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Bowtie

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(4)
Aligns short read geared toward mammalian re-sequencing. Bowtie is based on a Burrows-Wheeler index based on the full-text minute-space (FM) index. It follows two steps: an initial, ungapped seed-finding stage that derives advantage from the speed and memory efficiency of the full-text minute index and a gapped extension stage that employs dynamic programming and benefits from the efficiency of single-instruction multiple-data (SIMD) parallel processing available on modern processors.

BWA / Burrows-Wheeler Aligner

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(3)
Maps low-divergent sequences against a large reference genome, such as the human genome. BWA consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. The first algorithm is designed for Illumina sequence reads up to 100bp, while the rest two for longer sequences ranged from 70bp to 1Mbp. BWA-MEM and BWA-SW share similar features such as long-read support and split alignment, but BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads.

BLAT / BLAST-Like Alignment Tool

Finds genomic sequences that match a protein or DNA sequence submitted by the user. BLAT is a very fast sequence alignment tool similar to BLAST typically used for searching similar sequences within the same or closely related species. It was developed to align millions of expressed sequence tags and mouse whole-genome random reads to the human genome at a higher speed. BLAT is commonly used to look up the location of a sequence in the genome or determine the exon structure of an mRNA, but expert users can run large batch jobs and make internal parameter sensitivity changes by installing command line it on Linux server.

LAST-TRAIN

Improves sequence alignment accuracy by inferring substitution and gap scores that fit the frequencies of substitutions, insertions, and deletions in a given dataset. LAST-TRAIN uses a standard iterative approach: it first aligns the sequences using some initial score parameters, then infers better score parameters from the alignments, then re-aligns and repeats until the parameters stop changing. It achieves adequate speed by an X-drop heuristic, depletes paralogs using LASTSPLIT, and allows different insertion and deletion parameters and non-strand-symmetric substitution parameters.