LAF: Logic Alignment Free and its application to bacterial genomes classification
Identifieur interne : 001726 ( Main/Exploration ); précédent : 001725; suivant : 001727LAF: Logic Alignment Free and its application to bacterial genomes classification
Auteurs : Emanuel Weitschek [Italie] ; Fabio Cunial [Finlande] ; Giovanni Felici [Italie]Source :
- BioData Mining [ 1756-0381 ] ; 2015.
Abstract
Alignment-free algorithms can be used to estimate the similarity of biological sequences and hence are often applied to the phylogenetic reconstruction of genomes. Most of these algorithms rely on comparing the frequency of all the distinct substrings of fixed length (
In this paper, we present Logic Alignment Free (
We apply
State of the art methods to adjust the frequency of
Url:
DOI: 10.1186/s13040-015-0073-1
PubMed: 26664519
PubMed Central: 4673791
Affiliations:
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">LAF: Logic Alignment Free and its application to bacterial genomes classification</title>
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<front><div type="abstract" xml:lang="en"><p>Alignment-free algorithms can be used to estimate the similarity of biological sequences and hence are often applied to the phylogenetic reconstruction of genomes. Most of these algorithms rely on comparing the frequency of all the distinct substrings of fixed length (<italic>k</italic>
-mers) that occur in the analyzed sequences.</p>
<p>In this paper, we present Logic Alignment Free (<sc>LAF</sc>
), a method that combines alignment-free techniques and rule-based classification algorithms in order to assign biological samples to their taxa. This method searches for a minimal subset of <italic>k</italic>
-mers whose relative frequencies are used to build classification models as disjunctive-normal-form logic formulas (<italic>if-then rules</italic>
).</p>
<p>We apply <sc>LAF</sc>
successfully to the classification of bacterial genomes to their corresponding taxonomy. In particular, we succeed in obtaining reliable classification at different taxonomic levels by extracting a handful of rules, each one based on the frequency of just few <italic>k</italic>
-mers.</p>
<p>State of the art methods to adjust the frequency of <italic>k</italic>
-mers to the character distribution of the underlying genomes have negligible impact on classification performance, suggesting that the signal of each class is strong and that <sc>LAF</sc>
is effective in identifying it.</p>
</div>
</front>
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