Abstract
Motivation: Structural variation is common in human and cancer genomes. High-throughput DNA sequencing has enabled genome-scale surveys of structural variation. However, the short reads produced by these technologies limit the study of complex variants, particularly those involving repetitive regions. Recent 'third-generation' sequencing technologies provide single-molecule templates and longer sequencing reads, but at the cost of higher per-nucleotide error rates. Results: We present MultiBreak-SV, an algorithm to detect structural variants (SVs) from single molecule sequencing data, paired read sequencing data, or a combination of sequencing data from different platforms. We demonstrate that combining low-coverage third-generation data from Pacific Biosciences (PacBio) with high-coverage paired read data is advantageous on simulated chromosomes. We apply MultiBreak-SV to PacBio data from four human fosmids and show that it detects known SVs with high sensitivity and specificity. Finally, we perform a whole-genome analysis on PacBio data from a complete hydatidiform mole cell line and predict 1002 high-probability SVs, over half of which are confirmed by an Illumina-based assembly.
Original language | English (US) |
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Pages (from-to) | 3458-3466 |
Number of pages | 9 |
Journal | Bioinformatics |
Volume | 30 |
Issue number | 24 |
DOIs | |
State | Published - Dec 15 2014 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Computational Mathematics
- Molecular Biology
- Biochemistry
- Statistics and Probability
- Computer Science Applications
- Computational Theory and Mathematics