Search

Summary Improvements in next‐generation sequencing technologies have resulted in dramatically reduced sequencing costs. This has led to an explosion of ‘‐seq’‐based methods, of which RNA sequencing (RNA‐seq) for generating transcriptomic data is the most popular. By analysing global patterns of gene expression in organs/tissues/cells of interest or in response to chemical or environmental perturbations, researchers can better understand an organism's biology. Tools designed to work with large RNA‐seq data sets enable analyses and visualizations to help generate hypotheses about a gene's function. We present here a user‐friendly RNA‐seq data exploration tool, called the ‘eFP‐Seq Browser’, that shows the read map coverage of a gene of interest in each of the samples along with ‘electronic fluorescent pictographic’ (eFP) images that serve as visual representations of expression levels. The tool also summarizes the details of each RNA‐seq experiment, providing links to archival databases and publications. It automatically computes the reads per kilobase per million reads mapped expression‐level summaries and point biserial correlation scores to sort the samples based on a gene's expression level or by how dissimilar the read map profile is from a gene splice variant, to quickly identify samples with the strongest expression level or where alternative splicing might be occurring. Links to the Integrated Genome Browser desktop visualization tool allow researchers to visualize and explore the details of RNA‐seq alignments summarized in eFP‐Seq Browser as coverage graphs. We present four cases of use of the eFP‐Seq Browser for ABI3,SR34,SR45a and U2AF65B, where we examine expression levels and identify alternative splicing. The URL for the browser is https://bar.utoronto.ca/eFP-Seq_Browser/. Open research badges This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. Tool is at http://sps:urlprefix::https; RNA‐seq data at http://sps:urlprefix::https and http://sps:urlprefix::https. Code is available at http://sps:urlprefix::https, Significance Statement We present a tool, the eFP‐Seq Browser, for rapidly identifying RNA sequencing samples with strong expression levels of a given gene, or where the read maps for a given gene/sample best match a particular gene model. Details can be called up with convenient links to the Integrated Genome Browser.

The Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org/) contains uniformly annotated and processed NGS data related to gene transcription regulation: ChIP-seq, ChIP-exo, DNase-seq, MNase-seq, ATAC-seq and RNA-seq. With the latest release, the database has reached a new level of data integration. All cell types (cell lines and tissues) presented in the GTRD were arranged into a dictionary and linked with different ontologies (BRENDA, Cell Ontology, Uberon, Cellosaurus and Experimental Factor Ontology) and with related experiments in specialized databases on transcription regulation (FANTOM5, ENCODE and GTEx). The updated version of the GTRD provides an integrated view of transcription regulation through a dedicated web interface with advanced browsing and search capabilities, an integrated genome browser, and table reports by cell types, transcription factors, and genes of interest.

Genomics researchers do better work when they can interactively explore and visualize data. However, due to the vast size of experimental datasets, researchers are increasingly using powerful, cloud-based systems to process and analyze data. These remote systems, called science gateways, offer user-friendly, Web-based access to high performance computing and storage resources, but typically lack interactive visualization capability. In this paper, we present BioViz Connect, a middleware Web application that links the CyVerse science gateway to the Integrated Genome Browser (IGB), a highly interactive native application implemented in Java that runs on the user’s personal computer. Using BioViz Connect, users can (i) stream data from the CyVerse data store into IGB for visualization, (ii) improve the IGB user experience for themselves and others by adding IGB specific metadata to CyVerse data files, including genome version and track appearance, and (iii) run compute-intensive visual analytics functions on CyVerse infrastructure to create new datasets for visualization in IGB or other applications. To demonstrate how BioViz Connect facilitates interactive data visualization, we describe an example RNA-Seq data analysis investigating how heat and desiccation stresses affect gene expression in the model plant Arabidopsis thaliana. Lastly, we discuss limitations of the technologies used and suggest opportunities for future work. BioViz Connect is available from https://bioviz.org.

