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      Enhanced structural variant and breakpoint detection using SVMerge by integration of multiple detection methods and local assembly

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      Author(s): 1 , 1 , 1 , 1 ,
      Publication date (Electronic):
      Journal: Genome Biology
      Publisher: BioMed Central

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          Abstract

          We present a pipeline, SVMerge, to detect structural variants by integrating calls from several existing structural variant callers, which are then validated and the breakpoints refined using local de novo assembly. SVMerge is modular and extensible, allowing new callers to be incorporated as they become available. We applied SVMerge to the analysis of a HapMap trio, demonstrating enhanced structural variant detection, breakpoint refinement, and a lower false discovery rate. SVMerge can be downloaded from http://svmerge.sourceforge.net.

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          SSAHA: a fast search method for large DNA databases.

          Z. Ning, A Cox, J Mullikin (2001)
          We describe an algorithm, SSAHA (Sequence Search and Alignment by Hashing Algorithm), for performing fast searches on databases containing multiple gigabases of DNA. Sequences in the database are preprocessed by breaking them into consecutive k-tuples of k contiguous bases and then using a hash table to store the position of each occurrence of each k-tuple. Searching for a query sequence in the database is done by obtaining from the hash table the "hits" for each k-tuple in the query sequence and then performing a sort on the results. We discuss the effect of the tuple length k on the search speed, memory usage, and sensitivity of the algorithm and present the results of computational experiments which show that SSAHA can be three to four orders of magnitude faster than BLAST or FASTA, while requiring less memory than suffix tree methods. The SSAHA algorithm is used for high-throughput single nucleotide polymorphism (SNP) detection and very large scale sequence assembly. Also, it provides Web-based sequence search facilities for Ensembl projects.
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            Repbase update: a database and an electronic journal of repetitive elements.

            J Jurka (2000)
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              Sensitive and accurate detection of copy number variants using read depth of coverage.

              Seungtai Yoon, Zhenyu Xuan, Vladimir Makarov … (2009)
              Methods for the direct detection of copy number variation (CNV) genome-wide have become effective instruments for identifying genetic risk factors for disease. The application of next-generation sequencing platforms to genetic studies promises to improve sensitivity to detect CNVs as well as inversions, indels, and SNPs. New computational approaches are needed to systematically detect these variants from genome sequence data. Existing sequence-based approaches for CNV detection are primarily based on paired-end read mapping (PEM) as reported previously by Tuzun et al. and Korbel et al. Due to limitations of the PEM approach, some classes of CNVs are difficult to ascertain, including large insertions and variants located within complex genomic regions. To overcome these limitations, we developed a method for CNV detection using read depth of coverage. Event-wise testing (EWT) is a method based on significance testing. In contrast to standard segmentation algorithms that typically operate by performing likelihood evaluation for every point in the genome, EWT works on intervals of data points, rapidly searching for specific classes of events. Overall false-positive rate is controlled by testing the significance of each possible event and adjusting for multiple testing. Deletions and duplications detected in an individual genome by EWT are examined across multiple genomes to identify polymorphism between individuals. We estimated error rates using simulations based on real data, and we applied EWT to the analysis of chromosome 1 from paired-end shotgun sequence data (30x) on five individuals. Our results suggest that analysis of read depth is an effective approach for the detection of CNVs, and it captures structural variants that are refractory to established PEM-based methods.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2010
                Publication date (Electronic): 31 December 2010
                Volume: 11
                Issue: 12
                Page: R128
                Affiliations
                [1 ]Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
                Article
                Publisher ID: gb-2010-11-12-r128
                DOI: 10.1186/gb-2010-11-12-r128
                PMC ID: 3046488
                PubMed ID: 21194472
                SO-VID: 764e3bee-3603-4ea9-9867-6a92bc04a382
                Copyright © Copyright ©2010 Wong et al.; licensee BioMed Central Ltd.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 9 August 2010
                Date revision received : 9 November 2010
                Date accepted : 31 December 2010
                Categories
                Subject: Software

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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