Search

Background Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. Results We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt webcite), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist webcite). Conclusions DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes., Harvard Catalyst, National Institutes of Health (U.S.) (NIH R01 GM067761), National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (5K08DK78361), Dana-Farber/Harvard Cancer Center

Background: The advent of high-throughput sequencing has enabled sequencing based measurements of cellular function, with an individual measurement potentially consisting of more than 108 reads. While tools are available for aligning sets of reads to genomes and interpreting the results, fewer tools have been developed to address the storage and retrieval requirements of large collections of aligned datasets. We present ReadDB, a network accessible column store database system for aligned high-throughput read datasets. Results: ReadDB stores collections of aligned read positions and provides a client interface to support visualization and analysis. ReadDB is implemented as a network server that responds to queries on genomic intervals in an experiment with either the set of contained reads or a histogram based interval summary. Tests on datasets ranging from 105 to 108 reads demonstrate that ReadDB performance is generally within a factor of two of local-storage based methods and often three to five times better than other network-based methods. Conclusions: ReadDB is a high-performance foundation for ChIP-Seq and RNA-Seq analysis. The client-server model provides convenient access to compute cluster nodes or desktop visualization software without requiring a shared network filesystem or large amounts of local storage. The client code provides a simple interface for fast data access to visualization or analysis. ReadDB provides a new way to store genome-aligned reads for use in applications where read sequence and alignment mismatches are not needed., National Institutes of Health (U.S.) (grant 5TL1EB008540), National Institutes of Health (U.S.) (grant 5R01HG002668), National Institutes of Health (U.S.) (grant P01-NS055923-01), National Institutes of Health (U.S.) (grant 1-UL1-RR024920)

Background Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data. Results We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate. Conclusions The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia., National Human Genome Research Institute (U.S.) (R01HG003354), National Institute of Allergy and Infectious Diseases (U.S.) (U19 AI067854-05), National Heart, Lung, and Blood Institute (grant T32 HL007427-28), National Institutes of Health (U.S.) (grant K99 LM009826)

Background Image-based screens can produce hundreds of measured features for each of hundreds of millions of individual cells in a single experiment. Results Here, we describe CellProfiler Analyst, open-source software for the interactive exploration and analysis of multidimensional data, particularly data from high-throughput, image-based experiments. Conclusion The system enables interactive data exploration for image-based screens and automated scoring of complex phenotypes that require combinations of multiple measured features per cell.

Background: The regulatory map of a genome consists of the binding sites for proteins that determine the transcription of nearby genes. An initial regulatory map for S. cerevisiae was recently published using six motif discovery programs to analyze genome-wide chromatin immunoprecipitation data for 203 transcription factors. The programs were used to identify sequence motifs that were likely to correspond to the DNA-binding specificity of the immunoprecipitated proteins. We report improved versions of two conservation-based motif discovery algorithms, PhyloCon and Converge. Using these programs, we create a refined regulatory map for S. cerevisiae by reanalyzing the same chromatin immunoprecipitation data. Results: Applying the same conservative criteria that were applied in the original study, we find that PhyloCon and Converge each separately discover more known specificities than the combination of all six programs in the previous study. Combining the results of PhyloCon and Converge, we discover significant sequence motifs for 36 transcription factors that were previously missed. The new set of motifs identifies 636 more regulatory interactions than the previous one. The new network contains 28% more regulatory interactions among transcription factors, evidence of greater cross-talk between regulators. Conclusion: Combining two complementary computational strategies for conservation-based motif discovery improves the ability to identify the specificity of transcriptional regulators from genome-wide chromatin immunoprecipitation data. The increased sensitivity of these methods significantly expands the map of yeast regulatory sites without the need to alter any of the thresholds for statistical significance. The new map of regulatory sites reveals a more elaborate and complex view of the yeast genetic regulatory network than was observed previously., Pfizer Inc., National Institute of General Medical Sciences (U.S.) (NRSA award), National Institutes of Health (U.S) ( grant 1R01 HG002668-01 ), National Institutes of Health (U.S) ( grant HG00249 ), National Institutes of Health (U.S) ( training grant in genomic science 2T32HG00045 ), Society of Kauffman Fellows

Background An exponential growth of high-throughput biological information and data has occurred in the past decade, supported by technologies, such as microarrays and RNA-Seq. Most data generated using such methods are used to encode large amounts of rich information, and determine diagnostic and prognostic biomarkers. Although data storage costs have reduced, process of capturing data using aforementioned technologies is still expensive. Moreover, the time required for the assay, from sample preparation to raw value measurement is excessive (in the order of days). There is an opportunity to reduce both the cost and time for generating such expression datasets. Results We propose a framework in which complete gene expression values can be reliably predicted in-silico from partial measurements. This is achieved by modelling expression data as a low-rank matrix and then applying recently discovered techniques of matrix completion by using nonlinear convex optimisation. We evaluated prediction of gene expression data based on 133 studies, sourced from a combined total of 10,921 samples. It is shown that such datasets can be constructed with a low relative error even at high missing value rates (>50 %), and that such predicted datasets can be reliably used as surrogates for further analysis. Conclusion This method has potentially far-reaching applications including how bio-medical data is sourced and generated, and transcriptomic prediction by optimisation. We show that gene expression data can be computationally constructed, thereby potentially reducing the costs of gene expression profiling. In conclusion, this method shows great promise of opening new avenues in research on low-rank matrix completion in biological sciences. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1106-6) contains supplementary material, which is available to authorized users.