YASS : Enhancing the sensitivity of DNA similarity search
Identifieur interne : 003F07 ( Crin/Corpus ); précédent : 003F06; suivant : 003F08YASS : Enhancing the sensitivity of DNA similarity search
Auteurs : Laurent Noé ; Gregory KucherovSource :
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Abstract
We present YASS -- a new tool for computing local similarities in DNA sequences. Similar to many existent algorithms (e.g. BLAST), YASS first searches for small patterns (``seeds'') shared by input sequences and then extends some of those (``hits'') into larger alignments. New features of YASS include, on the one hand, a new transition-constrained seed model and, on the other hand, a flexible group criterion of forming hits out of individual seeds. Computer experiments confirm that the program achieves a good sensitivity/selectivity trade-off compared to existing programs, which makes of YASS an efficient tool for comparing long DNA sequences, such as eukaryotic chromosomes.
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" wicri:score="322">YASS : Enhancing the sensitivity of DNA similarity search</title></titleStmt><publicationStmt><idno type="RBID">CRIN:noe04b</idno><date when="2004" year="2004">2004</date><idno type="wicri:Area/Crin/Corpus">003F07</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">YASS : Enhancing the sensitivity of DNA similarity search</title><author><name sortKey="Noe, Laurent" sort="Noe, Laurent" uniqKey="Noe L" first="Laurent" last="Noé">Laurent Noé</name></author><author><name sortKey="Kucherov, Gregory" sort="Kucherov, Gregory" uniqKey="Kucherov G" first="Gregory" last="Kucherov">Gregory Kucherov</name></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>dna local alignment search tool</term><term>transition constrained spaced seeds</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en" wicri:score="1833">We present YASS -- a new tool for computing local similarities in DNA sequences. Similar to many existent algorithms (e.g. BLAST), YASS first searches for small patterns (``seeds'') shared by input sequences and then extends some of those (``hits'') into larger alignments. New features of YASS include, on the one hand, a new transition-constrained seed model and, on the other hand, a flexible group criterion of forming hits out of individual seeds. Computer experiments confirm that the program achieves a good sensitivity/selectivity trade-off compared to existing programs, which makes of YASS an efficient tool for comparing long DNA sequences, such as eukaryotic chromosomes.</div></front></TEI><BibTex type="inproceedings"><ref>noe04b</ref><crinnumber>A04-R-221</crinnumber><category>3</category><equipe>ADAGE</equipe><author><e>Noé, Laurent</e><e>Kucherov, Gregory</e></author><title>YASS : Enhancing the sensitivity of DNA similarity search</title><booktitle>{Proceedings of the 4th International conference on Bioinformatics of Genome Regulation and Structure - BGRS'2004, Novosibirsk, Russia}</booktitle><year>2004</year><volume>2</volume><pages>289-292</pages><month>Jul</month><organization>Institute of Cytology and Genetics</organization><keywords><e>dna local alignment search tool</e><e>transition constrained spaced seeds</e></keywords><abstract>We present YASS -- a new tool for computing local similarities in DNA sequences. Similar to many existent algorithms (e.g. BLAST), YASS first searches for small patterns (``seeds'') shared by input sequences and then extends some of those (``hits'') into larger alignments. New features of YASS include, on the one hand, a new transition-constrained seed model and, on the other hand, a flexible group criterion of forming hits out of individual seeds. Computer experiments confirm that the program achieves a good sensitivity/selectivity trade-off compared to existing programs, which makes of YASS an efficient tool for comparing long DNA sequences, such as eukaryotic chromosomes.</abstract></BibTex></record>Pour manipuler ce document sous Unix (Dilib)
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