Loading…
Identification Exon Skipping Events From High-Throughput RNA Sequencing Data
The emergence of next-generation high-throughput RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate identification of alternative splicing events from RNA-Seq data has remained an unresolved challenge i...
Saved in:
| Published in: | IEEE transactions on nanobioscience 2015-07, Vol.14 (5), p.562-569 |
|---|---|
| Main Authors: | , , , |
| Format: | Magazinearticle |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c413t-5514b01610e53451759dfa67d5f0212157b451e5613421e966d11b8e98edb53b3 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c413t-5514b01610e53451759dfa67d5f0212157b451e5613421e966d11b8e98edb53b3 |
| container_end_page | 569 |
| container_issue | 5 |
| container_start_page | 562 |
| container_title | IEEE transactions on nanobioscience |
| container_volume | 14 |
| creator | Bai, Yang Ji, Shufan Jiang, Qinghua Wang, Yadong |
| description | The emergence of next-generation high-throughput RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate identification of alternative splicing events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies. Identifying exon skipping (ES) events is an essential part in genome-wide alternative splicing event identification. In this paper, we propose a novel method ESFinder, a random forest classifier to identify ES events from RNA-Seq data. ESFinder conducts thorough studies on predicting features and figures out proper features according to their relevance for ES event identification. Experimental results on real human skeletal muscle and brain RNA-Seq data show that ESFinder could effectively predict ES events with high predictive accuracy. The codes of ESFinder are available at http://mlg.hit.edu.cn/ybai/ES/ESFinder.html. |
| doi_str_mv | 10.1109/TNB.2015.2419812 |
| format | magazinearticle |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmed_primary_25935040</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7097714</ieee_id><sourcerecordid>1703697910</sourcerecordid><originalsourceid>FETCH-LOGICAL-c413t-5514b01610e53451759dfa67d5f0212157b451e5613421e966d11b8e98edb53b3</originalsourceid><addsrcrecordid>eNqNkclLw0AUhwdRrNtdECTgxUvqe7Nmji7VCkXB1nPIMmlH2yRmEtH_3gmtPXjyMAvz-95jHh8hpwhDRNBXs6ebIQUUQ8pRR0h3yAEKEYVUMr3b35kM0WcDcujcGwAqKfQ-GVChmQAOB2TymJuytYXNktZWZTD68tv03da1LefB6NOHLrhvqlUwtvNFOFs0VTdf1F0bvDxdB1Pz0Zky69G7pE2OyV6RLJ052ZxH5PV-NLsdh5Pnh8fb60mYcWRtKATyFFAiGMG4QCV0XiRS5aIAihSFSv2rERIZp2i0lDliGhkdmTwVLGVH5HLdt24q_wHXxivrMrNcJqWpOhejYgBcAov-gYLul-L_QZnUSiN49OIP-lZ1Teln7imqIsqV8hSsqaypnGtMEdeNXSXNd4wQ9_5i7y_u_cUbf77kfNO4S1cm3xb8CvPA2Rqwxpht3A-gkLMfmf6aJA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>magazinearticle</recordtype><pqid>1702782477</pqid></control><display><type>magazinearticle</type><title>Identification Exon Skipping Events From High-Throughput RNA Sequencing Data</title><source>IEEE Xplore (Online service)</source><creator>Bai, Yang ; Ji, Shufan ; Jiang, Qinghua ; Wang, Yadong</creator><creatorcontrib>Bai, Yang ; Ji, Shufan ; Jiang, Qinghua ; Wang, Yadong</creatorcontrib><description>The emergence of next-generation high-throughput RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate identification of alternative splicing events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies. Identifying exon skipping (ES) events is an essential part in genome-wide alternative splicing event identification. In this paper, we propose a novel method ESFinder, a random forest classifier to identify ES events from RNA-Seq data. ESFinder conducts thorough studies on predicting features and figures out proper features according to their relevance for ES event identification. Experimental results on real human skeletal muscle and brain RNA-Seq data show that ESFinder could effectively predict ES events with high predictive accuracy. The codes of ESFinder are available at http://mlg.hit.edu.cn/ybai/ES/ESFinder.html.