Format

Send to

Choose Destination
BMC Bioinformatics. 2015 Jan 28;16:18. doi: 10.1186/s12859-014-0438-3.

Fast inexact mapping using advanced tree exploration on backward search methods.

Author information

1
GRyCAP department of I3M, Universitat Politècnica de València, Building 8B, Camino de vera s/n, Valencia, 46022, Spain. josator@i3m.upv.es.
2
GRyCAP department of I3M, Universitat Politècnica de València, Building 8B, Camino de vera s/n, Valencia, 46022, Spain. antodo@i3m.upv.es.
3
Bioinformatics department of Centro de Investigación Príncipe Felipe, Autopista del Saler 16, Valencia, 46012, Spain. jtarraga@cipf.es.
4
Bioinformatics department of Centro de Investigación Príncipe Felipe, Autopista del Saler 16, Valencia, 46012, Spain. imedina@cipf.es.
5
Bioinformatics department of Centro de Investigación Príncipe Felipe, Autopista del Saler 16, Valencia, 46012, Spain. jdopazo@cipf.es.
6
GRyCAP department of I3M, Universitat Politècnica de València, Building 8B, Camino de vera s/n, Valencia, 46022, Spain. iblanque@dsic.upv.es.

Abstract

BACKGROUND:

Short 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.

RESULTS:

The 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%.

CONCLUSIONS:

Although 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.

PMID:
25626517
PMCID:
PMC4384339
DOI:
10.1186/s12859-014-0438-3
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center