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Quantitative detection of low-abundance somatic structural variants in normal cells by high-throughput sequencing.

ABSTRACT: The detection and quantification of low-abundance somatic DNA mutations by high-throughput sequencing is challenging because of the difficulty of distinguishing errors from true mutations. There are several approaches available for analyzing somatic point mutations and small insertions or deletions, but an accurate genome-wide assessment of somatic structural variants (somSVs) in bulk DNA is still not possible. Here we present Structural Variant Search (SVS), a method to accurately detect rare somSVs by low-coverage sequencing. We demonstrate direct quantitative assessment of elevated somSV frequencies induced by known clastogenic compounds in human primary cells.

SUBMITTER: Quispe-Tintaya W

PROVIDER: S-EPMC4927357 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Quantitative detection of low-abundance somatic structural variants in normal cells by high-throughput sequencing.

Quispe-Tintaya Wilber W Gorbacheva Tatyana T Lee Moonsook M Makhortov Sergei S Popov Vasily N VN Vijg Jan J Maslov Alexander Y AY

Nature methods 20160606 7

The detection and quantification of low-abundance somatic DNA mutations by high-throughput sequencing is challenging because of the difficulty of distinguishing errors from true mutations. There are several approaches available for analyzing somatic point mutations and small insertions or deletions, but an accurate genome-wide assessment of somatic structural variants (somSVs) in bulk DNA is still not possible. Here we present Structural Variant Search (SVS), a method to accurately detect rare s ...[more]

PMID: 27271197

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Project description:Annotating Low Abundance and Transient RNAs in Yeast using Tiling Microarrays and Ultra High-throughput Sequencing Reveals New Transcripts that are not Conserved Across Closely Related Yeast Species A complete description of the transcriptome of an organism is crucial for a comprehensive understanding of how it functions, how its transcriptional networks are controlled, and may provide insights into the organism’s evolution. Despite the status of Saccharomyces cerevisiae as arguably the most well studied model eukaryote, we still do not have a full catalog or understanding of all its genes. In order to interrogate the transcriptome of S. cerevisiae for low abundance or rapidly turned over transcripts, we deleted elements of the RNA degradation machinery with the goal of preferentially increasing the relative abundance of such transcripts. We then used high-resolution tiling microarrays and ultra high-throughput sequencing (UHTS) to identify, map and validate unannotated transcripts that are more abundant in the RNA degradation mutants relative to wild-type cells. We identified 365 currently unannotated transcripts, the majority presumably representing low abundance or short-lived RNAs, of which 185 are previously unknown and unique to this study. It is likely that many of these are cryptic unstable transcripts (CUTs), which are rapidly degraded and whose function(s) within the cell are still unclear, while others may be novel functional transcripts. Of the 185 transcripts we identified as novel to our study, greater than 80 percent come from regions of the genome that have lower conservation scores amongst closely related yeast species than 85 percent of the verified ORFs in S. cerevisiae. Such regions of the genome have typically been less well studied, and by definition transcripts from these regions will distinguish S. cerevisiae from these closely related species. Keywords: Saccharomyces cerevisiae, transcriptome, RNA-Seq, RNA degradation, XRN1, RRP6, LSM1, PAT1, Salt Shock, NaCl

Dataset Information

Quantitative detection of low-abundance somatic structural variants in normal cells by high-throughput sequencing.

Publications

Quantitative detection of low-abundance somatic structural variants in normal cells by high-throughput sequencing.

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