Visualization and Probability-based Scoring of Structural Variants Within Repetitive Sequences

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2014 Feb 7

PMID 24501098

Citations 1

Authors

Eitan Halper-Stromberg

Jared Steranka

Kathleen H Burns

Sarven Sabunciyan

Rafael A Irizarry

Affiliations

Soon will be listed here.

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.

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.

Citing Articles

Visualization tools for human structural variations identified by whole-genome sequencing.

Yokoyama T, Kasahara M J Hum Genet. 2019; 65(1):49-60.

PMID: 31666648 PMC: 8075883. DOI: 10.1038/s10038-019-0687-0.

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