General Information
| Description | A fast and accurate SIMD-friendly filter to accelerate alignment verification in read mapping |
| Homepage | https://github.com/xhongyi/SHD_code |
Publications
Shifted Hamming distance: a fast and accurate SIMD-friendly filter to accelerate alignment verification in read mapping
Motivation
: Calculating the edit-distance (i.e. minimum number of insertions, deletions and substitutions) between short DNA sequences is the primary task performed by seed-and-extend based mappers, which compare billions of sequences. In practice, only sequence pairs with a small edit-distance provide useful scientific data. However, the majority of sequence pairs analyzed by seed-and-extend based mappers differ by significantly more errors than what is typically allowed. Such error-abundant sequence pairs needlessly waste resources and severely hinder the performance of read mappers. Therefore, it is crucial to develop a fast and accurate filter that can rapidly and efficiently detect error-abundant string pairs and remove them from consideration before more computationally expensive methods are used.
Results
: We present a simple and efficient algorithm, Shifted Hamming Distance (SHD), which accelerates the alignment verification procedure in read mapping, by quickly filtering out error-abundant sequence pairs using bit-parallel and SIMD-parallel operations. SHD only filters string pairs that contain more errors than a user-defined threshold, making it fully comprehensive. It also maintains high accuracy with moderate error threshold (up to 5% of the string length) while achieving a 3-fold speedup over the best previous algorithm (Gene Myers’s bit-vector algorithm). SHD is compatible with all mappers that perform sequence alignment for verification.
Availability and implementation
: We provide an implementation of SHD in C with Intel SSE instructions at: https://github.com/CMU-SAFARI/SHD.
Contact
: hxin@cmu.edu, calkan@cs.bilkent.edu.tr or onur@cmu.edu
Supplementary information
: Supplementary data are available at Bioinformatics online.
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