Mosaic variants resulting from postzygotic mutations are prevalent in the human genome and play important roles in human diseases. However, except for cancer-related variants, there is no collection of postzygotic mosaic variants in noncancer disease-related and healthy individuals. Here, we present MosaicBase, a comprehensive database that includes 6698 mosaic variants related to 266 noncancer diseases and 27,991 mosaic variants identified in 422 healthy individuals. Genomic and phenotypic information of each variant was manually extracted and curated from 383 publications. MosaicBase supports the query of variants with Online Mendelian Inheritance in Man (OMIM) entries, genomic coordinates, gene symbols, or Entrez IDs. We also provide an integrated genome browser for users to easily access mosaic variants and their related annotations for any genomic region. By analyzing the variants collected in MosaicBase, we find that mosaic variants that directly contribute to disease phenotype show features distinct from those of variants in individuals with mild or no phenotypes, in terms of their genomic distribution, mutation signatures, and fraction of mutant cells. MosaicBase will not only assist clinicians in genetic counseling and diagnosis but also provide a useful resource to understand the genomic baseline of postzygotic mutations in the general human population. MosaicBase is publicly available at http://mosaicbase.com/ or http://49.4.21.8:8000.

Mosaic variants resulting from postzygotic mutations are prevalent in the human genome and play important roles in human diseases. However, except for cancer-related variant collections, there are no collections of mosaic variants in noncancer diseases and asymptomatic individuals. Here, we present MosaicBase (http://mosaicbase.cbi.pku.edu.cn/ or http://49.4.21.8:8000/), a comprehensive database that includes 6,698 mosaic variants related to 269 noncancer diseases and 27,991 mosaic variants identified in 422 asymptomatic individuals. The genomic and phenotypic information for each variant was manually extracted and curated from 383 publications. MosaicBase supports the query of variants with Online Mendelian Inheritance in Man (OMIM) entries, genomic coordinates, gene symbols, or Entrez IDs. We also provide an integrated genome browser for users to easily access mosaic variants and their related annotations within any genomic region. By analyzing the variants collected in MosaicBase, we found that mosaic variants that directly contribute to disease phenotype showed features distinct from those of variants in individuals with a mild or no phenotype in terms of their genomic distribution, mutation signatures, and fraction of mutant cells. MosaicBase will not only assist clinicians in genetic counseling and diagnosis but also provide a useful resource to understand the genomic baseline of postzygotic mutations in the general human population.

The current version of the Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org) contains information about: (i) transcription factor binding sites (TFBSs) and transcription coactivators identified by ChIP-seq experiments for Homo sapiens, Mus musculus, Rattus norvegicus, Danio rerio, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae, Schizosaccharomyces pombe and Arabidopsis thaliana; (ii) regions of open chromatin and TFBSs (DNase footprints) identified by DNase-seq; (iii) unmappable regions where TFBSs cannot be identified due to repeats; (iv) potential TFBSs for both human and mouse using position weight matrices from the HOCOMOCO database. Raw ChIP-seq and DNase-seq data were obtained from ENCODE and SRA, and uniformly processed. ChIP-seq peaks were called using four different methods: MACS, SISSRs, GEM and PICS. Moreover, peaks for the same factor and peak calling method, albeit using different experiment conditions (cell line, treatment, etc.), were merged into clusters. To reduce noise, such clusters for different peak calling methods were merged into meta-clusters; these were considered to be non-redundant TFBS sets. Moreover, extended quality control was applied to all ChIP-seq data. Web interface to access GTRD was developed using the BioUML platform. It provides browsing and displaying information, advanced search possibilities and an integrated genome browser.

Motivation The generation of high-quality assemblies, even for large eukaryotic genomes, has become a routine task for many biologists thanks to recent advances in sequencing technologies. However, the annotation of these assemblies—a crucial step toward unlocking the biology of the organism of interest—has remained a complex challenge that often requires advanced bioinformatics expertise. Results Here, we present MOSGA (Modular Open-Source Genome Annotator), a genome annotation framework for eukaryotic genomes with a user-friendly web-interface that generates and integrates annotations from various tools. The aggregated results can be analyzed with a fully integrated genome browser and are provided in a format ready for submission to NCBI. MOSGA is built on a portable, customizable and easily extendible Snakemake backend, and thus, can be tailored to a wide range of users and projects. Availability and implementation We provide MOSGA as a web service at https://mosga.mathematik.uni-marburg.de and as a docker container at registry.gitlab.com/mosga/mosga: latest. Source code can be found at https://gitlab.com/mosga/mosga Supplementary information Supplementary data are available at Bioinformatics online.

