A bayesian approach to protein inference problem in shotgun proteomics
The protein inference problem represents a major challenge in shotgun proteomics. In this article, we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorious proba...
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| Veröffentlicht in: | Journal of computational biology 2009-08, Vol.16 (8), p.1183-1193 |
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| container_end_page | 1193 |
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| container_issue | 8 |
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| container_title | Journal of computational biology |
| container_volume | 16 |
| creator | Li, Yong Fuga Arnold, Randy J Li, Yixue Radivojac, Predrag Sheng, Quanhu Tang, Haixu |
| description | The protein inference problem represents a major challenge in shotgun proteomics. In this article, we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results. |
| doi_str_mv | 10.1089/cmb.2009.0018 |
| format | Article |
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In this article, we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results.</description><identifier>ISSN: 1066-5277</identifier><identifier>EISSN: 1557-8666</identifier><identifier>DOI: 10.1089/cmb.2009.0018</identifier><identifier>PMID: 19645593</identifier><language>eng</language><publisher>United States: Mary Ann Liebert, Inc</publisher><subject>Algorithms ; Bayesian statistical decision theory ; Computational biology ; Databases, Protein ; Models, Statistical ; Proteomics ; Proteomics - methods ; Sequence Analysis, Protein - methods</subject><ispartof>Journal of computational biology, 2009-08, Vol.16 (8), p.1183-1193</ispartof><rights>COPYRIGHT 2009 Mary Ann Liebert, Inc.</rights><rights>Copyright 2009, Mary Ann Liebert, Inc. 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c514t-81c8a01eae2e07085fbf93359420267078a7ab527cb13fababbecebb5826815d3</citedby><cites>FETCH-LOGICAL-c514t-81c8a01eae2e07085fbf93359420267078a7ab527cb13fababbecebb5826815d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,3029,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19645593$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Yong Fuga</creatorcontrib><creatorcontrib>Arnold, Randy J</creatorcontrib><creatorcontrib>Li, Yixue</creatorcontrib><creatorcontrib>Radivojac, Predrag</creatorcontrib><creatorcontrib>Sheng, Quanhu</creatorcontrib><creatorcontrib>Tang, Haixu</creatorcontrib><title>A bayesian approach to protein inference problem in shotgun proteomics</title><title>Journal of computational biology</title><addtitle>J Comput Biol</addtitle><description>The protein inference problem represents a major challenge in shotgun proteomics. In this article, we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results.</description><subject>Algorithms</subject><subject>Bayesian statistical decision theory</subject><subject>Computational biology</subject><subject>Databases, Protein</subject><subject>Models, Statistical</subject><subject>Proteomics</subject><subject>Proteomics - methods</subject><subject>Sequence Analysis, Protein - methods</subject><issn>1066-5277</issn><issn>1557-8666</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFksuLFDEQxsOiuA897lUahL31WEk6r4swLO4qLHjRc0gy1TOR7mTs9Aj735tmBtcFYckhReVXj3x8hFxTWFHQ5mMY_YoBmBUA1WfkggqhWi2lfFVjkLIVTKlzclnKz0pwCeoNOadGdkIYfkHu1o13j1iiS43b76fswq6Zc1OjGWNqYupxwhRwyfgBx5ppyi7P20M6QnmMobwlr3s3FHx3uq_Ij7vP32-_tA_f7r_erh_aIGg3t5oG7YCiQ4agQIve94ZzYToGTCpQ2inn68bBU94777zHgN4LzaSmYsOvyKdj3_3Bj7gJmObJDXY_xdFNjza7aJ-_pLiz2_zbMmVMZ1RtcHNqMOVfByyzHWMJOAwuYT4UK5Uw0HF4EeSdrlhV9CWQgeYglKzghyO4dQPaKmyuG4YFtmsGUtf_d8vc1X-oejZYhc4J-1jzzwraY0GYcikT9n_VoGAXi9hqEbtYxC4Wqfz7fyV8ok-e4H8AJ-225A</recordid><startdate>200908</startdate><enddate>200908</enddate><creator>Li, Yong Fuga</creator><creator>Arnold, Randy J</creator><creator>Li, Yixue</creator><creator>Radivojac, Predrag</creator><creator>Sheng, Quanhu</creator><creator>Tang, Haixu</creator><general>Mary Ann Liebert, Inc</general><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>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7SC</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>200908</creationdate><title>A bayesian approach to protein inference problem in shotgun proteomics</title><author>Li, Yong Fuga ; Arnold, Randy J ; Li, Yixue ; Radivojac, Predrag ; Sheng, Quanhu ; Tang, Haixu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c514t-81c8a01eae2e07085fbf93359420267078a7ab527cb13fababbecebb5826815d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Algorithms</topic><topic>Bayesian statistical decision theory</topic><topic>Computational biology</topic><topic>Databases, Protein</topic><topic>Models, Statistical</topic><topic>Proteomics</topic><topic>Proteomics - methods</topic><topic>Sequence Analysis, Protein - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Yong Fuga</creatorcontrib><creatorcontrib>Arnold, Randy J</creatorcontrib><creatorcontrib>Li, Yixue</creatorcontrib><creatorcontrib>Radivojac, Predrag</creatorcontrib><creatorcontrib>Sheng, Quanhu</creatorcontrib><creatorcontrib>Tang, Haixu</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Computer Science Collection</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Yong Fuga</au><au>Arnold, Randy J</au><au>Li, Yixue</au><au>Radivojac, Predrag</au><au>Sheng, Quanhu</au><au>Tang, Haixu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A bayesian approach to protein inference problem in shotgun proteomics</atitle><jtitle>Journal of computational biology</jtitle><addtitle>J Comput Biol</addtitle><date>2009-08</date><risdate>2009</risdate><volume>16</volume><issue>8</issue><spage>1183</spage><epage>1193</epage><pages>1183-1193</pages><issn>1066-5277</issn><eissn>1557-8666</eissn><abstract>The protein inference problem represents a major challenge in shotgun proteomics. In this article, we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results.</abstract><cop>United States</cop><pub>Mary Ann Liebert, Inc</pub><pmid>19645593</pmid><doi>10.1089/cmb.2009.0018</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Journal of computational biology, 2009-08, Vol.16 (8), p.1183-1193 |
| issn | 1066-5277 1557-8666 |
| language | eng |
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| source | Mary Ann Liebert Online Subscription; MEDLINE; Alma/SFX Local Collection |
| subjects | Algorithms Bayesian statistical decision theory Computational biology Databases, Protein Models, Statistical Proteomics Proteomics - methods Sequence Analysis, Protein - methods |
| title | A bayesian approach to protein inference problem in shotgun proteomics |
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