Helga Thorvaldsdottir

Integrative genomics viewer

Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration

Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today’s sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.

Molecular signatures database (MSigDB) 3.0

MOTIVATION Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets. RESULTS We report the availability of a new version of the database, MSigDB 3.0, with over 6700 gene sets, a complete revision of the collection of canonical pathways and experimental signatures from publications, enhanced annotations and upgrades to the web site. AVAILABILITY AND IMPLEMENTATION MSigDB is freely available for non-commercial use at http://www.broadinstitute.org/msigdb.

Quantitative visualization of alternative exon expression from RNA-seq data

MOTIVATION Analysis of RNA sequencing (RNA-Seq) data revealed that the vast majority of human genes express multiple mRNA isoforms, produced by alternative pre-mRNA splicing and other mechanisms, and that most alternative isoforms vary in expression between human tissues. As RNA-Seq datasets grow in size, it remains challenging to visualize isoform expression across multiple samples. RESULTS To help address this problem, we present Sashimi plots, a quantitative visualization of aligned RNA-Seq reads that enables quantitative comparison of exon usage across samples or experimental conditions. Sashimi plots can be made using the Broad Integrated Genome Viewer or with a stand-alone command line program. AVAILABILITY AND IMPLEMENTATION Software code and documentation freely available here: http://miso.readthedocs.org/en/fastmiso/sashimi.html

A genomic data viewer for iPad

The Integrative Genomics Viewer (IGV) for iPad, based on the popular IGV application for desktop and laptop computers, supports researchers who wish to take advantage of the mobility of today’s tablet computers to view genomic data and present findings to colleagues.

Juicebox.js Provides a Cloud-Based Visualization System for Hi-C Data

SUMMARY Contact mapping experiments such as Hi-C explore how genomes fold in 3D. Here, we introduce Juicebox.js, a cloud-based web application for exploring the resulting datasets. Like the original Juicebox application, Juicebox.js allows users to zoom in and out of such datasets using an interface similar to Google Earth. Juicebox.js also has many features designed to facilitate data reproducibility and sharing. Furthermore, Juicebox.js encodes the exact state of the browser in a shareable URL. Creating a public browser for a new Hi-C dataset does not require coding and can be accomplished in under a minute. The web app also makes it possible to create interactive figures online that can complement or replace ordinary journal figures. When combined with Juicer, this makes the entire process of data analysis transparent, insofar as every step from raw reads to published figure is publicly available as open source code.

Sashimi plots: Quantitative visualization of alternative isoform expression from RNA-seq data

Analysis of RNA sequencing (RNA-Seq) data revealed that the vast majority of human genes express multiple mRNA isoforms, produced by alternative pre-mRNA splicing and other mechanisms, and that most alternative isoforms vary in expression between human tissues. As RNA-Seq datasets grow in size, it remains challenging to visualize isoform expression across multiple samples. We present Sashimi plots, a quantitative multi-sample visualization of RNA-Seq reads aligned to gene annotations, which enables quantitative comparison of isoform usage across samples or experimental conditions. Given an input annotation and spliced alignments of reads from a sample, a region of interest is visualized in a Sashimi plot as follows: (i) alignments in exons are represented as read densities (optionally normalized by length of genomic region and coverage), and (ii) splice junction reads are drawn as arcs connecting a pair of exons, where arc width is drawn proportional to the number of reads aligning to the junction.

Variant Review with the Integrative Genomics Viewer

Manual review of aligned reads for confirmation and interpretation of variant calls is an important step in many variant calling pipelines for next-generation sequencing (NGS) data. Visual inspection can greatly increase the confidence in calls, reduce the risk of false positives, and help characterize complex events. The Integrative Genomics Viewer (IGV) was one of the first tools to provide NGS data visualization, and it currently provides a rich set of tools for inspection, validation, and interpretation of NGS datasets, as well as other types of genomic data. Here, we present a short overview of IGVs variant review features for both single-nucleotide variants and structural variants, with examples from both cancer and germline datasets. IGV is freely available at https://www.igv.org Cancer Res; 77(21); e31-34. ©2017 AACR.

Abstract 3968: Exploring cancer datasets in the integrative genomics viewer (IGV)

Cancer genome characterization studies, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), are producing a flood of diverse data including whole-genome sequencing scans, expression profiles, high-resolution SNP and copy number data, and epigenetic profiles. These diverse datasets are enabling researchers to study the cancer genome in unprecedented detail. However, their sheer size and diversity presents significant challenges to downstream interpretation. While much progress has been made in automated analysis, human review enabled by intuitive and responsive visualizations remains an essential component of data analysis. The Integrative Genomics Viewer (IGV) was developed to address this need by providing researchers with an intuitive, user-friendly, interactive visualization tool for exploration of diverse genomic datasets. IGV has extensive support for both next-generation and micro-array based platforms, and for integration of these data types with clinical and phenotypic data. It provides an intuitive interface similar to online mapping tools such as Google Maps, enabling smooth zooming and panning at all resolution scales, from whole genome to base pairs. Data can be annotated, filtered, grouped, and sorted in a variety of ways. This ability to dynamically and flexibly integrate multiple different datasets, and view them at any scale, allows investigators to elucidate complex biological relationships that are not otherwise readily apparent. In this presentation we describe the IGV, with emphasis on recent features focused on data integration and interpretation, developed in close collaboration with cancer researchers. Specifically (1) a multi-locus pathway view which supports simultaneous viewing of data in multiple genomic regions defined by pathways or gene sets, (2) integration with external tools such as Mutation Assessor and PolyPhen, to provide views and data to asses the functional significance of somatic events, and (3) a flexible, interactive charting capability to enable detailed exploration of inter-dependencies between data types at a specific locus or set of loci. These features will be illustrated in the context of use cases drawn from the TCGA glioblastoma multiforme and ovarian datasets. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3968. doi:1538-7445.AM2012-3968

A multi-tool recipe to identify regions of protein-DNA binding and their influence on associated gene expression

One commonly performed bioinformatics task is to infer functional regulation of transcription factors by observing differential expression under a knockout, and integrating DNA binding information of that transcription factor. However, until now, this task has required dedicated bioinformatics support to perform the necessary data integration. GenomeSpace provides a protocol, or “recipe”, and a user interface with inter-operating software tools to identify protein occupancies along the genome from a ChIP-seq experiment and associated differentially regulated genes from a RNA-Seq experiment. By integrating RNA-Seq and ChIP-seq analyses, a user is easily able to associate differing expression phenotypes with changing epigenetic landscapes.