Background Genome browsers are widely used for locating interesting genomic regions, but their interactive use is obviously limited to inspecting short genomic portions. An ideal interaction is to provide patterns of regions on the browser, and then extract other genomic regions over the whole genome where such patterns occur, ranked by similarity. Results We developed SimSearch, an optimized pattern-search method and an open source plugin for the Integrated Genome Browser (IGB), to find genomic region sets that are similar to a given region pattern. It provides efficient visual genome-wide analytics computation in large datasets; the plugin supports intuitive user interactions for selecting an interesting pattern on IGB tracks and visualizing the computed occurrences of similar patterns along the entire genome. SimSearch also includes functions for the annotation and enrichment of results, and is enhanced with a Quickload repository including numerous epigenomic feature datasets from ENCODE and Roadmap Epigenomics. The paper also includes some use cases to show multiple genome-wide analyses of biological interest, which can be easily performed by taking advantage of the presented approach. Conclusions The novel SimSearch method provides innovative support for effective genome-wide pattern search and visualization; its relevance and practical usefulness is demonstrated through a number of significant use cases of biological interest. The SimSearch IGB plugin, documentation, and code are freely available at https://deib-geco.github.io/simsearch-app/ and https://github.com/DEIB-GECO/simsearch-app/.

The GWAS Central resource provides a toolkit for integrative access and visualization of a uniquely extensive collection of genome-wide association study data, while ensuring safe open access to prevent research participant identification. GWAS Central is the world's most comprehensive openly accessible repository of summary-level GWAS association information, providing over 70 million P-values for over 3800 studies investigating over 1400 unique phenotypes. The database content comprises direct submissions received from GWAS authors and consortia, in addition to actively gathered data sets from various public sources. GWAS data are discoverable from the perspective of genetic markers, genes, genome regions or phenotypes, via graphical visualizations and detailed downloadable data reports. Tested genetic markers and relevant genomic features can be visually interrogated across up to sixteen multiple association data sets in a single view using the integrated genome browser. The semantic standardization of phenotype descriptions with Medical Subject Headings and the Human Phenotype Ontology allows the precise identification of genetic variants associated with diseases, phenotypes and traits of interest. Harmonization of the phenotype descriptions used across several GWAS-related resources has extended the phenotype search capabilities to enable cross-database study discovery using a range of ontologies. GWAS Central is updated regularly and available at https://www.gwascentral.org.

The Zebrafish Model Organism Database (ZFIN; zfin.org) was established in 1994 as the primary genetic and genomic resource for the zebrafish research community. Some of the earliest records in ZFIN were for people and laboratories. Since that time, services and data types provided by ZFIN have grown considerably. Today, ZFIN provides the official nomenclature for zebrafish genes, mutants, and transgenics and curates many data types including gene expression, phenotypes, Gene Ontology, models of human disease, orthology, knockdown reagents, transgenic constructs, and antibodies. Ontologies are used throughout ZFIN to structure these expertly curated data. An integrated genome browser provides genomic context for genes, transgenics, mutants, and knockdown reagents. ZFIN also supports a community wiki where the research community can post new antibody records and research protocols. Data in ZFIN are accessible via web pages, download files, and the ZebrafishMine (zebrafishmine.org), an installation of the InterMine data warehousing software. Searching for data at ZFIN utilizes both parameterized search forms and a single box search for searching or browsing data quickly. This chapter aims to describe the primary ZFIN data and services, and provide insight into how to use and interpret ZFIN searches, data, and web pages.

