Abstract
BackgroundShort sequence mapping methods for Next Generation Sequencing consist on a combination of seeding techniques followed by local alignment based on dynamic programming approaches. Most seeding algorithms are based on backward search alignment, using the Burrows Wheeler Transform, the Ferragina and Manzini Index or Suffix Arrays. All these backward search algorithms have excellent performance, but their computational cost highly increases when allowing errors. In this paper, we discuss an inexact mapping algorithm based on pruning strategies for search tree exploration over genomic data.ResultsThe proposed algorithm achieves a 13x speed-up over similar algorithms when allowing 6 base errors, including insertions, deletions and mismatches. This algorithm can deal with 400 bps reads with up to 9 errors in a high quality Illumina dataset. In this example, the algorithm works as a preprocessor that reduces by 55% the number of reads to be aligned. Depending on the aligner the overall execution time is reduced between 20–40%.ConclusionsAlthough not intended as a complete sequence mapping tool, the proposed algorithm could be used as a preprocessing step to modern sequence mappers. This step significantly reduces the number reads to be aligned, accelerating overall alignment time. Furthermore, this algorithm could be used for accelerating the seeding step of already available sequence mappers. In addition, an out-of-core index has been implemented for working with large genomes on systems without expensive memory configurations.
Highlights
Short sequence mapping methods for Generation Sequencing consist on a combination of seeding techniques followed by local alignment based on dynamic programming approaches
Comparison with other FM-Index only algorithms As we stated before, our algorithm is not intended as a full sequence mapper, only a preprocessing step for modern sequence mappers
The purpose of this study is to provide a fair comparison against similar algorithms based only on FM-Index backward search, performing the experiments under the same input, execution arguments and system environment
Summary
Short sequence mapping methods for Generation Sequencing consist on a combination of seeding techniques followed by local alignment based on dynamic programming approaches. Differences between reads and the reference appear, due to the natural genetic variability or failures in the sequence digitalisation phase For this reason, a mapping algorithm must allow a certain number of errors, Several inexact alignment solutions available in the literature focus on dynamic programming approaches, like the Smith-Waterman Algorithm [2,3] (SW) or the Hidden Markov Models [4] (HMM). A mapping algorithm must allow a certain number of errors, Several inexact alignment solutions available in the literature focus on dynamic programming approaches, like the Smith-Waterman Algorithm [2,3] (SW) or the Hidden Markov Models [4] (HMM) Their computational complexity depends on the length of the read multiplied by the length of the reference genome
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