Dataset Information

Integrative detection and analysis of structural variation in cancer genomes.

ABSTRACT: Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.

SUBMITTER: Dixon JR

PROVIDER: S-EPMC6301019 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

Integrative detection and analysis of structural variation in cancer genomes.

Dixon Jesse R JR Xu Jie J Dileep Vishnu V Zhan Ye Y Song Fan F Le Victoria T VT Yardımcı Galip Gürkan GG Chakraborty Abhijit A Bann Darrin V DV Wang Yanli Y Clark Royden R Zhang Lijun L Yang Hongbo H Liu Tingting T Iyyanki Sriranga S An Lin L Pool Christopher C Sasaki Takayo T Rivera-Mulia Juan Carlos JC Ozadam Hakan H Lajoie Bryan R BR Kaul Rajinder R Buckley Michael M Lee Kristen K Diegel Morgan M Pezic Dubravka D Ernst Christina C Hadjur Suzana S Odom Duncan T DT Stamatoyannopoulos John A JA Broach James R JR Hardison Ross C RC Ay Ferhat F Noble William Stafford WS Dekker Job J Gilbert David M DM Yue Feng F

Nature genetics 20180910 10

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative ap ...[more]

PMID: 30202056

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Project description:BackgroundStructural variations play an important role in bacterial genomes. They can mediate genome adaptation quickly in response to the external environment and thus can also play a role in antibiotic resistance. The detection of structural variations in bacteria is challenging, and the recognition of even small rearrangements can be important. Even though most detection tools are aimed at and benchmarked on eukaryotic genomes, they can also be used on prokaryotic genomes. The key features of detection are the ability to detect small rearrangements and support haploid genomes. Because of the limiting performance of a single detection tool, combining the detection abilities of multiple tools can lead to more robust results. There are already available workflows for structural variation detection for long-reads technologies and for the detection of single-nucleotide variation and indels, both aimed at bacteria. Yet we are unaware of structural variations detection workflows for the short-reads sequencing platform. Motivated by this gap we created our workflow. Further, we were interested in increasing the detection performance and providing more robust results.ResultsWe developed an open-source bioinformatics pipeline, ProcaryaSV, for the detection of structural variations in bacterial isolates from paired-end short sequencing reads. Multiple tools, starting with quality control and trimming of sequencing data, alignment to the reference genome, and multiple structural variation detection tools, are integrated. All the partial results are then processed and merged with an in-house merging algorithm. Compared with a single detection approach, ProcaryaSV has improved detection performance and is a reproducible easy-to-use tool.ConclusionsThe ProcaryaSV pipeline provides an integrative approach to structural variation detection from paired-end next-generation sequencing of bacterial samples. It can be easily installed and used on Linux machines. It is publicly available on GitHub at https://github.com/robinjugas/ProcaryaSV .

Dataset Information

Integrative detection and analysis of structural variation in cancer genomes.

Publications

Integrative detection and analysis of structural variation in cancer genomes.

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