Background: Structural variations in human genomes, such as insertions, deletion, or rearrangements, play animportant role in cancer development. Next-Generation Sequencing technologies have been central in providingways to detect such variations. Most existing methods however are limited to the analysis of a single genome, and itis only recently that the comparison of closely related genomes has been considered. In particular, a few recentworks considered the analysis of data sets obtained by sequencing both tumor and healthy tissues of the samecancer patient. In that context, the goal is to detect variations that are specific to exactly one of the genomes, forexample to differentiate between patient-specific and tumor-specific variations. This is a difficult task, especially whenfacing the additional challenge of the possible contamination of healthy tissues by tumor cells and conversely.Results: In the current work, we analyzed a data set of paired-end short-reads, obtained by sequencing tumortissues and healthy tissues, both from the same cancer patient. Based on a combinatorial notion of conflictbetween deletions, we show that in the tumor data, more deletions are predicted than there could actually be in adiploid genome. In contrast, the predictions for the data from normal tissues are almost conflict-free. We designedand applied a method, specific to the analysis of such pooled and contaminated data sets, to detect potentialtumor-specific deletions. Our method takes the deletion calls from both data sets and assigns reads from themixed tumor/normal data to the normal one with the goal to minimize the number of reads that need to bediscarded to obtain a set of conflict-free deletion clusters. We observed that, on the specific data set we analyze,only a very small fraction of the reads needs to be discarded to obtain a set of consistent deletions.Conclusions: We present a framework based on a rigorous definition of consistency between deletions and theassumption that the tumor sample also contains normal cells. A combined analysis of both data sets based on thismodel allowed a consistent explanation of almost all data, providing a detailed picture of candidate patient- andtumor-specific deletions.
Wittler and Chauve BMC Bioinformatics 2011, 12(Suppl 9):S21
Consistency-Based Detection of Potential Tumor-Specific Deletions in Matched Normal/Tumor Genomes
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