Integrative genomics viewer

JT Robinson, H Thorvaldsdóttir, W Winckler… - Nature …, 2011 - nature.com
JT Robinson, H Thorvaldsdóttir, W Winckler, M Guttman, ES Lander, G Getz, JP Mesirov
Nature biotechnology, 2011nature.com
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 …
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.
To address this challenge, we have developed the Integrative Genomics Viewer (IGV), a lightweight visualization tool that enables intuitive real-time exploration of diverse, large-scale genomic data sets on standard desktop computers. It supports flexible integration of a wide range of genomic data types including aligned sequence reads, mutations, copy number, RNA interference screens, gene expression, methylation and genomic annotations (Supplementary Fig. 1). The IGV makes use of efficient, multi-resolution file formats to enable real-time exploration of arbitrarily large data sets over all resolution scales, while consuming minimal resources on the client computer (Supplementary Notes). Navigation through a data set is similar to that of Google Maps, allowing the user to zoom and pan seamlessly across the genome at any level of detail from whole genome to base pair (Supplementary Fig. 2). Data sets can be loaded from local or remote sources, including cloud-based resources, enabling investigators to view their own genomic data sets alongside publicly available data from, for example, The Cancer Genome Atlas 1, 1000 Genomes 2 (http://www. 1000genomes. org/) and ENCODE 3 (http://www. genome. gov/10005107) projects. In addition, IGV allows collaborators to load and share data locally or remotely over the internet.
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