Summary: One gene can produce multiple transcript variants encoding proteins with different functions. To facilitate visual analysis of transcript variants, we developed ProtAnnot, which shows protein annotations in the context of genomic sequence. ProtAnnot searches InterPro and displays profile matches (protein annotations) alongside gene models, exposing how alternative promoters, splicing and 3′ end processing add, remove, or remodel functional motifs. To draw attention to these effects, ProtAnnot color-codes exons by frame and displays a cityscape graphic summarizing exonic sequence at each position. These techniques make visual analysis of alternative transcripts faster and more convenient for biologists. Availability and implementation: ProtAnnot is a plug-in App for Integrated Genome Browser, an open source desktop genome browser available from http://www.bioviz.org. Contact: aloraine@uncc.edu

The field of non-coding RNA biology has been hampered by the lack of availability of a comprehensive, up-to-date collection of accessioned RNA sequences. Here we present the first release of RNAcentral, a database that collates and integrates information from an international consortium of established RNA sequence databases. The initial release contains over 8.1 million sequences, including representatives of all major functional classes. A web portal (http://rnacentral.org) provides free access to data, search functionality, cross-references, source code and an integrated genome browser for selected species.

Summary: Prioritization of candidate genes emanating from large-scale screens requires integrated analyses at the genomics, molecular, network and structural biology levels. We have extended the Integrated Genome Browser (IGB) to facilitate these tasks. The graphical user interface greatly simplifies building disease networks and zooming in at atomic resolution to identify variations in molecular complexes that may affect molecular interactions in the context of genomic data. All results are summarized in genome tracks and can be visualized and analyzed at the transcript level. Availability and implementation: The MI Bundle is a plugin for the IGB. The plugin, help, video and tutorial are available at http://cru.genomics.iit.it/igbmibundle/ and https://github.com/CRUiit/igb-mi-bundle/wiki. The source code is released under the Apache License, Version 2. Contact: arnaud.ceol@iit.it Supplementary information: Supplementary data are available at Bioinformatics online.

GTRD-Gene Transcription Regulation Database (http://gtrd.biouml.org)-is a database of transcription factor binding sites (TFBSs) identified by ChIP-seq experiments for human and mouse. Raw ChIP-seq data were obtained from ENCODE and SRA and uniformly processed: (i) reads were aligned using Bowtie2; (ii) ChIP-seq peaks were called using peak callers MACS, SISSRs, GEM and PICS; (iii) peaks for the same factor and peak callers, but different experiment conditions (cell line, treatment, etc.), were merged into clusters; (iv) such clusters for different peak callers were merged into metaclusters that were considered as non-redundant sets of TFBSs. In addition to information on location in genome, the sets contain structured information about cell lines and experimental conditions extracted from descriptions of corresponding ChIP-seq experiments. A web interface to access GTRD was developed using the BioUML platform. It provides: (i) browsing and displaying information; (ii) advanced search possibilities, e.g. search of TFBSs near the specified gene or search of all genes potentially regulated by a specified transcription factor; (iii) integrated genome browser that provides visualization of the GTRD data: read alignments, peaks, clusters, metaclusters and information about gene structures from the Ensembl database and binding sites predicted using position weight matrices from the HOCOMOCO database.

Motivation: Genome browsers that support fast navigation through vast datasets and provide interactive visual analytics functions can help scientists achieve deeper insight into biological systems. Toward this end, we developed Integrated Genome Browser (IGB), a highly configurable, interactive and fast open source desktop genome browser. Results: Here we describe multiple updates to IGB, including all-new capabilities to display and interact with data from high-throughput sequencing experiments. To demonstrate, we describe example visualizations and analyses of datasets from RNA-Seq, ChIP-Seq and bisulfite sequencing experiments. Understanding results from genome-scale experiments requires viewing the data in the context of reference genome annotations and other related datasets. To facilitate this, we enhanced IGB’s ability to consume data from diverse sources, including Galaxy, Distributed Annotation and IGB-specific Quickload servers. To support future visualization needs as new genome-scale assays enter wide use, we transformed the IGB codebase into a modular, extensible platform for developers to create and deploy all-new visualizations of genomic data. Availability and implementation: IGB is open source and is freely available from http://bioviz.org/igb. Contact: aloraine@uncc.edu

