LAF: Logic Alignment Free and its application to bacterial genomes classification.
Identifieur interne : 001349 ( PubMed/Curation ); précédent : 001348; suivant : 001350LAF: 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 (k-mers) that occur in the analyzed sequences. In this paper, we present Logic Alignment Free (LAF), 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 k-mers whose relative frequencies are used to build classification models as disjunctive-normal-form logic formulas (if-then rules). We apply LAF 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 k-mers. State of the art methods to adjust the frequency of k-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 LAF is effective in identifying it.
DOI: 10.1186/s13040-015-0073-1
PubMed: 26664519
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Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185 Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Department of Engineering, Uninettuno International University, Corso Vittorio Emanuele II, 39, Rome, 00186 Italy ; Institute of Systems Analysis and Computer Science "A. Ruberti", National Research Council, Via dei Taurini 19, Rome</wicri:regionArea></affiliation></author><author><name sortKey="Cunial, Fabio" sort="Cunial, Fabio" uniqKey="Cunial F" first="Fabio" last="Cunial">Fabio Cunial</name><affiliation wicri:level="4"><nlm:affiliation>Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, P.O. Box 68 (Gustaf Hällströmin katu 2b), Helsinki, FI-00014 Finland.</nlm:affiliation><orgName type="university">Université d'Helsinki</orgName><country>Finlande</country><placeName><settlement type="city">Helsinki</settlement><region type="région" nuts="2">Uusimaa</region></placeName></affiliation></author><author><name sortKey="Felici, Giovanni" sort="Felici, Giovanni" uniqKey="Felici G" first="Giovanni" last="Felici">Giovanni Felici</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Systems Analysis and Computer Science "A. Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185 Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Institute of Systems Analysis and Computer Science "A. Ruberti", National Research Council, Via dei Taurini 19, Rome</wicri:regionArea></affiliation></author></analytic><series><title level="j">BioData mining</title><idno type="ISSN">1756-0381</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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 (k-mers) that occur in the analyzed sequences. In this paper, we present Logic Alignment Free (LAF), 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 k-mers whose relative frequencies are used to build classification models as disjunctive-normal-form logic formulas (if-then rules). We apply LAF 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 k-mers. State of the art methods to adjust the frequency of k-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 LAF is effective in identifying it. </div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">26664519</PMID><DateCompleted><Year>2015</Year><Month>12</Month><Day>15</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">1756-0381</ISSN><JournalIssue CitedMedium="Print"><Volume>8</Volume><PubDate><Year>2015</Year></PubDate></JournalIssue><Title>BioData mining</Title><ISOAbbreviation>BioData Min</ISOAbbreviation></Journal><ArticleTitle>LAF: Logic Alignment Free and its application to bacterial genomes classification.</ArticleTitle><Pagination><MedlinePgn>39</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s13040-015-0073-1</ELocationID><Abstract><AbstractText>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 (k-mers) that occur in the analyzed sequences. In this paper, we present Logic Alignment Free (LAF), 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 k-mers whose relative frequencies are used to build classification models as disjunctive-normal-form logic formulas (if-then rules). We apply LAF 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 k-mers. State of the art methods to adjust the frequency of k-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 LAF is effective in identifying it. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Weitschek</LastName><ForeName>Emanuel</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Department of Engineering, Uninettuno International University, Corso Vittorio Emanuele II, 39, Rome, 00186 Italy ; Institute of Systems Analysis and Computer Science "A. Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185 Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cunial</LastName><ForeName>Fabio</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, P.O. Box 68 (Gustaf Hällströmin katu 2b), Helsinki, FI-00014 Finland.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Felici</LastName><ForeName>Giovanni</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Institute of Systems Analysis and Computer Science "A. 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