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      PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data

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          Abstract

          Paired-End Mapper (PEMer) enables mapping of genomic structural variants at considerably enhanced sensitivity, specificity and resolution over previous approaches.

          Abstract

          Personal-genomics endeavors, such as the 1000 Genomes project, are generating maps of genomic structural variants by analyzing ends of massively sequenced genome fragments. To process these we developed Paired-End Mapper ( PEMer; http://sv.gersteinlab.org/pemer). This comprises an analysis pipeline, compatible with several next-generation sequencing platforms; simulation-based error models, yielding confidence-values for each structural variant; and a back-end database. The simulations demonstrated high structural variant reconstruction efficiency for PEMer's coverage-adjusted multi-cutoff scoring-strategy and showed its relative insensitivity to base-calling errors.

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          Identification of common molecular subsequences.

          T.F. Smith, M.S. Waterman (1981)
          Article 0 comments Cited 1704 times – based on 0 reviews     Review now
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            Global variation in copy number in the human genome.

            Richard Redon, Shumpei Ishikawa, Karen R Fitch … (2006)
            Copy number variation (CNV) of DNA sequences is functionally significant but has yet to be fully ascertained. We have constructed a first-generation CNV map of the human genome through the study of 270 individuals from four populations with ancestry in Europe, Africa or Asia (the HapMap collection). DNA from these individuals was screened for CNV using two complementary technologies: single-nucleotide polymorphism (SNP) genotyping arrays, and clone-based comparative genomic hybridization. A total of 1,447 copy number variable regions (CNVRs), which can encompass overlapping or adjacent gains or losses, covering 360 megabases (12% of the genome) were identified in these populations. These CNVRs contained hundreds of genes, disease loci, functional elements and segmental duplications. Notably, the CNVRs encompassed more nucleotide content per genome than SNPs, underscoring the importance of CNV in genetic diversity and evolution. The data obtained delineate linkage disequilibrium patterns for many CNVs, and reveal marked variation in copy number among populations. We also demonstrate the utility of this resource for genetic disease studies.
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              Structural variation in the human genome.

              Lars Feuk, Andrew R. Carson, Stephen W Scherer (2006)
              The first wave of information from the analysis of the human genome revealed SNPs to be the main source of genetic and phenotypic human variation. However, the advent of genome-scanning technologies has now uncovered an unexpectedly large extent of what we term 'structural variation' in the human genome. This comprises microscopic and, more commonly, submicroscopic variants, which include deletions, duplications and large-scale copy-number variants - collectively termed copy-number variants or copy-number polymorphisms - as well as insertions, inversions and translocations. Rapidly accumulating evidence indicates that structural variants can comprise millions of nucleotides of heterogeneity within every genome, and are likely to make an important contribution to human diversity and disease susceptibility.
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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): 2009
                Publication date (Electronic): 23 February 2009
                Volume: 10
                Issue: 2
                Page: R23
                Affiliations
                [1 ]Gene Expression Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr., Heidelberg, 69117, Germany
                [2 ]EMBL Outstation Hinxton, EMBL-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
                [3 ]Molecular Biophysics and Biochemistry Department, Yale University, Whitney Ave, New Haven, CT 06520, USA
                [4 ]Department of Molecular, Cellular, and Developmental Biology, Yale University, Whitney Ave, New Haven, CT 06520, USA
                [5 ]Department of Computer Science, Yale University, Prospect Street, New Haven, CT 06511, USA
                [6 ]Program in Computational Biology and Bioinformatics, Yale University, Whitney Ave, New Haven, CT 06520, USA
                Article
                Publisher ID: gb-2009-10-2-r23
                DOI: 10.1186/gb-2009-10-2-r23
                PMC ID: 2688268
                PubMed ID: 19236709
                SO-VID: fe0fd73d-d082-4b09-a50c-0274b2a00a2d
                Copyright © Copyright © 2009 Korbel 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 : 1 September 2008
                Date revision received : 22 December 2008
                Date accepted : 23 February 2009
                Categories
                Subject: Software

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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