Alignment-free comparison of next-generation sequencing data using compression-based distance measures

摘要

Enormous volumes of short reads data from next-generation sequencing (NGS)technologies have posed new challenges to the area of genomic sequencecomparison. The multiple sequence alignment approach is hardly applicable to NGS data dueto the challenging problem of short read assembly. Thus alignment-free methods need to be developed for the comparison of NGSsamples of short reads. Recently, new $k$-mer based distance measures such as {\it CVTree},$d_{2}^{S}$, {\it co-phylog} have been proposed to address this problem. However, those distances depend considerably on the parameter $k$, and how tochoose the optimal $k$ is not trivial since it may depend on different aspectsof the sequence data. Hence, in this paper we consider an alternative parameter-free approach:compression-based distance measures. These measures have shown impressive performance on long genome sequences inprevious studies, but they have not been tested on NGS short reads. In this study we perform extensive validation and show that thecompression-based distances are highly consistent with those distances obtainedfrom the $k$-mer based methods, from the alignment-based approach, and fromexisting benchmarks in the literature. Moreover, as these measures are parameter-free, no optimization is requiredand they still perform consistently well on multiple types of sequence data,for different kinds of species and taxonomy levels. The compression-based distance measures are assembly-free, alignment-free,parameter-free, and thus represent useful tools for the comparison of longgenome sequences and NGS samples of short reads.