SummaryOne gene can produce multiple transcript variants encoding proteins with different functions. To facilitate visual analysis of transcript variants, we developed ProtAnnot, which shows protein annotations in the context of genomic sequence. ProtAnnot searches InterPro and displays profile matches (protein annotations) alongside gene models, exposing how alternative promoters, splicing, and 3’ end processing add, remove, or remodel functional motifs. To draw attention to these effects, ProtAnnot color-codes exons by frame and displays a cityscape graphic summarizing exonic sequence at each position. These techniques make visual analysis of alternative transcripts faster and more convenient for biologists.Availability and ImplementationProtAnnot is a plug-in App for Integrated Genome Browser, an open source desktop genome browser available from http://www.bioviz.org.Contactaloraine@uncc.edu

Alternative splicing enables a single gene to produce multiple mRNA isoforms by varying splice site selection. In animals, alternative splicing of mRNA isoforms between cell types is widespread and supports cellular differentiation. In plants, at least 20% of multi-exon genes are alternatively spliced, but the extent and significance of tissue-specific splicing is less well understood, partly because it is difficult to isolate cells of a single type. Pollen is a useful model system to study tissue-specific splicing in higher plants because pollen grains contain only two cell types and can be collected in large amounts without damaging cells. Previously, we identified pollen-specific splicing patterns by comparing RNA-Seq data from Arabidopsis pollen and leaves. Here, we used semi-quantitative PCR to validate pollen-specific splicing patterns among genes where RNA-Seq data analysis indicated splicing was most different between pollen and leaves. PCR testing confirmed eight of nine alternative splicing patterns, and results from the ninth were inconclusive. In four genes, alternative transcriptional start sites coincided with alternative splicing. This study highlights the value of the low-cost PCR assay as a method of validating RNA-Seq results.

Sequencing costs are falling, but the cost of data analysis remains high, often because unforeseen problems arise, such as insufficient depth of sequencing or batch effects. Experimenting with data analysis methods during the planning phase of an experiment can reveal unanticipated problems and build valuable bioinformatics expertise in the organism or process being studied. This protocol describes using R Markdown and RStudio, user-friendly tools for statistical analysis and reproducible research in bioinformatics, to analyze and document the analysis of an example RNA-Seq data set from tomato pollen undergoing chronic heat stress. Also, we show how to use Integrated Genome Browser to visualize read coverage graphs for differentially expressed genes. Applying the protocol described here and using the provided data sets represent a useful first step toward building RNA-Seq data analysis expertise in a research group.

Background: cis-NATs (cis-natural antisense transcripts) are transcribed from opposite strands of adjacent genes and have been shown to regulate gene expression by generating small RNAs from the overlapping region. cis-NATs are important for plant development and resistance to pathogens and stress. Several genome-wide investigations identified a number of cis-NAT pairs, but these investigations predicted cis-NATS using expression data from bulk samples that included lots of cell types. Some cis-NAT pairs identified from those investigations might not be functional, because both transcripts of cis-NAT pairs need to be co-expressed in the same cell. Pollen only contains two cell types, two sperm and one vegetative cell, which makes cell-specific investigation of cis-NATs possible. Methods: We investigated potential protein-coding cis-NATs in pollen and sperm using pollen RNA-seq data and TAIR10 gene models using the Integrated Genome Browser. We then used sperm microarray data and sRNAs in sperm and pollen to determine possibly functional cis-NATs in the sperm or vegetative cell, respectively. Results: We identified 1471 potential protein-coding cis-NAT pairs, including 131 novel pairs that were not present in TAIR10 gene models. In pollen, 872 possibly functional pairs were identified. 72 and 56 pairs were potentially functional in sperm and vegetative cells, respectively. sRNAs were detected at 794 genes, belonging to 739 pairs. Conclusion: These potential candidates in sperm and the vegetative cell are tools for understanding gene expression mechanisms in pollen.

