Novel biologically relevant small RNA-sequencing alignment tool LevenMap for alignment to database of non-coding RNAs

A crucial aspect of the bioinformatics workflow in small RNA-sequencing is the alignment of reads to a database of reference ncRNAs. Alignment algorithms such as Bowtie, Burrows-Wheeler Aligner (BWA), and Spliced Transcripts Alignment to a Reference (STAR) - which are designed for aligning reads to a reference genome - are typically used. Aligning short RNA-sequenced reads to a database of non-coding RNAs (ncRNAs) is fundamentally a different task than aligning longer reads to a genome due to ncRNAs (i) having roughly the same number of nucleotides as the reads being aligned and (ii) being subsequences of other ncRNAs. To account for these differences, we developed the novel alignment algorithm LevenMap. Of all reads which exactly matched a reference ncRNA in a publicly available dataset, LevenMap aligned 100.0% of them to their respective ncRNA while all other aligners mapped less than 40% of these reads to their corresponding ncRNA. Furthermore, the mean ratio (length of read) / (length of corresponding reference ncRNA) of all aligned reads was 1.0 and 0.998 for LevenMap with at most zero and one mismatch(es) allowed, respectively; this ratio was no more than 0.51 for all other aligners. Overall, LevenMap is designed to account for the nuances of aligning small RNA-sequencing data to a database of reference ncRNAs and yields more biologically relevant counts compared to traditional aligners in this context. LevenMap is free and publicly available on GitHub: https://github.com/hdlugas/LevenMap.