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GSVseq - Detection Genomic Structural Variation Using Multi-features of Next Generation Sequencing

Wednesday, September 24, 2014 — Poster Session IV

10:00 a.m. –12:00 p.m.

FAES Academic Center



* FARE Award Winner


  • Kai Ying
  • Z.D. Wang
  • Nancy Hansen
  • Jim Mullikin


Copy number variation (CNV) play an important role in many complex diseases such as autism, alzheimer and cancer. Recent advances in high-throughput DNA sequencing technologies such as Whole Genome Shotgun(WGS) and Exome -Capture have enabled detect CNV in large samples with reasonable cost. However, current analytical methods of NGS CNV detection is still unsatisfied both in sensitivity and accuracy. Those methods usually only consider one feature of NGS data in their CNV calling. However one set of NGS data has several features that can be used to detect CNV, such as Read Depth (RD), Allele Frequency Aberration(AFA), Pair End(PE) distribution and Split Reads(SR). Here we present an integrated CNV detection method that combine both Read Depth (RD), Allele Frequency Aberration(AFA) information in a unified statistical model. Our method "hard" combine different NGS features in each base pair and apply HMM model to detect contiguous genomic region that show CNV signals. Our method can also consider family pedigree information to increase detection power based on mendelian inheritance laws. Both simulated and real data show that our method has significant advantage comparing to other methods that consider only one sequence feature or just "soft" combine several features.

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