Driven by new technologies for data acquisition, integrative genomic studies comprising large numbers of samples are poised to revolutionize the way we approach the study of cancer and other diseases and chart the way to novel treatment regimens. They require the high-throughput generation and analysis of multiple types of genomic data. To address this challenge, and in active collaboration with end users, we developed a broadly applicable Integrative Genomics Viewer (IGV). IGV is a high-performance, user-friendly tool used by thousands of investigators world-wide for the interactive exploration of large, integrated genomic datasets. The goal of this project is to develop the next-generation of the IGV for extracting biological knowledge from the explosion of genomic data facing biomedical researchers both today and in the future. Specifically, we plan to take IGV to the next level with the addition of analysis-driven navigation, new complementary non-genomic views, multi-level representations of large numbers of samples from multiple studies, and a rich collaboration environment.
Aim 1. To enable intuitive exploration of large-scale, multi-study datasets with IGV through intelligent, analysis-driven navigation combined with new functional, structural, and multi-level views.
Aim 2. To provide the capability to annotate views and share insights on genomic datasets to enhance the speed and ease of research collaborations via IGV.
Aim 3. To provide developer outreach and continued maintenance and support of the IGV software and users. We have extensive experience in software engineering, including the development and distribution of software used by tens of thousands of scientists world-wide. We will continue our user-driven development approach working closely with many large genomics projects like The Cancer Genome Atlas, the International Cancer Consortium, the 1000 Genomes, and ENCODE, as well as numerous single-investigator studies. IGV's current success, flexible architecture, and our plan to support external development make us well poised to accomplish our aims to further transform data visualization and enable and accelerate the pace of biomedical discovery.

Public Health Relevance

We propose to develop the next-generation of the Integrative Genomics Viewer (IGV) to support biomedical research through the interactive and collaborative visualization and exploration of genomic data. Given the enormous amount of data generated by studies today, visualization is often a key element in gaining insight into the genomic basis and mechanisms of disease. These insights will help to develop hypotheses for further study and point the way to new therapeutic targets.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BST-P (02))
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Li, Jerry
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Broad Institute, Inc.
United States
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Robinson, James T; Turner, Douglass; Durand, Neva C et al. (2018) Juicebox.js Provides a Cloud-Based Visualization System for Hi-C Data. Cell Syst 6:256-258.e1
Robinson, James T; Thorvaldsdóttir, Helga; Wenger, Aaron M et al. (2017) Variant Review with the Integrative Genomics Viewer. Cancer Res 77:e31-e34
Durand, Neva C; Robinson, James T; Shamim, Muhammad S et al. (2016) Juicebox Provides a Visualization System for Hi-C Contact Maps with Unlimited Zoom. Cell Syst 3:99-101
Lu, Zhipeng; Zhang, Qiangfeng Cliff; Lee, Byron et al. (2016) RNA Duplex Map in Living Cells Reveals Higher-Order Transcriptome Structure. Cell 165:1267-1279
Thorvaldsdóttir, Helga; Robinson, James T; Turner, Douglass et al. (2015) A genomic data viewer for iPad. Genome Biol 16:46
Katz, Yarden; Wang, Eric T; Silterra, Jacob et al. (2015) Quantitative visualization of alternative exon expression from RNA-seq data. Bioinformatics 31:2400-2
Lawrence, Michael S; Stojanov, Petar; Mermel, Craig H et al. (2014) Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505:495-501
Campagne, Fabien; Dorff, Kevin C; Chambwe, Nyasha et al. (2013) Compression of structured high-throughput sequencing data. PLoS One 8:e79871