Statistically Consistent k-mer Methods for Phylogenetic Tree Reconstruction.
Identifieur interne : 000B82 ( Main/Exploration ); précédent : 000B81; suivant : 000B83Statistically Consistent k-mer Methods for Phylogenetic Tree Reconstruction.
Auteurs : Elizabeth S. Allman [États-Unis] ; John A. Rhodes [États-Unis] ; Seth Sullivant [États-Unis]Source :
- Journal of computational biology : a journal of computational molecular cell biology [ 1557-8666 ] ; 2017.
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Abstract
Frequencies of k-mers in sequences are sometimes used as a basis for inferring phylogenetic trees without first obtaining a multiple sequence alignment. We show that a standard approach of using the squared Euclidean distance between k-mer vectors to approximate a tree metric can be statistically inconsistent. To remedy this, we derive model-based distance corrections for orthologous sequences without gaps, which lead to consistent tree inference. The identifiability of model parameters from k-mer frequencies is also studied. Finally, we report simulations showing that the corrected distance outperforms many other k-mer methods, even when sequences are generated with an insertion and deletion process. These results have implications for multiple sequence alignment as well since k-mer methods are usually the first step in constructing a guide tree for such algorithms.
DOI: 10.1089/cmb.2015.0216
PubMed: 27387364
Affiliations:
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Rhodes</name><affiliation wicri:level="2"><nlm:affiliation>1 Department of Mathematics and Statistics, University of Alaska Fairbanks , Fairbanks, Alaska.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Alaska</region></placeName><wicri:cityArea>1 Department of Mathematics and Statistics, University of Alaska Fairbanks , Fairbanks</wicri:cityArea></affiliation></author><author><name sortKey="Sullivant, Seth" sort="Sullivant, Seth" uniqKey="Sullivant S" first="Seth" last="Sullivant">Seth Sullivant</name><affiliation wicri:level="2"><nlm:affiliation>2 Department of Mathematics, North Carolina State University , Raleigh, North Carolina.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Caroline du Nord</region></placeName><wicri:cityArea>2 Department of Mathematics, North Carolina State University , Raleigh</wicri:cityArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2017">2017</date><idno type="RBID">pubmed:27387364</idno><idno type="pmid">27387364</idno><idno type="doi">10.1089/cmb.2015.0216</idno><idno type="wicri:Area/PubMed/Corpus">001056</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001056</idno><idno type="wicri:Area/PubMed/Curation">001056</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001056</idno><idno type="wicri:Area/PubMed/Checkpoint">000B22</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000B22</idno><idno type="wicri:Area/Ncbi/Merge">001690</idno><idno type="wicri:Area/Ncbi/Curation">001690</idno><idno type="wicri:Area/Ncbi/Checkpoint">001690</idno><idno type="wicri:Area/Main/Merge">000B85</idno><idno type="wicri:Area/Main/Curation">000B82</idno><idno type="wicri:Area/Main/Exploration">000B82</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Statistically Consistent k-mer Methods for Phylogenetic Tree Reconstruction.</title><author><name sortKey="Allman, Elizabeth S" sort="Allman, Elizabeth S" uniqKey="Allman E" first="Elizabeth S" last="Allman">Elizabeth S. Allman</name><affiliation wicri:level="2"><nlm:affiliation>1 Department of Mathematics and Statistics, University of Alaska Fairbanks , Fairbanks, Alaska.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Alaska</region></placeName><wicri:cityArea>1 Department of Mathematics and Statistics, University of Alaska Fairbanks , Fairbanks</wicri:cityArea></affiliation></author><author><name sortKey="Rhodes, John A" sort="Rhodes, John A" uniqKey="Rhodes J" first="John A" last="Rhodes">John A. 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We show that a standard approach of using the squared Euclidean distance between k-mer vectors to approximate a tree metric can be statistically inconsistent. To remedy this, we derive model-based distance corrections for orthologous sequences without gaps, which lead to consistent tree inference. The identifiability of model parameters from k-mer frequencies is also studied. Finally, we report simulations showing that the corrected distance outperforms many other k-mer methods, even when sequences are generated with an insertion and deletion process. These results have implications for multiple sequence alignment as well since k-mer methods are usually the first step in constructing a guide tree for such algorithms.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Alaska</li><li>Caroline du Nord</li></region></list><tree><country name="États-Unis"><region name="Alaska"><name sortKey="Allman, Elizabeth S" sort="Allman, Elizabeth S" uniqKey="Allman E" first="Elizabeth S" last="Allman">Elizabeth S. Allman</name></region><name sortKey="Rhodes, John A" sort="Rhodes, John A" uniqKey="Rhodes J" first="John A" last="Rhodes">John A. Rhodes</name><name sortKey="Sullivant, Seth" sort="Sullivant, Seth" uniqKey="Sullivant S" first="Seth" last="Sullivant">Seth Sullivant</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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