Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding
Identifieur interne : 000B18 ( Pmc/Curation ); précédent : 000B17; suivant : 000B19Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding
Auteurs : Xu Min [République populaire de Chine] ; Wanwen Zeng [République populaire de Chine] ; Ning Chen [République populaire de Chine] ; Ting Chen [République populaire de Chine, États-Unis] ; Rui Jiang [République populaire de Chine]Source :
- Bioinformatics [ 1367-4803 ] ; 2017.
Abstract
Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted
We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with
The source code can be downloaded from
Url:
DOI: 10.1093/bioinformatics/btx234
PubMed: 28881969
PubMed Central: 5870572
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PMC:5870572Le document en format XML
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-mer embedding</title>
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<affiliation wicri:level="1"><nlm:aff id="btx234-aff4">Program in Computational Biology and Bioinformatics, University of Southern California, CA, USA</nlm:aff>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Chromatin accessibility prediction via convolutional long short-term memory networks with <italic>k</italic>
-mer embedding</title>
<author><name sortKey="Min, Xu" sort="Min, Xu" uniqKey="Min X" first="Xu" last="Min">Xu Min</name>
<affiliation wicri:level="1"><nlm:aff id="btx234-aff1">MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="btx234-aff2">Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Zeng, Wanwen" sort="Zeng, Wanwen" uniqKey="Zeng W" first="Wanwen" last="Zeng">Wanwen Zeng</name>
<affiliation wicri:level="1"><nlm:aff id="btx234-aff1">MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="btx234-aff3">Department of Automation, Tsinghua University, Beijing, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Automation, Tsinghua University, Beijing</wicri:regionArea>
</affiliation>
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<author><name sortKey="Chen, Ning" sort="Chen, Ning" uniqKey="Chen N" first="Ning" last="Chen">Ning Chen</name>
<affiliation wicri:level="1"><nlm:aff id="btx234-aff1">MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing</wicri:regionArea>
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<affiliation wicri:level="1"><nlm:aff id="btx234-aff2">Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Chen, Ting" sort="Chen, Ting" uniqKey="Chen T" first="Ting" last="Chen">Ting Chen</name>
<affiliation wicri:level="1"><nlm:aff id="btx234-aff1">MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing</wicri:regionArea>
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<affiliation wicri:level="1"><nlm:aff id="btx234-aff2">Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing</wicri:regionArea>
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<affiliation wicri:level="1"><nlm:aff id="btx234-aff4">Program in Computational Biology and Bioinformatics, University of Southern California, CA, USA</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Program in Computational Biology and Bioinformatics, University of Southern California, CA</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Jiang, Rui" sort="Jiang, Rui" uniqKey="Jiang R" first="Rui" last="Jiang">Rui Jiang</name>
<affiliation wicri:level="1"><nlm:aff id="btx234-aff1">MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="btx234-aff3">Department of Automation, Tsinghua University, Beijing, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Department of Automation, Tsinghua University, Beijing</wicri:regionArea>
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<series><title level="j">Bioinformatics</title>
<idno type="ISSN">1367-4803</idno>
<idno type="eISSN">1367-4811</idno>
<imprint><date when="2017">2017</date>
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<front><div type="abstract" xml:lang="en"><title>Abstract</title>
<sec id="SA1"><title>Motivation</title>
<p>Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted <italic>k</italic>
-mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful <italic>k</italic>
-mer co-occurrence information with recent advances in deep learning.</p>
</sec>
<sec id="SA2"><title>Results</title>
<p>We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with <italic>k</italic>
-mer embedding. We first split DNA sequences into <italic>k</italic>
-mers and pre-train <italic>k</italic>
-mer embedding vectors based on the co-occurrence matrix of <italic>k</italic>
-mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that <italic>k</italic>
-mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility.</p>
</sec>
<sec id="SA3"><title>Availability and implementation</title>
<p>The source code can be downloaded from <ext-link ext-link-type="uri" xlink:href="https://github.com/minxueric/ismb2017_lstm">https://github.com/minxueric/ismb2017_lstm</ext-link>
.</p>
</sec>
<sec id="SA4"><title>Supplementary information</title>
<p><xref ref-type="supplementary-material" rid="sup1">Supplementary materials</xref>
are available at <italic>Bioinformatics</italic>
online.</p>
</sec>
</div>
</front>
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<front><journal-meta><journal-id journal-id-type="nlm-ta">Bioinformatics</journal-id>
<journal-id journal-id-type="iso-abbrev">Bioinformatics</journal-id>
<journal-id journal-id-type="publisher-id">bioinformatics</journal-id>
<journal-title-group><journal-title>Bioinformatics</journal-title>