</description><identifier>ISSN: 1536-1241</identifier><identifier>EISSN: 1558-2639</identifier><identifier>DOI: 10.1109/TNB.2015.2419812</identifier><identifier>PMID: 25935040</identifier><identifier>CODEN: ITMCEL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>alternative splicing ; Alternative Splicing - genetics ; Bioinformatics ; Brain ; Brain - metabolism ; classifier ; Computational Biology - methods ; Databases, Genetic ; Decision Trees ; exon skipping ; Exons - genetics ; Feature extraction ; feature selection ; Forests ; Gene sequencing ; Genomics ; High-Throughput Nucleotide Sequencing - methods ; Human ; Humans ; MICROSTRUCTURES ; Muscle, Skeletal - metabolism ; Muscles ; Nanobioscience ; Nanostructure ; Ribonucleic acids ; RNA-Seq ; ROC Curve ; Sequence Analysis, RNA - methods ; Silicon ; Splicing ; YTTRIUM OXIDE</subject><ispartof>IEEE transactions on nanobioscience, 2015-07, Vol.14 (5), p.562-569</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2015</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c413t-5514b01610e53451759dfa67d5f0212157b451e5613421e966d11b8e98edb53b3</citedby><cites>FETCH-LOGICAL-c413t-5514b01610e53451759dfa67d5f0212157b451e5613421e966d11b8e98edb53b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7097714$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>780,784,27925,54796</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25935040$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bai, Yang</creatorcontrib><creatorcontrib>Ji, Shufan</creatorcontrib><creatorcontrib>Jiang, Qinghua</creatorcontrib><creatorcontrib>Wang, Yadong</creatorcontrib><title>Identification Exon Skipping Events From High-Throughput RNA Sequencing Data</title><title>IEEE transactions on nanobioscience</title><addtitle>TNB</addtitle><addtitle>IEEE Trans Nanobioscience</addtitle><description>The emergence of next-generation high-throughput RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate identification of alternative splicing events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies. Identifying exon skipping (ES) events is an essential part in genome-wide alternative splicing event identification. In this paper, we propose a novel method ESFinder, a random forest classifier to identify ES events from RNA-Seq data. ESFinder conducts thorough studies on predicting features and figures out proper features according to their relevance for ES event identification. Experimental results on real human skeletal muscle and brain RNA-Seq data show that ESFinder could effectively predict ES events with high predictive accuracy. The codes of ESFinder are available at http://mlg.hit.edu.cn/ybai/ES/ESFinder.html.</description><subject>alternative splicing</subject><subject>Alternative Splicing - genetics</subject><subject>Bioinformatics</subject><subject>Brain</subject><subject>Brain - metabolism</subject><subject>classifier</subject><subject>Computational Biology - methods</subject><subject>Databases, Genetic</subject><subject>Decision Trees</subject><subject>exon skipping</subject><subject>Exons - genetics</subject><subject>Feature extraction</subject><subject>feature selection</subject><subject>Forests</subject><subject>Gene sequencing</subject><subject>Genomics</subject><subject>High-Throughput Nucleotide Sequencing - methods</subject><subject>Human</subject><subject>Humans</subject><subject>MICROSTRUCTURES</subject><subject>Muscle, Skeletal - metabolism</subject><subject>Muscles</subject><subject>Nanobioscience</subject><subject>Nanostructure</subject><subject>Ribonucleic acids</subject><subject>RNA-Seq</subject><subject>ROC Curve</subject><subject>Sequence Analysis, RNA - methods</subject><subject>Silicon</subject><subject>Splicing</subject><subject>YTTRIUM OXIDE</subject><issn>1536-1241</issn><issn>1558-2639</issn><fulltext>true</fulltext><rsrctype>magazinearticle</rsrctype><creationdate>2015</creationdate><recordtype>magazinearticle</recordtype><recordid>eNqNkclLw0AUhwdRrNtdECTgxUvqe7Nmji7VCkXB1nPIMmlH2yRmEtH_3gmtPXjyMAvz-95jHh8hpwhDRNBXs6ebIQUUQ8pRR0h3yAEKEYVUMr3b35kM0WcDcujcGwAqKfQ-GVChmQAOB2TymJuytYXNktZWZTD68tv03da1LefB6NOHLrhvqlUwtvNFOFs0VTdf1F0bvDxdB1Pz0Zky69G7pE2OyV6RLJ052ZxH5PV-NLsdh5Pnh8fb60mYcWRtKATyFFAiGMG4QCV0XiRS5aIAihSFSv2rERIZp2i0lDliGhkdmTwVLGVH5HLdt24q_wHXxivrMrNcJqWpOhejYgBcAov-gYLul-L_QZnUSiN49OIP-lZ1Teln7imqIsqV8hSsqaypnGtMEdeNXSXNd4wQ9_5i7y_u_cUbf77kfNO4S1cm3xb8CvPA2Rqwxpht3A-gkLMfmf6aJA</recordid><startdate>20150701</startdate><enddate>20150701</enddate><creator>Bai, Yang</creator><creator>Ji, Shufan</creator><creator>Jiang, Qinghua</creator><creator>Wang, Yadong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><scope>7TM</scope><scope>H8G</scope></search><sort><creationdate>20150701</creationdate><title>Identification Exon Skipping Events From High-Throughput RNA Sequencing Data</title><author>Bai, Yang ; Ji, Shufan ; Jiang, Qinghua ; Wang, Yadong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c413t-5514b01610e53451759dfa67d5f0212157b451e5613421e966d11b8e98edb53b3</frbrgroupid><rsrctype>magazinearticle</rsrctype><prefilter>magazinearticle</prefilter><language>eng</language><creationdate>2015</creationdate><topic>alternative