Dataset Information

BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers.

ABSTRACT: Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for 'targeted' resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a 'kmer' strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings.

SUBMITTER: Abo RP

PROVIDER: S-EPMC4330340 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers.

Abo Ryan P RP Ducar Matthew M Garcia Elizabeth P EP Thorner Aaron R AR Rojas-Rudilla Vanesa V Lin Ling L Sholl Lynette M LM Hahn William C WC Meyerson Matthew M Lindeman Neal I NI Van Hummelen Paul P MacConaill Laura E LE

Nucleic acids research 20141126 3

Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for 'targeted' resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approac ...[more]

PMID: 25428359

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Project description:MOTIVATION:Cancer is a complex disease that involves rapidly evolving cells, often forming multiple distinct clones. In order to effectively understand progression of a patient-specific tumor, one needs to comprehensively sample tumor DNA at multiple time points, ideally obtained through inexpensive and minimally invasive techniques. Current sequencing technologies make the 'liquid biopsy' possible, which involves sampling a patient's blood or urine and sequencing the circulating cell free DNA (cfDNA). A certain percentage of this DNA originates from the tumor, known as circulating tumor DNA (ctDNA). The ratio of ctDNA may be extremely low in the sample, and the ctDNA may originate from multiple tumors or clones. These factors present unique challenges for applying existing tools and workflows to the analysis of ctDNA, especially in the detection of structural variations which rely on sufficient read coverage to be detectable. RESULTS:Here we introduce SViCT?, a structural variation (SV) detection tool designed to handle the challenges associated with cfDNA analysis. SViCT?can detect breakpoints and sequences of various structural variations including deletions, insertions, inversions, duplications and translocations. SViCT?extracts discordant read pairs, one-end anchors and soft-clipped/split reads, assembles them into contigs, and re-maps contig intervals to a reference genome using an efficient k-mer indexing approach. The intervals are then joined using a combination of graph and greedy algorithms to identify specific structural variant signatures. We assessed the performance of SViCT?and compared it to state-of-the-art tools using simulated cfDNA datasets with properties matching those of real cfDNA samples. The positive predictive value and sensitivity of our tool was superior to all the tested tools and reasonable performance was maintained down to the lowest dilution of 0.01% tumor DNA in simulated datasets. Additionally, SViCT?was able to detect all known SVs in two real cfDNA reference datasets (at 0.6-5% ctDNA) and predict a novel structural variant in a prostate cancer cohort. AVAILABILITY:SViCT?is available at https://github.com/vpc-ccg/svict. Contact:faraz.hach@ubc.ca.

Dataset Information

BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers.

Publications

BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers.

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