being supplied to a load. The output of the voltage-to frequency converter controls the ``reset`` input of a R-S flip flop, while an ``0`` crossing detector controls the ``set`` input. The output of the flip flop triggers a monostable multivibrator controlling the SCR or TRIAC firing circuit connected to the load. Logic gates prevent the direct triggering of the multivibrator in the rare instance where the ``reset`` and ``set`` inputs of the flip flop are in coincidence. The control circuit can be supplemented with a control loop, providing compensation for line voltage variations. 9 figs.

Integrative Functional Genomics for Systems Genetics in GeneWeaver.org.

PubMed

Bubier, Jason A; Langston, Michael A; Baker, Erich J; Chesler, Elissa J

2017-01-01

The abundance of existing functional genomics studies permits an integrative approach to interpreting and resolving the results of diverse systems genetics studies. However, a major challenge lies in assembling and harmonizing heterogeneous data sets across species for facile comparison to the positional candidate genes and coexpression networks that come from systems genetic studies. GeneWeaver is an online database and suite of tools at www.geneweaver.org that allows for fast aggregation and analysis of gene set-centric data. GeneWeaver contains curated experimental data together with resource-level data such as GO annotations, MP annotations, and KEGG pathways, along with persistent stores of user entered data sets. These can be entered directly into GeneWeaver or transferred from widely used resources such as GeneNetwork.org. Data are analyzed using statistical tools and advanced graph algorithms to discover new relations, prioritize candidate genes, and generate function hypotheses. Here we use GeneWeaver to find genes common to multiple gene sets, prioritize candidate genes from a quantitative trait locus, and characterize a set of differentially expressed genes. Coupling a large multispecies repository curated and empirical functional genomics data to fast computational tools allows for the rapid integrative analysis of heterogeneous data for interpreting and extrapolating systems genetics results.

Automated Structural Optimization System (ASTROS). Volume 1. Theoretical Manual

DTIC Science & Technology

1988-12-01

corresponding frequency list are given by Equation C-9. The second set of parameters is the frequency list used in solving Equation C-3 to obtain the response...vector (u(w)). This frequency list is: w - 2*fo, 2wfi, 2wf2, 2wfn (C-20) The frequency lists (^ and w are not necessarily equal. While setting...alternative methods are used to input the frequency list u. For the first method, the frequency list u is input via two parameters: Aff (C-21

Gene selection for tumor classification using neighborhood rough sets and entropy measures.

PubMed

Chen, Yumin; Zhang, Zunjun; Zheng, Jianzhong; Ma, Ying; Xue, Yu

2017-03-01

With the development of bioinformatics, tumor classification from gene expression data becomes an important useful technology for cancer diagnosis. Since a gene expression data often contains thousands of genes and a small number of samples, gene selection from gene expression data becomes a key step for tumor classification. Attribute reduction of rough sets has been successfully applied to gene selection field, as it has the characters of data driving and requiring no additional information. However, traditional rough set method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, we propose a novel gene selection method based on the neighborhood rough set model, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. Moreover, this paper addresses an entropy measure under the frame of neighborhood rough sets for tackling the uncertainty and noisy of gene expression data. The utilization of this measure can bring about a discovery of compact gene subsets. Finally, a gene selection algorithm is designed based on neighborhood granules and the entropy measure. Some experiments on two gene expression data show that the proposed gene selection is an effective method for improving the accuracy of tumor classification. Copyright © 2017 Elsevier Inc. All rights reserved.

Curated eutherian third party data gene data sets.

PubMed

Premzl, Marko

2016-03-01

The free available eutherian genomic sequence data sets advanced scientific field of genomics. Of note, future revisions of gene data sets were expected, due to incompleteness of public eutherian genomic sequence assemblies and potential genomic sequence errors. The eutherian comparative genomic analysis protocol was proposed as guidance in protection against potential genomic sequence errors in public eutherian genomic sequences. The protocol was applicable in updates of 7 major eutherian gene data sets, including 812 complete coding sequences deposited in European Nucleotide Archive as curated third party data gene data sets.

Transcriptional responses in thyroid tissues from rats treated with a tumorigenic and a non-tumorigenic triazole conazole fungicide.

PubMed

Hester, Susan D; Nesnow, Stephen

2008-03-15

Conazoles are azole-containing fungicides that are used in agriculture and medicine. Conazoles can induce follicular cell adenomas of the thyroid in rats after chronic bioassay. The goal of this study was to identify pathways and networks of genes that were associated with thyroid tumorigenesis through transcriptional analyses. To this end, we compared transcriptional profiles from tissues of rats treated with a tumorigenic and a non-tumorigenic conazole. Triadimefon, a rat thyroid tumorigen, and myclobutanil, which was not tumorigenic in rats after a 2-year bioassay, were administered in the feed to male Wistar/Han rats for 30 or 90 days similar to the treatment conditions previously used in their chronic bioassays. Thyroid gene expression was determined using high density Affymetrix GeneChips (Rat 230_2). Gene expression was analyzed by the Gene Set Expression Analyses method which clearly separated the tumorigenic treatments (tumorigenic response group (TRG)) from the non-tumorigenic treatments (non-tumorigenic response group (NRG)). Core genes from these gene sets were mapped to canonical, metabolic, and GeneGo processes and these processes compared across group and treatment time. Extensive analyses were performed on the 30-day gene sets as they represented the major perturbations. Gene sets in the 30-day TRG group had over representation of fatty acid metabolism, oxidation, and degradation processes (including PPARgamma and CYP involvement), and of cell proliferation responses. Core genes from these gene sets were combined into networks and found to possess signaling interactions. In addition, the core genes in each gene set were compared with genes known to be associated with human thyroid cancer. Among the genes that appeared in both rat and human data sets were: Acaca, Asns, Cebpg, Crem, Ddit3, Gja1, Grn, Jun, Junb, and Vegf. These genes were major contributors in the previously developed network from triadimefon-treated rat thyroids. It is postulated that triadimefon induces oxidative response genes and activates the nuclear receptor, Ppargamma, initiating transcription of gene products and signaling to a series of genes involved in cell proliferation.

Involvement of astrocyte metabolic coupling in Tourette syndrome pathogenesis.

PubMed

de Leeuw, Christiaan; Goudriaan, Andrea; Smit, August B; Yu, Dongmei; Mathews, Carol A; Scharf, Jeremiah M; Verheijen, Mark H G; Posthuma, Danielle

2015-11-01

Tourette syndrome is a heritable neurodevelopmental disorder whose pathophysiology remains unknown. Recent genome-wide association studies suggest that it is a polygenic disorder influenced by many genes of small effect. We tested whether these genes cluster in cellular function by applying gene-set analysis using expert curated sets of brain-expressed genes in the current largest available Tourette syndrome genome-wide association data set, involving 1285 cases and 4964 controls. The gene sets included specific synaptic, astrocytic, oligodendrocyte and microglial functions. We report association of Tourette syndrome with a set of genes involved in astrocyte function, specifically in astrocyte carbohydrate metabolism. This association is driven primarily by a subset of 33 genes involved in glycolysis and glutamate metabolism through which astrocytes support synaptic function. Our results indicate for the first time that the process of astrocyte-neuron metabolic coupling may be an important contributor to Tourette syndrome pathogenesis.

Involvement of astrocyte metabolic coupling in Tourette syndrome pathogenesis

PubMed Central

de Leeuw, Christiaan; Goudriaan, Andrea; Smit, August B; Yu, Dongmei; Mathews, Carol A; Scharf, Jeremiah M; Scharf, J M; Pauls, D L; Yu, D; Illmann, C; Osiecki, L; Neale, B M; Mathews, C A; Reus, V I; Lowe, T L; Freimer, N B; Cox, N J; Davis, L K; Rouleau, G A; Chouinard, S; Dion, Y; Girard, S; Cath, D C; Posthuma, D; Smit, J H; Heutink, P; King, R A; Fernandez, T; Leckman, J F; Sandor, P; Barr, C L; McMahon, W; Lyon, G; Leppert, M; Morgan, J; Weiss, R; Grados, M A; Singer, H; Jankovic, J; Tischfield, J A; Heiman, G A; Verheijen, Mark H G; Posthuma, Danielle

2015-01-01

Tourette syndrome is a heritable neurodevelopmental disorder whose pathophysiology remains unknown. Recent genome-wide association studies suggest that it is a polygenic disorder influenced by many genes of small effect. We tested whether these genes cluster in cellular function by applying gene-set analysis using expert curated sets of brain-expressed genes in the current largest available Tourette syndrome genome-wide association data set, involving 1285 cases and 4964 controls. The gene sets included specific synaptic, astrocytic, oligodendrocyte and microglial functions. We report association of Tourette syndrome with a set of genes involved in astrocyte function, specifically in astrocyte carbohydrate metabolism. This association is driven primarily by a subset of 33 genes involved in glycolysis and glutamate metabolism through which astrocytes support synaptic function. Our results indicate for the first time that the process of astrocyte-neuron metabolic coupling may be an important contributor to Tourette syndrome pathogenesis. PMID:25735483

Spot 42 Small RNA Regulates Arabinose-Inducible araBAD Promoter Activity by Repressing Synthesis of the High-Affinity Low-Capacity Arabinose Transporter

PubMed Central

Chen, Jiandong

2016-01-01

ABSTRACT The l-arabinose-inducible araBAD promoter (PBAD) enables tightly controlled and tunable expression of genes of interest in a broad range of bacterial species. It has been used successfully to study bacterial sRNA regulation, where PBAD drives expression of target mRNA translational fusions. Here we report that in Escherichia coli, Spot 42 sRNA regulates PBAD promoter activity by affecting arabinose uptake. We demonstrate that Spot 42 sRNA represses araF, a gene encoding the AraF subunit of the high-affinity low-capacity arabinose transporter AraFGH, through direct base-pairing interactions. We further show that endogenous Spot 42 sRNA is sufficient to repress araF expression under various growth conditions. Finally, we demonstrate this posttranscriptional repression has a biological consequence, decreasing the induction of PBAD at low levels of arabinose. This problem can be circumvented using strategies reported previously for avoiding all-or-none induction behavior, such as through constitutive expression of the low-affinity high-capacity arabinose transporter AraE or induction with a higher concentration of inducers. This work adds araF to the set of Spot 42-regulated genes, in agreement with previous studies suggesting that Spot 42, itself negatively regulated by the cyclic AMP (cAMP) receptor protein-cAMP complex, reinforces the catabolite repression network. IMPORTANCE The bacterial arabinose-inducible system is widely used for titratable control of gene expression. We demonstrate here that a posttranscriptional mechanism mediated by Spot 42 sRNA contributes to the functionality of the PBAD system at subsaturating inducer concentrations by affecting inducer uptake. Our finding extends the inputs into the known transcriptional control for the PBAD system and has implications for improving its usage for tunable gene expression. PMID:27849174

Detecting regulatory gene-environment interactions with unmeasured environmental factors.

PubMed

Fusi, Nicoló; Lippert, Christoph; Borgwardt, Karsten; Lawrence, Neil D; Stegle, Oliver

2013-06-01

Genomic studies have revealed a substantial heritable component of the transcriptional state of the cell. To fully understand the genetic regulation of gene expression variability, it is important to study the effect of genotype in the context of external factors such as alternative environmental conditions. In model systems, explicit environmental perturbations have been considered for this purpose, allowing to directly test for environment-specific genetic effects. However, such experiments are limited to species that can be profiled in controlled environments, hampering their use in important systems such as human. Moreover, even in seemingly tightly regulated experimental conditions, subtle environmental perturbations cannot be ruled out, and hence unknown environmental influences are frequent. Here, we propose a model-based approach to simultaneously infer unmeasured environmental factors from gene expression profiles and use them in genetic analyses, identifying environment-specific associations between polymorphic loci and individual gene expression traits. In extensive simulation studies, we show that our method is able to accurately reconstruct environmental factors and their interactions with genotype in a variety of settings. We further illustrate the use of our model in a real-world dataset in which one environmental factor has been explicitly experimentally controlled. Our method is able to accurately reconstruct the true underlying environmental factor even if it is not given as an input, allowing to detect genuine genotype-environment interactions. In addition to the known environmental factor, we find unmeasured factors involved in novel genotype-environment interactions. Our results suggest that interactions with both known and unknown environmental factors significantly contribute to gene expression variability. and implementation: Software available at http://pmbio.github.io/envGPLVM/. Supplementary data are available at Bioinformatics online.

Neural Progenitors Adopt Specific Identities by Directly Repressing All Alternative Progenitor Transcriptional Programs.

PubMed

Kutejova, Eva; Sasai, Noriaki; Shah, Ankita; Gouti, Mina; Briscoe, James

2016-03-21

In the vertebrate neural tube, a morphogen-induced transcriptional network produces multiple molecularly distinct progenitor domains, each generating different neuronal subtypes. Using an in vitro differentiation system, we defined gene expression signatures of distinct progenitor populations and identified direct gene-regulatory inputs corresponding to locations of specific transcription factor binding. Combined with targeted perturbations of the network, this revealed a mechanism in which a progenitor identity is installed by active repression of the entire transcriptional programs of other neural progenitor fates. In the ventral neural tube, sonic hedgehog (Shh) signaling, together with broadly expressed transcriptional activators, concurrently activates the gene expression programs of several domains. The specific outcome is selected by repressive input provided by Shh-induced transcription factors that act as the key nodes in the network, enabling progenitors to adopt a single definitive identity from several initially permitted options. Together, the data suggest design principles relevant to many developing tissues. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

User's manual for master: Modeling of aerodynamic surfaces by 3-dimensional explicit representation. [input to three dimensional computational fluid dynamics

NASA Technical Reports Server (NTRS)

Gibson, S. G.

1983-01-01

A system of computer programs was developed to model general three dimensional surfaces. Surfaces are modeled as sets of parametric bicubic patches. There are also capabilities to transform coordinates, to compute mesh/surface intersection normals, and to format input data for a transonic potential flow analysis. A graphical display of surface models and intersection normals is available. There are additional capabilities to regulate point spacing on input curves and to compute surface/surface intersection curves. Input and output data formats are described; detailed suggestions are given for user input. Instructions for execution are given, and examples are shown.

WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.

PubMed

Chi, Sang-Mun; Nam, Dougu

2012-04-01

We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc smchiks@ks.ac.kr; dougnam@unist.ac.kr Supplementary data is available at http://www.btool.org/WegoLoc.

It’s Time for An Epigenomics Roadmap of Heart Failure

PubMed Central

Papait, Roberto; Corrado, Nadia; Rusconi, Francesca; Serio, Simone; V.G. Latronico, Michael

2015-01-01

The post-genomic era has completed its first decade. During this time, we have seen an attempt to understand life not just through the study of individual isolated processes, but through the appreciation of the amalgam of complex networks, within which each process can influence others. Greatly benefiting this view has been the study of the epigenome, the set of DNA and histone protein modifications that regulate gene expression and the function of regulatory non-coding RNAs without altering the DNA sequence itself. Indeed, the availability of reference genome assemblies of many species has led to the development of methodologies such as ChIP-Seq and RNA-Seq that have allowed us to define with high resolution the genomic distribution of several epigenetic elements and to better comprehend how they are interconnected for the regulation of gene expression. In the last few years, the use of these methodologies in the cardiovascular field has contributed to our understanding of the importance of epigenetics in heart diseases, giving new input to this area of research. Here, we review recently acquired knowledge on the role of the epigenome in heart failure, and discuss the need of an epigenomics roadmap for cardiovascular disease. PMID:27006627

Sequential Logic Model Deciphers Dynamic Transcriptional Control of Gene Expressions

PubMed Central

Yeo, Zhen Xuan; Wong, Sum Thai; Arjunan, Satya Nanda Vel; Piras, Vincent; Tomita, Masaru; Selvarajoo, Kumar; Giuliani, Alessandro; Tsuchiya, Masa

2007-01-01

Background Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. Methodology Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. Principal Findings SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin) during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. Conclusions/Significance The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet providing rich biological insight. The demonstration of the efficacy of this approach in endo16 is a promising step for further application of the proposed method. PMID:17712424

Case-based retrieval framework for gene expression data.

PubMed

Anaissi, Ali; Goyal, Madhu; Catchpoole, Daniel R; Braytee, Ali; Kennedy, Paul J

2015-01-01

The process of retrieving similar cases in a case-based reasoning system is considered a big challenge for gene expression data sets. The huge number of gene expression values generated by microarray technology leads to complex data sets and similarity measures for high-dimensional data are problematic. Hence, gene expression similarity measurements require numerous machine-learning and data-mining techniques, such as feature selection and dimensionality reduction, to be incorporated into the retrieval process. This article proposes a case-based retrieval framework that uses a k-nearest-neighbor classifier with a weighted-feature-based similarity to retrieve previously treated patients based on their gene expression profiles. The herein-proposed methodology is validated on several data sets: a childhood leukemia data set collected from The Children's Hospital at Westmead, as well as the Colon cancer, the National Cancer Institute (NCI), and the Prostate cancer data sets. Results obtained by the proposed framework in retrieving patients of the data sets who are similar to new patients are as follows: 96% accuracy on the childhood leukemia data set, 95% on the NCI data set, 93% on the Colon cancer data set, and 98% on the Prostate cancer data set. The designed case-based retrieval framework is an appropriate choice for retrieving previous patients who are similar to a new patient, on the basis of their gene expression data, for better diagnosis and treatment of childhood leukemia. Moreover, this framework can be applied to other gene expression data sets using some or all of its steps.

Building accurate historic and future climate MEPDG input files for Louisiana DOTD.

DOT National Transportation Integrated Search

2017-02-01

The pavement design process (originally MEPDG, then DARWin-ME, and now Pavement ME Design) requires a multi-year set of hourly : climate input data that influence pavement material properties. In Louisiana, the software provides nine locations with c...

Consistency of gene starts among Burkholderia genomes

PubMed Central

2011-01-01

Background Evolutionary divergence in the position of the translational start site among orthologous genes can have significant functional impacts. Divergence can alter the translation rate, degradation rate, subcellular location, and function of the encoded proteins. Results Existing Genbank gene maps for Burkholderia genomes suggest that extensive divergence has occurred--53% of ortholog sets based on Genbank gene maps had inconsistent gene start sites. However, most of these inconsistencies appear to be gene-calling errors. Evolutionary divergence was the most plausible explanation for only 17% of the ortholog sets. Correcting probable errors in the Genbank gene maps decreased the percentage of ortholog sets with inconsistent starts by 68%, increased the percentage of ortholog sets with extractable upstream intergenic regions by 32%, increased the sequence similarity of intergenic regions and predicted proteins, and increased the number of proteins with identifiable signal peptides. Conclusions Our findings highlight an emerging problem in comparative genomics: single-digit percent errors in gene predictions can lead to double-digit percentages of inconsistent ortholog sets. The work demonstrates a simple approach to evaluate and improve the quality of gene maps. PMID:21342528

23S rRNA gene-based enterococci community signatures in Lake Pontchartrain, Louisiana, USA, following urban runoff inputs after Hurricane Katrina.