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<issn pub-type="ppub">1367-4803</issn>
<issn pub-type="epub">1367-4811</issn>
<publisher><publisher-name>Oxford University Press</publisher-name>
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<article-meta><article-id pub-id-type="pmid">28881969</article-id>
<article-id pub-id-type="pmc">5870572</article-id>
<article-id pub-id-type="doi">10.1093/bioinformatics/btx234</article-id>
<article-id pub-id-type="publisher-id">btx234</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Ismb/Eccb 2017: The 25th Annual Conference Intelligent Systems for Molecular Biology Held Jointly with the 16th Annual European Conference on Computational Biology, Prague, Czech Republic, July 21–25, 2017</subject>
<subj-group subj-group-type="category-toc-heading"><subject>Hitseq</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>Chromatin accessibility prediction via convolutional long short-term memory networks with <italic>k</italic>
-mer embedding</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Min</surname>
<given-names>Xu</given-names>
</name>
<xref ref-type="aff" rid="btx234-aff1">1</xref>
<xref ref-type="aff" rid="btx234-aff2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zeng</surname>
<given-names>Wanwen</given-names>
</name>
<xref ref-type="aff" rid="btx234-aff1">1</xref>
<xref ref-type="aff" rid="btx234-aff3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chen</surname>
<given-names>Ning</given-names>
</name>
<xref ref-type="aff" rid="btx234-aff1">1</xref>
<xref ref-type="aff" rid="btx234-aff2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chen</surname>
<given-names>Ting</given-names>
</name>
<xref ref-type="aff" rid="btx234-aff1">1</xref>
<xref ref-type="aff" rid="btx234-aff2">2</xref>
<xref ref-type="aff" rid="btx234-aff4">4</xref>
<xref ref-type="corresp" rid="btx234-cor1"></xref>
<pmc-comment>tingchen@tsinghua.edu.cn </pmc-comment>
</contrib>
<contrib contrib-type="author"><name><surname>Jiang</surname>
<given-names>Rui</given-names>
</name>
<xref ref-type="aff" rid="btx234-aff1">1</xref>
<xref ref-type="aff" rid="btx234-aff3">3</xref>
<xref ref-type="corresp" rid="btx234-cor1"></xref>
<pmc-comment>ruijiang@tsinghua.edu.cn </pmc-comment>
</contrib>
</contrib-group>
<aff id="btx234-aff1"><label>1</label>
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China</aff>
<aff id="btx234-aff2"><label>2</label>
Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing, China</aff>
<aff id="btx234-aff3"><label>3</label>
Department of Automation, Tsinghua University, Beijing, China</aff>
<aff id="btx234-aff4"><label>4</label>
Program in Computational Biology and Bioinformatics, University of Southern California, CA, USA</aff>
<author-notes><corresp id="btx234-cor1">To whom correspondence should be addressed. Email: <email>tingchen@tsinghua.edu.cn</email>
or <email>ruijiang@tsinghua.edu.cn</email>
</corresp>
</author-notes>
<pub-date pub-type="ppub"><day>15</day>
<month>7</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub" iso-8601-date="2017-07-12"><day>12</day>
<month>7</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>12</day>
<month>7</month>
<year>2017</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on the . </pmc-comment>
<volume>33</volume>
<issue>14</issue>
<fpage>i92</fpage>
<lpage>i101</lpage>
<permissions><copyright-statement>© The Author 2017. Published by Oxford University Press.</copyright-statement>
<copyright-year>2017</copyright-year>
<license license-type="cc-by-nc" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/">http://creativecommons.org/licenses/by-nc/4.0/</ext-link>
), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com</license-p>
</license>
</permissions>
<self-uri xlink:href="btx234.pdf"></self-uri>
<abstract><title>Abstract</title>
<sec id="SA1"><title>Motivation</title>
<p>Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted <italic>k</italic>
-mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful <italic>k</italic>
-mer co-occurrence information with recent advances in deep learning.</p>
</sec>
<sec id="SA2"><title>Results</title>
<p>We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with <italic>k</italic>
-mer embedding. We first split DNA sequences into <italic>k</italic>
-mers and pre-train <italic>k</italic>
-mer embedding vectors based on the co-occurrence matrix of <italic>k</italic>
-mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that <italic>k</italic>
-mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility.</p>
</sec>
<sec id="SA3"><title>Availability and implementation</title>
<p>The source code can be downloaded from <ext-link ext-link-type="uri" xlink:href="https://github.com/minxueric/ismb2017_lstm">https://github.com/minxueric/ismb2017_lstm</ext-link>
.</p>
</sec>
<sec id="SA4"><title>Supplementary information</title>
<p><xref ref-type="supplementary-material" rid="sup1">Supplementary materials</xref>
are available at <italic>Bioinformatics</italic>
online.</p>
</sec>
</abstract>
<funding-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">National Natural Science Foundation of China</named-content>
<named-content content-type="funder-identifier">10.13039/501100001809</named-content>
</funding-source>
<award-id>61573207</award-id>
<award-id>61175002</award-id>
<award-id>71101010</award-id>
<award-id>61673241</award-id>
<award-id>61561146396</award-id>
</award-group>
</funding-group>
<counts><page-count count="10"></page-count>
</counts>
</article-meta>
</front>
</pmc>
</record>
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