Integrative genomics viewer

Journal name:
Nature Biotechnology
Volume:
29,
Pages:
24–26
Year published:
DOI:
doi:10.1038/nbt.1754
Published online

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 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.

At a glance

Figures

  1. Copy number, expression and mutation data grouped by tumor subtype.
    Figure 1: Copy number, expression and mutation data grouped by tumor subtype.

    This figure illustrates an integrated, multi-modal view of 202 glioblastoma multiforme samples from The Cancer Genome Atlas (TCGA). Copy number data are segmented values from Affymetrix (Santa Clara, CA, USA) SNP6.0 arrays. Expression data are limited to genes represented on all TCGA-employed platforms and displayed across the entire gene locus. Red shading indicates relative upregulation of a gene and the degree of copy gain of a region; blue shading indicates relative downregulation and copy loss. Small black squares indicate the position of point missense mutations. Samples are grouped by tumor subtype (2nd annotation column) and data type (1st sample annotation column) and sorted by copy number of the EGFR locus. Linking by sample attributes ensures that the order of sample tracks is consistent across data types within their respective tumor subtypes.

  2. View of aligned reads at 20-kb resolution.
    Figure 2: View of aligned reads at 20-kb resolution.

    Coverage plot and alignments from paired-end reads for a matched tumor/normal pair. Sequencing was performed on an Illumina (San Diego, CA) GA2 platform and aligned with Maq (http://maq.sourceforge.net/). Alignments are represented as gray polygons with reads mismatching the reference indicated by color. Loci with a large percentage of mismatches relative to the reference are flagged in the coverage plot as color-coded bars. Alignments with unexpected inferred insert sizes are indicated by color. There is evidence for a ~10-kb deletion (removing two exons of AIDA) in the tumor sample not present in the normal.

References

  1. Cancer Genome Atlas Research Network. Nature 455, 10611068 (2008).
  2. Durbin, R.M. et al. Nature 467, 10611073 (2010).
  3. The ENCODE Project Consortium. Science 306, 636640 (2004).
  4. Verhaak, R.G. et al. Cancer Cell 17, 98110 (2010).
  5. Guttman, M. et al. Nature 458, 223227 (2009).
  6. Berger, M.F. et al. Genome Res. 20, 413427 (2010).
  7. Nielsen, C., Cantor, M., Dubchak, I., Gordon, D. & Wang, T. Nat. Methods 7, S5S15 (2010).
  8. Rutherford, K. et al. Bioinformatics 16, 944945 (2000).
  9. Huang, W. & Marth, G. Genome Res. 18, 15381543 (2008).
  10. Bao, H. et al. Bioinformatics 25, 15541555 (2009).
  11. Milne, I. et al. Bioinformatics 26, 401402 (2010).
  12. Fiume, M., Williams, V., Brook, A. & Brudno, M. Bioinformatics 26, 19381944 (2010).
  13. Lewis, S.E. et al. Genome Biol. 3, RESEARCH0082.1–0082.14 (2002).
  14. Nicol, J.W., Helt, G.A., Blanchard, S.G. Jr., Raja, A. & Loraine, A.E. Bioinformatics 25, 27302731 (2009).

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Author information

Affiliations

  1. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA.

    • James T Robinson,
    • Helga Thorvaldsdóttir,
    • Wendy Winckler,
    • Mitchell Guttman,
    • Eric S Lander,
    • Gad Getz &
    • Jill P Mesirov
  2. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.

    • Mitchell Guttman &
    • Eric S Lander
  3. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA.

    • Eric S Lander

Competing financial interests

The authors declare no competing financial interests.

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Supplementary information

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  1. Supplementary Text and Figures (8.5M)

    Supplementary Figs. 1–9, Supplementary Table 1 and Supplementary Notes

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