SNPs Detection by eBWT Positional Clustering

Background: Sequencing technologies keep on turning cheaper and faster, thus putting a growing pressure for data structures designed to eciently store raw data, and possibly perform analysis therein. In this view, there is a growing interest in alignment-free and reference-free variants calling methods that only make use of (suitably indexed) raw reads data. Results: We develop the positional clustering theory that (i) describes how the extended Burrows-Wheeler Transform (eBWT) of a collection of reads tends to cluster together bases that cover the same genome position, (ii) predicts the size of such clusters, and (iii) exhibits an elegant and precise LCP array based procedure to locate such clusters in the eBWT. Based on this theory, we designed and implemented an alignment-free and reference-free SNPs calling method, and we devised a consequent SNPs calling pipeline. Experiments on both synthetic and real data show that SNPs can be detected with a simple scan of the eBWT and LCP arrays as, in accordance with our theoretical framework, they are within clusters in the eBWT of the reads. Finally, our tool intrinsically performs a reference-free evaluation of its accuracy by returning the coverage of each SNP. Conclusions: Based on the results of the experiments on synthetic and real data, we conclude that the positional clustering framework can be eectively used for the problem of identifying SNPs, and it appears to be a promising approach for calling other type of variants directly on raw sequencing data. Availability: The software ebwt2snp is freely available for academic use at: https://github.com/nicolaprezza/ebwt2snp.