PubMed

Bae, Hee-Sung; Hou, Aixin

2013-02-01

Little is known about the impacts of fecal polluted urban runoff inputs on the structure of enterococci communities in estuarine waters. This study employed a 23S rRNA gene-based polymerase chain reaction (PCR) assay with newly designed genus-specific primers, Ent127F-Ent907R, to determine the possible impacts of Hurricane Katrina floodwaters via the 17th Street Canal discharge on the community structure of enterococci in Lake Pontchartrain. A total of 94 phylotypes were identified through the restriction fragment length polymorphism (RFLP) screening of 494 clones while only 8 phylotypes occurred among 88 cultivated isolates. Sequence analyses of representative phylotypes and their temporal and spatial distribution in the lake and the canal indicated the Katrina floodwater input introduced a large portion of Enterococcus flavescens, Enterococcus casseliflavus, and Enterococcus dispar into the lake; typical fecal groups Enterococcus faecium, Enterococcus durans, Enterococcus hirae, and Enterococcus mundtii were detected primarily in the floodwater-impacted waters. This study provides a global picture of enterococci in estuarine waters impacted by Hurricane Katrina-derived urban runoff. It also demonstrates the culture-independent PCR approach using 23S rRNA gene as a molecular marker could be a good alternative in ecological studies of enterococci in natural environments to overcome the limitation of conventional cultivation methods.

An autonomous molecular computer for logical control of gene expression

PubMed Central

Benenson, Yaakov; Gil, Binyamin; Ben-Dor, Uri; Adar, Rivka; Shapiro, Ehud

2013-01-01

Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems1–7. Recently, simple molecular-scale autonomous programmable computers were demonstrated8–15 allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for ‘logical’ control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton12–17; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes18–22 associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug. PMID:15116117

A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling

DOE PAGES

Pelletier, Jon D.; Broxton, Patrick D.; Hazenberg, Pieter; ...

2016-01-22

Earth’s terrestrial near-subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high-resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsecmore » (~ 1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. As a result, we anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models.« less

A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling

NASA Astrophysics Data System (ADS)

Pelletier, Jon D.; Broxton, Patrick D.; Hazenberg, Pieter; Zeng, Xubin; Troch, Peter A.; Niu, Guo-Yue; Williams, Zachary; Brunke, Michael A.; Gochis, David

2016-03-01

Earth's terrestrial near-subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high-resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsec (˜1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. We anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models. This article was corrected on 2 FEB 2016. See the end of the full text for details.

Method and apparatus for smart battery charging including a plurality of controllers each monitoring input variables

DOEpatents

Hammerstrom, Donald J.

2013-10-15

A method for managing the charging and discharging of batteries wherein at least one battery is connected to a battery charger, the battery charger is connected to a power supply. A plurality of controllers in communication with one and another are provided, each of the controllers monitoring a subset of input variables. A set of charging constraints may then generated for each controller as a function of the subset of input variables. A set of objectives for each controller may also be generated. A preferred charge rate for each controller is generated as a function of either the set of objectives, the charging constraints, or both, using an algorithm that accounts for each of the preferred charge rates for each of the controllers and/or that does not violate any of the charging constraints. A current flow between the battery and the battery charger is then provided at the actual charge rate.

Uncertainty Analysis for a Jet Flap Airfoil

NASA Technical Reports Server (NTRS)

Green, Lawrence L.; Cruz, Josue

2006-01-01

An analysis of variance (ANOVA) study was performed to quantify the potential uncertainties of lift and pitching moment coefficient calculations from a computational fluid dynamics code, relative to an experiment, for a jet flap airfoil configuration. Uncertainties due to a number of factors including grid density, angle of attack and jet flap blowing coefficient were examined. The ANOVA software produced a numerical model of the input coefficient data, as functions of the selected factors, to a user-specified order (linear, 2-factor interference, quadratic, or cubic). Residuals between the model and actual data were also produced at each of the input conditions, and uncertainty confidence intervals (in the form of Least Significant Differences or LSD) for experimental, computational, and combined experimental / computational data sets were computed. The LSD bars indicate the smallest resolvable differences in the functional values (lift or pitching moment coefficient) attributable solely to changes in independent variable, given just the input data points from selected data sets. The software also provided a collection of diagnostics which evaluate the suitability of the input data set for use within the ANOVA process, and which examine the behavior of the resultant data, possibly suggesting transformations which should be applied to the data to reduce the LSD. The results illustrate some of the key features of, and results from, the uncertainty analysis studies, including the use of both numerical (continuous) and categorical (discrete) factors, the effects of the number and range of the input data points, and the effects of the number of factors considered simultaneously.

CYP1A1, GCLC, AGT, AGTR1 gene-gene interactions in community-acquired pneumonia pulmonary complications.

PubMed

Salnikova, Lyubov E; Smelaya, Tamara V; Golubev, Arkadiy M; Rubanovich, Alexander V; Moroz, Viktor V

2013-11-01

This study was conducted to establish the possible contribution of functional gene polymorphisms in detoxification/oxidative stress and vascular remodeling pathways to community-acquired pneumonia (CAP) susceptibility in the case-control study (350 CAP patients, 432 control subjects) and to predisposition to the development of CAP complications in the prospective study. All subjects were genotyped for 16 polymorphic variants in the 14 genes of xenobiotics detoxification CYP1A1, AhR, GSTM1, GSTT1, ABCB1, redox-status SOD2, CAT, GCLC, and vascular homeostasis ACE, AGT, AGTR1, NOS3, MTHFR, VEGFα. Risk of pulmonary complications (PC) in the single locus analysis was associated with CYP1A1, GCLC and AGTR1 genes. Extra PC (toxic shock syndrome and myocarditis) were not associated with these genes. We evaluated gene-gene interactions using multi-factor dimensionality reduction, and cumulative gene risk score approaches. The final model which included >5 risk alleles in the CYP1A1 (rs2606345, rs4646903, rs1048943), GCLC, AGT, and AGTR1 genes was associated with pleuritis, empyema, acute respiratory distress syndrome, all PC and acute respiratory failure (ARF). We considered CYP1A1, GCLC, AGT, AGTR1 gene set using Set Distiller mode implemented in GeneDecks for discovering gene-set relations via the degree of sharing descriptors within a given gene set. N-acetylcysteine and oxygen were defined by Set Distiller as the best descriptors for the gene set associated in the present study with PC and ARF. Results of the study are in line with literature data and suggest that genetically determined oxidative stress exacerbation may contribute to the progression of lung inflammation.

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

Pelletier, Jon D.; Broxton, Patrick D.; Hazenberg, Pieter

Earth’s terrestrial near-subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high-resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsecmore » (~ 1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. As a result, we anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models.« less

Repression of Middle Sporulation Genes in Saccharomyces cerevisiae by the Sum1-Rfm1-Hst1 Complex Is Maintained by Set1 and H3K4 Methylation

PubMed Central

Jaiswal, Deepika; Jezek, Meagan; Quijote, Jeremiah; Lum, Joanna; Choi, Grace; Kulkarni, Rushmie; Park, DoHwan; Green, Erin M.

2017-01-01

The conserved yeast histone methyltransferase Set1 targets H3 lysine 4 (H3K4) for mono, di, and trimethylation and is linked to active transcription due to the euchromatic distribution of these methyl marks and the recruitment of Set1 during transcription. However, loss of Set1 results in increased expression of multiple classes of genes, including genes adjacent to telomeres and middle sporulation genes, which are repressed under normal growth conditions because they function in meiotic progression and spore formation. The mechanisms underlying Set1-mediated gene repression are varied, and still unclear in some cases, although repression has been linked to both direct and indirect action of Set1, associated with noncoding transcription, and is often dependent on the H3K4me2 mark. We show that Set1, and particularly the H3K4me2 mark, are implicated in repression of a subset of middle sporulation genes during vegetative growth. In the absence of Set1, there is loss of the DNA-binding transcriptional regulator Sum1 and the associated histone deacetylase Hst1 from chromatin in a locus-specific manner. This is linked to increased H4K5ac at these loci and aberrant middle gene expression. These data indicate that, in addition to DNA sequence, histone modification status also contributes to proper localization of Sum1. Our results also show that the role for Set1 in middle gene expression control diverges as cells receive signals to undergo meiosis. Overall, this work dissects an unexplored role for Set1 in gene-specific repression, and provides important insights into a new mechanism associated with the control of gene expression linked to meiotic differentiation. PMID:29066473

A Simple Screening Approach To Prioritize Genes for Functional Analysis Identifies a Role for Interferon Regulatory Factor 7 in the Control of Respiratory Syncytial Virus Disease

PubMed Central

McDonald, Jacqueline U.; Kaforou, Myrsini; Clare, Simon; Hale, Christine; Ivanova, Maria; Huntley, Derek; Dorner, Marcus; Wright, Victoria J.; Levin, Michael; Martinon-Torres, Federico; Herberg, Jethro A.

2016-01-01

ABSTRACT Greater understanding of the functions of host gene products in response to infection is required. While many of these genes enable pathogen clearance, some enhance pathogen growth or contribute to disease symptoms. Many studies have profiled transcriptomic and proteomic responses to infection, generating large data sets, but selecting targets for further study is challenging. Here we propose a novel data-mining approach combining multiple heterogeneous data sets to prioritize genes for further study by using respiratory syncytial virus (RSV) infection as a model pathogen with a significant health care impact. The assumption was that the more frequently a gene is detected across multiple studies, the more important its role is. A literature search was performed to find data sets of genes and proteins that change after RSV infection. The data sets were standardized, collated into a single database, and then panned to determine which genes occurred in multiple data sets, generating a candidate gene list. This candidate gene list was validated by using both a clinical cohort and in vitro screening. We identified several genes that were frequently expressed following RSV infection with no assigned function in RSV control, including IFI27, IFIT3, IFI44L, GBP1, OAS3, IFI44, and IRF7. Drilling down into the function of these genes, we demonstrate a role in disease for the gene for interferon regulatory factor 7, which was highly ranked on the list, but not for IRF1, which was not. Thus, we have developed and validated an approach for collating published data sets into a manageable list of candidates, identifying novel targets for future analysis. IMPORTANCE Making the most of “big data” is one of the core challenges of current biology. There is a large array of heterogeneous data sets of host gene responses to infection, but these data sets do not inform us about gene function and require specialized skill sets and training for their utilization. Here we describe an approach that combines and simplifies these data sets, distilling this information into a single list of genes commonly upregulated in response to infection with RSV as a model pathogen. Many of the genes on the list have unknown functions in RSV disease. We validated the gene list with new clinical, in vitro, and in vivo data. This approach allows the rapid selection of genes of interest for further, more-detailed studies, thus reducing time and costs. Furthermore, the approach is simple to use and widely applicable to a range of diseases. PMID:27822537

Digital data used to relate nutrient inputs to water quality in the Chesapeake Bay watershed

USGS Publications Warehouse

Brakebill, John W.; Preston, Stephen D.

1999-01-01

Digital data sets were compiled by the U. S. Geological Survey (USGS) and used as input for a collection of Spatially Referenced Regressions On Watershed attributes for the Chesapeake Bay region. These regressions relate streamwater loads to nutrient sources and the factors that affect the transport of these nutrients throughout the watershed. A digital segmented network based on watershed boundaries serves as the primary foundation for spatially referencing total nitrogen and total phosphorus source and land-surface characteristic data sets within a Geographic Information System. Digital data sets of atmospheric wet deposition of nitrate, point-source discharge locations, land cover, and agricultural sources such as fertilizer and manure were created and compiled from numerous sources and represent nitrogen and phosphorus inputs. Some land-surface characteristics representing factors that affect the transport of nutrients include land use, land cover, average annual precipitation and temperature, slope, and soil permeability. Nutrient input and land-surface characteristic data sets merged with the segmented watershed network provide the spatial detail by watershed segment required by the models. Nutrient stream loads were estimated for total nitrogen, total phosphorus, nitrate/nitrite, amonium, phosphate, and total suspended soilds at as many as 109 sites within the Chesapeake Bay watershed. The total nitrogen and total phosphorus load estimates are the dependent variables for the regressions and were used for model calibration. Other nutrient-load estimates may be used for calibration in future applications of the models.

Screening of dementia genes by whole-exome sequencing in early-onset Alzheimer disease: input and lessons.

PubMed

Nicolas, Gaël; Wallon, David; Charbonnier, Camille; Quenez, Olivier; Rousseau, Stéphane; Richard, Anne-Claire; Rovelet-Lecrux, Anne; Coutant, Sophie; Le Guennec, Kilan; Bacq, Delphine; Garnier, Jean-Guillaume; Olaso, Robert; Boland, Anne; Meyer, Vincent; Deleuze, Jean-François; Munter, Hans Markus; Bourque, Guillaume; Auld, Daniel; Montpetit, Alexandre; Lathrop, Mark; Guyant-Maréchal, Lucie; Martinaud, Olivier; Pariente, Jérémie; Rollin-Sillaire, Adeline; Pasquier, Florence; Le Ber, Isabelle; Sarazin, Marie; Croisile, Bernard; Boutoleau-Bretonnière, Claire; Thomas-Antérion, Catherine; Paquet, Claire; Sauvée, Mathilde; Moreaud, Olivier; Gabelle, Audrey; Sellal, François; Ceccaldi, Mathieu; Chamard, Ludivine; Blanc, Frédéric; Frebourg, Thierry; Campion, Dominique; Hannequin, Didier

2016-05-01

Causative variants in APP, PSEN1 or PSEN2 account for a majority of cases of autosomal dominant early-onset Alzheimer disease (ADEOAD, onset before 65 years). Variant detection rates in other EOAD patients, that is, with family history of late-onset AD (LOAD) (and no incidence of EOAD) and sporadic cases might be much lower. We analyzed the genomes from 264 patients using whole-exome sequencing (WES) with high depth of coverage: 90 EOAD patients with family history of LOAD and no incidence of EOAD in the family and 174 patients with sporadic AD starting between 51 and 65 years. We found three PSEN1 and one PSEN2 causative, probably or possibly causative variants in four patients (1.5%). Given the absence of PSEN1, PSEN2 and APP causative variants, we investigated whether these 260 patients might be burdened with protein-modifying variants in 20 genes that were previously shown to cause other types of dementia when mutated. For this analysis, we included an additional set of 160 patients who were previously shown to be free of causative variants in PSEN1, PSEN2 and APP: 107 ADEOAD patients and 53 sporadic EOAD patients with an age of onset before 51 years. In these 420 patients, we detected no variant that might modify the function of the 20 dementia-causing genes. We conclude that EOAD patients with family history of LOAD and no incidence of EOAD in the family or patients with sporadic AD starting between 51 and 65 years have a low variant-detection rate in AD genes.

Precision matters for position decoding in the early fly embryo

NASA Astrophysics Data System (ADS)

Petkova, Mariela D.; Tkacik, Gasper; Wieschaus, Eric F.; Bialek, William; Gregor, Thomas

Genetic networks can determine cell fates in multicellular organisms with precision that often reaches the physical limits of the system. However, it is unclear how the organism uses this precision and whether it has biological content. Here we address this question in the developing fly embryo, in which a genetic network of patterning genes reaches 1% precision in positioning cells along the embryo axis. The network consists of three interconnected layers: an input layer of maternal gradients, a processing layer of gap genes, and an output layer of pair-rule genes with seven-striped patterns. From measurements of gap gene protein expression in hundreds of wild-type embryos we construct a ``decoder'', which is a look-up table that determines cellular positions from the concentration means, variances and co-variances. When we apply the decoder to measurements in mutant embryos lacking various combinations of the maternal inputs, we predict quantitative changes in the output layer such as missing, altered or displaced stripes. We confirm these predictions by measuring pair-rule expression in the mutant embryos. Our results thereby show that the precision of the patterning network is biologically meaningful and a necessary feature for decoding cell positions in the early fly embryo.

Gaussian-input Gaussian mixture model for representing density maps and atomic models.

PubMed

Kawabata, Takeshi

2018-07-01

A new Gaussian mixture model (GMM) has been developed for better representations of both atomic models and electron microscopy 3D density maps. The standard GMM algorithm employs an EM algorithm to determine the parameters. It accepted a set of 3D points with weights, corresponding to voxel or atomic centers. Although the standard algorithm worked reasonably well; however, it had three problems. First, it ignored the size (voxel width or atomic radius) of the input, and thus it could lead to a GMM with a smaller spread than the input. Second, the algorithm had a singularity problem, as it sometimes stopped the iterative procedure due to a Gaussian function with almost zero variance. Third, a map with a large number of voxels required a long computation time for conversion to a GMM. To solve these problems, we have introduced a Gaussian-input GMM algorithm, which considers the input atoms or voxels as a set of Gaussian functions. The standard EM algorithm of GMM was extended to optimize the new GMM. The new GMM has identical radius of gyration to the input, and does not suddenly stop due to the singularity problem. For fast computation, we have introduced a down-sampled Gaussian functions (DSG) by merging neighboring voxels into an anisotropic Gaussian function. It provides a GMM with thousands of Gaussian functions in a short computation time. We also have introduced a DSG-input GMM: the Gaussian-input GMM with the DSG as the input. This new algorithm is much faster than the standard algorithm. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Automated crystallographic system for high-throughput protein structure determination.

PubMed

Brunzelle, Joseph S; Shafaee, Padram; Yang, Xiaojing; Weigand, Steve; Ren, Zhong; Anderson, Wayne F

2003-07-01

High-throughput structural genomic efforts require software that is highly automated, distributive and requires minimal user intervention to determine protein structures. Preliminary experiments were set up to test whether automated scripts could utilize a minimum set of input parameters and produce a set of initial protein coordinates. From this starting point, a highly distributive system was developed that could determine macromolecular structures at a high throughput rate, warehouse and harvest the associated data. The system uses a web interface to obtain input data and display results. It utilizes a relational database to store the initial data needed to start the structure-determination process as well as generated data. A distributive program interface administers the crystallographic programs which determine protein structures. Using a test set of 19 protein targets, 79% were determined automatically.

Analog Input Data Acquisition Software

NASA Technical Reports Server (NTRS)

Arens, Ellen

2009-01-01

DAQ Master Software allows users to easily set up a system to monitor up to five analog input channels and save the data after acquisition. This program was written in LabVIEW 8.0, and requires the LabVIEW runtime engine 8.0 to run the executable.

cluster trials v. 1.0

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

Mitchell, John; Castillo, Andrew

2016-09-21

This software contains a set of python modules – input, search, cluster, analysis; these modules read input files containing spatial coordinates and associated attributes which can be used to perform nearest neighbor search (spatial indexing via kdtree), cluster analysis/identification, and calculation of spatial statistics for analysis.

Programmable electronic synthesized capacitance

NASA Technical Reports Server (NTRS)

Kleinberg, Leonard L. (Inventor)

1987-01-01

A predetermined and variable synthesized capacitance which may be incorporated into the resonant portion of an electronic oscillator for the purpose of tuning the oscillator comprises a programmable operational amplifier circuit. The operational amplifier circuit has its output connected to its inverting input, in a follower configuration, by a network which is low impedance at the operational frequency of the circuit. The output of the operational amplifier is also connected to the noninverting input by a capacitor. The noninverting input appears as a synthesized capacitance which may be varied with a variation in gain-bandwidth product of the operational amplifier circuit. The gain-bandwidth product may, in turn, be varied with a variation in input set current with a digital to analog converter whose output is varied with a command word. The output impedance of the circuit may also be varied by the output set current. This circuit may provide very small ranges in oscillator frequency with relatively large control voltages unaffected by noise.

