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Updated: May 22, 2026
11:02
Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
Published on: October 18, 2013
Reconstructing cancer genomes from paired-end sequencing data.
Layla Oesper1, Anna Ritz, Sarah J Aerni
1Department of Computer Science, Brown University, Providence, RI, USA. layla@cs.brown.edu
BMC Bioinformatics
|April 28, 2012
Summary
We developed PREGO, an efficient algorithm to reconstruct cancer genome structure from DNA sequencing data. It identifies complex rearrangements, aiding our understanding of cancer genome organization and evolution.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Cancer genomes arise from germline genomes via somatic mutations.
- Somatic structural variants scramble germline genome sequences into blocks.
- Reconstructing cancer genome block organization is crucial for understanding cancer development.
Purpose of the Study:
- To develop an efficient algorithm for reconstructing cancer genome block organization.
- To analyze structural variants and their mechanisms in cancer genomes.
- To provide a tool for cancer genome analysis.
Main Methods:
- Developed the Paired-end Reconstruction of Genome Organization (PREGO) algorithm.
- Formulated cancer genome reconstruction as an optimization problem on an interval-adjacency graph.
- Applied PREGO to ovarian cancer genomes from The Cancer Genome Atlas.
Main Results:
- PREGO efficiently reconstructs cancer genome block organization from paired-end sequencing data.
- Identified numerous structural variants, including duplications and other rearrangements.
- Analyzed reciprocal vs. non-reciprocal rearrangements and duplication mechanisms.
Conclusions:
- PREGO efficiently identifies complex and biologically relevant rearrangements in cancer genome sequencing data.
- The algorithm aids in understanding cancer genome complexity.
- PREGO software is publicly available for research use.
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