FSH: fast spaced seed hashing exploiting adjacent hashes
Identifieur interne : 000579 ( Pmc/Checkpoint ); précédent : 000578; suivant : 000580FSH: fast spaced seed hashing exploiting adjacent hashes
Auteurs : Samuele Girotto ; Matteo Comin ; Cinzia PizziSource :
- Algorithms for Molecular Biology : AMB [ 1748-7188 ] ; 2018.
Abstract
Patterns with wildcards in specified positions, namely
The method proposed in this paper, fast spaced-seed hashing (FSH), exploits the similarity of the hash values of spaced seeds computed at adjacent positions in the input sequence. In our experiments we compute the hash for each positions of metagenomics reads from several datasets, with respect to different spaced seeds. We also propose a generalized version of the algorithm for the simultaneous computation of multiple spaced seeds hashing. In the experiments, our algorithm can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.6
Spaced seed hashing is a routine task for several bioinformatics application. FSH allows to perform this task efficiently and raise the question of whether other hashing can be exploited to further improve the speed up. This has the potential of major impact in the field, making spaced seed applications not only accurate, but also faster and more efficient.
The software FSH is freely available for academic use at:
Url:
DOI: 10.1186/s13015-018-0125-4
PubMed: 29588651
PubMed Central: 5863468
Affiliations:
Links toward previous steps (curation, corpus...)
Links to Exploration step
PMC:5863468Le document en format XML
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<author><name sortKey="Girotto, Samuele" sort="Girotto, Samuele" uniqKey="Girotto S" first="Samuele" last="Girotto">Samuele Girotto</name>
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<author><name sortKey="Comin, Matteo" sort="Comin, Matteo" uniqKey="Comin M" first="Matteo" last="Comin">Matteo Comin</name>
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<author><name sortKey="Pizzi, Cinzia" sort="Pizzi, Cinzia" uniqKey="Pizzi C" first="Cinzia" last="Pizzi">Cinzia Pizzi</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">FSH: fast spaced seed hashing exploiting adjacent hashes</title>
<author><name sortKey="Girotto, Samuele" sort="Girotto, Samuele" uniqKey="Girotto S" first="Samuele" last="Girotto">Samuele Girotto</name>
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<author><name sortKey="Comin, Matteo" sort="Comin, Matteo" uniqKey="Comin M" first="Matteo" last="Comin">Matteo Comin</name>
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<author><name sortKey="Pizzi, Cinzia" sort="Pizzi, Cinzia" uniqKey="Pizzi C" first="Cinzia" last="Pizzi">Cinzia Pizzi</name>
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<series><title level="j">Algorithms for Molecular Biology : AMB</title>
<idno type="eISSN">1748-7188</idno>
<imprint><date when="2018">2018</date>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Patterns with wildcards in specified positions, namely <italic>spaced seeds</italic>
, are increasingly used instead of <italic>k</italic>
-mers in many bioinformatics applications that require indexing, querying and rapid similarity search, as they can provide better sensitivity. Many of these applications require to compute the hashing of each position in the input sequences with respect to the given spaced seed, or to multiple spaced seeds. While the hashing of <italic>k</italic>
-mers can be rapidly computed by exploiting the large overlap between consecutive <italic>k</italic>
-mers, spaced seeds hashing is usually computed from scratch for each position in the input sequence, thus resulting in slower processing.</p>
</sec>
<sec><title>Results</title>
<p> The method proposed in this paper, fast spaced-seed hashing (FSH), exploits the similarity of the hash values of spaced seeds computed at adjacent positions in the input sequence. In our experiments we compute the hash for each positions of metagenomics reads from several datasets, with respect to different spaced seeds. We also propose a generalized version of the algorithm for the simultaneous computation of multiple spaced seeds hashing. In the experiments, our algorithm can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.6<inline-formula id="IEq1"><alternatives><tex-math id="M1">\documentclass[12pt]{minimal}
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, depending on the structure of the spaced seed.</p>
</sec>
<sec><title>Conclusions</title>
<p>Spaced seed hashing is a routine task for several bioinformatics application. FSH allows to perform this task efficiently and raise the question of whether other hashing can be exploited to further improve the speed up. This has the potential of major impact in the field, making spaced seed applications not only accurate, but also faster and more efficient.</p>
</sec>
<sec><title>Availability</title>
<p>The software FSH is freely available for academic use at: <ext-link ext-link-type="uri" xlink:href="https://bitbucket.org/samu661/fsh/overview">https://bitbucket.org/samu661/fsh/overview</ext-link>
.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Algorithms Mol Biol</journal-id>
<journal-id journal-id-type="iso-abbrev">Algorithms Mol Biol</journal-id>
<journal-title-group><journal-title>Algorithms for Molecular Biology : AMB</journal-title>
</journal-title-group>
<issn pub-type="epub">1748-7188</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">29588651</article-id>
<article-id pub-id-type="pmc">5863468</article-id>
<article-id pub-id-type="publisher-id">125</article-id>
<article-id pub-id-type="doi">10.1186/s13015-018-0125-4</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research</subject>
</subj-group>
</article-categories>