Background: Blueberries are a rich source of antioxidants and other beneficial compounds that can protect against disease. Identifying genes involved in synthesis of bioactive compounds could enable breeding berry varieties with enhanced health benefits. Results: Toward this end, we annotated a draft blueberry genome assembly using RNA-Seq data from five stages of berry fruit development and ripening. Genome-guided assembly of RNA-Seq read alignments combined with output from ab initio gene finders produced around 60,000 gene models, of which more than half were similar to proteins from other species, typically the grape Vitis vinifera. Comparison of gene models to the PlantCyc database of metabolic pathway enzymes identified candidate genes involved in synthesis of bioactive compounds, including bixin, an apocarotenoid with potential disease-fighting properties, and defense-related cyanogenic glycosides, which are toxic. Cyanogenic glycoside (CG) biosynthetic enzymes were highly expressed in green fruit, and a candidate CG detoxification enzyme was up regulated during fruit ripening. Candidate genes for ethylene, anthocyanin, and 400 other biosynthetic pathways were also identified. Homology-based annotation using Blast2GO and InterPro assigned Gene Ontology terms to around 15,000 genes. RNA-Seq expression profiling showed that blueberry growth, maturation, and ripening involve dynamic gene expression changes, including coordinated up and down regulation of metabolic pathway enzymes and transcriptional regulators. Analysis of RNA-seq alignments identified developmentally regulated alternative splicing, promoter use, and 3? end formation. Conclusions: We report genome sequence, gene models, functional annotations, and RNA-Seq expression data that provide an important new resource enabling high throughput studies in blueberry. RNA-Seq data are freely available for visualization in Integrated Genome Browser, and analysis code is available from the git repository at http://bitbucket.org/lorainelab/blueberrygenome.

Ultra-high throughput sequencing of cDNA (RNA-Seq) is an invaluable resource for investigating alternative splicing in an organism. Alternative splicing is a form of post-transcriptional regulation in which primary RNA transcripts from a single gene can be spliced in multiple ways leading to different RNA and protein products. In plants and other species, it has been shown that many genes involved in circadian regulation are alternatively spliced. As new RNA-Seq data sets become available, these data will lead to new insights into links between regulation RNA splicing and the circadian system. Analyzing RNA-Seq data sets requires software tools that can display RNA-Seq read alignments alongside gene models, enabling assessment of how treatments or developmental stages affect splicing patterns and production of novel variants. The Integrated Genome Browser software program (IGB) is a free and flexible desktop tool that enables discovery and quantification of alternative splicing. In this protocol, we use IGB and a cold-stress RNA-Seq data set to examine alternative splicing of Arabidopsis thaliana LHY, a circadian clock regulator. Integrated Genome Browser is freely available from http://www.bioviz.org.

Previous and current research studies have been reporting or refuting susceptibility genes which have any minor or major genetic significance to Alzheimer disease. In our study we have obtained datasets from R. N. A. sequencing analysis performed on post-mortem brain tissues of clinically diagnosed Alzheimer’s male patients above the age of 80. The samples included in our study are grouped into three datasets as frontal brain, temporal brain and whole brain. Our sole purpose of the study was to decipher new S. N. PS if any and report any unreported or untested S. N. Ps related to genes. We have performed mRNA sequencing analysis with the help of Next Generation Sequencing GALAXY tool available online. In addition, we have utilized Integrated Genome Browser (IGB) to visualize our results. P. E. RL Programming algorithm was generated and NCBI’s dbSNP (Database of Single Nucleotide Polymorphisms) and Clin V.A. R (Clinical Variation) databases helped to obtain rsids and related information for galaxy analyses results. Our study is reporting and/or confirming pathogenicity of P. R. N. P, A. P. O. E, CST3, TUBB2B and F. T. L in the AD brain tissues. A growing study of PRNP gene has indicated a strong genetic association with AD yet more research with a bigger sample size in different populations will be of certain significance. Further research is needed for studying the TUBB2B gene due to its uncertain significance in the earlier research in Alzheimer’s. F. T. L is a recent gene which has been linked to A. D and future research in AD should be conducted targeting this specific gene.

