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ABySS: A parallel assembler for short read sequence data

ABySS: A parallel assembler for short read sequence data

  1. Jared T. Simpson,1,
  2. Kim Wong,
  3. Shaun D. Jackman,
  4. Jacqueline E. Schein,
  5. Steven J.M. Jones and
  6. İnanç Birol,2
  1. Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia V5Z 4E6, Canada

    Abstract

    Widespread adoption of massively parallel deoxyribonucleic acid (DNA) sequencing instruments has prompted the recent development of de novo short read assembly algorithms. A common shortcoming of the available tools is their inability to efficiently assemble vast amounts of data generated from large-scale sequencing projects, such as the sequencing of individual human genomes to catalog natural genetic variation. To address this limitation, we developed ABySS (Assembly By Short Sequences), a parallelized sequence assembler. As a demonstration of the capability of our software, we assembled 3.5 billion paired-end reads from the genome of an African male publicly released by Illumina, Inc. Approximately 2.76 million contigs ≥100 base pairs (bp) in length were created with an N50 size of 1499 bp, representing 68% of the reference human genome. Analysis of these contigs identified polymorphic and novel sequences not present in the human reference assembly, which were validated by alignment to alternate human assemblies and to other primate genomes.

    Footnotes

    • 1 Present address: Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom.

    • 2 Corresponding author.

      E-mail ibirol{at}bcgsc.ca; fax (604) 876-3561.

    • [Supplemental material is available online at www.genome.org. Software binaries and instructions are available at http://www.bcgsc.ca/platform/bioinfo/software/abyss.]

    • Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.089532.108.

      • Received November 26, 2008.
      • Accepted February 24, 2009.
    • Freely available online through the Genome Research Open Access option.

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