<title-group><article-title>FSH: fast spaced seed hashing exploiting adjacent hashes</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Girotto</surname>
<given-names>Samuele</given-names>
</name>
<address><email>samuele.girotto@gmail.com</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>Comin</surname>
<given-names>Matteo</given-names>
</name>
<address><email>comin@dei.unipd.it</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-6616-4003</contrib-id>
<name><surname>Pizzi</surname>
<given-names>Cinzia</given-names>
</name>
<address><email>cinzia.pizzi@dei.unipd.it</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1757 3470</institution-id>
<institution-id institution-id-type="GRID">grid.5608.b</institution-id>
<institution>Department of Information Engineering,</institution>
<institution>University of Padova,</institution>
</institution-wrap>
via Gradenigo 6/A, Padova, Italy</aff>
</contrib-group>
<pub-date pub-type="epub"><day>22</day>
<month>3</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>22</day>
<month>3</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="collection"><year>2018</year>
</pub-date>
<volume>13</volume>
<elocation-id>8</elocation-id>
<history><date date-type="received"><day>31</day>
<month>10</month>
<year>2017</year>
</date>
<date date-type="accepted"><day>12</day>
<month>3</month>
<year>2018</year>
</date>
</history>
<permissions><copyright-statement>© The Author(s) 2018</copyright-statement>
<license license-type="OpenAccess"><license-p><bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p>Patterns with wildcards in specified positions, namely <italic>spaced seeds</italic>
, are increasingly used instead of <italic>k</italic>
-mers in many bioinformatics applications that require indexing, querying and rapid similarity search, as they can provide better sensitivity. Many of these applications require to compute the hashing of each position in the input sequences with respect to the given spaced seed, or to multiple spaced seeds. While the hashing of <italic>k</italic>
-mers can be rapidly computed by exploiting the large overlap between consecutive <italic>k</italic>
-mers, spaced seeds hashing is usually computed from scratch for each position in the input sequence, thus resulting in slower processing.</p>
</sec>
<sec><title>Results</title>
<p> The method proposed in this paper, fast spaced-seed hashing (FSH), exploits the similarity of the hash values of spaced seeds computed at adjacent positions in the input sequence. In our experiments we compute the hash for each positions of metagenomics reads from several datasets, with respect to different spaced seeds. We also propose a generalized version of the algorithm for the simultaneous computation of multiple spaced seeds hashing. In the experiments, our algorithm can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.6<inline-formula id="IEq1"><alternatives><tex-math id="M1">\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\times$$\end{document}</tex-math>
<mml:math id="M2"><mml:mo>×</mml:mo>
</mml:math>
<inline-graphic xlink:href="13015_2018_125_Article_IEq1.gif"></inline-graphic>
</alternatives>
</inline-formula>
to 5.3<inline-formula id="IEq2"><alternatives><tex-math id="M3">\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\times$$\end{document}</tex-math>
<mml:math id="M4"><mml:mo>×</mml:mo>
</mml:math>
<inline-graphic xlink:href="13015_2018_125_Article_IEq2.gif"></inline-graphic>
</alternatives>
</inline-formula>
, depending on the structure of the spaced seed.</p>
</sec>
<sec><title>Conclusions</title>
<p>Spaced seed hashing is a routine task for several bioinformatics application. FSH allows to perform this task efficiently and raise the question of whether other hashing can be exploited to further improve the speed up. This has the potential of major impact in the field, making spaced seed applications not only accurate, but also faster and more efficient.</p>
</sec>
<sec><title>Availability</title>
<p>The software FSH is freely available for academic use at: <ext-link ext-link-type="uri" xlink:href="https://bitbucket.org/samu661/fsh/overview">https://bitbucket.org/samu661/fsh/overview</ext-link>
.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Spaced seeds</kwd>
<kwd>K-mers</kwd>
<kwd>Efficient hashing</kwd>
</kwd-group>
<funding-group><award-group><funding-source><institution>MIUR - Ministero dell’Istruzione dell’Università e della Ricerca</institution>
</funding-source>
<award-id>PRIN20122F87B2</award-id>
<principal-award-recipient><name><surname>Pizzi</surname>
<given-names>Cinzia</given-names>
</name>
</principal-award-recipient>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2018</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations><list></list>
<tree><noCountry><name sortKey="Comin, Matteo" sort="Comin, Matteo" uniqKey="Comin M" first="Matteo" last="Comin">Matteo Comin</name>
<name sortKey="Girotto, Samuele" sort="Girotto, Samuele" uniqKey="Girotto S" first="Samuele" last="Girotto">Samuele Girotto</name>
<name sortKey="Pizzi, Cinzia" sort="Pizzi, Cinzia" uniqKey="Pizzi C" first="Cinzia" last="Pizzi">Cinzia Pizzi</name>
</noCountry>
</tree>
</affiliations>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/MersV1/Data/Pmc/Checkpoint
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000579 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Pmc/Checkpoint/biblio.hfd -nk 000579 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Sante |area= MersV1 |flux= Pmc |étape= Checkpoint |type= RBID |clé= PMC:5863468 |texte= FSH: fast spaced seed hashing exploiting adjacent hashes }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Pmc/Checkpoint/RBID.i -Sk "pubmed:29588651" \ | HfdSelect -Kh $EXPLOR_AREA/Data/Pmc/Checkpoint/biblio.hfd \ | NlmPubMed2Wicri -a MersV1
This area was generated with Dilib version V0.6.33. |