Embedding global barrier and collective in torus network with each node combining input from receivers according to class map for output to senders

DOEpatents

Chen, Dong; Coteus, Paul W; Eisley, Noel A; Gara, Alan; Heidelberger, Philip; Senger, Robert M; Salapura, Valentina; Steinmacher-Burow, Burkhard; Sugawara, Yutaka; Takken, Todd E

2013-08-27

Embodiments of the invention provide a method, system and computer program product for embedding a global barrier and global interrupt network in a parallel computer system organized as a torus network. The computer system includes a multitude of nodes. In one embodiment, the method comprises taking inputs from a set of receivers of the nodes, dividing the inputs from the receivers into a plurality of classes, combining the inputs of each of the classes to obtain a result, and sending said result to a set of senders of the nodes. Embodiments of the invention provide a method, system and computer program product for embedding a collective network in a parallel computer system organized as a torus network. In one embodiment, the method comprises adding to a torus network a central collective logic to route messages among at least a group of nodes in a tree structure.

Inference of Evolutionary Forces Acting on Human Biological Pathways

PubMed Central

Daub, Josephine T.; Dupanloup, Isabelle; Robinson-Rechavi, Marc; Excoffier, Laurent

2015-01-01

Because natural selection is likely to act on multiple genes underlying a given phenotypic trait, we study here the potential effect of ongoing and past selection on the genetic diversity of human biological pathways. We first show that genes included in gene sets are generally under stronger selective constraints than other genes and that their evolutionary response is correlated. We then introduce a new procedure to detect selection at the pathway level based on a decomposition of the classical McDonald–Kreitman test extended to multiple genes. This new test, called 2DNS, detects outlier gene sets and takes into account past demographic effects and evolutionary constraints specific to gene sets. Selective forces acting on gene sets can be easily identified by a mere visual inspection of the position of the gene sets relative to their two-dimensional null distribution. We thus find several outlier gene sets that show signals of positive, balancing, or purifying selection but also others showing an ancient relaxation of selective constraints. The principle of the 2DNS test can also be applied to other genomic contrasts. For instance, the comparison of patterns of polymorphisms private to African and non-African populations reveals that most pathways show a higher proportion of nonsynonymous mutations in non-Africans than in Africans, potentially due to different demographic histories and selective pressures. PMID:25971280

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

PubMed Central

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.; Taylor, Ronald C.; Weisenhorn, Pamela; Olson, Robert D.; Stevens, Rick L.; Rocha, Miguel; Rocha, Isabel; Best, Aaron A.; DeJongh, Matthew; Tintle, Nathan L.; Parrello, Bruce; Overbeek, Ross; Henry, Christopher S.

2016-01-01

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain. PMID:27933038

Metagenomics Reveals the Impact of Wastewater Treatment Plants on the Dispersal of Microorganisms and Genes in Aquatic Sediments.

PubMed

Chu, Binh T T; Petrovich, Morgan L; Chaudhary, Adit; Wright, Dorothy; Murphy, Brian; Wells, George; Poretsky, Rachel

2018-03-01

Wastewater treatment plants (WWTPs) release treated effluent containing mobile genetic elements (MGEs), antibiotic resistance genes (ARGs), and microorganisms into the environment, yet little is known about their influence on nearby microbial communities and the retention of these factors in receiving water bodies. Our research aimed to characterize the genes and organisms from two different WWTPs that discharge into Lake Michigan, as well as from surrounding lake sediments to determine the dispersal and fate of these factors with respect to distance from the effluent outfall. Shotgun metagenomics coupled to distance-decay analyses showed a higher abundance of genes identical to those in WWTP effluent genes in sediments closer to outfall sites than in sediments farther away, indicating their possible WWTP origin. We also found genes attributed to organisms, such as those belonging to Helicobacteraceae , Legionellaceae , Moraxellaceae , and Neisseriaceae , in effluent from both WWTPs and decreasing in abundance in lake sediments with increased distance from WWTPs. Moreover, our results showed that the WWTPs likely influence the ARG composition in lake sediments close to the effluent discharge. Many of these ARGs were located on MGEs in both the effluent and sediment samples, indicating a relatively broad propensity for horizontal gene transfer (HGT). Our approach allowed us to specifically link genes to organisms and their genetic context, providing insight into WWTP impacts on natural microbial communities. Overall, our results suggest a substantial influence of wastewater effluent on gene content and microbial community structure in the sediments of receiving water bodies. IMPORTANCE Wastewater treatment plants (WWTPs) release their effluent into aquatic environments. Although treated, effluent retains many genes and microorganisms that have the potential to influence the receiving water in ways that are poorly understood. Here, we tracked the genetic footprint, including genes specific to antibiotic resistance and mobile genetic elements and their associated organisms, from WWTPs to lake sediments. Our work is novel in that we used metagenomic data sets to comprehensively evaluate total gene content and the genetic and taxonomic context of specific genes in environmental samples putatively impacted by WWTP inputs. Based on two different WWTPs with different treatment processes, our findings point to an influence of WWTPs on the presence, abundance, and composition of these factors in the environment. Copyright © 2018 Chu et al.

Metagenomics Reveals the Impact of Wastewater Treatment Plants on the Dispersal of Microorganisms and Genes in Aquatic Sediments

PubMed Central

Chu, Binh T. T.; Petrovich, Morgan L.; Chaudhary, Adit; Wright, Dorothy; Murphy, Brian; Wells, George

2017-01-01

ABSTRACT Wastewater treatment plants (WWTPs) release treated effluent containing mobile genetic elements (MGEs), antibiotic resistance genes (ARGs), and microorganisms into the environment, yet little is known about their influence on nearby microbial communities and the retention of these factors in receiving water bodies. Our research aimed to characterize the genes and organisms from two different WWTPs that discharge into Lake Michigan, as well as from surrounding lake sediments to determine the dispersal and fate of these factors with respect to distance from the effluent outfall. Shotgun metagenomics coupled to distance-decay analyses showed a higher abundance of genes identical to those in WWTP effluent genes in sediments closer to outfall sites than in sediments farther away, indicating their possible WWTP origin. We also found genes attributed to organisms, such as those belonging to Helicobacteraceae, Legionellaceae, Moraxellaceae, and Neisseriaceae, in effluent from both WWTPs and decreasing in abundance in lake sediments with increased distance from WWTPs. Moreover, our results showed that the WWTPs likely influence the ARG composition in lake sediments close to the effluent discharge. Many of these ARGs were located on MGEs in both the effluent and sediment samples, indicating a relatively broad propensity for horizontal gene transfer (HGT). Our approach allowed us to specifically link genes to organisms and their genetic context, providing insight into WWTP impacts on natural microbial communities. Overall, our results suggest a substantial influence of wastewater effluent on gene content and microbial community structure in the sediments of receiving water bodies. IMPORTANCE Wastewater treatment plants (WWTPs) release their effluent into aquatic environments. Although treated, effluent retains many genes and microorganisms that have the potential to influence the receiving water in ways that are poorly understood. Here, we tracked the genetic footprint, including genes specific to antibiotic resistance and mobile genetic elements and their associated organisms, from WWTPs to lake sediments. Our work is novel in that we used metagenomic data sets to comprehensively evaluate total gene content and the genetic and taxonomic context of specific genes in environmental samples putatively impacted by WWTP inputs. Based on two different WWTPs with different treatment processes, our findings point to an influence of WWTPs on the presence, abundance, and composition of these factors in the environment. PMID:29269503

E-Learning: Students Input for Using Mobile Devices in Science Instructional Settings

ERIC Educational Resources Information Center

Yilmaz, Ozkan

2016-01-01

A variety of e-learning theories, models, and strategy have been developed to support educational settings. There are many factors for designing good instructional settings. This study set out to determine functionality of mobile devices, students who already have, and the student needs and views in relation to e-learning settings. The study…

GenePRIMP: A Gene Prediction Improvement Pipeline For Prokaryotic Genomes

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

Kyrpides, Nikos C.; Ivanova, Natalia N.; Pati, Amrita

2010-07-08

GenePRIMP (Gene Prediction Improvement Pipeline, Http://geneprimp.jgi-psf.org), a computational process that performs evidence-based evaluation of gene models in prokaryotic genomes and reports anomalies including inconsistent start sites, missing genes, and split genes. We show that manual curation of gene models using the anomaly reports generated by GenePRIMP improves their quality and demonstrate the applicability of GenePRIMP in improving finishing quality and comparing different genome sequencing and annotation technologies. Keywords in context: Gene model, Quality Control, Translation start sites, Automatic correction. Hardware requirements; PC, MAC; Operating System: UNIX/LINUX; Compiler/Version: Perl 5.8.5 or higher; Special requirements: NCBI Blast and nr installation; File Types:more » Source Code, Executable module(s), Sample problem input data; installation instructions other; programmer documentation. Location/transmission: http://geneprimp.jgi-psf.org/gp.tar.gz« less

SkData: data sets and algorithm evaluation protocols in Python

NASA Astrophysics Data System (ADS)

Bergstra, James; Pinto, Nicolas; Cox, David D.

2015-01-01

Machine learning benchmark data sets come in all shapes and sizes, whereas classification algorithms assume sanitized input, such as (x, y) pairs with vector-valued input x and integer class label y. Researchers and practitioners know all too well how tedious it can be to get from the URL of a new data set to a NumPy ndarray suitable for e.g. pandas or sklearn. The SkData library handles that work for a growing number of benchmark data sets (small and large) so that one-off in-house scripts for downloading and parsing data sets can be replaced with library code that is reliable, community-tested, and documented. The SkData library also introduces an open-ended formalization of training and testing protocols that facilitates direct comparison with published research. This paper describes the usage and architecture of the SkData library.

Design of a fragment library that maximally represents available chemical space.

PubMed

Schulz, M N; Landström, J; Bright, K; Hubbard, R E

2011-07-01

Cheminformatics protocols have been developed and assessed that identify a small set of fragments which can represent the compounds in a chemical library for use in fragment-based ligand discovery. Six different methods have been implemented and tested on Input Libraries of compounds from three suppliers. The resulting Fragment Sets have been characterised on the basis of computed physico-chemical properties and their similarity to the Input Libraries. A method that iteratively identifies fragments with the maximum number of similar compounds in the Input Library (Nearest Neighbours) produces the most diverse library. This approach could increase the success of experimental ligand discovery projects, by providing fragments that can be progressed rapidly to larger compounds through access to available similar compounds (known as SAR by Catalog).

Entanglement in channel discrimination with restricted measurements

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

Matthews, William; Piani, Marco; Watrous, John

2010-09-15

We study the power of measurements implementable with local quantum operations and classical communication (LOCC) measurements in the setting of quantum channel discrimination. More precisely, we consider discrimination procedures that attempt to identify an unknown channel, chosen uniformly from two known alternatives, that take the following form: (i) the input to the unknown channel is prepared in a possibly entangled state with an ancillary system, (ii) the unknown channel is applied to the input system, and (iii) an LOCC measurement is performed on the output and ancillary systems, resulting in a guess for which of the two channels was given.more » The restriction of the measurement in such a procedure to be an LOCC measurement is of interest because it isolates the entanglement in the initial input-ancillary systems as a resource in the setting of channel discrimination. We prove that there exist channel discrimination problems for which restricted procedures of this sort can be at either of the two extremes: they may be optimal within the set of all discrimination procedures (and simultaneously outperform all strategies that make no use of entanglement), or they may be no better than unentangled strategies (and simultaneously suboptimal within the set of all discrimination procedures).« less

Genome-Wide Temporal Expression Profiling in Caenorhabditis elegans Identifies a Core Gene Set Related to Long-Term Memory.

PubMed

Freytag, Virginie; Probst, Sabine; Hadziselimovic, Nils; Boglari, Csaba; Hauser, Yannick; Peter, Fabian; Gabor Fenyves, Bank; Milnik, Annette; Demougin, Philippe; Vukojevic, Vanja; de Quervain, Dominique J-F; Papassotiropoulos, Andreas; Stetak, Attila

2017-07-12

The identification of genes related to encoding, storage, and retrieval of memories is a major interest in neuroscience. In the current study, we analyzed the temporal gene expression changes in a neuronal mRNA pool during an olfactory long-term associative memory (LTAM) in Caenorhabditis elegans hermaphrodites. Here, we identified a core set of 712 (538 upregulated and 174 downregulated) genes that follows three distinct temporal peaks demonstrating multiple gene regulation waves in LTAM. Compared with the previously published positive LTAM gene set (Lakhina et al., 2015), 50% of the identified upregulated genes here overlap with the previous dataset, possibly representing stimulus-independent memory-related genes. On the other hand, the remaining genes were not previously identified in positive associative memory and may specifically regulate aversive LTAM. Our results suggest a multistep gene activation process during the formation and retrieval of long-term memory and define general memory-implicated genes as well as conditioning-type-dependent gene sets. SIGNIFICANCE STATEMENT The identification of genes regulating different steps of memory is of major interest in neuroscience. Identification of common memory genes across different learning paradigms and the temporal activation of the genes are poorly studied. Here, we investigated the temporal aspects of Caenorhabditis elegans gene expression changes using aversive olfactory associative long-term memory (LTAM) and identified three major gene activation waves. Like in previous studies, aversive LTAM is also CREB dependent, and CREB activity is necessary immediately after training. Finally, we define a list of memory paradigm-independent core gene sets as well as conditioning-dependent genes. Copyright © 2017 the authors 0270-6474/17/376661-12$15.00/0.

Wireless and acoustic hearing with bone-anchored hearing devices.

PubMed

Bosman, Arjan J; Mylanus, Emmanuel A M; Hol, Myrthe K S; Snik, Ad F M

2015-07-01

The efficacy of wireless connectivity in bone-anchored hearing was studied by comparing the wireless and acoustic performance of the Ponto Plus sound processor from Oticon Medical relative to the acoustic performance of its predecessor, the Ponto Pro. Nineteen subjects with more than two years' experience with a bone-anchored hearing device were included. Thirteen subjects were fitted unilaterally and six bilaterally. Subjects served as their own control. First, subjects were tested with the Ponto Pro processor. After a four-week acclimatization period performance the Ponto Plus processor was measured. In the laboratory wireless and acoustic input levels were made equal. In daily life equal settings of wireless and acoustic input were used when watching TV, however when using the telephone the acoustic input was reduced by 9 dB relative to the wireless input. Speech scores for microphone with Ponto Pro and for both input modes of the Ponto Plus processor were essentially equal when equal input levels of wireless and microphone inputs were used. Only the TV-condition showed a statistically significant (p <5%) lower speech reception threshold for wireless relative to microphone input. In real life, evaluation of speech quality, speech intelligibility in quiet and noise, and annoyance by ambient noise, when using landline phone, mobile telephone, and watching TV showed a clear preference (p <1%) for the Ponto Plus system with streamer over the microphone input. Due to the small number of respondents with landline phone (N = 7) the result for noise annoyance was only significant at the 5% level. Equal input levels for acoustic and wireless inputs results in equal speech scores, showing a (near) equivalence for acoustic and wireless sound transmission with Ponto Pro and Ponto Plus. The default 9-dB difference between microphone and wireless input when using the telephone results in a substantial wireless benefit when using the telephone. The preference of wirelessly transmitted audio when watching TV can be attributed to the relatively poor sound quality of backward facing loudspeakers in flat screen TVs. The ratio of wireless and acoustic input can be easily set to the user's preference with the streamer's volume control.

Developmental Self-Construction and -Configuration of Functional Neocortical Neuronal Networks

PubMed Central

Bauer, Roman; Zubler, Frédéric; Pfister, Sabina; Hauri, Andreas; Pfeiffer, Michael; Muir, Dylan R.; Douglas, Rodney J.

2014-01-01

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative (‘winner-take-all’, WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data. PMID:25474693

75 FR 44930 - Stakeholder Input; Revisions to Water Quality Standards Regulation

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-30

... Input; Revisions to Water Quality Standards Regulation AGENCY: Environmental Protection Agency. ACTION... national rulemaking to make a limited set of targeted changes to EPA's water quality standards regulation... rulemaking, and to hear views from the public regarding possible changes to EPA's water quality standards...

Intracellular calcium dynamics permit a Purkinje neuron model to perform toggle and gain computations upon its inputs

PubMed Central

Forrest, Michael D.

2014-01-01

Without synaptic input, Purkinje neurons can spontaneously fire in a repeating trimodal pattern that consists of tonic spiking, bursting and quiescence. Climbing fiber input (CF) switches Purkinje neurons out of the trimodal firing pattern and toggles them between a tonic firing and a quiescent state, while setting the gain of their response to Parallel Fiber (PF) input. The basis to this transition is unclear. We investigate it using a biophysical Purkinje cell model under conditions of CF and PF input. The model can replicate these toggle and gain functions, dependent upon a novel account of intracellular calcium dynamics that we hypothesize to be applicable in real Purkinje cells. PMID:25191262

Minimal Power Latch for Single-Slope ADCs

NASA Technical Reports Server (NTRS)

Hancock, Bruce R.

2013-01-01

Column-parallel analog-to-digital converters (ADCs) for imagers involve simultaneous operation of many ADCs. Single-slope ADCs are well adapted to this use because of their simplicity. Each ADC contains a comparator, comparing its input signal level to an increasing reference signal (ramp). When the ramp is equal to the input, the comparator triggers a latch that captures an encoded counter value (code). Knowing the captured code, the ramp value and hence the input signal are determined. In a column-parallel ADC, each column contains only the comparator and the latches; the ramp and code generation are shared. In conventional latch or flip-flop circuits, there is an input stage that tracks the input signal, and this stage consumes switching current every time the input changes. With many columns, many bits, and high code rates, this switching current can be substantial. It will also generate noise that may corrupt the analog signals. A latch was designed that does not track the input, and consumes power only at the instant of latching the data value. The circuit consists of two S-R (set-reset) latches, gated by the comparator. One is set by high data values and the other by low data values. The latches are cross-coupled so that the first one to set blocks the other. In order that the input data not need an inversion, which would consume power, the two latches are made in complementary polarity. This requires complementary gates from the comparator, instead of complementary data values, but the comparator only triggers once per conversion, and usually has complementary outputs to begin with. An efficient CMOS (complementary metal oxide semiconductor) implementation of this circuit is shown in the figure, where C is the comparator output, D is the data (code), and Q0 and Q1 are the outputs indicating the capture of a zero or one value. The latch for Q0 has a negative-true set signal and output, and is implemented using OR-AND-INVERT logic, while the latch for Q1 uses positive- true signals and is implemented using AND-OR-INVERT logic. In this implementation, both latches are cleared when the comparator is reset. Two redundant transistors are removed from the reset side of each latch, making for a compact layout. CMOS imagers with column-parallel ADCs have demonstrated high performance for remote sensing applications. With this latch circuit, the power consumption and noise can be further reduced. This innovation can be used in CMOS imagers and very-low-power electronics

nocte Is Required for Integrating Light and Temperature Inputs in Circadian Clock Neurons of Drosophila.

