Homer, N., Merriman, B. & Nelson, S. F. BFAST: an alignment tool for large scale genome resequencing. PLoS One 4, e7767

Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
PLoS ONE (Impact Factor: 3.23). 11/2009; 4(11):e7767. DOI: 10.1371/journal.pone.0007767
Source: PubMed


The new generation of massively parallel DNA sequencers, combined with the challenge of whole human genome resequencing, result in the need for rapid and accurate alignment of billions of short DNA sequence reads to a large reference genome. Speed is obviously of great importance, but equally important is maintaining alignment accuracy of short reads, in the 25-100 base range, in the presence of errors and true biological variation.
We introduce a new algorithm specifically optimized for this task, as well as a freely available implementation, BFAST, which can align data produced by any of current sequencing platforms, allows for user-customizable levels of speed and accuracy, supports paired end data, and provides for efficient parallel and multi-threaded computation on a computer cluster. The new method is based on creating flexible, efficient whole genome indexes to rapidly map reads to candidate alignment locations, with arbitrary multiple independent indexes allowed to achieve robustness against read errors and sequence variants. The final local alignment uses a Smith-Waterman method, with gaps to support the detection of small indels.
We compare BFAST to a selection of large-scale alignment tools -- BLAT, MAQ, SHRiMP, and SOAP -- in terms of both speed and accuracy, using simulated and real-world datasets. We show BFAST can achieve substantially greater sensitivity of alignment in the context of errors and true variants, especially insertions and deletions, and minimize false mappings, while maintaining adequate speed compared to other current methods. We show BFAST can align the amount of data needed to fully resequence a human genome, one billion reads, with high sensitivity and accuracy, on a modest computer cluster in less than 24 hours. BFAST is available at (

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Available from: Stanley F Nelson, Jul 30, 2014
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    • "The location with the highest score is chosen as the alignment location for a short read. For example, BLAST [4] uses a hash table of all fixed length k-mers in the reference to find seeds, and a banded version of the Smith-Waterman algorithm to compute high scoring gapped alignments. RMAP [15] uses a hash table of non-overlapping k-mers of length m/(k þ 1) in the reads to find seeds. "
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    • "To detect similarities in bio-sequences, in the so called hit and extend strategy framework, spaced seeds are now a frequently used technique to define the hit (Keich et al., 2004). Several tools have been proposed that use spaced seeds (Chen et al., 2009, David et al., 2011, Harris, 2007, Homer et al., 2009, Ilie et al., 2013, Kiee lbasa et al., 2011, Li et al., 2004, Lin et al., 2008, Zhou et al., 2010), or to design spaced seeds (Buhler et al., 2005, Do Duc et al., 2012, Ilie et al., 2011, Kucherov et al., 2006, Marschall et al., 2012, Nuel, 2011). "
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