Accurate identification of genetic variants such as single nucleotide polymorphisms (SNPs) or RNA editing sites from RNA-Seq reads is important, yet challenging, because it necessitates a very low false positive rate in read mapping. Although many read aligners are available, no single aligner was specifically developed or tested as an effective tool for SNP and RNA editing prediction.
Researchers at UCLA have developed RASER, an accurate read aligner with novel mapping schemes and index tree structure that aims to reduce false positive mappings due to existence of highly similar regions. They demonstrate that RASER shows the best mapping accuracy compared to other popular algorithms and highest sensitivity in identifying multiply mapped reads. As a result, RASER displays superb efficacy in unbiased mapping of the alternative alleles of SNPs and in identification of RNA editing sites.
Index building. s+16 bases at every k steps of genome and/or transcriptome reference sequences are indexed, where s and k are user parameters with default values 8 and 4, respectively. Nodes of level (lv) n (n > 0) store positions of sequences of length (s+n) within the reference. For example, the positions of ‘AACTGCTTT’ are stored in the lv1 node (which is a leaf node). The node is a leaf node if the number of stored positions is <32 or the maximum number of levels (16) is reached.
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