Dataset Information

Detection of structural mosaicism from targeted and whole-genome sequencing data.

ABSTRACT: Structural mosaic abnormalities are large post-zygotic mutations present in a subset of cells and have been implicated in developmental disorders and cancer. Such mutations have been conventionally assessed in clinical diagnostics using cytogenetic or microarray testing. Modern disease studies rely heavily on exome sequencing, yet an adequate method for the detection of structural mosaicism using targeted sequencing data is lacking. Here, we present a method, called MrMosaic, to detect structural mosaic abnormalities using deviations in allele fraction and read coverage from next-generation sequencing data. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) simulations were used to calculate detection performance across a range of mosaic event sizes, types, clonalities, and sequencing depths. The tool was applied to 4911 patients with undiagnosed developmental disorders, and 11 events among nine patients were detected. For eight of these 11 events, mosaicism was observed in saliva but not blood, suggesting that assaying blood alone would miss a large fraction, possibly >50%, of mosaic diagnostic chromosomal rearrangements.

SUBMITTER: King DA

PROVIDER: S-EPMC5630034 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Publications

Detection of structural mosaicism from targeted and whole-genome sequencing data.

King Daniel A DA Sifrim Alejandro A Fitzgerald Tomas W TW Rahbari Raheleh R Hobson Emma E Homfray Tessa T Mansour Sahar S Mehta Sarju G SG Shehla Mohammed M Tomkins Susan E SE Vasudevan Pradeep C PC Hurles Matthew E ME

Genome research 20170830 10

Structural mosaic abnormalities are large post-zygotic mutations present in a subset of cells and have been implicated in developmental disorders and cancer. Such mutations have been conventionally assessed in clinical diagnostics using cytogenetic or microarray testing. Modern disease studies rely heavily on exome sequencing, yet an adequate method for the detection of structural mosaicism using targeted sequencing data is lacking. Here, we present a method, called MrMosaic, to detect structura ...[more]

PMID: 28855261

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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

Detection of structural mosaicism from targeted and whole-genome sequencing data.

Publications

Detection of structural mosaicism from targeted and whole-genome sequencing data.

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