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

• Published in: Bioinformatics Vol. 30; no. 11; pp. 1514 - 1521
• Main Authors: Halper-Stromberg, Eitan, Steranka, Jared, Burns, Kathleen H., Sabunciyan, Sarven, Irizarry, Rafael A.
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.06.2014
• Subjects: Bioinformatics; Biotechnology; Cell Line, Transformed; Cell Line, Tumor; DNA - chemistry; Genome, Human; Genomic Structural Variation; Genomics - methods; High-Throughput Nucleotide Sequencing - methods; Humans; Loci; Mathematical analysis; Original Papers; Probability; Repetitive Sequences, Nucleic Acid; Scoring; Sequence Alignment - methods; Sequence Analysis, DNA - methods; Sequencing; Upstream; Visualization
• ISSN: 1367-4803; 1367-4811; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btu054

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. Availability: We implement our method as an R package available at https://github.com/Eitan177/targetSeqView. Code to reproduce the figures and results are also available. Contact:  ehalper2@jhmi.edu Supplementary information:  Supplementary data are available at Bioinformatics online.