A Fast Alignment-Free Approach for De Novo Detection of Protein Conserved Regions
Identifieur interne : 001755 ( Ncbi/Merge ); précédent : 001754; suivant : 001756A Fast Alignment-Free Approach for De Novo Detection of Protein Conserved Regions
Auteurs : Armen Abnousi [États-Unis] ; Shira L. Broschat [États-Unis] ; Ananth Kalyanaraman [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2016.
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Abstract
Identifying conserved regions in protein sequences is a fundamental operation, occurring in numerous sequence-driven analysis pipelines. It is used as a way to decode domain-rich regions within proteins, to compute protein clusters, to annotate sequence function, and to compute evolutionary relationships among protein sequences. A number of approaches exist for identifying and characterizing protein families based on their domains, and because domains represent conserved portions of a protein sequence, the primary computation involved in protein family characterization is identification of such conserved regions. However, identifying conserved regions from large collections (millions) of protein sequences presents significant challenges.
In this paper we present a new, alignment-free method for detecting conserved regions in protein sequences called NADDA (No-Alignment Domain Detection Algorithm). Our method exploits the abundance of exact matching short subsequences (
We have compared NADDA with Pfam and InterPro databases. For known domains annotated by Pfam, accuracy is 83%, sensitivity 96%, and specificity 44%. For sequences with new domains not present in the training set an average accuracy of 63% is achieved when compared to Pfam. A boost in results in comparison with InterPro demonstrates the ability of NADDA to capture conserved regions beyond those present in Pfam. We have also compared NADDA with ADDA and MKDOM2, assuming Pfam as ground-truth. On average NADDA shows comparable accuracy, more balanced sensitivity and specificity, and being alignment-free, is significantly faster. Excluding the one-time cost of training, runtimes on a single processor were 49s, 10,566s, and 456s for NADDA, ADDA, and MKDOM2, respectively, for a data set comprised of approximately 2500 sequences.
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DOI: 10.1371/journal.pone.0161338
PubMed: 27552220
PubMed Central: 4995020
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Broschat</name><affiliation wicri:level="2"><nlm:aff id="aff001"><addr-line>School of EECS, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="aff002"><addr-line>Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Paul G. 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Allen School for Global Animal Health, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Conserved Sequence (genetics)</term><term>Databases, Protein</term><term>Protein Domains</term><term>Protein Structure, Tertiary</term><term>Sequence Alignment</term><term>Sequence Analysis, Protein</term><term>Sequence Homology, Amino Acid</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Alignement de séquences</term><term>Analyse de séquence de protéine</term><term>Bases de données de protéines</term><term>Domaines protéiques</term><term>Logiciel</term><term>Similitude de séquences d'acides aminés</term><term>Structure tertiaire des protéines</term><term>Séquence conservée (génétique)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Conserved Sequence</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Séquence conservée</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Databases, Protein</term><term>Protein Domains</term><term>Protein Structure, Tertiary</term><term>Sequence Alignment</term><term>Sequence Analysis, Protein</term><term>Sequence Homology, Amino Acid</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Alignement de séquences</term><term>Analyse de séquence de protéine</term><term>Bases de données de protéines</term><term>Domaines protéiques</term><term>Logiciel</term><term>Similitude de séquences d'acides aminés</term><term>Structure tertiaire des protéines</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec id="sec001"><title>Background</title><p>Identifying conserved regions in protein sequences is a fundamental operation, occurring in numerous sequence-driven analysis pipelines. It is used as a way to decode domain-rich regions within proteins, to compute protein clusters, to annotate sequence function, and to compute evolutionary relationships among protein sequences. A number of approaches exist for identifying and characterizing protein families based on their domains, and because domains represent conserved portions of a protein sequence, the primary computation involved in protein family characterization is identification of such conserved regions. However, identifying conserved regions from large collections (millions) of protein sequences presents significant challenges.</p></sec><sec id="sec002"><title>Methods</title><p>In this paper we present a new, alignment-free method for detecting conserved regions in protein sequences called NADDA (No-Alignment Domain Detection Algorithm). Our method exploits the abundance of exact matching short subsequences (<italic>k</italic>-mers) to quickly detect conserved regions, and the power of machine learning is used to improve the prediction accuracy of detection. We present a parallel implementation of NADDA using the MapReduce framework and show that our method is highly scalable.</p></sec><sec id="sec003"><title>Results</title><p>We have compared NADDA with Pfam and InterPro databases. For known domains annotated by Pfam, accuracy is 83%, sensitivity 96%, and specificity 44%. For sequences with new domains not present in the training set an average accuracy of 63% is achieved when compared to Pfam. A boost in results in comparison with InterPro demonstrates the ability of NADDA to capture conserved regions beyond those present in Pfam. We have also compared NADDA with ADDA and MKDOM2, assuming Pfam as ground-truth. On average NADDA shows comparable accuracy, more balanced sensitivity and specificity, and being alignment-free, is significantly faster. Excluding the one-time cost of training, runtimes on a single processor were 49s, 10,566s, and 456s for NADDA, ADDA, and MKDOM2, respectively, for a data set comprised of approximately 2500 sequences.