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      • Record: found
      • Abstract: found
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      Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads

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          Abstract

          Motivation: There is a strong demand in the genomic community to develop effective algorithms to reliably identify genomic variants. Indel detection using next-gen data is difficult and identification of long structural variations is extremely challenging.

          Results: We present Pindel, a pattern growth approach, to detect breakpoints of large deletions and medium-sized insertions from paired-end short reads. We use both simulated reads and real data to demonstrate the efficiency of the computer program and accuracy of the results.

          Availability: The binary code and a short user manual can be freely downloaded from http://www.ebi.ac.uk/∼kye/pindel/.

          Contact: k.ye@ 123456lumc.nl ; zn1@ 123456sanger.ac.uk

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          The complete genome of an individual by massively parallel DNA sequencing.

          David Wheeler, Maithreyan Srinivasan, Michael Egholm … (2008)
          The association of genetic variation with disease and drug response, and improvements in nucleic acid technologies, have given great optimism for the impact of 'genomic medicine'. However, the formidable size of the diploid human genome, approximately 6 gigabases, has prevented the routine application of sequencing methods to deciphering complete individual human genomes. To realize the full potential of genomics for human health, this limitation must be overcome. Here we report the DNA sequence of a diploid genome of a single individual, James D. Watson, sequenced to 7.4-fold redundancy in two months using massively parallel sequencing in picolitre-size reaction vessels. This sequence was completed in two months at approximately one-hundredth of the cost of traditional capillary electrophoresis methods. Comparison of the sequence to the reference genome led to the identification of 3.3 million single nucleotide polymorphisms, of which 10,654 cause amino-acid substitution within the coding sequence. In addition, we accurately identified small-scale (2-40,000 base pair (bp)) insertion and deletion polymorphism as well as copy number variation resulting in the large-scale gain and loss of chromosomal segments ranging from 26,000 to 1.5 million base pairs. Overall, these results agree well with recent results of sequencing of a single individual by traditional methods. However, in addition to being faster and significantly less expensive, this sequencing technology avoids the arbitrary loss of genomic sequences inherent in random shotgun sequencing by bacterial cloning because it amplifies DNA in a cell-free system. As a result, we further demonstrate the acquisition of novel human sequence, including novel genes not previously identified by traditional genomic sequencing. This is the first genome sequenced by next-generation technologies. Therefore it is a pilot for the future challenges of 'personalized genome sequencing'.
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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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              Mapping and sequencing of structural variation from eight human genomes.

              Jeffrey M. Kidd, Gregory M. Cooper, William F Donahue … (2008)
              Genetic variation among individual humans occurs on many different scales, ranging from gross alterations in the human karyotype to single nucleotide changes. Here we explore variation on an intermediate scale--particularly insertions, deletions and inversions affecting from a few thousand to a few million base pairs. We employed a clone-based method to interrogate this intermediate structural variation in eight individuals of diverse geographic ancestry. Our analysis provides a comprehensive overview of the normal pattern of structural variation present in these genomes, refining the location of 1,695 structural variants. We find that 50% were seen in more than one individual and that nearly half lay outside regions of the genome previously described as structurally variant. We discover 525 new insertion sequences that are not present in the human reference genome and show that many of these are variable in copy number between individuals. Complete sequencing of 261 structural variants reveals considerable locus complexity and provides insights into the different mutational processes that have shaped the human genome. These data provide the first high-resolution sequence map of human structural variation--a standard for genotyping platforms and a prelude to future individual genome sequencing projects.
              Article 0 comments Cited 362 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 1 November 2009
                Publication date (Electronic): 26 June 2009
                Publication date PMC-release: 26 June 2009
                Volume: 25
                Issue: 21
                Pages: 2865-2871
                Affiliations
                1 EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, 2 Departments of Molecular Epidemiology, Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands, 3 Max Planck Institute for Molecular Genetics and International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany and 4 The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: Alfonso Valencia

                Article
                Publisher ID: btp394
                DOI: 10.1093/bioinformatics/btp394
                PMC ID: 2781750
                PubMed ID: 19561018
                SO-VID: e9227eb9-175a-418f-870f-e371d2b8c3d5
                Copyright statement: © The Author(s) 2009. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 27 April 2009
                Date revision received : 20 June 2009
                Date accepted : 21 June 2009
                Categories
                Subject: Ismb/Eccb 2009 Special Interest Group on Short Read Sequencing
                Subject: Original Papers
                Subject: Genome Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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