FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods

Becker, Timothy J.; Lee, Wan‐Ping; Leone, Joseph F.; Zhu, Qihui; Zhang, Chengsheng; Liu, Silvia; Sargent, J.W.; Shanker, Kritika; Mil-homens, Adam; Cerveira, Eliza; Ryan, Mallory; Cha, Jane; Navarro, Fábio C. P.; Galeev, Timur R.; Gerstein, Mark; Mills, Ryan E.; Shin, Dong-Guk; Lee, Charles; Malhotra, Ankit

doi:10.1186/s13059-018-1404-6

Cited by 57 publications

(62 citation statements)

References 25 publications

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“…5A). This level of discrepancy is not unexpected [29] and is indicative of the difficulty of detecting SVs in general. When we intersect these 14 consensus WGS-based translocations with those detected by HiNT, we find that 5 are in common (Fig.…”

Section: Resultsmentioning

confidence: 90%

HiNT: a computational method for detecting copy number variations and translocations from Hi-C data

Wang

Lee

Chu

et al. 2019

Preprint

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11The three-dimensional conformation of a genome can be profiled using Hi-C, a technique that 12 combines chromatin conformation capture with high-throughput sequencing. However, structural 13 variations (SV) often yield features that can be mistaken for chromosomal interactions. Here, we 14 describe a computational method HiNT (Hi-C for copy Number variation and Translocation 15 detection), which detects copy number variations and inter-chromosomal translocations within 16Hi-C data with breakpoints at single base-pair resolution. We demonstrate that HiNT 17 outperforms existing methods on both simulated and real data. We also show that Hi-C can 18 supplement whole-genome sequencing in SV detection by locating breakpoints in repetitive 19 regions. 20 21Keywords 22 chromosomal interactions, structural variation, whole-genome sequencing, repetitive region 23 24 outperforms existing computational methods both in sensitivity and false discovery rate (FDR). 57HiNT also provides translocation breakpoints at single base-pair resolution, a feature not 58 available in existing methods that utilize only Hi-C data. Furthermore, HiNT supports 59 parallelization, utilizes efficient storage formats for interaction matrices, and accepts multiple 60 input formats including raw FASTQ, BAM, and contact matrix. HiNT is available at 61 https://github.com/parklab/HiNT. 62 63 RESULTS 64

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Section: Resultsmentioning

confidence: 90%

HiNT: a computational method for detecting copy number variations and translocations from Hi-C data

Wang

Lee

Chu

et al. 2019

Preprint

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“…For example, if one algorithm is known to produce very few false calls, all SVs detected by this algorithm can be included in the final call set. Some tools, such as Par-liament2 (Zarate et al, 2018) and FusorSV (Becker et al, 2018), run a suite of individual SV callers and provide an ensemble call set.…”

Section: Variant Callingmentioning

confidence: 99%

A Practical Guide for Structural Variation Detection in the Human Genome

Yang

2020

CP Human Genetics

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Profiling genetic variants—including single nucleotide variants, small insertions and deletions, copy number variations, and structural variations (SVs)—from both healthy individuals and individuals with disease is a key component of genetic and biomedical research. SVs are large‐scale changes in the genome and involve breakage and rejoining of DNA fragments. They may affect thousands to millions of nucleotides and can lead to loss, gain, and reshuffling of genes and regulatory elements. SVs are known to impact gene expression and potentially result in altered phenotypes and diseases. Therefore, identifying SVs from the human genomes is particularly important. In this review, I describe advantages and disadvantages of the available high‐throughput assays for the discovery of SVs, which are the most challenging genetic alterations to detect. A practical guide is offered to suggest the most suitable strategies for discovering different types of SVs including common germline, rare, somatic, and complex variants. I also discuss factors to be considered, such as cost and performance, for different strategies when designing experiments. Last, I present several approaches to identify potential SV artifacts caused by samples, experimental procedures, and computational analysis. © 2020 Wiley Periodicals LLC.

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“…Furthermore, callers such as Manta (Chen et al, 2016) and GRIDSS (Cameron et al, 2017) also incorporate short-read assembly. To obtain a more comprehensive and/or accurate callset, ensemble approaches have yielded promising results (English et al, 2015;Mohiyuddin et al, 2015;Becker et al, 2018;Fang et al, 2018). In such an approach, i) a range of SV callers are executed, and ii) their results are combined into a single callset.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data (v0.2)"

2020

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Structural variants (SVs) are an important class of genetic variation implicated in a wide array of genetic diseases including cancer. Despite the advances in whole genome sequencing, comprehensive and accurate detection of SVs in short-read data still poses some practical and computational challenges. We present sv-callers, a highly portable workflow that enables parallel execution of multiple SV detection tools as well as provide users with example analyses of detected SV callsets in a Jupyter Notebook. This workflow supports easy deployment of software dependencies, configuration and addition of new analysis tools. Moreover, porting it to different computing systems requires minimal effort. Finally, we demonstrate the utility of the workflow by performing both somatic and germline SV analyses using different high-performance computing systems.

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FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods

Cited by 57 publications

References 25 publications

HiNT: a computational method for detecting copy number variations and translocations from Hi-C data

HiNT: a computational method for detecting copy number variations and translocations from Hi-C data

A Practical Guide for Structural Variation Detection in the Human Genome

Peer Review #1 of "sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data (v0.2)"

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