Visualization and probability-based scoring of structural variants within repetitive sequences

Eitan Halper-Stromberg, Jared Steranka, Kathleen H. Burns, Sarven Sabunciyan, Rafael A. Irizarry

Research output: Contribution to journalArticle

Abstract

Motivation: Repetitive sequences account for approximately half of the human genome. Accurately ascertaining sequences in these regions with next generation sequencers is challenging, and requires a different set of analytical techniques than for reads originating from unique sequences. Complicating the matter are repetitive regions subject to programmed rearrangements, as is the case with the antigen-binding domains in the Immunoglobulin (Ig) and T-cell receptor (TCR) loci. Results: We developed a probability-based score and visualization method to aid in distinguishing true structural variants from alignment artifacts. We demonstrate the usefulness of this method in its ability to separate real structural variants from false positives generated with existing upstream analysis tools. We validated our approach using both target-capture and whole-genome experiments. Capture sequencing reads were generated from primary lymphoid tumors, cancer cell lines and an EBV-transformed lymphoblast cell line over the Ig and TCR loci. Whole-genome sequencing reads were from a lymphoblastoid cell-line.

Original languageEnglish (US)
Pages (from-to)1514-1521
Number of pages8
JournalBioinformatics
Volume30
Issue number11
DOIs
StatePublished - Jun 1 2014

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ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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