SUMMARY: To address the difficulty in assessing the implication of regulatory variants in diseases, a scoring scheme previously published allows the calculation of the Regulatory Variant Evidence score (RVE-score). The score represents the accumulated evidence for a causative role of a regulatory variant in a disease. Regulatory Evidence for Variants Underlying Phenotypes was built to calculate the RVE-score of regulatory variants, based on the 24 criteria, with a hybrid approach combining information retrieved from public databases and user input. AVAILABILITY AND IMPLEMENTATION: RevUP is freely available at http://www.revup-classifier.ca. The source code is available at https://github.com/wassermanlab/revup. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Across the life sciences, processing next generation sequencing data commonly relies upon a computationally expensive process where reads are mapped onto a reference sequence. Prior to such processing, however, there is a vast amount of information that can be ascertained from the reads, potentially obviating the need for processing, or allowing optimized mapping approaches to be deployed. Here, we present a method termed FlexTyper which facilitates a "reverse mapping" approach in which high throughput sequence queries, in the form of k-mer searches, are run against indexed short-read datasets in order to extract useful information. This reverse mapping approach enables the rapid counting of target sequences of interest. We demonstrate FlexTyper's utility for recovering depth of coverage, and accurate genotyping of SNP sites across the human genome. We show that genotyping unmapped reads can correctly inform a sample's population, sex, and relatedness in a family setting. Detection of pathogen sequences within RNA-seq data was sensitive and accurate, performing comparably to existing methods, but with increased flexibility. We present two examples of ways in which this flexibility allows the analysis of genome features not well-represented in a linear reference. First, we analyze contigs from African genome sequencing studies, showing how they distribute across families from three distinct populations. Second, we show how gene-marking k-mers for the killer immune receptor locus allow allele detection in a region that is challenging for standard read mapping pipelines. The future adoption of the reverse mapping approach represented by FlexTyper will be enabled by more efficient methods for FM-index generation and biology-informed collections of reference queries. In the long-term, selection of population-specific references or weighting of edges in pan-population reference genome graphs will be possible using the FlexTyper approach. FlexTyper is available at https://github.com/wassermanlab/OpenFlexTyper.

SUMMARY: The computational identification of the transcription factors (TFs) [more generally, transcription regulators, (TR)] responsible for the co-regulation of a specific set of genes is a common problem found in genomic analysis. Herein, we describe TFEA.ChIP, a tool that makes use of ChIP-seq datasets to estimate and visualize TR enrichment in gene lists representing transcriptional profiles. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TR was correctly identified in 126 of a total of 174 analyzed. Comparison with other TR enrichment tools demonstrates that TFEA.ChIP is an highly customizable package with an outstanding performance. AVAILABILITY AND IMPLEMENTATION: TFEA.ChIP is implemented as an R package available at Bioconductor https://www.bioconductor.org/packages/devel/bioc/html/TFEA.ChIP.html and github https://github.com/LauraPS1/TFEA.ChIP_downloads. A web-based GUI to the package is also available at https://www.iib.uam.es/TFEA.ChIP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Deciphering the functional roles of cis-regulatory variants is a critical challenge in genome analysis and interpretation. It has been hypothesized that altered transcription factor (TF) binding events are a central mechanism by which cis-regulatory variants impact gene expression levels. However, we lack a computational framework to understand and quantify such mechanistic contributions. RESULTS: We present TF2Exp, a gene-based framework to predict the impact of altered TF-binding events on gene expression levels. Using data from lymphoblastoid cell lines, TF2Exp models were applied successfully to predict the expression levels of 3196 genes. Alterations within DNase I hypersensitive, CTCF-bound and tissue-specific TF-bound regions were the greatest contributing features to the models. TF2Exp models performed as well as models based on common variants, both in cross-validation and external validation. Combining TF alteration and common variant features can further improve model performance. Unlike variant-based models, TF2Exp models have the unique advantage to evaluate the functional impact of variants in linkage disequilibrium and uncommon variants. We find that adding TF-binding events altered only by uncommon variants could increase the number of predictable genes (R2 > 0.05). Taken together, TF2Exp represents a key step towards interpreting the functional roles of cis-regulatory variants in the human genome. AVAILABILITY AND IMPLEMENTATION: The code and model training results are publicly available at https://github.com/wqshi/TF2Exp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Objective: The clinical diagnosis of genetic disorders is undergoing transformation, driven by whole exome sequencing and whole genome sequencing (WES/WGS). However, such nucleotide-level resolution technologies create an interpretive challenge. Prior literature suggests that clinicians may employ characteristic cognitive processes during WES/WGS investigations to identify disruptions in genes causal for the observed disease. Based on cognitive ergonomics, we designed and evaluated a gene prioritization workflow that supported these cognitive processes. Materials and Methods: We designed a novel workflow in which clinicians recalled known genetic diseases with similarity to patient phenotypes to inform WES/WGS data interpretation. This prototype-based workflow was evaluated against the common computational approach based on physician-specified sets of individual patient phenotypes. The evaluation was conducted as a web-based user study, in which 18 clinicians analyzed 2 simulated patient scenarios using a randomly assigned workflow. Data analysis compared the 2 workflows with respect to accuracy and efficiency in diagnostic interpretation, efficacy in collecting detailed phenotypic information, and user satisfaction. Results: Participants interpreted genetic diagnoses faster using prototype-based workflows. The 2 workflows did not differ in other evaluated aspects. Discussion: The user study findings indicate that prototype-based approaches, which are designed to model experts' cognitive processes, can expedite gene prioritization and provide utility in synergy with common phenotype-driven variant/gene prioritization approaches. However, further research of the extent of this effect across diverse genetic diseases is required. Conclusion: The findings demonstrate potential for prototype-based phenotype description to accelerate computer-assisted variant/gene prioritization through complementation of skills and knowledge of clinical experts via human-computer interaction.

