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This article is part of the supplement: Proceedings of the Second Annual RECOMB Satellite Workshop on Massively Parallel Sequencing (RECOMB-seq 2012)

Open Access Proceedings

Reconstructing cancer genomes from paired-end sequencing data

Layla Oesper1*, Anna Ritz1, Sarah J Aerni2, Ryan Drebin1 and Benjamin J Raphael13*

Author Affiliations

1 Department of Computer Science, Brown University, Providence, RI, USA

2 BioMedical Informatics Program, Stanford University, Stanford, CA, USA

3 Center for Computational Molecular Biology, Brown University, Providence, RI, USA

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BMC Bioinformatics 2012, 13(Suppl 6):S10  doi:10.1186/1471-2105-13-S6-S10

Published: 19 April 2012

Abstract

Background

A cancer genome is derived from the germline genome through a series of somatic mutations. Somatic structural variants - including duplications, deletions, inversions, translocations, and other rearrangements - result in a cancer genome that is a scrambling of intervals, or "blocks" of the germline genome sequence. We present an efficient algorithm for reconstructing the block organization of a cancer genome from paired-end DNA sequencing data.

Results

By aligning paired reads from a cancer genome - and a matched germline genome, if available - to the human reference genome, we derive: (i) a partition of the reference genome into intervals; (ii) adjacencies between these intervals in the cancer genome; (iii) an estimated copy number for each interval. We formulate the Copy Number and Adjacency Genome Reconstruction Problem of determining the cancer genome as a sequence of the derived intervals that is consistent with the measured adjacencies and copy numbers. We design an efficient algorithm, called

    P
aired-end
    Re
construction of
    G
enome
    O
rganization (PREGO), to solve this problem by reducing it to an optimization problem on an interval-adjacency graph constructed from the data. The solution to the optimization problem results in an Eulerian graph, containing an alternating Eulerian tour that corresponds to a cancer genome that is consistent with the sequencing data. We apply our algorithm to five ovarian cancer genomes that were sequenced as part of The Cancer Genome Atlas. We identify numerous rearrangements, or structural variants, in these genomes, analyze reciprocal vs. non-reciprocal rearrangements, and identify rearrangements consistent with known mechanisms of duplication such as tandem duplications and breakage/fusion/bridge (B/F/B) cycles.

Conclusions

We demonstrate that PREGO efficiently identifies complex and biologically relevant rearrangements in cancer genome sequencing data. An implementation of the PREGO algorithm is available at http://compbio.cs.brown.edu/software/ webcite.