The Integrative Genomics Viewer (IGV) is a lightweight visualization tool designed for intuitive real-time exploration of diverse, large-scale genomic datasets on standard desktop computers. It supports the integration of various genomic data types, including aligned sequence reads, mutations, copy number, RNAi screens, gene expression, methylation, and genomic annotations. IGV enables efficient navigation through datasets, similar to Google Maps, allowing users to zoom and pan across the genome at any level of detail, from whole-genome to base pair resolution. Datasets can be loaded from local or remote sources, including cloud-based resources, enabling investigators to view their own genomic datasets alongside publicly available data from projects such as The Cancer Genome Atlas (TCGA), 1000 Genomes, and ENCODE. IGV allows collaborators to load and share data locally or remotely over the web. IGV supports the concurrent visualization of diverse data types across hundreds, and up to thousands of samples, and the correlation of these integrated datasets with clinical and phenotypic variables. Researchers can define arbitrary sample annotations and associate them with data tracks using a simple tab-delimited file format. These annotations can include sample identifiers, phenotypes, outcomes, cluster membership, or other clinical or experimental labels. Annotations are displayed as heatmaps and are used for grouping, sorting, filtering, and overlaying diverse data types to yield a comprehensive picture of the integrated dataset. IGV's scalable architecture makes it well suited for genome-wide exploration of next-generation sequencing (NGS) datasets, including both basic aligned read data and derived results such as read coverage. IGV varies the displayed level of detail according to resolution scale, representing NGS data by a simple coverage plot at very wide views, such as the whole genome. As the user zooms below the ~50 kb range, individual aligned reads become visible, and putative SNPs are highlighted as allele counts in the coverage plot. Alignment details for each read are available in popup windows. Zooming further, individual base mismatches become visible, highlighted by color and intensity according to base call and quality. IGV uses paired end information to color-code paired ends if their insert sizes are larger than expected, fall on different chromosomes, or have unexpected pair orientations. Such pairs, when consistent across multiple reads, can be indicative of a genomic rearrangement. IGV also allows the visualization of misalignments, particularly in repeat regions, which can yield unexpected insert sizes. IGV is open source software and freely available at http://www.broadinstitute.org/igv/, including full documentation on use of the software.The Integrative Genomics Viewer (IGV) is a lightweight visualization tool designed for intuitive real-time exploration of diverse, large-scale genomic datasets on standard desktop computers. It supports the integration of various genomic data types, including aligned sequence reads, mutations, copy number, RNAi screens, gene expression, methylation, and genomic annotations. IGV enables efficient navigation through datasets, similar to Google Maps, allowing users to zoom and pan across the genome at any level of detail, from whole-genome to base pair resolution. Datasets can be loaded from local or remote sources, including cloud-based resources, enabling investigators to view their own genomic datasets alongside publicly available data from projects such as The Cancer Genome Atlas (TCGA), 1000 Genomes, and ENCODE. IGV allows collaborators to load and share data locally or remotely over the web. IGV supports the concurrent visualization of diverse data types across hundreds, and up to thousands of samples, and the correlation of these integrated datasets with clinical and phenotypic variables. Researchers can define arbitrary sample annotations and associate them with data tracks using a simple tab-delimited file format. These annotations can include sample identifiers, phenotypes, outcomes, cluster membership, or other clinical or experimental labels. Annotations are displayed as heatmaps and are used for grouping, sorting, filtering, and overlaying diverse data types to yield a comprehensive picture of the integrated dataset. IGV's scalable architecture makes it well suited for genome-wide exploration of next-generation sequencing (NGS) datasets, including both basic aligned read data and derived results such as read coverage. IGV varies the displayed level of detail according to resolution scale, representing NGS data by a simple coverage plot at very wide views, such as the whole genome. As the user zooms below the ~50 kb range, individual aligned reads become visible, and putative SNPs are highlighted as allele counts in the coverage plot. Alignment details for each read are available in popup windows. Zooming further, individual base mismatches become visible, highlighted by color and intensity according to base call and quality. IGV uses paired end information to color-code paired ends if their insert sizes are larger than expected, fall on different chromosomes, or have unexpected pair orientations. Such pairs, when consistent across multiple reads, can be indicative of a genomic rearrangement. IGV also allows the visualization of misalignments, particularly in repeat regions, which can yield unexpected insert sizes. IGV is open source software and freely available at http://www.broadinstitute.org/igv/, including full documentation on use of the software.