| Summary: | Detection and characterization of genomic structural variation are important for understanding the landscape of genetic variation in human populations and in complex diseases such as cancer. Recent studies demonstrate the feasibility of detecting structural variation using next-generation, short-insert, paired-end sequencing reads. However, the utility of these reads is not entirely clear, nor are the analysis methods under which accurate detection can be achieved. The algorithm BreakDancer predicts a wide variety of structural variants including indels, inversions, and translocations. We examined BreakDancer's performance in simulation, comparison with other methods, analysis of an acute myeloid leukemia sample, and the 1,000 Genomes trio individuals. We found that it substantially improved the detection of small and intermediate size indels from 10 bp to 1 Mbp that are difficult to detect via a single conventional approach.
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