PubMed

Chen, Chenghao; Xu, Min; Anantaprakorn, Yuto; Rosing, Mechthild; Stanewsky, Ralf

2018-05-21

Circadian clocks organize biological processes to occur at optimized times of day and thereby contribute to overall fitness. While the regular daily changes of environmental light and temperature synchronize circadian clocks, extreme external conditions can bypass the temporal constraints dictated by the clock. Despite advanced knowledge about how the daily light-dark changes synchronize the clock, relatively little is known with regard to how the daily temperature changes influence daily timing and how temperature and light signals are integrated. In Drosophila, a network of ∼150 brain clock neurons exhibit 24-hr oscillations of clock gene expression to regulate daily activity and sleep. We show here that a temperature input pathway from peripheral sensory organs, which depends on the gene nocte, targets specific subsets of these clock neurons to synchronize molecular and behavioral rhythms to temperature cycles. Strikingly, while nocte 1 mutant flies synchronize normally to light-dark cycles at constant temperatures, the combined presence of light-dark and temperature cycles inhibits synchronization. nocte 1 flies exhibit altered siesta sleep, suggesting that the sleep-regulating clock neurons are an important target for nocte-dependent temperature input, which dominates a parallel light input into these cells. In conclusion, we reveal a nocte-dependent temperature input pathway to central clock neurons and show that this pathway and its target neurons are important for the integration of sensory light and temperature information in order to temporally regulate activity and sleep during daily light and temperature cycles. Copyright © 2018 Elsevier Ltd. All rights reserved.

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

NASA Astrophysics Data System (ADS)

Pakyuz-Charrier, Evren; Lindsay, Mark; Ogarko, Vitaliy; Giraud, Jeremie; Jessell, Mark

2018-04-01

Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the properties of the subsurface matters; they express our understanding of geometries in depth. For that reason, 3-D geological models have a wide range of practical applications including but not restricted to civil engineering, the oil and gas industry, the mining industry, and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) and the inherent lack of knowledge in areas where there are no observations combined with input uncertainty (observational, conceptual and technical errors). Because 3-D geological models are often used for impactful decision-making it is critical that all 3-D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data measurement uncertainty propagation in implicit 3-D geological modeling. This aim is achieved using Monte Carlo simulation for uncertainty estimation (MCUE), a stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3-D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of incorrect disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e., scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminish the occurrence of artifacts.

Genome-wide identification of lineage-specific genes in Arabidopsis, Oryza and Populus

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

Yang, Xiaohan; Jawdy, Sara; Tschaplinski, Timothy J

2009-01-01

Protein sequences were compared among Arabidopsis, Oryza and Populus to identify differential gene (DG) sets that are in one but not the other two genomes. The DG sets were screened against a plant transcript database, the NR protein database and six newly-sequenced genomes (Carica, Glycine, Medicago, Sorghum, Vitis and Zea) to identify a set of species-specific genes (SS). Gene expression, protein motif and intron number were examined. 192, 641 and 109 SS genes were identified in Arabidopsis, Oryza and Populus, respectively. Some SS genes were preferentially expressed in flowers, roots, xylem and cambium or up-regulated by stress. Six conserved motifsmore » in Arabidopsis and Oryza SS proteins were found in other distant lineages. The SS gene sets were enriched with intronless genes. The results reflect functional and/or anatomical differences between monocots and eudicots or between herbaceous and woody plants. The Populus-specific genes are candidates for carbon sequestration and biofuel research.« less

Feeding State, Insulin and NPR-1 Modulate Chemoreceptor Gene Expression via Integration of Sensory and Circuit Inputs

PubMed Central

Gruner, Matthew; Nelson, Dru; Winbush, Ari; Hintz, Rebecca; Ryu, Leesun; Chung, Samuel H.; Kim, Kyuhyung; Gabel, Chrisopher V.; van der Linden, Alexander M.

2014-01-01

Feeding state and food availability can dramatically alter an animals' sensory response to chemicals in its environment. Dynamic changes in the expression of chemoreceptor genes may underlie some of these food and state-dependent changes in chemosensory behavior, but the mechanisms underlying these expression changes are unknown. Here, we identified a KIN-29 (SIK)-dependent chemoreceptor, srh-234, in C. elegans whose expression in the ADL sensory neuron type is regulated by integration of sensory and internal feeding state signals. We show that in addition to KIN-29, signaling is mediated by the DAF-2 insulin-like receptor, OCR-2 TRPV channel, and NPR-1 neuropeptide receptor. Cell-specific rescue experiments suggest that DAF-2 and OCR-2 act in ADL, while NPR-1 acts in the RMG interneurons. NPR-1-mediated regulation of srh-234 is dependent on gap-junctions, implying that circuit inputs regulate the expression of chemoreceptor genes in sensory neurons. Using physical and genetic manipulation of ADL neurons, we show that sensory inputs from food presence and ADL neural output regulate srh-234 expression. While KIN-29 and DAF-2 act primarily via the MEF-2 (MEF2) and DAF-16 (FOXO) transcription factors to regulate srh-234 expression in ADL neurons, OCR-2 and NPR-1 likely act via a calcium-dependent but MEF-2- and DAF-16-independent pathway. Together, our results suggest that sensory- and circuit-mediated regulation of chemoreceptor genes via multiple pathways may allow animals to precisely regulate and fine-tune their chemosensory responses as a function of internal and external conditions. PMID:25357003

Cross-Study Homogeneity of Psoriasis Gene Expression in Skin across a Large Expression Range

PubMed Central

Kerkof, Keith; Timour, Martin; Russell, Christopher B.

2013-01-01

Background In psoriasis, only limited overlap between sets of genes identified as differentially expressed (psoriatic lesional vs. psoriatic non-lesional) was found using statistical and fold-change cut-offs. To provide a framework for utilizing prior psoriasis data sets we sought to understand the consistency of those sets. Methodology/Principal Findings Microarray expression profiling and qRT-PCR were used to characterize gene expression in PP and PN skin from psoriasis patients. cDNA (three new data sets) and cRNA hybridization (four existing data sets) data were compared using a common analysis pipeline. Agreement between data sets was assessed using varying qualitative and quantitative cut-offs to generate a DEG list in a source data set and then using other data sets to validate the list. Concordance increased from 67% across all probe sets to over 99% across more than 10,000 probe sets when statistical filters were employed. The fold-change behavior of individual genes tended to be consistent across the multiple data sets. We found that genes with <2-fold change values were quantitatively reproducible between pairs of data-sets. In a subset of transcripts with a role in inflammation changes detected by microarray were confirmed by qRT-PCR with high concordance. For transcripts with both PN and PP levels within the microarray dynamic range, microarray and qRT-PCR were quantitatively reproducible, including minimal fold-changes in IL13, TNFSF11, and TNFRSF11B and genes with >10-fold changes in either direction such as CHRM3, IL12B and IFNG. Conclusions/Significance Gene expression changes in psoriatic lesions were consistent across different studies, despite differences in patient selection, sample handling, and microarray platforms but between-study comparisons showed stronger agreement within than between platforms. We could use cut-offs as low as log10(ratio) = 0.1 (fold-change = 1.26), generating larger gene lists that validate on independent data sets. The reproducibility of PP signatures across data sets suggests that different sample sets can be productively compared. PMID:23308107

Hydraulic actuator mechanism to control aircraft spoiler movements through dual input commands

NASA Technical Reports Server (NTRS)

Irick, S. C. (Inventor)

1981-01-01

An aircraft flight spoiler control mechanism is described. The invention enables the conventional, primary spoiler control system to retain its operational characteristics while accommodating a secondary input controlled by a conventional computer system to supplement the settings made by the primary input. This is achieved by interposing springs between the primary input and the spoiler control unit. The springs are selected to have a stiffness intermediate to the greater force applied by the primary control linkage and the lesser resistance offered by the spoiler control unit. Thus, operation of the primary input causes the control unit to yield before the springs, yet, operation of the secondary input, acting directly on the control unit, causes the springs to yield and absorb adjustments before they are transmitted into the primary control system.

Discovering monotonic stemness marker genes from time-series stem cell microarray data.

PubMed

Wang, Hsei-Wei; Sun, Hsing-Jen; Chang, Ting-Yu; Lo, Hung-Hao; Cheng, Wei-Chung; Tseng, George C; Lin, Chin-Teng; Chang, Shing-Jyh; Pal, Nikhil; Chung, I-Fang

2015-01-01

Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics. We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages. We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/.

Feature space trajectory for distorted-object classification and pose estimation in synthetic aperture radar

NASA Astrophysics Data System (ADS)

Casasent, David P.; Shenoy, Rajesh

1997-10-01

Classification and pose estimation of distorted input objects are considered. The feature space trajectory representation of distorted views of an object is used with a new eigenfeature space. For a distorted input object, the closest trajectory denotes the class of the input and the closest line segment on it denotes its pose. If an input point is too far from a trajectory, it is rejected as clutter. New methods for selecting Fukunaga-Koontz discriminant vectors, the number of dominant eigenvectors per class and for determining training, and test set compatibility are presented.

Combining Gene Signatures Improves Prediction of Breast Cancer Survival

PubMed Central

Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

2011-01-01

Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set. PMID:21423775

A statistical approach to identify, monitor, and manage incomplete curated data sets.

PubMed

Howe, Douglas G

2018-04-02

Many biological knowledge bases gather data through expert curation of published literature. High data volume, selective partial curation, delays in access, and publication of data prior to the ability to curate it can result in incomplete curation of published data. Knowing which data sets are incomplete and how incomplete they are remains a challenge. Awareness that a data set may be incomplete is important for proper interpretation, to avoiding flawed hypothesis generation, and can justify further exploration of published literature for additional relevant data. Computational methods to assess data set completeness are needed. One such method is presented here. In this work, a multivariate linear regression model was used to identify genes in the Zebrafish Information Network (ZFIN) Database having incomplete curated gene expression data sets. Starting with 36,655 gene records from ZFIN, data aggregation, cleansing, and filtering reduced the set to 9870 gene records suitable for training and testing the model to predict the number of expression experiments per gene. Feature engineering and selection identified the following predictive variables: the number of journal publications; the number of journal publications already attributed for gene expression annotation; the percent of journal publications already attributed for expression data; the gene symbol; and the number of transgenic constructs associated with each gene. Twenty-five percent of the gene records (2483 genes) were used to train the model. The remaining 7387 genes were used to test the model. One hundred and twenty-two and 165 of the 7387 tested genes were identified as missing expression annotations based on their residuals being outside the model lower or upper 95% confidence interval respectively. The model had precision of 0.97 and recall of 0.71 at the negative 95% confidence interval and precision of 0.76 and recall of 0.73 at the positive 95% confidence interval. This method can be used to identify data sets that are incompletely curated, as demonstrated using the gene expression data set from ZFIN. This information can help both database resources and data consumers gauge when it may be useful to look further for published data to augment the existing expertly curated information.

Modeling and validation of autoinducer-mediated bacterial gene expression in microfluidic environments

PubMed Central

Austin, Caitlin M.; Stoy, William; Su, Peter; Harber, Marie C.; Bardill, J. Patrick; Hammer, Brian K.; Forest, Craig R.

2014-01-01

Biosensors exploiting communication within genetically engineered bacteria are becoming increasingly important for monitoring environmental changes. Currently, there are a variety of mathematical models for understanding and predicting how genetically engineered bacteria respond to molecular stimuli in these environments, but as sensors have miniaturized towards microfluidics and are subjected to complex time-varying inputs, the shortcomings of these models have become apparent. The effects of microfluidic environments such as low oxygen concentration, increased biofilm encapsulation, diffusion limited molecular distribution, and higher population densities strongly affect rate constants for gene expression not accounted for in previous models. We report a mathematical model that accurately predicts the biological response of the autoinducer N-acyl homoserine lactone-mediated green fluorescent protein expression in reporter bacteria in microfluidic environments by accommodating these rate constants. This generalized mass action model considers a chain of biomolecular events from input autoinducer chemical to fluorescent protein expression through a series of six chemical species. We have validated this model against experimental data from our own apparatus as well as prior published experimental results. Results indicate accurate prediction of dynamics (e.g., 14% peak time error from a pulse input) and with reduced mean-squared error with pulse or step inputs for a range of concentrations (10 μM–30 μM). This model can help advance the design of genetically engineered bacteria sensors and molecular communication devices. PMID:25379076

Expression of the histone chaperone SET/TAF-Iβ during the strobilation process of Mesocestoides corti (Platyhelminthes, Cestoda).

PubMed

Costa, Caroline B; Monteiro, Karina M; Teichmann, Aline; da Silva, Edileuza D; Lorenzatto, Karina R; Cancela, Martín; Paes, Jéssica A; Benitz, André de N D; Castillo, Estela; Margis, Rogério; Zaha, Arnaldo; Ferreira, Henrique B

2015-08-01

The histone chaperone SET/TAF-Iβ is implicated in processes of chromatin remodelling and gene expression regulation. It has been associated with the control of developmental processes, but little is known about its function in helminth parasites. In Mesocestoides corti, a partial cDNA sequence related to SET/TAF-Iβ was isolated in a screening for genes differentially expressed in larvae (tetrathyridia) and adult worms. Here, the full-length coding sequence of the M. corti SET/TAF-Iβ gene was analysed and the encoded protein (McSET/TAF) was compared with orthologous sequences, showing that McSET/TAF can be regarded as a SET/TAF-Iβ family member, with a typical nucleosome-assembly protein (NAP) domain and an acidic tail. The expression patterns of the McSET/TAF gene and protein were investigated during the strobilation process by RT-qPCR, using a set of five reference genes, and by immunoblot and immunofluorescence, using monospecific polyclonal antibodies. A gradual increase in McSET/TAF transcripts and McSET/TAF protein was observed upon development induction by trypsin, demonstrating McSET/TAF differential expression during strobilation. These results provided the first evidence for the involvement of a protein from the NAP family of epigenetic effectors in the regulation of cestode development.

oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes

PubMed Central

Ho Sui, Shannan J.; Mortimer, James R.; Arenillas, David J.; Brumm, Jochen; Walsh, Christopher J.; Kennedy, Brian P.; Wasserman, Wyeth W.

2005-01-01

Targeted transcript profiling studies can identify sets of co-expressed genes; however, identification of the underlying functional mechanism(s) is a significant challenge. Established methods for the analysis of gene annotations, particularly those based on the Gene Ontology, can identify functional linkages between genes. Similar methods for the identification of over-represented transcription factor binding sites (TFBSs) have been successful in yeast, but extension to human genomics has largely proved ineffective. Creation of a system for the efficient identification of common regulatory mechanisms in a subset of co-expressed human genes promises to break a roadblock in functional genomics research. We have developed an integrated system that searches for evidence of co-regulation by one or more transcription factors (TFs). oPOSSUM combines a pre-computed database of conserved TFBSs in human and mouse promoters with statistical methods for identification of sites over-represented in a set of co-expressed genes. The algorithm successfully identified mediating TFs in control sets of tissue-specific genes and in sets of co-expressed genes from three transcript profiling studies. Simulation studies indicate that oPOSSUM produces few false positives using empirically defined thresholds and can tolerate up to 50% noise in a set of co-expressed genes. PMID:15933209

L1000CDS2: LINCS L1000 characteristic direction signatures search engine

PubMed Central

Duan, Qiaonan; Reid, St Patrick; Clark, Neil R; Wang, Zichen; Fernandez, Nicolas F; Rouillard, Andrew D; Readhead, Ben; Tritsch, Sarah R; Hodos, Rachel; Hafner, Marc; Niepel, Mario; Sorger, Peter K; Dudley, Joel T; Bavari, Sina; Panchal, Rekha G; Ma’ayan, Avi

2016-01-01

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS2. The L1000CDS2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS2, we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource. PMID:28413689

Gene integrated set profile analysis: a context-based approach for inferring biological endpoints

PubMed Central

Kowalski, Jeanne; Dwivedi, Bhakti; Newman, Scott; Switchenko, Jeffery M.; Pauly, Rini; Gutman, David A.; Arora, Jyoti; Gandhi, Khanjan; Ainslie, Kylie; Doho, Gregory; Qin, Zhaohui; Moreno, Carlos S.; Rossi, Michael R.; Vertino, Paula M.; Lonial, Sagar; Bernal-Mizrachi, Leon; Boise, Lawrence H.

2016-01-01

The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an ‘integrate by intersection’ (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance. PMID:26826710

Discovery of cancer common and specific driver gene sets

PubMed Central

2017-01-01

Abstract Cancer is known as a disease mainly caused by gene alterations. Discovery of mutated driver pathways or gene sets is becoming an important step to understand molecular mechanisms of carcinogenesis. However, systematically investigating commonalities and specificities of driver gene sets among multiple cancer types is still a great challenge, but this investigation will undoubtedly benefit deciphering cancers and will be helpful for personalized therapy and precision medicine in cancer treatment. In this study, we propose two optimization models to de novo discover common driver gene sets among multiple cancer types (ComMDP) and specific driver gene sets of one certain or multiple cancer types to other cancers (SpeMDP), respectively. We first apply ComMDP and SpeMDP to simulated data to validate their efficiency. Then, we further apply these methods to 12 cancer types from The Cancer Genome Atlas (TCGA) and obtain several biologically meaningful driver pathways. As examples, we construct a common cancer pathway model for BRCA and OV, infer a complex driver pathway model for BRCA carcinogenesis based on common driver gene sets of BRCA with eight cancer types, and investigate specific driver pathways of the liquid cancer lymphoblastic acute myeloid leukemia (LAML) versus other solid cancer types. In these processes more candidate cancer genes are also found. PMID:28168295

Oscillator or Amplifier With Wide Frequency Range

NASA Technical Reports Server (NTRS)

Kleinberg, L.; Sutton, J.

1987-01-01

Inductive and capacitive effects synthesized with feedback circuits. Oscillator/amplifier resistively tunable over wide frequency range. Feedback circuits containing operational amplifiers, resistors, and capacitors synthesize electrical effects of inductance and capacitance in parallel between input terminals. Synthetic inductance and capacitance, and, therefore, resonant frequency of input admittance, adjusted by changing potentiometer setting.

ASDIR-II. Volume I. User Manual

DTIC Science & Technology

1975-12-01

normally the most significant part of the overall aircraft IR signature. The 4 radiance is directly dependent upon the geometric view factors , a set...tactors as punched card output in. a view factor computer run. For the view factor computer run IB49 through 53 and all IDS input A, from IDS-2 to IDS-6...may be excluded from the input string if the * program execution is requested to stop after punching the viewv factors . Inputs required for punching

Comparative genomic analysis of SET domain family reveals the origin, expansion, and putative function of the arthropod-specific SmydA genes as histone modifiers in insects.

PubMed

Jiang, Feng; Liu, Qing; Wang, Yanli; Zhang, Jie; Wang, Huimin; Song, Tianqi; Yang, Meiling; Wang, Xianhui; Kang, Le

2017-06-01

The SET domain is an evolutionarily conserved motif present in histone lysine methyltransferases, which are important in the regulation of chromatin and gene expression in animals. In this study, we searched for SET domain-containing genes (SET genes) in all of the 147 arthropod genomes sequenced at the time of carrying out this experiment to understand the evolutionary history by which SET domains have evolved in insects. Phylogenetic and ancestral state reconstruction analysis revealed an arthropod-specific SET gene family, named SmydA, that is ancestral to arthropod animals and specifically diversified during insect evolution. Considering that pseudogenization is the most probable fate of the new emerging gene copies, we provided experimental and evolutionary evidence to demonstrate their essential functions. Fluorescence in situ hybridization analysis and in vitro methyltransferase activity assays showed that the SmydA-2 gene was transcriptionally active and retained the original histone methylation activity. Expression knockdown by RNA interference significantly increased mortality, implying that the SmydA genes may be essential for insect survival. We further showed predominantly strong purifying selection on the SmydA gene family and a potential association between the regulation of gene expression and insect phenotypic plasticity by transcriptome analysis. Overall, these data suggest that the SmydA gene family retains essential functions that may possibly define novel regulatory pathways in insects. This work provides insights into the roles of lineage-specific domain duplication in insect evolution. © The Authors 2017. Published by Oxford University Press.

Comparative genomic analysis of SET domain family reveals the origin, expansion, and putative function of the arthropod-specific SmydA genes as histone modifiers in insects

PubMed Central

Jiang, Feng; Liu, Qing; Wang, Yanli; Zhang, Jie; Wang, Huimin; Song, Tianqi; Yang, Meiling

2017-01-01

Abstract The SET domain is an evolutionarily conserved motif present in histone lysine methyltransferases, which are important in the regulation of chromatin and gene expression in animals. In this study, we searched for SET domain–containing genes (SET genes) in all of the 147 arthropod genomes sequenced at the time of carrying out this experiment to understand the evolutionary history by which SET domains have evolved in insects. Phylogenetic and ancestral state reconstruction analysis revealed an arthropod-specific SET gene family, named SmydA, that is ancestral to arthropod animals and specifically diversified during insect evolution. Considering that pseudogenization is the most probable fate of the new emerging gene copies, we provided experimental and evolutionary evidence to demonstrate their essential functions. Fluorescence in situ hybridization analysis and in vitro methyltransferase activity assays showed that the SmydA-2 gene was transcriptionally active and retained the original histone methylation activity. Expression knockdown by RNA interference significantly increased mortality, implying that the SmydA genes may be essential for insect survival. We further showed predominantly strong purifying selection on the SmydA gene family and a potential association between the regulation of gene expression and insect phenotypic plasticity by transcriptome analysis. Overall, these data suggest that the SmydA gene family retains essential functions that may possibly define novel regulatory pathways in insects. This work provides insights into the roles of lineage-specific domain duplication in insect evolution. PMID:28444351

ACTG: novel peptide mapping onto gene models.

PubMed

Choi, Seunghyuk; Kim, Hyunwoo; Paek, Eunok

2017-04-15

In many proteogenomic applications, mapping peptide sequences onto genome sequences can be very useful, because it allows us to understand origins of the gene products. Existing software tools either take the genomic position of a peptide start site as an input or assume that the peptide sequence exactly matches the coding sequence of a given gene model. In case of novel peptides resulting from genomic variations, especially structural variations such as alternative splicing, these existing tools cannot be directly applied unless users supply information about the variant, either its genomic position or its transcription model. Mapping potentially novel peptides to genome sequences, while allowing certain genomic variations, requires introducing novel gene models when aligning peptide sequences to gene structures. We have developed a new tool called ACTG (Amino aCids To Genome), which maps peptides to genome, assuming all possible single exon skipping, junction variation allowing three edit distances from the original splice sites, exon extension and frame shift. In addition, it can also consider SNVs (single nucleotide variations) during mapping phase if a user provides the VCF (variant call format) file as an input. Available at http://prix.hanyang.ac.kr/ACTG/search.jsp . eunokpaek@hanyang.ac.kr. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Antagonistic control of a dual-input mammalian gene switch by food additives.

PubMed

Xie, Mingqi; Ye, Haifeng; Hamri, Ghislaine Charpin-El; Fussenegger, Martin

2014-08-01

Synthetic biology has significantly advanced the design of mammalian trigger-inducible transgene-control devices that are able to programme complex cellular behaviour. Fruit-based benzoate derivatives licensed as food additives, such as flavours (e.g. vanillate) and preservatives (e.g. benzoate), are a particularly attractive class of trigger compounds for orthogonal mammalian transgene control devices because of their innocuousness, physiological compatibility and simple oral administration. Capitalizing on the genetic componentry of the soil bacterium Comamonas testosteroni, which has evolved to catabolize a variety of aromatic compounds, we have designed different mammalian gene expression systems that could be induced and repressed by the food additives benzoate and vanillate. When implanting designer cells engineered for gene switch-driven expression of the human placental secreted alkaline phosphatase (SEAP) into mice, blood SEAP levels of treated animals directly correlated with a benzoate-enriched drinking programme. Additionally, the benzoate-/vanillate-responsive device was compatible with other transgene control systems and could be assembled into higher-order control networks providing expression dynamics reminiscent of a lap-timing stopwatch. Designer gene switches using licensed food additives as trigger compounds to achieve antagonistic dual-input expression profiles and provide novel control topologies and regulation dynamics may advance future gene- and cell-based therapies. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

shRNA-Induced Gene Knockdown In Vivo to Investigate Neutrophil Function.

PubMed

Basit, Abdul; Tang, Wenwen; Wu, Dianqing

2016-01-01

To silence genes in neutrophils efficiently, we exploited the RNA interference and developed an shRNA-based gene knockdown technique. This method involves transfection of mouse bone marrow-derived hematopoietic stem cells with retroviral vector carrying shRNA directed at a specific gene. Transfected stem cells are then transplanted into irradiated wild-type mice. After engraftment of stem cells, the transplanted mice have two sets of circulating neutrophils. One set has a gene of interest knocked down while the other set has full complement of expressed genes. This efficient technique provides a unique way to directly compare the response of neutrophils with a knocked-down gene to that of neutrophils with the full complement of expressed genes in the same environment.

Beyond main effects of gene-sets: harsh parenting moderates the association between a dopamine gene-set and child externalizing behavior.

PubMed

Windhorst, Dafna A; Mileva-Seitz, Viara R; Rippe, Ralph C A; Tiemeier, Henning; Jaddoe, Vincent W V; Verhulst, Frank C; van IJzendoorn, Marinus H; Bakermans-Kranenburg, Marian J

2016-08-01

In a longitudinal cohort study, we investigated the interplay of harsh parenting and genetic variation across a set of functionally related dopamine genes, in association with children's externalizing behavior. This is one of the first studies to employ gene-based and gene-set approaches in tests of Gene by Environment (G × E) effects on complex behavior. This approach can offer an important alternative or complement to candidate gene and genome-wide environmental interaction (GWEI) studies in the search for genetic variation underlying individual differences in behavior. Genetic variants in 12 autosomal dopaminergic genes were available in an ethnically homogenous part of a population-based cohort. Harsh parenting was assessed with maternal (n = 1881) and paternal (n = 1710) reports at age 3. Externalizing behavior was assessed with the Child Behavior Checklist (CBCL) at age 5 (71 ± 3.7 months). We conducted gene-set analyses of the association between variation in dopaminergic genes and externalizing behavior, stratified for harsh parenting. The association was statistically significant or approached significance for children without harsh parenting experiences, but was absent in the group with harsh parenting. Similarly, significant associations between single genes and externalizing behavior were only found in the group without harsh parenting. Effect sizes in the groups with and without harsh parenting did not differ significantly. Gene-environment interaction tests were conducted for individual genetic variants, resulting in two significant interaction effects (rs1497023 and rs4922132) after correction for multiple testing. Our findings are suggestive of G × E interplay, with associations between dopamine genes and externalizing behavior present in children without harsh parenting, but not in children with harsh parenting experiences. Harsh parenting may overrule the role of genetic factors in externalizing behavior. Gene-based and gene-set analyses offer promising new alternatives to analyses focusing on single candidate polymorphisms when examining the interplay between genetic and environmental factors.

About miRNAs, miRNA seeds, target genes and target pathways.

PubMed

Kehl, Tim; Backes, Christina; Kern, Fabian; Fehlmann, Tobias; Ludwig, Nicole; Meese, Eckart; Lenhof, Hans-Peter; Keller, Andreas

2017-12-05

miRNAs are typically repressing gene expression by binding to the 3' UTR, leading to degradation of the mRNA. This process is dominated by the eight-base seed region of the miRNA. Further, miRNAs are known not only to target genes but also to target significant parts of pathways. A logical line of thoughts is: miRNAs with similar (seed) sequence target similar sets of genes and thus similar sets of pathways. By calculating similarity scores for all 3.25 million pairs of 2,550 human miRNAs, we found that this pattern frequently holds, while we also observed exceptions. Respective results were obtained for both, predicted target genes as well as experimentally validated targets. We note that miRNAs target gene set similarity follows a bimodal distribution, pointing at a set of 282 miRNAs that seems to target genes with very high specificity. Further, we discuss miRNAs with different (seed) sequences that nonetheless regulate similar gene sets or pathways. Most intriguingly, we found miRNA pairs that regulate different gene sets but similar pathways such as miR-6886-5p and miR-3529-5p. These are jointly targeting different parts of the MAPK signaling cascade. The main goal of this study is to provide a general overview on the results, to highlight a selection of relevant results on miRNAs, miRNA seeds, target genes and target pathways and to raise awareness for artifacts in respective comparisons. The full set of information that allows to infer detailed results on each miRNA has been included in miRPathDB, the miRNA target pathway database (https://mpd.bioinf.uni-sb.de).

Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification.

PubMed

Shimoni, Yishai

2018-02-01

One of the goals of cancer research is to identify a set of genes that cause or control disease progression. However, although multiple such gene sets were published, these are usually in very poor agreement with each other, and very few of the genes proved to be functional therapeutic targets. Furthermore, recent findings from a breast cancer gene-expression cohort showed that sets of genes selected randomly can be used to predict survival with a much higher probability than expected. These results imply that many of the genes identified in breast cancer gene expression analysis may not be causal of cancer progression, even though they can still be highly predictive of prognosis. We performed a similar analysis on all the cancer types available in the cancer genome atlas (TCGA), namely, estimating the predictive power of random gene sets for survival. Our work shows that most cancer types exhibit the property that random selections of genes are more predictive of survival than expected. In contrast to previous work, this property is not removed by using a proliferation signature, which implies that proliferation may not always be the confounder that drives this property. We suggest one possible solution in the form of data-driven sub-classification to reduce this property significantly. Our results suggest that the predictive power of random gene sets may be used to identify the existence of sub-classes in the data, and thus may allow better understanding of patient stratification. Furthermore, by reducing the observed bias this may allow more direct identification of biologically relevant, and potentially causal, genes.

Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification

PubMed Central

2018-01-01

One of the goals of cancer research is to identify a set of genes that cause or control disease progression. However, although multiple such gene sets were published, these are usually in very poor agreement with each other, and very few of the genes proved to be functional therapeutic targets. Furthermore, recent findings from a breast cancer gene-expression cohort showed that sets of genes selected randomly can be used to predict survival with a much higher probability than expected. These results imply that many of the genes identified in breast cancer gene expression analysis may not be causal of cancer progression, even though they can still be highly predictive of prognosis. We performed a similar analysis on all the cancer types available in the cancer genome atlas (TCGA), namely, estimating the predictive power of random gene sets for survival. Our work shows that most cancer types exhibit the property that random selections of genes are more predictive of survival than expected. In contrast to previous work, this property is not removed by using a proliferation signature, which implies that proliferation may not always be the confounder that drives this property. We suggest one possible solution in the form of data-driven sub-classification to reduce this property significantly. Our results suggest that the predictive power of random gene sets may be used to identify the existence of sub-classes in the data, and thus may allow better understanding of patient stratification. Furthermore, by reducing the observed bias this may allow more direct identification of biologically relevant, and potentially causal, genes. PMID:29470520

Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data.

PubMed

Lee, Hyeonjeong; Shin, Miyoung

2017-01-01

The problem of discovering genetic markers as disease signatures is of great significance for the successful diagnosis, treatment, and prognosis of complex diseases. Even if many earlier studies worked on identifying disease markers from a variety of biological resources, they mostly focused on the markers of genes or gene-sets (i.e., pathways). However, these markers may not be enough to explain biological interactions between genetic variables that are related to diseases. Thus, in this study, our aim is to investigate distinctive associations among active pathways (i.e., pathway-sets) shown each in case and control samples which can be observed from gene expression and/or methylation data. The pathway-sets are obtained by identifying a set of associated pathways that are often active together over a significant number of class samples. For this purpose, gene expression or methylation profiles are first analyzed to identify significant (active) pathways via gene-set enrichment analysis. Then, regarding these active pathways, an association rule mining approach is applied to examine interesting pathway-sets in each class of samples (case or control). By doing so, the sets of associated pathways often working together in activity profiles are finally chosen as our distinctive signature of each class. The identified pathway-sets are aggregated into a pathway activity network (PAN), which facilitates the visualization of differential pathway associations between case and control samples. From our experiments with two publicly available datasets, we could find interesting PAN structures as the distinctive signatures of breast cancer and uterine leiomyoma cancer, respectively. Our pathway-set markers were shown to be superior or very comparable to other genetic markers (such as genes or gene-sets) in disease classification. Furthermore, the PAN structure, which can be constructed from the identified markers of pathway-sets, could provide deeper insights into distinctive associations between pathway activities in case and control samples.

Microgravity and Immunity: Changes in Lymphocyte Gene Expression

NASA Technical Reports Server (NTRS)

Risin, D.; Pellis, N. R.; Ward, N. E.; Risin, S. A.

2006-01-01

Earlier studies had shown that modeled and true microgravity (MG) cause multiple direct effects on human lymphocytes. MG inhibits lymphocyte locomotion, suppresses polyclonal and antigen-specific activation, affects signal transduction mechanisms, as well as activation-induced apoptosis. In this study we assessed changes in gene expression associated with lymphocyte exposure to microgravity in an attempt to identify microgravity-sensitive genes (MGSG) in general and specifically those genes that might be responsible for the functional and structural changes observed earlier. Two sets of experiments targeting different goals were conducted. In the first set, T-lymphocytes from normal donors were activated with antiCD3 and IL2 and then cultured in 1g (static) and modeled MG (MMG) conditions (Rotating Wall Vessel bioreactor) for 24 hours. This setting allowed searching for MGSG by comparison of gene expression patterns in zero and 1 g gravity. In the second set - activated T-cells after culturing for 24 hours in 1g and MMG were exposed three hours before harvesting to a secondary activation stimulus (PHA) thus triggering the apoptotic pathway. Total RNA was extracted using the RNeasy isolation kit (Qiagen, Valencia, CA). Affymetrix Gene Chips (U133A), allowing testing for 18,400 human genes, were used for microarray analysis. In the first set of experiments MMG exposure resulted in altered expression of 89 genes, 10 of them were up-regulated and 79 down-regulated. In the second set, changes in expression were revealed in 85 genes, 20 were up-regulated and 65 were down-regulated. The analysis revealed that significant numbers of MGS genes are associated with signal transduction and apoptotic pathways. Interestingly, the majority of genes that responded by up- or down-regulation in the alternative sets of experiments were not the same, possibly reflecting different functional states of the examined T-lymphocyte populations. The responder genes (MGSG) might play an essential role in adaptation to MG and/or be responsible for pathologic changes encountered in Space and thus represent potential targets for molecular-based countermeasures

Identifying prognostic signature in ovarian cancer using DirGenerank

PubMed Central

Wang, Jian-Yong; Chen, Ling-Ling; Zhou, Xiong-Hui

2017-01-01

Identifying the prognostic genes in cancer is essential not only for the treatment of cancer patients, but also for drug discovery. However, it's still a big challenge to select the prognostic genes that can distinguish the risk of cancer patients across various data sets because of tumor heterogeneity. In this situation, the selected genes whose expression levels are statistically related to prognostic risks may be passengers. In this paper, based on gene expression data and prognostic data of ovarian cancer patients, we used conditional mutual information to construct gene dependency network in which the nodes (genes) with more out-degrees have more chances to be the modulators of cancer prognosis. After that, we proposed DirGenerank (Generank in direct netowrk) algorithm, which concerns both the gene dependency network and genes’ correlations to prognostic risks, to identify the gene signature that can predict the prognostic risks of ovarian cancer patients. Using ovarian cancer data set from TCGA (The Cancer Genome Atlas) as training data set, 40 genes with the highest importance were selected as prognostic signature. Survival analysis of these patients divided by the prognostic signature in testing data set and four independent data sets showed the signature can distinguish the prognostic risks of cancer patients significantly. Enrichment analysis of the signature with curated cancer genes and the drugs selected by CMAP showed the genes in the signature may be drug targets for therapy. In summary, we have proposed a useful pipeline to identify prognostic genes of cancer patients. PMID:28615526

Vesicular Glutamate Transporters: Spatio-Temporal Plasticity following Hearing Loss

PubMed Central

Fyk-Kolodziej, Bozena; Shimano, Takashi; Gong, Tzy-Wen; Holt, Avril Genene

2011-01-01

An immunocytochemical comparison of vGluT1 and vGluT3 in the cochlear nucleus (CN) of deafened versus normal hearing rats showed the first example of vGluT3 immunostaining in the dorsal and ventral CN and revealed temporal and spatial changes in vGluT1 localization in the CN after cochlear injury. In normal hearing rats vGluT1 immunostaining was restricted to terminals on CN neurons while vGluT3 immunolabeled the somata of the neurons. This changed in the VCN three days following deafness, where vGluT1 immunostaining was no longer seen in large auditory nerve terminals but was instead found in somata of VCN neurons. In the DCN, while vGluT1 labeling of terminals decreased, there was no labeling of neuronal somata. Therefore, loss of peripheral excitatory input results in co-localization of vGluT1 and vGluT3 in VCN neuronal somata. Postsynaptic glutamatergic neurons can use retrograde signaling to control their presynaptic inputs and these results suggest vGluTs could play a role in regulating retrograde signaling in the CN under different conditions of excitatory input. Changes in vGluT gene expression in CN neurons were found three weeks following deafness using qRT-PCR with significant increases in vGluT1 gene expression in both ventral and dorsal CN while vGluT3 gene expression decreased in VCN but increased in DCN. PMID:21211553

Pathway Distiller - multisource biological pathway consolidation

PubMed Central

2012-01-01

Background One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. Methods After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. Results We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. Conclusions By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments. PMID:23134636

Pathway Distiller - multisource biological pathway consolidation.

PubMed

Doderer, Mark S; Anguiano, Zachry; Suresh, Uthra; Dashnamoorthy, Ravi; Bishop, Alexander J R; Chen, Yidong

2012-01-01

One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments.

A Meta-Analysis of Multiple Matched Copy Number and Transcriptomics Data Sets for Inferring Gene Regulatory Relationships

PubMed Central

Newton, Richard; Wernisch, Lorenz

2014-01-01

Inferring gene regulatory relationships from observational data is challenging. Manipulation and intervention is often required to unravel causal relationships unambiguously. However, gene copy number changes, as they frequently occur in cancer cells, might be considered natural manipulation experiments on gene expression. An increasing number of data sets on matched array comparative genomic hybridisation and transcriptomics experiments from a variety of cancer pathologies are becoming publicly available. Here we explore the potential of a meta-analysis of thirty such data sets. The aim of our analysis was to assess the potential of in silico inference of trans-acting gene regulatory relationships from this type of data. We found sufficient correlation signal in the data to infer gene regulatory relationships, with interesting similarities between data sets. A number of genes had highly correlated copy number and expression changes in many of the data sets and we present predicted potential trans-acted regulatory relationships for each of these genes. The study also investigates to what extent heterogeneity between cell types and between pathologies determines the number of statistically significant predictions available from a meta-analysis of experiments. PMID:25148247

Adopting Cut Scores: Post-Standard-Setting Panel Considerations for Decision Makers

ERIC Educational Resources Information Center

Geisinger, Kurt F.; McCormick, Carina M.

2010-01-01

Standard-setting studies utilizing procedures such as the Bookmark or Angoff methods are just one component of the complete standard-setting process. Decision makers ultimately must determine what they believe to be the most appropriate standard or cut score to use, employing the input of the standard-setting panelists as one piece of information…

Analog current mode analog/digital converter

NASA Technical Reports Server (NTRS)

Hadidi, Khayrollah (Inventor)

1996-01-01

An improved subranging or comparator circuit is provided for an analog-to-digital converter. As a subranging circuit, the circuit produces a residual signal representing the difference between an analog input signal and an analog of a digital representation. This is achieved by subdividing the digital representation into two or more parts and subtracting from the analog input signal analogs of each of the individual digital portions. In another aspect of the present invention, the subranging circuit comprises two sets of differential input pairs in which the transconductance of one differential input pair is scaled relative to the transconductance of the other differential input pair. As a consequence, the same resistor string may be used for two different digital-to-analog converters of the subranging circuit.

Organization of monosynaptic inputs to the serotonin and dopamine neuromodulatorysystems

PubMed Central

Ogawa, Sachie K.; Cohen, Jeremiah Y.; Hwang, Dabin; Uchida, Naoshige; Watabe-Uchida, Mitsuko

2014-01-01

SUMMARY Serotonin and dopamine are major neuromodulators. Here we used a modified rabies virus to identify monosynaptic inputs to serotonin neurons in the dorsal and median raphe (DR and MR). We found that inputs to DR and MR serotonin neurons are spatially shiftedin the forebrain, with MRserotonin neurons receiving inputs from more medial structures. We then compared these data with inputs to dopamine neurons in the ventral tegmental area (VTA) and substantianigra pars compacta (SNc). We found that DR serotonin neurons receive inputs from a remarkably similar set of areas as VTA dopamine neurons, apart from the striatum, which preferentially targets dopamine neurons. Ourresults suggest three majorinput streams: amedial stream regulates MR serotonin neurons, anintermediate stream regulatesDR serotonin and VTA dopamine neurons, and alateral stream regulatesSNc dopamine neurons. These results providefundamental organizational principlesofafferent control forserotonin and dopamine. PMID:25108805

Ethical issues in haemophilia.

PubMed

DiMichele, D; Chuansumrit, A; London, A J; Thompson, A R; Cooper, C G; Killian, R M; Ross, L F; Lillicrap, D; Kimmelman, J

2006-07-01

Ethical issues surrounding both the lack of global access to care as well as the implementation of advancing technologies, continue to challenge the international haemophilia community. Haemophilia is not given the priority it deserves in most developing countries. Given the heavy burdens of sickness and disease and severe resource constraints, it may not be possible to provide effective treatment to all who suffer from the various 'orphan' diseases. Nevertheless, through joint efforts, some package of effective interventions can be deployed for a significant number of those who are afflicted with 'orphan' diseases. With cost-effective utilization of limited resources, a national standard of care is possible and affordable. Gene-based diagnosis carries attendant ethical concerns whether for clinical testing or for research purposes, even as the list of its potential benefits to the haemophilia community grows rapidly. As large-scale genetic sequencing becomes quicker and cheaper, moving from the research to the clinic, we will face decisions about the implementation of prenatal, neonatal and other screening programs. Such debates will require input from not just the health care professionals but from all stakeholders in the haemophilia community. Finally, long-term therapeutic success gene transfer in small and large animal models raises the question of when and in which patient population the novel therapeutic approach should first be studied in humans with haemophilia. Although gene therapy represents a worthy goal, the central question for the haemophilia community should be whether it wishes to volunteer itself as a model for a much broader set of innovations.

MINE: Module Identification in Networks

PubMed Central

2011-01-01

Background Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks. Results MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the C. elegans protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties. Conclusions MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both S. cerevisiae and C. elegans. PMID:21605434

Searching for statistically significant regulatory modules.

PubMed

Bailey, Timothy L; Noble, William Stafford

2003-10-01

The regulatory machinery controlling gene expression is complex, frequently requiring multiple, simultaneous DNA-protein interactions. The rate at which a gene is transcribed may depend upon the presence or absence of a collection of transcription factors bound to the DNA near the gene. Locating transcription factor binding sites in genomic DNA is difficult because the individual sites are small and tend to occur frequently by chance. True binding sites may be identified by their tendency to occur in clusters, sometimes known as regulatory modules. We describe an algorithm for detecting occurrences of regulatory modules in genomic DNA. The algorithm, called mcast, takes as input a DNA database and a collection of binding site motifs that are known to operate in concert. mcast uses a motif-based hidden Markov model with several novel features. The model incorporates motif-specific p-values, thereby allowing scores from motifs of different widths and specificities to be compared directly. The p-value scoring also allows mcast to only accept motif occurrences with significance below a user-specified threshold, while still assigning better scores to motif occurrences with lower p-values. mcast can search long DNA sequences, modeling length distributions between motifs within a regulatory module, but ignoring length distributions between modules. The algorithm produces a list of predicted regulatory modules, ranked by E-value. We validate the algorithm using simulated data as well as real data sets from fruitfly and human. http://meme.sdsc.edu/MCAST/paper

Analysis of genetic association using hierarchical clustering and cluster validation indices.

PubMed

Pagnuco, Inti A; Pastore, Juan I; Abras, Guillermo; Brun, Marcel; Ballarin, Virginia L

2017-10-01

It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, based on some criteria of similarity. This task is usually performed by clustering algorithms, where the genes are clustered into meaningful groups based on their expression values in a set of experiment. In this work, we propose a method to find sets of co-expressed genes, based on cluster validation indices as a measure of similarity for individual gene groups, and a combination of variants of hierarchical clustering to generate the candidate groups. We evaluated its ability to retrieve significant sets on simulated correlated and real genomics data, where the performance is measured based on its detection ability of co-regulated sets against a full search. Additionally, we analyzed the quality of the best ranked groups using an online bioinformatics tool that provides network information for the selected genes. Copyright © 2017 Elsevier Inc. All rights reserved.

Superior Cross-Species Reference Genes: A Blueberry Case Study

PubMed Central

Die, Jose V.; Rowland, Lisa J.

2013-01-01

The advent of affordable Next Generation Sequencing technologies has had major impact on studies of many crop species, where access to genomic technologies and genome-scale data sets has been extremely limited until now. The recent development of genomic resources in blueberry will enable the application of high throughput gene expression approaches that should relatively quickly increase our understanding of blueberry physiology. These studies, however, require a highly accurate and robust workflow and make necessary the identification of reference genes with high expression stability for correct target gene normalization. To create a set of superior reference genes for blueberry expression analyses, we mined a publicly available transcriptome data set from blueberry for orthologs to a set of Arabidopsis genes that showed the most stable expression in a developmental series. In total, the expression stability of 13 putative reference genes was evaluated by qPCR and a set of new references with high stability values across a developmental series in fruits and floral buds of blueberry were identified. We also demonstrated the need to use at least two, preferably three, reference genes to avoid inconsistencies in results, even when superior reference genes are used. The new references identified here provide a valuable resource for accurate normalization of gene expression in Vaccinium spp. and may be useful for other members of the Ericaceae family as well. PMID:24058469

Glutamatergic and GABAergic gene sets in attention-deficit/hyperactivity disorder: association to overlapping traits in ADHD and autism.

PubMed

Naaijen, J; Bralten, J; Poelmans, G; Glennon, J C; Franke, B; Buitelaar, J K

2017-01-10

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASD) often co-occur. Both are highly heritable; however, it has been difficult to discover genetic risk variants. Glutamate and GABA are main excitatory and inhibitory neurotransmitters in the brain; their balance is essential for proper brain development and functioning. In this study we investigated the role of glutamate and GABA genetics in ADHD severity, autism symptom severity and inhibitory performance, based on gene set analysis, an approach to investigate multiple genetic variants simultaneously. Common variants within glutamatergic and GABAergic genes were investigated using the MAGMA software in an ADHD case-only sample (n=931), in which we assessed ASD symptoms and response inhibition on a Stop task. Gene set analysis for ADHD symptom severity, divided into inattention and hyperactivity/impulsivity symptoms, autism symptom severity and inhibition were performed using principal component regression analyses. Subsequently, gene-wide association analyses were performed. The glutamate gene set showed an association with severity of hyperactivity/impulsivity (P=0.009), which was robust to correcting for genome-wide association levels. The GABA gene set showed nominally significant association with inhibition (P=0.04), but this did not survive correction for multiple comparisons. None of single gene or single variant associations was significant on their own. By analyzing multiple genetic variants within candidate gene sets together, we were able to find genetic associations supporting the involvement of excitatory and inhibitory neurotransmitter systems in ADHD and ASD symptom severity in ADHD.

geneCommittee: a web-based tool for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification.

PubMed

Reboiro-Jato, Miguel; Arrais, Joel P; Oliveira, José Luis; Fdez-Riverola, Florentino

2014-01-30

The diagnosis and prognosis of several diseases can be shortened through the use of different large-scale genome experiments. In this context, microarrays can generate expression data for a huge set of genes. However, to obtain solid statistical evidence from the resulting data, it is necessary to train and to validate many classification techniques in order to find the best discriminative method. This is a time-consuming process that normally depends on intricate statistical tools. geneCommittee is a web-based interactive tool for routinely evaluating the discriminative classification power of custom hypothesis in the form of biologically relevant gene sets. While the user can work with different gene set collections and several microarray data files to configure specific classification experiments, the tool is able to run several tests in parallel. Provided with a straightforward and intuitive interface, geneCommittee is able to render valuable information for diagnostic analyses and clinical management decisions based on systematically evaluating custom hypothesis over different data sets using complementary classifiers, a key aspect in clinical research. geneCommittee allows the enrichment of microarrays raw data with gene functional annotations, producing integrated datasets that simplify the construction of better discriminative hypothesis, and allows the creation of a set of complementary classifiers. The trained committees can then be used for clinical research and diagnosis. Full documentation including common use cases and guided analysis workflows is freely available at http://sing.ei.uvigo.es/GC/.

Real Time Trajectory Planning for Groups of Unmanned Vehicles

DTIC Science & Technology

2005-08-22

positions of the robots, and a third set determines the local relations of the neighboring robots. They have used a nonholonomic kinematic model for the...Note that the vehicles are not physically coupled in any way. A feedback control law for control inputs F.2 and T2 miust be determined to control... Vb12 71 = 01 + 01 2 - 02 The goal is finding a control law for the two inputs [F2 , T2] to stabilize the outputs. Therefore, the input-output equations

A Review of Feature Extraction Software for Microarray Gene Expression Data

PubMed Central

Tan, Ching Siang; Ting, Wai Soon; Mohamad, Mohd Saberi; Chan, Weng Howe; Deris, Safaai; Ali Shah, Zuraini

2014-01-01

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method. PMID:25250315

FLUXCOM - Overview and First Synthesis

NASA Astrophysics Data System (ADS)

Jung, M.; Ichii, K.; Tramontana, G.; Camps-Valls, G.; Schwalm, C. R.; Papale, D.; Reichstein, M.; Gans, F.; Weber, U.

2015-12-01

We present a community effort aiming at generating an ensemble of global gridded flux products by upscaling FLUXNET data using an array of different machine learning methods including regression/model tree ensembles, neural networks, and kernel machines. We produced products for gross primary production, terrestrial ecosystem respiration, net ecosystem exchange, latent heat, sensible heat, and net radiation for two experimental protocols: 1) at a high spatial and 8-daily temporal resolution (5 arc-minute) using only remote sensing based inputs for the MODIS era; 2) 30 year records of daily, 0.5 degree spatial resolution by incorporating meteorological driver data. Within each set-up, all machine learning methods were trained with the same input data for carbon and energy fluxes respectively. Sets of input driver variables were derived using an extensive formal variable selection exercise. The performance of the extrapolation capacities of the approaches is assessed with a fully internally consistent cross-validation. We perform cross-consistency checks of the gridded flux products with independent data streams from atmospheric inversions (NEE), sun-induced fluorescence (GPP), catchment water balances (LE, H), satellite products (Rn), and process-models. We analyze the uncertainties of the gridded flux products and for example provide a breakdown of the uncertainty of mean annual GPP originating from different machine learning methods, different climate input data sets, and different flux partitioning methods. The FLUXCOM archive will provide an unprecedented source of information for water, energy, and carbon cycle studies.

Solving Common Mathematical Problems

NASA Technical Reports Server (NTRS)

Luz, Paul L.

2005-01-01

Mathematical Solutions Toolset is a collection of five software programs that rapidly solve some common mathematical problems. The programs consist of a set of Microsoft Excel worksheets. The programs provide for entry of input data and display of output data in a user-friendly, menu-driven format, and for automatic execution once the input data has been entered.

Unitary vs Multiple Semantics: PET Studies of Word and Picture Processing

ERIC Educational Resources Information Center

Bright, P.; Moss, H.; Tyler, L. K.

2004-01-01

In this paper we examine a central issue in cognitive neuroscience: are there separate conceptual representations associated with different input modalities (e.g., Paivio, 1971, 1986; Warrington & Shallice, 1984) or do inputs from different modalities converge on to the same set of representations (e.g., Caramazza, Hillis, Rapp, & Romani, 1990;…

Minimum Input Techniques for Valley Oak Restocking

Treesearch

Elizabeth A. Bernhardt; Tedmund J. Swiecki

1991-01-01

We set up experiments at four locations in northern California to demonstrate minimum input techniques for restocking valley oak, Quercus lobata. Overall emergence of acorns planted in 1989 ranged from 47 to 61 percent. Use of supplemental irrigation had a significant positive effect on seedling growth at two of three sites. Mulch, of organic...

DEVELOPING NUTRIENT CRIETERIA FOR ESTUARIES WITH VARIABLE OCEAN INPUTS: AN EXAMPLE FROM THE PACIFIC NORTHWEST

EPA Science Inventory

Estuaries in the Pacific Northwest have major intraannual and within estuary variation in sources and magnitudes of nutrient inputs. To develop an approach for setting nutrient criteria for these systems, we conducted a case study for Yaquina Bay, OR based on a synthesis of resea...

Gene Therapy for Fracture Repair

DTIC Science & Technology

2007-05-01

Methods: We have adopted the Agilent rat oligomer chip to analyze our fracture RNA in our microarray analysis. This chip has 20,046 unique gene...signal during fluorescent labeling of the cDNA. This approach is highly advantageous for reducing the RNA input into the system, minimizing the numbers...perform the analysis on these extremely limited samples without pooling the RNA from multiple individuals. We are therefore able to analyze the

The Him gene reveals a balance of inputs controlling muscle differentiation in Drosophila.

PubMed

Liotta, David; Han, Jun; Elgar, Stuart; Garvey, Clare; Han, Zhe; Taylor, Michael V

2007-08-21

Tissue development requires the controlled regulation of cell-differentiation programs. In muscle, the Mef2 transcription factor binds to and activates the expression of many genes and has a major positive role in the orchestration of differentiation. However, little is known about how Mef2 activity is regulated in vivo during development. Here, we characterize a gene, Holes in muscle (Him), which our results indicate is part of this control in Drosophila. Him expression rapidly declines as embryonic muscle differentiates, and consistent with this, Him overexpression inhibits muscle differentiation. This inhibitory effect is suppressed by mef2, implicating Him in the mef2 pathway. We then found that Him downregulates the transcriptional activity of Mef2 in both cell culture and in vivo. Furthermore, Him protein binds Groucho, a conserved, transcriptional corepressor, through a WRPW motif and requires this motif and groucho function to inhibit both muscle differentiation and Mef2 activity during development. Together, our results identify a mechanism that can inhibit muscle differentiation in vivo. We conclude that a balance of positive and negative inputs, including Mef2, Him, and Groucho, controls muscle differentiation during Drosophila development and suggest that one outcome is to hold developing muscle cells in a state with differentiation genes poised to be expressed.

The Him Gene Reveals a Balance of Inputs Controlling Muscle Differentiation in Drosophila

PubMed Central

Liotta, David; Han, Jun; Elgar, Stuart; Garvey, Clare; Han, Zhe; Taylor, Michael V.

2007-01-01

Summary Tissue development requires the controlled regulation of cell-differentiation programs. In muscle, the Mef2 transcription factor binds to and activates the expression of many genes and has a major positive role in the orchestration of differentiation [1–4]. However, little is known about how Mef2 activity is regulated in vivo during development. Here, we characterize a gene, Holes in muscle (Him), which our results indicate is part of this control in Drosophila. Him expression rapidly declines as embryonic muscle differentiates, and consistent with this, Him overexpression inhibits muscle differentiation. This inhibitory effect is suppressed by mef2, implicating Him in the mef2 pathway. We then found that Him downregulates the transcriptional activity of Mef2 in both cell culture and in vivo. Furthermore, Him protein binds Groucho, a conserved, transcriptional corepressor, through a WRPW motif and requires this motif and groucho function to inhibit both muscle differentiation and Mef2 activity during development. Together, our results identify a mechanism that can inhibit muscle differentiation in vivo. We conclude that a balance of positive and negative inputs, including Mef2, Him, and Groucho, controls muscle differentiation during Drosophila development and suggest that one outcome is to hold developing muscle cells in a state with differentiation genes poised to be expressed. PMID:17702578

Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data.

PubMed

Cha, Kihoon; Hwang, Taeho; Oh, Kimin; Yi, Gwan-Su

2015-01-01

It has been reported that several brain diseases can be treated as transnosological manner implicating possible common molecular basis under those diseases. However, molecular level commonality among those brain diseases has been largely unexplored. Gene expression analyses of human brain have been used to find genes associated with brain diseases but most of those studies were restricted either to an individual disease or to a couple of diseases. In addition, identifying significant genes in such brain diseases mostly failed when it used typical methods depending on differentially expressed genes. In this study, we used a correlation-based biclustering approach to find coexpressed gene sets in five neurodegenerative diseases and three psychiatric disorders. By using biclustering analysis, we could efficiently and fairly identified various gene sets expressed specifically in both single and multiple brain diseases. We could find 4,307 gene sets correlatively expressed in multiple brain diseases and 3,409 gene sets exclusively specified in individual brain diseases. The function enrichment analysis of those gene sets showed many new possible functional bases as well as neurological processes that are common or specific for those eight diseases. This study introduces possible common molecular bases for several brain diseases, which open the opportunity to clarify the transnosological perspective assumed in brain diseases. It also showed the advantages of correlation-based biclustering analysis and accompanying function enrichment analysis for gene expression data in this type of investigation.

Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data

PubMed Central

2015-01-01

Background It has been reported that several brain diseases can be treated as transnosological manner implicating possible common molecular basis under those diseases. However, molecular level commonality among those brain diseases has been largely unexplored. Gene expression analyses of human brain have been used to find genes associated with brain diseases but most of those studies were restricted either to an individual disease or to a couple of diseases. In addition, identifying significant genes in such brain diseases mostly failed when it used typical methods depending on differentially expressed genes. Results In this study, we used a correlation-based biclustering approach to find coexpressed gene sets in five neurodegenerative diseases and three psychiatric disorders. By using biclustering analysis, we could efficiently and fairly identified various gene sets expressed specifically in both single and multiple brain diseases. We could find 4,307 gene sets correlatively expressed in multiple brain diseases and 3,409 gene sets exclusively specified in individual brain diseases. The function enrichment analysis of those gene sets showed many new possible functional bases as well as neurological processes that are common or specific for those eight diseases. Conclusions This study introduces possible common molecular bases for several brain diseases, which open the opportunity to clarify the transnosological perspective assumed in brain diseases. It also showed the advantages of correlation-based biclustering analysis and accompanying function enrichment analysis for gene expression data in this type of investigation. PMID:26043779

Processing oscillatory signals by incoherent feedforward loops

NASA Astrophysics Data System (ADS)

Zhang, Carolyn; Wu, Feilun; Tsoi, Ryan; Shats, Igor; You, Lingchong

From the timing of amoeba development to the maintenance of stem cell pluripotency,many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression.While networks underlying this signal decoding are diverse,many are built around a common motif, the incoherent feedforward loop (IFFL),where an input simultaneously activates an output and an inhibitor of the output.With appropriate parameters,this motif can generate temporal adaptation,where the system is desensitized to a sustained input.This property serves as the foundation for distinguishing signals with varying temporal profiles.Here,we use quantitative modeling to examine another property of IFFLs,the ability to process oscillatory signals.Our results indicate that the system's ability to translate pulsatile dynamics is limited by two constraints.The kinetics of IFFL components dictate the input range for which the network can decode pulsatile dynamics.In addition,a match between the network parameters and signal characteristics is required for optimal ``counting''.We elucidate one potential mechanism by which information processing occurs in natural networks with implications in the design of synthetic gene circuits for this purpose. This work was partially supported by the National Science Foundation Graduate Research Fellowship (CZ).

Two- and three-input TALE-based AND logic computation in embryonic stem cells.

PubMed

Lienert, Florian; Torella, Joseph P; Chen, Jan-Hung; Norsworthy, Michael; Richardson, Ryan R; Silver, Pamela A

2013-11-01

Biological computing circuits can enhance our ability to control cellular functions and have potential applications in tissue engineering and medical treatments. Transcriptional activator-like effectors (TALEs) represent attractive components of synthetic gene regulatory circuits, as they can be designed de novo to target a given DNA sequence. We here demonstrate that TALEs can perform Boolean logic computation in mammalian cells. Using a split-intein protein-splicing strategy, we show that a functional TALE can be reconstituted from two inactive parts, thus generating two-input AND logic computation. We further demonstrate three-piece intein splicing in mammalian cells and use it to perform three-input AND computation. Using methods for random as well as targeted insertion of these relatively large genetic circuits, we show that TALE-based logic circuits are functional when integrated into the genome of mouse embryonic stem cells. Comparing construct variants in the same genomic context, we modulated the strength of the TALE-responsive promoter to improve the output of these circuits. Our work establishes split TALEs as a tool for building logic computation with the potential of controlling expression of endogenous genes or transgenes in response to a combination of cellular signals.

Modal Parameter Identification of a Flexible Arm System

NASA Technical Reports Server (NTRS)

Barrington, Jason; Lew, Jiann-Shiun; Korbieh, Edward; Wade, Montanez; Tantaris, Richard

1998-01-01

In this paper an experiment is designed for the modal parameter identification of a flexible arm system. This experiment uses a function generator to provide input signal and an oscilloscope to save input and output response data. For each vibrational mode, many sets of sine-wave inputs with frequencies close to the natural frequency of the arm system are used to excite the vibration of this mode. Then a least-squares technique is used to analyze the experimental input/output data to obtain the identified parameters for this mode. The identified results are compared with the analytical model obtained by applying finite element analysis.

Density-Dependent Quantized Least Squares Support Vector Machine for Large Data Sets.

PubMed

Nan, Shengyu; Sun, Lei; Chen, Badong; Lin, Zhiping; Toh, Kar-Ann

2017-01-01

Based on the knowledge that input data distribution is important for learning, a data density-dependent quantization scheme (DQS) is proposed for sparse input data representation. The usefulness of the representation scheme is demonstrated by using it as a data preprocessing unit attached to the well-known least squares support vector machine (LS-SVM) for application on big data sets. Essentially, the proposed DQS adopts a single shrinkage threshold to obtain a simple quantization scheme, which adapts its outputs to input data density. With this quantization scheme, a large data set is quantized to a small subset where considerable sample size reduction is generally obtained. In particular, the sample size reduction can save significant computational cost when using the quantized subset for feature approximation via the Nyström method. Based on the quantized subset, the approximated features are incorporated into LS-SVM to develop a data density-dependent quantized LS-SVM (DQLS-SVM), where an analytic solution is obtained in the primal solution space. The developed DQLS-SVM is evaluated on synthetic and benchmark data with particular emphasis on large data sets. Extensive experimental results show that the learning machine incorporating DQS attains not only high computational efficiency but also good generalization performance.

AlignMe—a membrane protein sequence alignment web server

PubMed Central

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

2014-01-01

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

Combining Shigella Tn-seq data with gold-standard E. coli gene deletion data suggests rare transitions between essential and non-essential gene functionality.

PubMed

Freed, Nikki E; Bumann, Dirk; Silander, Olin K

2016-09-06

Gene essentiality - whether or not a gene is necessary for cell growth - is a fundamental component of gene function. It is not well established how quickly gene essentiality can change, as few studies have compared empirical measures of essentiality between closely related organisms. Here we present the results of a Tn-seq experiment designed to detect essential protein coding genes in the bacterial pathogen Shigella flexneri 2a 2457T on a genome-wide scale. Superficial analysis of this data suggested that 481 protein-coding genes in this Shigella strain are critical for robust cellular growth on rich media. Comparison of this set of genes with a gold-standard data set of essential genes in the closely related Escherichia coli K12 BW25113 revealed that an excessive number of genes appeared essential in Shigella but non-essential in E. coli. Importantly, and in converse to this comparison, we found no genes that were essential in E. coli and non-essential in Shigella, implying that many genes were artefactually inferred as essential in Shigella. Controlling for such artefacts resulted in a much smaller set of discrepant genes. Among these, we identified three sets of functionally related genes, two of which have previously been implicated as critical for Shigella growth, but which are dispensable for E. coli growth. The data presented here highlight the small number of protein coding genes for which we have strong evidence that their essentiality status differs between the closely related bacterial taxa E. coli and Shigella. A set of genes involved in acetate utilization provides a canonical example. These results leave open the possibility of developing strain-specific antibiotic treatments targeting such differentially essential genes, but suggest that such opportunities may be rare in closely related bacteria.

Automatic image equalization and contrast enhancement using Gaussian mixture modeling.

PubMed

Celik, Turgay; Tjahjadi, Tardi

2012-01-01

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.

A five-layer users' need hierarchy of computer input device selection: a contextual observation survey of computer users with cervical spinal injuries (CSI).

PubMed

Tsai, Tsai-Hsuan; Nash, Robert J; Tseng, Kevin C

2009-05-01

This article presents how the researcher goes about answering the research question, 'how assistive technology impacts computer use among individuals with cervical spinal cord injury?' through an in-depth investigation into the real-life situations among computer operators with cervical spinal cord injuries (CSI). An in-depth survey was carried out to provide an insight into the function abilities and limitation, habitual practice and preference, choices and utilisation of input devices, personal and/or technical assistance, environmental set-up and arrangements and special requirements among 20 experienced computer users with cervical spinal cord injuries. Following the survey findings, a five-layer CSI users' needs hierarchy of input device selection and use was proposed. These needs were ranked in order: beginning with the most basic criterion at the bottom of the pyramid; lower-level criteria must be met before one moves onto the higher level. The users' needs hierarchy for CSI computer users, which had not been applied by previous research work and which has established a rationale for the development of alternative input devices. If an input device achieves the criteria set up in the needs hierarchy, then a good match of person and technology will be achieved.

Soil Microbe Active Community Composition and Capability of Responding to Litter Addition after 12 Years of No Inputs

PubMed Central

Brewer, Elizabeth; Yarwood, Rockie; Lajtha, Kate; Myrold, David

2013-01-01

One explanation given for the high microbial diversity found in soils is that they contain a large inactive biomass that is able to persist in soils for long periods of time. This persistent microbial fraction may help to buffer the functionality of the soil community during times of low nutrients by providing a reservoir of specialized functions that can be reactivated when conditions improve. A study was designed to test the hypothesis: in soils lacking fresh root or detrital inputs, microbial community composition may persist relatively unchanged. Upon addition of new inputs, this community will be stimulated to grow and break down litter similarly to control soils. Soils from two of the Detrital Input and Removal Treatments (DIRT) at the H. J. Andrews Experimental Forest, the no-input and control treatment plots, were used in a microcosm experiment where Douglas-fir needles were added to soils. After 3 and 151 days of incubation, soil microbial DNA and RNA was extracted and characterized using quantitative PCR (qPCR) and 454 pyrosequencing. The abundance of 16S and 28S gene copies and RNA copies did not vary with soil type or amendment; however, treatment differences were observed in the abundance of archaeal ammonia-oxidizing amoA gene abundance. Analysis of ∼110,000 bacterial sequences showed a significant change in the active (RNA-based) community between day 3 and day 151, but microbial composition was similar between soil types. These results show that even after 12 years of plant litter exclusion, the legacy of community composition was well buffered against a dramatic disturbance. PMID:23263952

Systematic computation with functional gene-sets among leukemic and hematopoietic stem cells reveals a favorable prognostic signature for acute myeloid leukemia.

PubMed

Yang, Xinan Holly; Li, Meiyi; Wang, Bin; Zhu, Wanqi; Desgardin, Aurelie; Onel, Kenan; de Jong, Jill; Chen, Jianjun; Chen, Luonan; Cunningham, John M

2015-03-24

Genes that regulate stem cell function are suspected to exert adverse effects on prognosis in malignancy. However, diverse cancer stem cell signatures are difficult for physicians to interpret and apply clinically. To connect the transcriptome and stem cell biology, with potential clinical applications, we propose a novel computational "gene-to-function, snapshot-to-dynamics, and biology-to-clinic" framework to uncover core functional gene-sets signatures. This framework incorporates three function-centric gene-set analysis strategies: a meta-analysis of both microarray and RNA-seq data, novel dynamic network mechanism (DNM) identification, and a personalized prognostic indicator analysis. This work uses complex disease acute myeloid leukemia (AML) as a research platform. We introduced an adjustable "soft threshold" to a functional gene-set algorithm and found that two different analysis methods identified distinct gene-set signatures from the same samples. We identified a 30-gene cluster that characterizes leukemic stem cell (LSC)-depleted cells and a 25-gene cluster that characterizes LSC-enriched cells in parallel; both mark favorable-prognosis in AML. Genes within each signature significantly share common biological processes and/or molecular functions (empirical p = 6e-5 and 0.03 respectively). The 25-gene signature reflects the abnormal development of stem cells in AML, such as AURKA over-expression. We subsequently determined that the clinical relevance of both signatures is independent of known clinical risk classifications in 214 patients with cytogenetically normal AML. We successfully validated the prognosis of both signatures in two independent cohorts of 91 and 242 patients respectively (log-rank p < 0.0015 and 0.05; empirical p < 0.015 and 0.08). The proposed algorithms and computational framework will harness systems biology research because they efficiently translate gene-sets (rather than single genes) into biological discoveries about AML and other complex diseases.

Accurately Assessing the Risk of Schizophrenia Conferred by Rare Copy-Number Variation Affecting Genes with Brain Function

PubMed Central

Raychaudhuri, Soumya; Korn, Joshua M.; McCarroll, Steven A.; Altshuler, David; Sklar, Pamela; Purcell, Shaun; Daly, Mark J.

2010-01-01

Investigators have linked rare copy number variation (CNVs) to neuropsychiatric diseases, such as schizophrenia. One hypothesis is that CNV events cause disease by affecting genes with specific brain functions. Under these circumstances, we expect that CNV events in cases should impact brain-function genes more frequently than those events in controls. Previous publications have applied “pathway” analyses to genes within neuropsychiatric case CNVs to show enrichment for brain-functions. While such analyses have been suggestive, they often have not rigorously compared the rates of CNVs impacting genes with brain function in cases to controls, and therefore do not address important confounders such as the large size of brain genes and overall differences in rates and sizes of CNVs. To demonstrate the potential impact of confounders, we genotyped rare CNV events in 2,415 unaffected controls with Affymetrix 6.0; we then applied standard pathway analyses using four sets of brain-function genes and observed an apparently highly significant enrichment for each set. The enrichment is simply driven by the large size of brain-function genes. Instead, we propose a case-control statistical test, cnv-enrichment-test, to compare the rate of CNVs impacting specific gene sets in cases versus controls. With simulations, we demonstrate that cnv-enrichment-test is robust to case-control differences in CNV size, CNV rate, and systematic differences in gene size. Finally, we apply cnv-enrichment-test to rare CNV events published by the International Schizophrenia Consortium (ISC). This approach reveals nominal evidence of case-association in neuronal-activity and the learning gene sets, but not the other two examined gene sets. The neuronal-activity genes have been associated in a separate set of schizophrenia cases and controls; however, testing in independent samples is necessary to definitively confirm this association. Our method is implemented in the PLINK software package. PMID:20838587

Radiation Quality Effects on Transcriptome Profiles in 3-d Cultures After Particle Irradiation

NASA Technical Reports Server (NTRS)

Patel, Z. S.; Kidane, Y. H.; Huff, J. L.

2014-01-01

In this work, we evaluate the differential effects of low- and high-LET radiation on 3-D organotypic cultures in order to investigate radiation quality impacts on gene expression and cellular responses. Reducing uncertainties in current risk models requires new knowledge on the fundamental differences in biological responses (the so-called radiation quality effects) triggered by heavy ion particle radiation versus low-LET radiation associated with Earth-based exposures. We are utilizing novel 3-D organotypic human tissue models that provide a format for study of human cells within a realistic tissue framework, thereby bridging the gap between 2-D monolayer culture and animal models for risk extrapolation to humans. To identify biological pathway signatures unique to heavy ion particle exposure, functional gene set enrichment analysis (GSEA) was used with whole transcriptome profiling. GSEA has been used extensively as a method to garner biological information in a variety of model systems but has not been commonly used to analyze radiation effects. It is a powerful approach for assessing the functional significance of radiation quality-dependent changes from datasets where the changes are subtle but broad, and where single gene based analysis using rankings of fold-change may not reveal important biological information. We identified 45 statistically significant gene sets at 0.05 q-value cutoff, including 14 gene sets common to gamma and titanium irradiation, 19 gene sets specific to gamma irradiation, and 12 titanium-specific gene sets. Common gene sets largely align with DNA damage, cell cycle, early immune response, and inflammatory cytokine pathway activation. The top gene set enriched for the gamma- and titanium-irradiated samples involved KRAS pathway activation and genes activated in TNF-treated cells, respectively. Another difference noted for the high-LET samples was an apparent enrichment in gene sets involved in cycle cycle/mitotic control. It is plausible that the enrichment in these particular pathways results from the complex DNA damage resulting from high-LET exposure where repair processes are not completed during the same time scale as the less complex damage resulting from low-LET radiation.

Source-independent full waveform inversion of seismic data

DOEpatents

Lee, Ki Ha

2006-02-14

A set of seismic trace data is collected in an input data set that is first Fourier transformed in its entirety into the frequency domain. A normalized wavefield is obtained for each trace of the input data set in the frequency domain. Normalization is done with respect to the frequency response of a reference trace selected from the set of seismic trace data. The normalized wavefield is source independent, complex, and dimensionless. The normalized wavefield is shown to be uniquely defined as the normalized impulse response, provided that a certain condition is met for the source. This property allows construction of the inversion algorithm disclosed herein, without any source or source coupling information. The algorithm minimizes the error between data normalized wavefield and the model normalized wavefield. The methodology is applicable to any 3-D seismic problem, and damping may be easily included in the process.

Influence Diagram Use With Respect to Technology Planning and Investment

NASA Technical Reports Server (NTRS)

Levack, Daniel J. H.; DeHoff, Bryan; Rhodes, Russel E.

2009-01-01

Influence diagrams are relatively simple, but powerful, tools for assessing the impact of choices or resource allocations on goals or requirements. They are very general and can be used on a wide range of problems. They can be used for any problem that has defined goals, a set of factors that influence the goals or the other factors, and a set of inputs. Influence diagrams show the relationship among a set of results and the attributes that influence them and the inputs that influence the attributes. If the results are goals or requirements of a program, then the influence diagram can be used to examine how the requirements are affected by changes to technology investment. This paper uses an example to show how to construct and interpret influence diagrams, how to assign weights to the inputs and attributes, how to assign weights to the transfer functions (influences), and how to calculate the resulting influences of the inputs on the results. A study is also presented as an example of how using influence diagrams can help in technology planning and investment. The Space Propulsion Synergy Team (SPST) used this technique to examine the impact of R&D spending on the Life Cycle Cost (LCC) of a space transportation system. The question addressed was the effect on the recurring and the non-recurring portions of LCC of the proportion of R&D resources spent to impact technology objectives versus the proportion spent to impact operational dependability objectives. The goals, attributes, and the inputs were established. All of the linkages (influences) were determined. The weighting of each of the attributes and each of the linkages was determined. Finally the inputs were varied and the impacts on the LCC determined and are presented. The paper discusses how each of these was accomplished both for credibility and as an example for future studies using influence diagrams for technology planning and investment planning.

