RT Journal Article
SR Electronic
T1 Improving the performance of minimizers and winnowing schemes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 104075
DO 10.1101/104075
A1 Guillaume Marçais
A1 David Pellow
A1 Daniel Bork
A1 Yaron Orenstein
A1 Ron Shamir
A1 Carl Kingsford
YR 2017
UL http://biorxiv.org/content/early/2017/01/29/104075.abstract
AB The minimizers scheme is a method for selecting k-mers from sequences. It is used in many bioinformatics software tools to bin comparable sequences or to sample a sequence in a deterministic fashion at approximately regular intervals, in order to reduce memory consumption and processing time. Although very useful, the minimizers selection procedure has undesirable behaviors (e.g., too many k-mers are selected when processing certain sequences). Some of these problems were already known to the authors of the minimizers technique, and the natural lexicographic ordering of k-mers used by minimizers was recognized as their origin. Many software tools using minimizers employ ad hoc variations of the lexicographic order to alleviate those issues.We provide an in-depth analysis of the effect of k-mer ordering on the performance of the minimizers technique. By using small universal hitting sets (a recently defined concept), we show how to significantly improve the performance of minimizers and avoid some of its worse behaviors. Based on these results, we encourage bioinformatics software developers to use an ordering based on a universal hitting set or, if not possible, a randomized ordering, rather than the lexicographic order. This analysis also settles negatively a conjecture (by Schleimer et al.) on the expected density of minimizers in a random sequence.The software used for this analysis is available on GitHub: https://github.com/gmarcais/minimizers.git.Contact: gmarcais{at}cs.cmu.edu