This newly submitted arxiv paper (h/t: @lexnederbragt) is interesting, but we are unable to connect between various logical flow. The authors first mention that compression-based alignment-free multiple sequence comparison methods could be better than k-mer based alignment-free multiple sequence comparison methods.
Another class of alignment-free distances arises from information theory, in particular, from data compression. Those distances are calculated from the relative information between the input sequences using Kolmogorov complexity [?, ?, ?], Lempel-Ziv complexity [?]. Unlike k-mer based distance measures which depend on the parameter k, compression-based distance measures are parameter-free and hence more consistent.
Then in the final section, it says -
One possible drawback of the compression-based distance measures is the running time. Faster compression tools, especially those developed speci?cally for NGS short reads such as BEETL [?], SCALCE [?], etc, can be applied to improve the running time.
When we combine those paragraphs, don’t we come a full circle to the introductory paragraphs of Jared Simpson’s SGA papers? After all, string graph algorithm could not work for NGS because of enormous number of pairwise comparisons, and a major contribution of Simpson/Durbin was to figure out the way to make that manageable.
May be we are missing something, and would like to hear from more knowledgeable people. The full abstract follows.
Enormous volumes of short reads data from next-generation sequencing (NGS) technologies have posed new challenges to the area of genomic sequence comparison. The multiple sequence alignment approach is hardly applicable to NGS data due to the challenging problem of short read assembly. Thus alignment-free methods need to be developed for the comparison of NGS samples of short reads. Recently, new k-mer based distance measures such as CVTree, dS^2, co-phylog have been proposed to address this problem. However, those distances depend considerably on the parameter k, and how to choose the optimal k is not trivial since it may depend on di?erent aspects of 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 in previous studies, but they have not been tested on NGS short reads. In this study we perform extensive validation and show that the compression-based distances are highly consistent with those distances obtained from the k-mer based methods, from the alignment-based approach, and from existing benchmarks in the literature. Moreover, as these measures are parameter-free, no optimization is required and they still perform consistently well on multiple types of sequence data, for di?erent 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 long genome sequences and NGS samples of short reads.