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  SVIM: Structural Variant Identification using Mapped Long Reads

Heller, D., & Vingron, M. (2019). SVIM: Structural Variant Identification using Mapped Long Reads. Bioinformatics, 35(17), 2907-2915. doi:10.1093/bioinformatics/btz041.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-01BE-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-01BF-2
Genre: Journal Article

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 Creators:
Heller, David1, Author              
Vingron, Martin1, Author              
Affiliations:
1Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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 Abstract: Motivation: Structural variants are defined as genomic variants larger than 50bp. They have been shown to affect more bases in any given genome than SNPs or small indels. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities. Results: We present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from PacBio and Nanopore sequencing machines. Availability and implementation: The source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package Index. Supplementary information: Supplementary data are available at Bioinformatics online.

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Language(s): eng - English
 Dates: 2010-01-212019-09-01
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btz041
ISSN: 1367-4811 (Electronic)1367-4803 (Print)
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: 9 Volume / Issue: 35 (17) Sequence Number: - Start / End Page: 2907 - 2915 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991