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Consortium, U" uniqKey="Consortium U">U Consortium</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Doolittle, Rf" uniqKey="Doolittle R">RF Doolittle</name></author><author><name sortKey="Bork, P" uniqKey="Bork P">P Bork</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Heger, A" uniqKey="Heger A">A Heger</name></author><author><name sortKey="Holm, L" uniqKey="Holm L">L Holm</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Portugaly, E" uniqKey="Portugaly E">E Portugaly</name></author><author><name sortKey="Harel, A" uniqKey="Harel A">A Harel</name></author><author><name 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Allen School for Global Animal Health, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="aff003"><addr-line>Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Kalyanaraman, Ananth" sort="Kalyanaraman, Ananth" uniqKey="Kalyanaraman A" first="Ananth" last="Kalyanaraman">Ananth Kalyanaraman</name><affiliation wicri:level="2"><nlm:aff id="aff001"><addr-line>School of EECS, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="aff002"><addr-line>Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">27552220</idno><idno type="pmc">4995020</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995020</idno><idno type="RBID">PMC:4995020</idno><idno type="doi">10.1371/journal.pone.0161338</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">001025</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001025</idno><idno type="wicri:Area/Pmc/Curation">001025</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">001025</idno><idno type="wicri:Area/Pmc/Checkpoint">000C74</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000C74</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A Fast Alignment-Free Approach for De Novo Detection of Protein Conserved Regions</title><author><name sortKey="Abnousi, Armen" sort="Abnousi, Armen" uniqKey="Abnousi A" first="Armen" last="Abnousi">Armen Abnousi</name><affiliation wicri:level="2"><nlm:aff id="aff001"><addr-line>School of EECS, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Broschat, Shira L" sort="Broschat, Shira L" uniqKey="Broschat S" first="Shira L." last="Broschat">Shira L. Broschat</name><affiliation wicri:level="2"><nlm:aff id="aff001"><addr-line>School of EECS, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="aff002"><addr-line>Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="aff003"><addr-line>Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Kalyanaraman, Ananth" sort="Kalyanaraman, Ananth" uniqKey="Kalyanaraman A" first="Ananth" last="Kalyanaraman">Ananth Kalyanaraman</name><affiliation wicri:level="2"><nlm:aff id="aff001"><addr-line>School of EECS, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="aff002"><addr-line>Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec id="sec001"><title>Background</title><p>Identifying conserved regions in protein sequences is a fundamental operation, occurring in numerous sequence-driven analysis pipelines. It is used as a way to decode domain-rich regions within proteins, to compute protein clusters, to annotate sequence function, and to compute evolutionary relationships among protein sequences. A number of approaches exist for identifying and characterizing protein families based on their domains, and because domains represent conserved portions of a protein sequence, the primary computation involved in protein family characterization is identification of such conserved regions. However, identifying conserved regions from large collections (millions) of protein sequences presents significant challenges.</p></sec><sec id="sec002"><title>Methods</title><p>In this paper we present a new, alignment-free method for detecting conserved regions in protein sequences called NADDA (No-Alignment Domain Detection Algorithm). Our method exploits the abundance of exact matching short subsequences (<italic>k</italic>-mers) to quickly detect conserved regions, and the power of machine learning is used to improve the prediction accuracy of detection. We present a parallel implementation of NADDA using the MapReduce framework and show that our method is highly scalable.</p></sec><sec id="sec003"><title>Results</title><p>We have compared NADDA with Pfam and InterPro databases. For known domains annotated by Pfam, accuracy is 83%, sensitivity 96%, and specificity 44%. For sequences with new domains not present in the training set an average accuracy of 63% is achieved when compared to Pfam. A boost in results in comparison with InterPro demonstrates the ability of NADDA to capture conserved regions beyond those present in Pfam. We have also compared NADDA with ADDA and MKDOM2, assuming Pfam as ground-truth. On average NADDA shows comparable accuracy, more balanced sensitivity and specificity, and being alignment-free, is significantly faster. Excluding the one-time cost of training, runtimes on a single processor were 49s, 10,566s, and 456s for NADDA, ADDA, and MKDOM2, respectively, for a data set comprised of approximately 2500 sequences.