BACKGROUND: In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. RESULTS: Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). CONCLUSION: The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

UNLABELLED: With the emergence of large-scale Cap Analysis of Gene Expression (CAGE) datasets from individual labs and the FANTOM consortium, one can now analyze the cis-regulatory regions associated with gene transcription at an unprecedented level of refinement. By coupling transcription factor binding site (TFBS) enrichment analysis with CAGE-derived genomic regions, CAGEd-oPOSSUM can identify TFs that act as key regulators of genes involved in specific mammalian cell and tissue types. The webtool allows for the analysis of CAGE-derived transcription start sites (TSSs) either provided by the user or selected from ∼1300 mammalian samples from the FANTOM5 project with pre-computed TFBS predicted with JASPAR TF binding profiles. The tool helps power insights into the regulation of genes through the study of the specific usage of TSSs within specific cell types and/or under specific conditions. AVAILABILITY AND IMPLEMENTATION: The CAGEd-oPOSUM web tool is implemented in Perl, MySQL and Apache and is available at http://cagedop.cmmt.ubc.ca/CAGEd_oPOSSUM CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The transition of whole-exome and whole-genome sequencing (WES/WGS) from the research setting to routine clinical practice remains challenging. OBJECTIVES: With almost no previous research specifically assessing interface designs and functionalities of WES and WGS software tools, the authors set out to ascertain perspectives from healthcare professionals in distinct domains on optimal clinical genomics user interfaces. METHODS: A series of semi-scripted focus groups, structured around professional challenges encountered in clinical WES and WGS, were conducted with bioinformaticians (n = 8), clinical geneticists (n = 9), genetic counselors (n = 5), and general physicians (n = 4). RESULTS: Contrary to popular existing system designs, bioinformaticians preferred command line over graphical user interfaces for better software compatibility and customization flexibility. Clinical geneticists and genetic counselors desired an overarching interactive graphical layout to prioritize candidate variants--a "tiered" system where only functionalities relevant to the user domain are made accessible. They favored a system capable of retrieving consistent representations of external genetic information from third-party sources. To streamline collaboration and patient exchanges, the authors identified user requirements toward an automated reporting system capable of summarizing key evidence-based clinical findings among the vast array of technical details. CONCLUSIONS: Successful adoption of a clinical WES/WGS system is heavily dependent on its ability to address the diverse necessities and predilections among specialists in distinct healthcare domains. Tailored software interfaces suitable for each group is likely more appropriate than the current popular "one size fits all" generic framework. This study provides interfaces for future intervention studies and software engineering opportunities.

OBJECTIVES: New DNA sequencing technologies have revolutionized the search for genetic disruptions. Targeted sequencing of all protein coding regions of the genome, called exome analysis, is actively used in research-oriented genetics clinics, with the transition to exomes as a standard procedure underway. This transition is challenging; identification of potentially causal mutation(s) amongst ∼10(6) variants requires specialized computation in combination with expert assessment. This study analyzes the usability of user interfaces for clinical exome analysis software. There are two study objectives: (1) To ascertain the key features of successful user interfaces for clinical exome analysis software based on the perspective of expert clinical geneticists, (2) To assess user-system interactions in order to reveal strengths and weaknesses of existing software, inform future design, and accelerate the clinical uptake of exome analysis. METHODS: Surveys, interviews, and cognitive task analysis were performed for the assessment of two next-generation exome sequence analysis software packages. The subjects included ten clinical geneticists who interacted with the software packages using the "think aloud" method. Subjects' interactions with the software were recorded in their clinical office within an urban research and teaching hospital. All major user interface events (from the user interactions with the packages) were time-stamped and annotated with coding categories to identify usability issues in order to characterize desired features and deficiencies in the user experience. RESULTS: We detected 193 usability issues, the majority of which concern interface layout and navigation, and the resolution of reports. Our study highlights gaps in specific software features typical within exome analysis. The clinicians perform best when the flow of the system is structured into well-defined yet customizable layers for incorporation within the clinical workflow. The results highlight opportunities to dramatically accelerate clinician analysis and interpretation of patient genomic data. CONCLUSION: We present the first application of usability methods to evaluate software interfaces in the context of exome analysis. Our results highlight how the study of user responses can lead to identification of usability issues and challenges and reveal software reengineering opportunities for improving clinical next-generation sequencing analysis. While the evaluation focused on two distinctive software tools, the results are general and should inform active and future software development for genome analysis software. As large-scale genome analysis becomes increasingly common in healthcare, it is critical that efficient and effective software interfaces are provided to accelerate clinical adoption of the technology. Implications for improved design of such applications are discussed.

Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs) is based on basic position weight matrices (PWMs) which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make the strong assumption that each nucleotide participates independently in the corresponding DNA-protein interaction and do not account for flexible length motifs. We introduce transcription factor flexible models (TFFMs) to represent TF binding properties. Based on hidden Markov models, TFFMs are flexible, and can model both position interdependence within TFBSs and variable length motifs within a single dedicated framework. The availability of thousands of experimentally validated DNA-TF interaction sequences from ChIP-seq allows for the generation of models that perform as well as PWMs for stereotypical TFs and can improve performance for TFs with flexible binding characteristics. We present a new graphical representation of the motifs that convey properties of position interdependence. TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that they can perform better than both PWMs and the dinucleotide weight matrix extension in discriminating ChIP-seq from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we demonstrate that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. Finally, TFFMs allow for the straightforward computation of an integrated TF occupancy score across a sequence. These results demonstrate the capacity of TFFMs to accurately model DNA-protein interactions, while providing a single unified framework suitable for the next generation of TFBS prediction.

BACKGROUND: MEDLINE®/PubMed® indexes over 20 million biomedical articles, providing curated annotation of its contents using a controlled vocabulary known as Medical Subject Headings (MeSH). The MeSH vocabulary, developed over 50+ years, provides a broad coverage of topics across biomedical research. Distilling the essential biomedical themes for a topic of interest from the relevant literature is important to both understand the importance of related concepts and discover new relationships. RESULTS: We introduce a novel method for determining enriched curator-assigned MeSH annotations in a set of papers associated to a topic, such as a gene, an author or a disease. We generate MeSH Over-representation Profiles (MeSHOPs) to quantitatively summarize the annotations in a form convenient for further computational analysis and visualization. Based on a hypergeometric distribution of assigned terms, MeSHOPs statistically account for the prevalence of the associated biomedical annotation while highlighting unusually prevalent terms based on a specified background. MeSHOPs can be visualized using word clouds, providing a succinct quantitative graphical representation of the relative importance of terms. Using the publication dates of articles, MeSHOPs track changing patterns of annotation over time. Since MeSHOPs are quantitative vectors, MeSHOPs can be compared using standard techniques such as hierarchical clustering. The reliability of MeSHOP annotations is assessed based on the capacity to re-derive the subset of the Gene Ontology annotations with equivalent MeSH terms. CONCLUSIONS: MeSHOPs allows quantitative measurement of the degree of association between any entity and the annotated medical concepts, based directly on relevant primary literature. Comparison of MeSHOPs allows entities to be related based on shared medical themes in their literature. A web interface is provided for generating and visualizing MeSHOPs.

BACKGROUND: To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. RESULTS: To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease. CONCLUSIONS: MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis.

We performed a genome-wide scan for muscle-specific cis-regulatory modules (CRMs) using three computational prediction programs. Based on the predictions, 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes. A subset of 19 CRMs validated as functional in the assay. The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery. Motif-based methods performed no better than predictions based only on sequence conservation. Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity. Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions. Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions.

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

Serial analysis of gene expression (SAGE) not only is a method for profiling the global expression of genes, but also offers the opportunity for the discovery of novel transcripts. SAGE tags are mapped to known transcripts to determine the gene of origin. Tags that map neither to a known transcript nor to the genome were hypothesized to span a splice junction, for which the exon combination or exon(s) are unknown. To test this hypothesis, we have developed an algorithm, SAGE2Splice, to efficiently map SAGE tags to potential splice junctions in a genome. The algorithm consists of three search levels. A scoring scheme was designed based on position weight matrices to assess the quality of candidates. Using optimized parameters for SAGE2Splice analysis and two sets of SAGE data, candidate junctions were discovered for 5%-6% of unmapped tags. Candidates were classified into three categories, reflecting the previous annotations of the putative splice junctions. Analysis of predicted tags extracted from EST sequences demonstrated that candidate junctions having the splice junction located closer to the center of the tags are more reliable. Nine of these 12 candidates were validated by RT-PCR and sequencing, and among these, four revealed previously uncharacterized exons. Thus, SAGE2Splice provides a new functionality for the identification of novel transcripts and exons. SAGE2Splice is available online at http://www.cisreg.ca.

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 http://www.cisreg.ca/gsb/.

MOTIVATION: TFBS is a set of integrated, object-oriented Perl modules for transcription factor binding site detection and analysis. It implements objects representing specificity profile matrices, binding sites and sets thereof, pattern generators, and pattern database interfaces. The modules are interoperable with the BioPerl open source system. AVAILABILITY AND SUPPLEMENTARY INFORMATION: The module package with documentation and example scripts are available at http://forkhead.cgb.ki.se/TFBS/