MersV1, Main, Exploration, bibRecord, 000A57

Asymptotically optimal minimizers schemes

Identifieur interne : 000A57 ( Main/Exploration ); précédent : 000A56; suivant : 000A58

Asymptotically optimal minimizers schemes

Auteurs : Guillaume Marçais [États-Unis] ; Dan Deblasio [États-Unis] ; Carl Kingsford [États-Unis]

Source :

Bioinformatics [ 1367-4803 ] ; 2018.

RBID : PMC:6037127

Descripteurs français

KwdFr :
- Algorithmes, Biologie informatique ().
MESH :
- Algorithmes, Biologie informatique.

English descriptors

KwdEn :
- Algorithms, Computational Biology (methods).
MESH :
- methods : Computational Biology.
- Algorithms.

Abstract

AbstractMotivation

The minimizers technique is a method to sample k-mers that is used in many bioinformatics software to reduce computation, memory usage and run time. The number of applications using minimizers keeps on growing steadily. Despite its many uses, the theoretical understanding of minimizers is still very limited. In many applications, selecting as few k-mers as possible (i.e. having a low density) is beneficial. The density is highly dependent on the choice of the order on the k-mers. Different applications use different orders, but none of these orders are optimal. A better understanding of minimizers schemes, and the related local and forward schemes, will allow designing schemes with lower density and thereby making existing and future bioinformatics tools even more efficient.

Results

From the analysis of the asymptotic behavior of minimizers, forward and local schemes, we show that the previously believed lower bound on minimizers schemes does not hold, and that schemes with density lower than thought possible actually exist. The proof is constructive and leads to an efficient algorithm to compare k-mers. These orders are the first known orders that are asymptotically optimal. Additionally, we give improved bounds on the density achievable by the three type of schemes.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6037127

DOI: 10.1093/bioinformatics/bty258
PubMed: 29949995
PubMed Central: 6037127

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en"><title>Abstract</title>
<sec id="s1"><title>Motivation</title>
<p>The minimizers technique is a method to sample <italic>k</italic>
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-mers. Different applications use different orders, but none of these orders are optimal. A better understanding of minimizers schemes, and the related local and forward schemes, will allow designing schemes with lower density and thereby making existing and future bioinformatics tools even more efficient.</p>
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<p>From the analysis of the asymptotic behavior of minimizers, forward and local schemes, we show that the previously believed lower bound on minimizers schemes does not hold, and that schemes with density lower than thought possible actually exist. The proof is constructive and leads to an efficient algorithm to compare <italic>k</italic>
-mers. These orders are the first known orders that are asymptotically optimal. Additionally, we give improved bounds on the density achievable by the three type of schemes.</p>
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Asymptotically optimal minimizers schemes

Asymptotically optimal minimizers schemes

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

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Pour manipuler ce document sous Unix (Dilib)

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