To the Editor
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 non-coding RNAs, SNP and copy
number profiling, and functional assays. Analysis of these large, diverse datasets
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 datasets and to flexibly
integrate multiple data types, including clinical data. However, the sheer volume
and scope of data poses a significant challenge to the development of such tools.
To address this challenge we developed the Integrative Genomics Viewer (IGV), a lightweight
visualization tool that enables intuitive real-time exploration of diverse, large-scale
genomic datasets on standard desktop computers. It supports flexible integration of
a wide range of genomic data types including aligned sequence reads, mutations, copy
number, RNAi screens, gene expression, methylation, and genomic annotations (Figure
S1). The IGV makes use of efficient, multi-resolution file formats to enable real-time
exploration of arbitrarily large datasets over all resolution scales, while consuming
minimal resources on the client computer (see Supplementary Text). Navigation through
a dataset is similar to Google Maps, allowing the user to zoom and pan seamlessly
across the genome at any level of detail from whole-genome to base pair (Figure S2).
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, for example, The Cancer Genome Atlas (TCGA)
, 1000 Genomes (www.1000genomes.org/), and ENCODE
(www.genome.gov/10005107) projects. In addition, IGV allows collaborators to load
and share data locally or remotely over the Web.
IGV supports concurrent visualization of diverse data types across hundreds, and up
to thousands of samples, and correlation of these integrated datasets with clinical
and phenotypic variables. A researcher can define arbitrary sample annotations and
associate them with data tracks using a simple tab-delimited file format (see Supplementary
Text). These might include, for example, sample identifier (used to link different
types of data for the same patient or tissue sample), phenotype, outcome, cluster
membership, or any other clinical or experimental label. Annotations are displayed
as a heatmap but more importantly are used for grouping, sorting, filtering, and overlaying
diverse data types to yield a comprehensive picture of the integrated dataset. This
is illustrated in Figure 1, a view of copy number, expression, mutation, and clinical
data from 202 glioblastoma samples from the TCGA project in a 3 kb region around the
. The investigator first grouped samples by tumor subtype, then by data type (copy
number and expression), and finally sorted them by median copy number over the EGFR
locus. A shared sample identifier links the copy number and expression tracks, maintaining
their relative sort order within the subtypes. Mutation data is overlaid on corresponding
copy number and expression tracks, based on shared participant identifier annotations.
Several trends in the data stand out, such as a strong correlation between copy number
and expression and an overrepresentation of EGFR amplified samples in the Classical
IGV’s scalable architecture makes it well suited for genome-wide exploration of next-generation
sequencing (NGS) datasets, including both basic aligned read data as well as derived
results, such as read coverage. NGS datasets can approach terabytes in size, so careful
management of data is necessary to conserve compute resources and to prevent information
overload. IGV varies the displayed level of detail according to resolution scale.
At very wide views, such as the whole genome, IGV represents NGS data by a simple
coverage plot. Coverage data is often useful for assessing overall quality and diagnosing
technical issues in sequencing runs (Figure S3), as well as analysis of ChIP-Seq
experiments (Figures S4 and S5).
As the user zooms below the ~50 kb range, individual aligned reads become visible
(Figure 2) and putative SNPs are highlighted as allele counts in the coverage plot.
Alignment details for each read are available in popup windows (Figures S6 and S7).
Zooming further, individual base mismatches become visible, highlighted by color and
intensity according to base call and quality. At this level, the investigator may
sort reads by base, quality, strand, sample and other attributes to assess the evidence
of a variant. This type of visual inspection can be an efficient and powerful tool
for variant call validation, eliminating many false positives and aiding in confirmation
of true findings (Figures S6 and S7).
Many sequencing protocols produce reads from both ends (“paired ends”) of genomic
fragments of known size distribution. IGV uses this 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. When coloring aberrant paired
ends, each chromosome is assigned a unique color, so that intra- (same color) and
inter- (different color) chromosomal events are readily distinguished (Figures 2 and
S8). We note that misalignments, particularly in repeat regions, can also yield unexpected
insert sizes, and can be diagnosed with the IGV (Figure S9).
There are a number of stand-alone, desktop genome browsers available today
, and the Integrated Genome Browser
. Many of them have features that overlap with IGV, particularly for NGS sequence
alignment and genome annotation viewing. The Integrated Genome Browser also supports
viewing array-based data. See Supplementary Table 1 and Supplementary Text for more
detail. IGV focuses on the emerging integrative nature of genomic studies, placing
equal emphasis on array-based platforms, such as expression and copy-number arrays,
next-generation sequencing, as well as clinical and other sample metadata. Indeed,
an important and unique feature of IGV is the ability to view all these different
data types together and to use the sample metadata to dynamically group, sort, and
filter datasets (Figure 1 above). Another important characteristic of IGV is fast
data loading and real-time pan and zoom – at all scales of genome resolution and all
dataset sizes, including datasets comprising hundreds of samples. Finally, we have
placed great emphasis on the ease of installation and use of IGV, with the goal of
making both the viewing and sharing of their data accessible to non-informatics end
IGV is open source software and freely available at http://www.broadinstitute.org/igv/,
including full documentation on use of the software.