Unraveling overlapping deletions by agglomerative clustering.

ABSTRACT:

Background

Structural variations in human genomes, such as deletions, play an important role in cancer development. Next-Generation Sequencing technologies have been central in providing ways to detect such variations. Methods like paired-end mapping allow to simultaneously analyze data from several samples in order to, e.g., distinguish tumor from patient specific variations. However, it has been shown that, especially in this setting, there is a need to explicitly take overlapping deletions into consideration. Existing tools have only minor capabilities to call overlapping deletions, unable to unravel complex signals to obtain consistent predictions.

Result

We present a first approach specifically designed to cluster short-read paired-end data into possibly overlapping del

SUBMITTER: Wittler R 

PROVIDER: S-EPMC3549816 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature