ArticleNucleic acids research2012
SVM²: an improved paired-end-based tool for the detection of small genomic structural variations using high-throughput single-genome resequencing data.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Comparisons of performances of structural variants detection algorithms in solitary or combination strategy.PloS one · 2025Article
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software.Nature communications · 2019Article
- Article
- GPA: A Microbial Genetic Polymorphisms Assignments Tool in Metagenomic Analysis by Bayesian Estimation.Genomics, proteomics & bioinformatics · 2019Article
- InDel marker detection by integration of multiple softwares using machine learning techniques.BMC bioinformatics · 2016Article
- Identification of copy number variants in whole-genome data using Reference Coverage Profiles.Frontiers in genetics · 2015Article
- A gradient-boosting approach for filtering de novo mutations in parent-offspring trios.Bioinformatics (Oxford, England) · 2014Article
- Unraveling overlapping deletions by agglomerative clustering.BMC genomics · 2013Article
- MATCHCLIP: locate precise breakpoints for copy number variation using CIGAR string by matching soft clipped reads.Frontiers in genetics · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several bioinformatics methods have been proposed for the detection and characterization of genomic structural variation (SV) from ultra high-throughput genome resequencing data. Recent surveys show that comprehensive detection of SV events of different types between an individual resequenced genome and a reference sequence is best achieved through the combination of methods based on different principles (split mapping, reassembly, read depth, insert size, etc.). The improvement of individual predictors is thus an important objective. In this study, we propose a new method that combines deviations from expected library insert sizes and additional information from local patterns of read mapping and uses supervised learning to predict the position and nature of structural variants. We show that our approach provides greatly increased sensitivity with respect to other tools based on paired end read mapping at no cost in specificity, and it makes reliable predictions of very short insertions and deletions in repetitive and low-complexity genomic contexts that can confound tools based on split mapping of reads.
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Registered trials
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