How to make stripes: deciphering the transition from non-periodic to periodic patterns in Drosophila segmentation

PubMed Central

Schroeder, Mark D.; Greer, Christina; Gaul, Ulrike

2011-01-01

The generation of metameric body plans is a key process in development. In Drosophila segmentation, periodicity is established rapidly through the complex transcriptional regulation of the pair-rule genes. The ‘primary’ pair-rule genes generate their 7-stripe expression through stripe-specific cis-regulatory elements controlled by the preceding non-periodic maternal and gap gene patterns, whereas ‘secondary’ pair-rule genes are thought to rely on 7-stripe elements that read off the already periodic primary pair-rule patterns. Using a combination of computational and experimental approaches, we have conducted a comprehensive systems-level examination of the regulatory architecture underlying pair-rule stripe formation. We find that runt (run), fushi tarazu (ftz) and odd skipped (odd) establish most of their pattern through stripe-specific elements, arguing for a reclassification of ftz and odd as primary pair-rule genes. In the case of run, we observe long-range cis-regulation across multiple intervening genes. The 7-stripe elements of run, ftz and odd are active concurrently with the stripe-specific elements, indicating that maternal/gap-mediated control and pair-rule gene cross-regulation are closely integrated. Stripe-specific elements fall into three distinct classes based on their principal repressive gap factor input; stripe positions along the gap gradients correlate with the strength of predicted input. The prevalence of cis-elements that generate two stripes and their genomic organization suggest that single-stripe elements arose by splitting and subfunctionalization of ancestral dual-stripe elements. Overall, our study provides a greatly improved understanding of how periodic patterns are established in the Drosophila embryo. PMID:21693522

A Novel Strategy for Selection and Validation of Reference Genes in Dynamic Multidimensional Experimental Design in Yeast

PubMed Central

Cankorur-Cetinkaya, Ayca; Dereli, Elif; Eraslan, Serpil; Karabekmez, Erkan; Dikicioglu, Duygu; Kirdar, Betul

2012-01-01

Background Understanding the dynamic mechanism behind the transcriptional organization of genes in response to varying environmental conditions requires time-dependent data. The dynamic transcriptional response obtained by real-time RT-qPCR experiments could only be correctly interpreted if suitable reference genes are used in the analysis. The lack of available studies on the identification of candidate reference genes in dynamic gene expression studies necessitates the identification and the verification of a suitable gene set for the analysis of transient gene expression response. Principal Findings In this study, a candidate reference gene set for RT-qPCR analysis of dynamic transcriptional changes in Saccharomyces cerevisiae was determined using 31 different publicly available time series transcriptome datasets. Ten of the twelve candidates (TPI1, FBA1, CCW12, CDC19, ADH1, PGK1, GCN4, PDC1, RPS26A and ARF1) we identified were not previously reported as potential reference genes. Our method also identified the commonly used reference genes ACT1 and TDH3. The most stable reference genes from this pool were determined as TPI1, FBA1, CDC19 and ACT1 in response to a perturbation in the amount of available glucose and as FBA1, TDH3, CCW12 and ACT1 in response to a perturbation in the amount of available ammonium. The use of these newly proposed gene sets outperformed the use of common reference genes in the determination of dynamic transcriptional response of the target genes, HAP4 and MEP2, in response to relaxation from glucose and ammonium limitations, respectively. Conclusions A candidate reference gene set to be used in dynamic real-time RT-qPCR expression profiling in yeast was proposed for the first time in the present study. Suitable pools of stable reference genes to be used under different experimental conditions could be selected from this candidate set in order to successfully determine the expression profiles for the genes of interest. PMID:22675547

Method of and apparatus for generating an interstitial point in a data stream having an even number of data points

NASA Technical Reports Server (NTRS)

Edwards, T. R. (Inventor)

1985-01-01

Apparatus for doubling the data density rate of an analog to digital converter or doubling the data density storage capacity of a memory deviced is discussed. An interstitial data point midway between adjacent data points in a data stream having an even number of equal interval data points is generated by applying a set of predetermined one-dimensional convolute integer coefficients which can include a set of multiplier coefficients and a normalizer coefficient. Interpolator means apply the coefficients to the data points by weighting equally on each side of the center of the even number of equal interval data points to obtain an interstital point value at the center of the data points. A one-dimensional output data set, which is twice as dense as a one-dimensional equal interval input data set, can be generated where the output data set includes interstitial points interdigitated between adjacent data points in the input data set. The method for generating the set of interstital points is a weighted, nearest-neighbor, non-recursive, moving, smoothing averaging technique, equivalent to applying a polynomial regression calculation to the data set.

OGEE v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines.

PubMed

Chen, Wei-Hua; Lu, Guanting; Chen, Xiao; Zhao, Xing-Ming; Bork, Peer

2017-01-04

OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased numbers of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http://ogee.medgenius.info. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Network-based differential gene expression analysis suggests cell cycle related genes regulated by E2F1 underlie the molecular difference between smoker and non-smoker lung adenocarcinoma

PubMed Central

2013-01-01

Background Differential gene expression (DGE) analysis is commonly used to reveal the deregulated molecular mechanisms of complex diseases. However, traditional DGE analysis (e.g., the t test or the rank sum test) tests each gene independently without considering interactions between them. Top-ranked differentially regulated genes prioritized by the analysis may not directly relate to the coherent molecular changes underlying complex diseases. Joint analyses of co-expression and DGE have been applied to reveal the deregulated molecular modules underlying complex diseases. Most of these methods consist of separate steps: first to identify gene-gene relationships under the studied phenotype then to integrate them with gene expression changes for prioritizing signature genes, or vice versa. It is warrant a method that can simultaneously consider gene-gene co-expression strength and corresponding expression level changes so that both types of information can be leveraged optimally. Results In this paper, we develop a gene module based method for differential gene expression analysis, named network-based differential gene expression (nDGE) analysis, a one-step integrative process for prioritizing deregulated genes and grouping them into gene modules. We demonstrate that nDGE outperforms existing methods in prioritizing deregulated genes and discovering deregulated gene modules using simulated data sets. When tested on a series of smoker and non-smoker lung adenocarcinoma data sets, we show that top differentially regulated genes identified by the rank sum test in different sets are not consistent while top ranked genes defined by nDGE in different data sets significantly overlap. nDGE results suggest that a differentially regulated gene module, which is enriched for cell cycle related genes and E2F1 targeted genes, plays a role in the molecular differences between smoker and non-smoker lung adenocarcinoma. Conclusions In this paper, we develop nDGE to prioritize deregulated genes and group them into gene modules by simultaneously considering gene expression level changes and gene-gene co-regulations. When applied to both simulated and empirical data, nDGE outperforms the traditional DGE method. More specifically, when applied to smoker and non-smoker lung cancer sets, nDGE results illustrate the molecular differences between smoker and non-smoker lung cancer. PMID:24341432

Multiconstrained gene clustering based on generalized projections

PubMed Central

2010-01-01

Background Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem. Results We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The generalized projector iteratively projects the clustering solution onto these sets in order to find a consistent solution included in the intersection set that satisfies all constraints. Compared with previous MGC methods, POCS can integrate multiple constraints from different nature without distorting the original constraints. To evaluate the clustering solution, we also propose a new performance measure referred to as Gene Log Likelihood (GLL) that considers genes having more than one function and hence in more than one cluster. Comparative experimental results show that our POCS-based gene clustering method outperforms current state-of-the-art MGC methods. Conclusions The POCS-based MGC method can successfully combine multiple constraints from different nature for gene clustering. Also, the proposed GLL is an effective performance measure for the soft clustering solutions. PMID:20356386

QTL and QTL x environment effects on agronomic and nitrogen acquisition traits in rice.

PubMed

Senthilvel, Senapathy; Vinod, Kunnummal Kurungara; Malarvizhi, Palaniappan; Maheswaran, Marappa

2008-09-01

Agricultural environments deteriorate due to excess nitrogen application. Breeding for low nitrogen responsive genotypes can reduce soil nitrogen input. Rice genotypes respond variably to soil available nitrogen. The present study attempted quantification of genotype x nitrogen level interaction and mapping of quantitative trait loci (QTLs) associated with nitrogen use efficiency (NUE) and other associated agronomic traits. Twelve parameters were observed across a set of 82 double haploid (DH) lines derived from IR64/Azucena. Three nitrogen regimes namely, native (0 kg/ha; no nitrogen applied), optimum (100 kg/ha) and high (200 kg/ha) replicated thrice were the environments. The parents and DH lines were significantly varying for all traits under different nitrogen regimes. All traits except plant height recorded significant genotype x environment interaction. Individual plant yield was positively correlated with nitrogen use efficiency and nitrogen uptake. Sixteen QTLs were detected by composite interval mapping. Eleven QTLs showed significant QTL x environment interactions. On chromosome 3, seven QTLs were detected associated with nitrogen use, plant yield and associated traits. A QTL region between markers RZ678, RZ574 and RZ284 was associated with nitrogen use and yield. This chromosomal region was enriched with expressed gene sequences of known key nitrogen assimilation genes.

A clustering package for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Model.

PubMed

Bruneau, Marine; Mottet, Thierry; Moulin, Serge; Kerbiriou, Maël; Chouly, Franz; Chretien, Stéphane; Guyeux, Christophe

2018-02-01

In this article, a new Python package for nucleotide sequences clustering is proposed. This package, freely available on-line, implements a Laplacian eigenmap embedding and a Gaussian Mixture Model for DNA clustering. It takes nucleotide sequences as input, and produces the optimal number of clusters along with a relevant visualization. Despite the fact that we did not optimise the computational speed, our method still performs reasonably well in practice. Our focus was mainly on data analytics and accuracy and as a result, our approach outperforms the state of the art, even in the case of divergent sequences. Furthermore, an a priori knowledge on the number of clusters is not required here. For the sake of illustration, this method is applied on a set of 100 DNA sequences taken from the mitochondrially encoded NADH dehydrogenase 3 (ND3) gene, extracted from a collection of Platyhelminthes and Nematoda species. The resulting clusters are tightly consistent with the phylogenetic tree computed using a maximum likelihood approach on gene alignment. They are coherent too with the NCBI taxonomy. Further test results based on synthesized data are then provided, showing that the proposed approach is better able to recover the clusters than the most widely used software, namely Cd-hit-est and BLASTClust. Copyright © 2017 Elsevier Ltd. All rights reserved.

Successful Application of Microarray Technology to Microdissected Formalin-Fixed, Paraffin-Embedded Tissue

PubMed Central

Coudry, Renata A.; Meireles, Sibele I.; Stoyanova, Radka; Cooper, Harry S.; Carpino, Alan; Wang, Xianqun; Engstrom, Paul F.; Clapper, Margie L.

2007-01-01

The establishment of a reliable method for using RNA from formalin-fixed, paraffin-embedded (FFPE) tissue would provide an opportunity to obtain novel gene expression data from the vast amounts of archived tissue. A custom-designed 22,000 oligonucleotide array was used in the present study to compare the gene expression profile of colonic epithelial cells isolated by laser capture microdissection from FFPE-archived samples with that of the same cell population from matched frozen samples, the preferred source of RNA. Total RNA was extracted from FFPE tissues, amplified, and labeled using the Paradise Reagent System. The quality of the input RNA was assessed by the Bioanalyzer profile, reverse transcriptase-polymerase chain reaction, and agarose gel electrophoresis. The results demonstrate that it is possible to obtain reliable microarray data from FFPE samples using RNA acquired by laser capture microdissection. The concordance between matched FFPE and frozen samples was evaluated and expressed as a Pearson’s correlation coefficient, with values ranging from 0.80 to 0.97. The presence of ribosomal RNA peaks in FFPE-derived RNA was reflected by a high correlation with paired frozen samples. A set of practical recommendations for evaluating the RNA integrity and quality in FFPE samples is reported. PMID:17251338

Multiplexing of ChIP-Seq Samples in an Optimized Experimental Condition Has Minimal Impact on Peak Detection.

PubMed

Kacmarczyk, Thadeous J; Bourque, Caitlin; Zhang, Xihui; Jiang, Yanwen; Houvras, Yariv; Alonso, Alicia; Betel, Doron

2015-01-01

Multiplexing samples in sequencing experiments is a common approach to maximize information yield while minimizing cost. In most cases the number of samples that are multiplexed is determined by financial consideration or experimental convenience, with limited understanding on the effects on the experimental results. Here we set to examine the impact of multiplexing ChIP-seq experiments on the ability to identify a specific epigenetic modification. We performed peak detection analyses to determine the effects of multiplexing. These include false discovery rates, size, position and statistical significance of peak detection, and changes in gene annotation. We found that, for histone marker H3K4me3, one can multiplex up to 8 samples (7 IP + 1 input) at ~21 million single-end reads each and still detect over 90% of all peaks found when using a full lane for sample (~181 million reads). Furthermore, there are no variations introduced by indexing or lane batch effects and importantly there is no significant reduction in the number of genes with neighboring H3K4me3 peaks. We conclude that, for a well characterized antibody and, therefore, model IP condition, multiplexing 8 samples per lane is sufficient to capture most of the biological signal.

Multiplexing of ChIP-Seq Samples in an Optimized Experimental Condition Has Minimal Impact on Peak Detection

PubMed Central

Kacmarczyk, Thadeous J.; Bourque, Caitlin; Zhang, Xihui; Jiang, Yanwen; Houvras, Yariv; Alonso, Alicia; Betel, Doron

2015-01-01

Multiplexing samples in sequencing experiments is a common approach to maximize information yield while minimizing cost. In most cases the number of samples that are multiplexed is determined by financial consideration or experimental convenience, with limited understanding on the effects on the experimental results. Here we set to examine the impact of multiplexing ChIP-seq experiments on the ability to identify a specific epigenetic modification. We performed peak detection analyses to determine the effects of multiplexing. These include false discovery rates, size, position and statistical significance of peak detection, and changes in gene annotation. We found that, for histone marker H3K4me3, one can multiplex up to 8 samples (7 IP + 1 input) at ~21 million single-end reads each and still detect over 90% of all peaks found when using a full lane for sample (~181 million reads). Furthermore, there are no variations introduced by indexing or lane batch effects and importantly there is no significant reduction in the number of genes with neighboring H3K4me3 peaks. We conclude that, for a well characterized antibody and, therefore, model IP condition, multiplexing 8 samples per lane is sufficient to capture most of the biological signal. PMID:26066343

GeneSilico protein structure prediction meta-server.

PubMed

Kurowski, Michal A; Bujnicki, Janusz M

2003-07-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.

GeneSilico protein structure prediction meta-server

PubMed Central

Kurowski, Michal A.; Bujnicki, Janusz M.

2003-01-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta. PMID:12824313

Preliminary Genomic Characterization of Ten Hardwood Tree Species from Multiplexed Low Coverage Whole Genome Sequencing

PubMed Central

Staton, Margaret; Best, Teodora; Khodwekar, Sudhir; Owusu, Sandra; Xu, Tao; Xu, Yi; Jennings, Tara; Cronn, Richard; Arumuganathan, A. Kathiravetpilla; Coggeshall, Mark; Gailing, Oliver; Liang, Haiying; Romero-Severson, Jeanne; Schlarbaum, Scott; Carlson, John E.

2015-01-01

Forest health issues are on the rise in the United States, resulting from introduction of alien pests and diseases, coupled with abiotic stresses related to climate change. Increasingly, forest scientists are finding genetic/genomic resources valuable in addressing forest health issues. For a set of ten ecologically and economically important native hardwood tree species representing a broad phylogenetic spectrum, we used low coverage whole genome sequencing from multiplex Illumina paired ends to economically profile their genomic content. For six species, the genome content was further analyzed by flow cytometry in order to determine the nuclear genome size. Sequencing yielded a depth of 0.8X to 7.5X, from which in silico analysis yielded preliminary estimates of gene and repetitive sequence content in the genome for each species. Thousands of genomic SSRs were identified, with a clear predisposition toward dinucleotide repeats and AT-rich repeat motifs. Flanking primers were designed for SSR loci for all ten species, ranging from 891 loci in sugar maple to 18,167 in redbay. In summary, we have demonstrated that useful preliminary genome information including repeat content, gene content and useful SSR markers can be obtained at low cost and time input from a single lane of Illumina multiplex sequence. PMID:26698853

Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm.

PubMed

Huang, X N; Ren, H P

2016-05-13

Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation.

Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states.

PubMed

Yang, Yi; Hu, Xiao-Pan; Ma, Bin-Guang

2017-02-28

Bradyrhizobium diazoefficiens is a rhizobium able to convert atmospheric nitrogen into ammonium by establishing mutualistic symbiosis with soybean. It has been recognized as an important parent strain for microbial agents and is widely applied in agricultural and environmental fields. In order to study the metabolic properties of symbiotic nitrogen fixation and the differences between a free-living cell and a symbiotic bacteroid, a genome-scale metabolic network of B. diazoefficiens USDA110 was constructed and analyzed. The metabolic network, iYY1101, contains 1031 reactions, 661 metabolites, and 1101 genes in total. Metabolic models reflecting free-living and symbiotic states were determined by defining the corresponding objective functions and substrate input sets, and were further constrained by high-throughput transcriptomic and proteomic data. Constraint-based flux analysis was used to compare the metabolic capacities and the effects on the metabolic targets of genes and reactions between the two physiological states. The results showed that a free-living rhizobium possesses a steady state flux distribution for sustaining a complex supply of biomass precursors while a symbiotic bacteroid maintains a relatively condensed one adapted to nitrogen-fixation. Our metabolic models may serve as a promising platform for better understanding the symbiotic nitrogen fixation of this species.

Differentiating the mTOR inhibitors everolimus and sirolimus in the treatment of tuberous sclerosis complex

PubMed Central

MacKeigan, Jeffrey P.; Krueger, Darcy A.

2015-01-01

Tuberous sclerosis complex (TSC) is a genetic autosomal dominant disorder characterized by benign tumor-like lesions, called hamartomas, in multiple organ systems, including the brain, skin, heart, kidneys, and lung. These hamartomas cause a diverse set of clinical problems based on their location and often result in epilepsy, learning difficulties, and behavioral problems. TSC is caused by mutations within the TSC1 or TSC2 genes that inactivate the genes' tumor-suppressive function and drive hamartomatous cell growth. In normal cells, TSC1 and TSC2 integrate growth signals and nutrient inputs to downregulate signaling to mammalian target of rapamycin (mTOR), an evolutionarily conserved serine-threonine kinase that controls cell growth and cell survival. The molecular connection between TSC and mTOR led to the clinical use of allosteric mTOR inhibitors (sirolimus and everolimus) for the treatment of TSC. Everolimus is approved for subependymal giant cell astrocytomas and renal angiomyolipomas in patients with TSC. Sirolimus, though not approved for TSC, has undergone considerable investigation to treat various aspects of the disease. Everolimus and sirolimus selectively inhibit mTOR signaling with similar molecular mechanisms, but with distinct clinical profiles. This review differentiates mTOR inhibitors in TSC while describing the molecular mechanisms, pathogenic mutations, and clinical trial outcomes for managing TSC. PMID:26289591

Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving Melanoma

PubMed Central

Kaushik, Abhinav; Bhatia, Yashuma; Ali, Shakir; Gupta, Dinesh

2015-01-01

Metastatic melanoma patients have a poor prognosis, mainly attributable to the underlying heterogeneity in melanoma driver genes and altered gene expression profiles. These characteristics of melanoma also make the development of drugs and identification of novel drug targets for metastatic melanoma a daunting task. Systems biology offers an alternative approach to re-explore the genes or gene sets that display dysregulated behaviour without being differentially expressed. In this study, we have performed systems biology studies to enhance our knowledge about the conserved property of disease genes or gene sets among mutually exclusive datasets representing melanoma progression. We meta-analysed 642 microarray samples to generate melanoma reconstructed networks representing four different stages of melanoma progression to extract genes with altered molecular circuitry wiring as compared to a normal cellular state. Intriguingly, a majority of the melanoma network-rewired genes are not differentially expressed and the disease genes involved in melanoma progression consistently modulate its activity by rewiring network connections. We found that the shortlisted disease genes in the study show strong and abnormal network connectivity, which enhances with the disease progression. Moreover, the deviated network properties of the disease gene sets allow ranking/prioritization of different enriched, dysregulated and conserved pathway terms in metastatic melanoma, in agreement with previous findings. Our analysis also reveals presence of distinct network hubs in different stages of metastasizing tumor for the same set of pathways in the statistically conserved gene sets. The study results are also presented as a freely available database at http://bioinfo.icgeb.res.in/m3db/. The web-based database resource consists of results from the analysis presented here, integrated with cytoscape web and user-friendly tools for visualization, retrieval and further analysis. PMID:26558755