Towards Selective-Alignment: Producing Accurate And Sensitive Alignments Using Quasi-Mapping

We introduce a novel algorithm for selectively aligning high-throughput sequencing reads to a transcriptome. This algorithm attempts to bridge the gap between fast “mapping” algorithms and more traditional alignment procedures. The former of these simply provide the transcripts, loci and orientations that likely generated a read (or, perhaps, simply the transcripts that are “compatible” with a read), while the latter produces the optimal edit distance and nucleotide-level correspondence between the read and reference sequences. We adopt a hybrid approach that is able to produce accurate alignments while still retaining much of the efficiency of fast mapping algorithms. To achieve this, we make fundamental modifications to an existing mapping algorithm, quasi-mapping, which increases the sensitivity of the procedure. Additionally, unlike the strategies adopted in most aligners which first align the ends of paired-end reads independently, we introduce a notion of co-mapping. This procedure exploits relevant information between the “hits” from the left and right ends of paired-end reads before full alignments or mappings for each are generated, which improves the efficiency of filtering likely-spurious alignments. Finally, we demonstrate the utility of selective alignment in improving the accuracy of efficient transcript-level quantification from RNA-seq reads. Specifically, we show that selective-alignment is able to resolve certain complex mapping scenarios that can confound existing fast mapping procedures, while simultaneously eliminating spurious alignments that fast mapping approaches can produce. Availability and implementation: Selective-alignment is implemented in C++11 as a part of RapMap, and is available as open source software, under GPL v3, at https://github.com/COMBINE-lab/RapMap/tree/selective-alignment.