Summary: ArrayFusion annotates conventional CGH results and various types of microarray data from a range of platforms (cDNA, expression, exon, SNP, array-CGH and ChIP-on-chip) and converts them into standard formats which can be visualized in genome browsers (Affymetrix™ Integrated Genome Browser and GBrowse in the HapMap Project). Converted files can then be imported simultaneously into a single genome browser to benefit a collective interpretation between different array results. ArrayFusion therefore provides a new type of tool facilitating the integration of CGH and array results to provide new experimental directions. Availability: Contact: hwwang@ym.edu.tw

Summary: Experimental techniques that survey an entire genome demand flexible, highly interactive visualization tools that can display new data alongside foundation datasets, such as reference gene annotations. The Integrated Genome Browser (IGB) aims to meet this need. IGB is an open source, desktop graphical display tool implemented in Java that supports real-time zooming and panning through a genome; layout of genomic features and datasets in moveable, adjustable tiers; incremental or genome-scale data loading from remote web servers or local files; and dynamic manipulation of quantitative data via genome graphs. Availability: The application and source code are available from http://igb.bioviz.org and http://genoviz.sourceforge.net. Contact: aloraine@uncc.edu

Background Biologists generally interrogate genomics data using web-based genome browsers that have limited analytical potential. New generation genome browsers such as the Integrated Genome Browser (IGB) have largely overcome this limitation and permit customized analyses to be implemented using plugins. We illustrate the use of a plugin for IGB that exploits advanced visualization techniques to integrate the analysis of genomics data with network and structural approaches. Results We show how visualization technologies that combine both genomics and network biology can facilitate the selection of the key amino acid contacts from protein-protein and protein-drug interactions. Starting from the MDM2-P53 interaction, which is a high-value target for cancer therapy, and Nutlin, the parent small molecule of an MDM2 antagonist that is currently in clinical trials, we show that this method can be generalized to analyze how drugs and mutations can interfere with both protein-protein and drug-protein networks. We illustrate this point by two additional use-cases exploring the molecular basis of tamoxifen side effects and of drug resistance in chronic myeloid leukemia patients. Conclusions Combined network and structure biology approaches provide key insights into both the genetic and the edgetic roles of variants in diseases. 3D interactomes facilitate the identification of disease-relevant interactions that can then be specifically targeted by drugs. Recent advances in molecular interaction and structure visualization tools have greatly simplified the mapping of mutated residues to molecular interaction interfaces. Such approaches can now also be integrated with genome visualization tools to enable comparative analyses of interaction contacts. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0908-x) contains supplementary material, which is available to authorized users.

With the rapidly falling cost and availability of high throughput sequencing and microarray technologies, the bottleneck for effectively using genomic analysis in the laboratory and clinic is shifting to one of effectively managing, analyzing, and sharing genomic data. Here we present three open-source, platform independent, software tools for generating, analyzing, distributing, and visualizing genomic data. These include a next generation sequencing/microarray LIMS and analysis project center (GNomEx); an application for annotating and programmatically distributing genomic data using the community vetted DAS/2 data exchange protocol (GenoPub); and a standalone Java Swing application (GWrap) that makes cutting edge command line analysis tools available to those who prefer graphical user interfaces. Both GNomEx and GenoPub use the rich client Flex/Flash web browser interface to interact with Java classes and a relational database on a remote server. Both employ a public-private user-group security model enabling controlled distribution of patient and unpublished data alongside public resources. As such, they function as genomic data repositories that can be accessed manually or programmatically through DAS/2-enabled client applications such as the Integrated Genome Browser. These tools have gained wide use in our core facilities, research laboratories and clinics and are freely available for non-profit use. See http://sourceforge.net/projects/gnomex/ , http://sourceforge.net/projects/genoviz/ , and http://sourceforge.net/projects/useq .