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Consortium, U" uniqKey="Consortium U">U Consortium</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Doolittle, Rf" uniqKey="Doolittle R">RF Doolittle</name></author><author><name sortKey="Bork, P" uniqKey="Bork P">P Bork</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Heger, A" uniqKey="Heger A">A Heger</name></author><author><name sortKey="Holm, L" uniqKey="Holm L">L Holm</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Portugaly, E" uniqKey="Portugaly E">E Portugaly</name></author><author><name sortKey="Harel, A" uniqKey="Harel A">A Harel</name></author><author><name 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uniqKey="Miller W">W Miller</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gouzy, J" uniqKey="Gouzy J">J Gouzy</name></author><author><name sortKey="Eugene, P" uniqKey="Eugene P">P Eugene</name></author><author><name sortKey="Greene, Ea" uniqKey="Greene E">EA Greene</name></author><author><name sortKey="Kahn, D" uniqKey="Kahn D">D Kahn</name></author><author><name sortKey="Corpet, F" uniqKey="Corpet F">F Corpet</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sonnhammer, El" uniqKey="Sonnhammer E">EL Sonnhammer</name></author><author><name sortKey="Kahn, D" uniqKey="Kahn D">D Kahn</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bru, C" uniqKey="Bru C">C Bru</name></author><author><name sortKey="Courcelle, E" uniqKey="Courcelle E">E Courcelle</name></author><author><name sortKey="Carrere, S" uniqKey="Carrere S">S Carrère</name></author><author><name sortKey="Beausse, Y" 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Eh" uniqKey="Han E">EH Han</name></author><author><name sortKey="Kumar, V" uniqKey="Kumar V">V Kumar</name></author><author><name sortKey="Singh, V" uniqKey="Singh V">V Singh</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI></pmc><pubmed><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Fast Alignment-Free Approach for De Novo Detection of Protein Conserved Regions.</title><author><name sortKey="Abnousi, Armen" sort="Abnousi, Armen" uniqKey="Abnousi A" first="Armen" last="Abnousi">Armen Abnousi</name><affiliation wicri:level="2"><nlm:affiliation>School of EECS, Washington State University, Pullman, WA, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Broschat, Shira L" sort="Broschat, Shira L" uniqKey="Broschat S" first="Shira L" last="Broschat">Shira L. Broschat</name><affiliation wicri:level="2"><nlm:affiliation>School of EECS, Washington State University, Pullman, WA, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Kalyanaraman, Ananth" sort="Kalyanaraman, Ananth" uniqKey="Kalyanaraman A" first="Ananth" last="Kalyanaraman">Ananth Kalyanaraman</name><affiliation wicri:level="2"><nlm:affiliation>School of EECS, Washington State University, Pullman, WA, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2016">2016</date><idno type="RBID">pubmed:27552220</idno><idno type="pmid">27552220</idno><idno type="doi">10.1371/journal.pone.0161338</idno><idno type="wicri:Area/PubMed/Corpus">000F93</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000F93</idno><idno type="wicri:Area/PubMed/Curation">000F93</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000F93</idno><idno type="wicri:Area/PubMed/Checkpoint">001251</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">001251</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A Fast Alignment-Free Approach for De Novo Detection of Protein Conserved Regions.</title><author><name sortKey="Abnousi, Armen" sort="Abnousi, Armen" uniqKey="Abnousi A" first="Armen" last="Abnousi">Armen Abnousi</name><affiliation wicri:level="2"><nlm:affiliation>School of EECS, Washington State University, Pullman, WA, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Broschat, Shira L" sort="Broschat, Shira L" uniqKey="Broschat S" first="Shira L" last="Broschat">Shira L. Broschat</name><affiliation wicri:level="2"><nlm:affiliation>School of EECS, Washington State University, Pullman, WA, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Kalyanaraman, Ananth" sort="Kalyanaraman, Ananth" uniqKey="Kalyanaraman A" first="Ananth" last="Kalyanaraman">Ananth Kalyanaraman</name><affiliation wicri:level="2"><nlm:affiliation>School of EECS, Washington State University, Pullman, WA, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of EECS, Washington State University, Pullman, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Conserved Sequence (genetics)</term><term>Databases, Protein</term><term>Protein Domains</term><term>Protein Structure, Tertiary</term><term>Sequence Alignment</term><term>Sequence Analysis, Protein</term><term>Sequence Homology, Amino Acid</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Alignement de séquences</term><term>Analyse de séquence de protéine</term><term>Bases de données de protéines</term><term>Domaines protéiques</term><term>Logiciel</term><term>Similitude de séquences d'acides aminés</term><term>Structure tertiaire des protéines</term><term>Séquence conservée (génétique)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Conserved Sequence</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Séquence conservée</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Databases, Protein</term><term>Protein Domains</term><term>Protein Structure, Tertiary</term><term>Sequence Alignment</term><term>Sequence Analysis, Protein</term><term>Sequence Homology, Amino Acid</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Alignement de séquences</term><term>Analyse de séquence de protéine</term><term>Bases de données de protéines</term><term>Domaines protéiques</term><term>Logiciel</term><term>Similitude de séquences d'acides aminés</term><term>Structure tertiaire des protéines</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Identifying conserved regions in protein sequences is a fundamental operation, occurring in numerous sequence-driven analysis pipelines. It is used as a way to decode domain-rich regions within proteins, to compute protein clusters, to annotate sequence function, and to compute evolutionary relationships among protein sequences. A number of approaches exist for identifying and characterizing protein families based on their domains, and because domains represent conserved portions of a protein sequence, the primary computation involved in protein family characterization is identification of such conserved regions. However, identifying conserved regions from large collections (millions) of protein sequences presents significant challenges.</div></front></TEI></pubmed></double></record>Pour manipuler ce document sous Unix (Dilib)
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