Fast inexact mapping using advanced tree exploration on backward search methods

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.

A new parallel pipeline for DNA methylation analysis of long reads datasets

“…Later, it was used as a backward search method (from the last character of the query string to the first one) to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing errors (mismatches), insertions or deletions (EIDs) [ 8 ]. Many software tools and BWT implementations have been proposed for sequence alignment [ 14 , 15 ]. Although the computational cost of the BWT increases with the number of allowed EIDs due to the search tree exploration process, most of the existing implementations use pruning or greedy schemes to avoid an exponential cost [ 8 , 10 , 15 ].…”

“…Many software tools and BWT implementations have been proposed for sequence alignment [ 14 , 15 ]. Although the computational cost of the BWT increases with the number of allowed EIDs due to the search tree exploration process, most of the existing implementations use pruning or greedy schemes to avoid an exponential cost [ 8 , 10 , 15 ].…”

OCMA: Fast, Memory-Efficient Factorization of Prohibitively Large Relationship Matrices

“…Mmap has been extensively used in many computing fields such as high-performance computing (Van Essen et al 2013; Song et al 2016), graph computation (Lin et al 2014), system virtual machine (Wang et al 2015), and databases (Lou and Ludewigt 2015). Mmap has also been used in bioinformatics in recent years, including the indexing of next-generating sequencing reads (Salavert et al 2016) and tree-based backward search (Salavert et al 2015).…”

Efficient approximate string matching techniques for sequence alignment

“…Despite the simplicity of algorithm 7, neighbourhood searches using other distances can become tricky. Unsurprisingly, there are still a number of recent publications [Salavert et al, 2015;Tennakoon et al, 2012] that struggle to capture all the combinations of mismatches, insertions and deletions trying to avoid repeated cases (e.g. insertion followed by deletion).…”