splicing</topic><topic>Alternative Splicing - genetics</topic><topic>Bioinformatics</topic><topic>Brain</topic><topic>Brain - metabolism</topic><topic>classifier</topic><topic>Computational Biology - methods</topic><topic>Databases, Genetic</topic><topic>Decision Trees</topic><topic>exon skipping</topic><topic>Exons - genetics</topic><topic>Feature extraction</topic><topic>feature selection</topic><topic>Forests</topic><topic>Gene sequencing</topic><topic>Genomics</topic><topic>High-Throughput Nucleotide Sequencing - methods</topic><topic>Human</topic><topic>Humans</topic><topic>MICROSTRUCTURES</topic><topic>Muscle, Skeletal - metabolism</topic><topic>Muscles</topic><topic>Nanobioscience</topic><topic>Nanostructure</topic><topic>Ribonucleic acids</topic><topic>RNA-Seq</topic><topic>ROC Curve</topic><topic>Sequence Analysis, RNA - methods</topic><topic>Silicon</topic><topic>Splicing</topic><topic>YTTRIUM OXIDE</topic><toplevel>online_resources</toplevel><creatorcontrib>Bai, Yang</creatorcontrib><creatorcontrib>Ji, Shufan</creatorcontrib><creatorcontrib>Jiang, Qinghua</creatorcontrib><creatorcontrib>Wang, Yadong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Xplore</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Nucleic Acids Abstracts</collection><collection>Copper Technical Reference Library</collection><jtitle>IEEE transactions on nanobioscience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bai, Yang</au><au>Ji, Shufan</au><au>Jiang, Qinghua</au><au>Wang, Yadong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification Exon Skipping Events From High-Throughput RNA Sequencing Data</atitle><jtitle>IEEE transactions on nanobioscience</jtitle><stitle>TNB</stitle><addtitle>IEEE Trans Nanobioscience</addtitle><date>2015-07-01</date><risdate>2015</risdate><volume>14</volume><issue>5</issue><spage>562</spage><epage>569</epage><pages>562-569</pages><issn>1536-1241</issn><eissn>1558-2639</eissn><coden>ITMCEL</coden><abstract>The emergence of next-generation high-throughput RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate identification of alternative splicing events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies. Identifying exon skipping (ES) events is an essential part in genome-wide alternative splicing event identification. In this paper, we propose a novel method ESFinder, a random forest classifier to identify ES events from RNA-Seq data. ESFinder conducts thorough studies on predicting features and figures out proper features according to their relevance for ES event identification. Experimental results on real human skeletal muscle and brain RNA-Seq data show that ESFinder could effectively predict ES events with high predictive accuracy. The codes of ESFinder are available at http://mlg.hit.edu.cn/ybai/ES/ESFinder.html.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>25935040</pmid><doi>10.1109/TNB.2015.2419812</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1536-1241 |
| ispartof | IEEE transactions on nanobioscience, 2015-07, Vol.14 (5), p.562-569 |
| issn | 1536-1241 1558-2639 |
| language | eng |
| recordid | cdi_pubmed_primary_25935040 |
| source | IEEE Xplore (Online service) |
| subjects | alternative splicing Alternative Splicing - genetics Bioinformatics Brain Brain - metabolism classifier Computational Biology - methods Databases, Genetic Decision Trees exon skipping Exons - genetics Feature extraction feature selection Forests Gene sequencing Genomics High-Throughput Nucleotide Sequencing - methods Human Humans MICROSTRUCTURES Muscle, Skeletal - metabolism Muscles Nanobioscience Nanostructure Ribonucleic acids RNA-Seq ROC Curve Sequence Analysis, RNA - methods Silicon Splicing YTTRIUM OXIDE |
| title | Identification Exon Skipping Events From High-Throughput RNA Sequencing Data |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T22%3A56%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Identification%20Exon%20Skipping%20Events%20From%20High-Throughput%20RNA%20Sequencing%20Data&rft.jtitle=IEEE%20transactions%20on%20nanobioscience&rft.au=Bai,%20Yang&rft.date=2015-07-01&rft.volume=14&rft.issue=5&rft.spage=562&rft.epage=569&rft.pages=562-569&rft.issn=1536-1241&rft.eissn=1558-2639&rft.coden=ITMCEL&rft_id=info:doi/10.1109/TNB.2015.2419812&rft_dat=%3Cproquest_pubme%3E1703697910%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c413t-5514b01610e53451759dfa67d5f0212157b451e5613421e966d11b8e98edb53b3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1702782477&rft_id=info:pmid/25935040&rft_ieee_id=7097714&rfr_iscdi=true |