Assessment of metabolome annotation quality: a method for evaluating the false discovery rate of elemental composition searches
In metabolomics researches using mass spectrometry (MS), systematic searching of high-resolution mass data against compound databases is often the first step of metabolite annotation to determine elemental compositions possessing similar theoretical mass numbers. However, incorrect hits derived from...
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| creator | Matsuda, Fumio Shinbo, Yoko Oikawa, Akira Hirai, Masami Yokota Fiehn, Oliver Kanaya, Shigehiko Saito, Kazuki |
| description | In metabolomics researches using mass spectrometry (MS), systematic searching of high-resolution mass data against compound databases is often the first step of metabolite annotation to determine elemental compositions possessing similar theoretical mass numbers. However, incorrect hits derived from errors in mass analyses will be included in the results of elemental composition searches. To assess the quality of peak annotation information, a novel methodology for false discovery rates (FDR) evaluation is presented in this study. Based on the FDR analyses, several aspects of an elemental composition search, including setting a threshold, estimating FDR, and the types of elemental composition databases most reliable for searching are discussed.
The FDR can be determined from one measured value (i.e., the hit rate for search queries) and four parameters determined by Monte Carlo simulation. The results indicate that relatively high FDR values (30-50%) were obtained when searching time-of-flight (TOF)/MS data using the KNApSAcK and KEGG databases. In addition, searches against large all-in-one databases (e.g., PubChem) always produced unacceptable results (FDR >70%). The estimated FDRs suggest that the quality of search results can be improved not only by performing more accurate mass analysis but also by modifying the properties of the compound database. A theoretical analysis indicates that FDR could be improved by using compound database with smaller but higher completeness entries.
High accuracy mass analysis, such as Fourier transform (FT)-MS, is needed for reliable annotation (FDR |
| doi_str_mv | 10.1371/journal.pone.0007490 |
| format | Article |
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The FDR can be determined from one measured value (i.e., the hit rate for search queries) and four parameters determined by Monte Carlo simulation. The results indicate that relatively high FDR values (30-50%) were obtained when searching time-of-flight (TOF)/MS data using the KNApSAcK and KEGG databases. In addition, searches against large all-in-one databases (e.g., PubChem) always produced unacceptable results (FDR >70%). The estimated FDRs suggest that the quality of search results can be improved not only by performing more accurate mass analysis but also by modifying the properties of the compound database. A theoretical analysis indicates that FDR could be improved by using compound database with smaller but higher completeness entries.
High accuracy mass analysis, such as Fourier transform (FT)-MS, is needed for reliable annotation (FDR <10%). In addition, a small, customized compound database is preferable for high-quality annotation of metabolome data.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0007490</identifier><identifier>PMID: 19847304</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Analysis ; Annotations ; Arabidopsis ; Arabidopsis - metabolism ; Biotechnology ; Chemical composition ; Chromatography ; Computational Biology - methods ; Computational Biology/Metabolic Networks ; Computer simulation ; Data bases ; Data Interpretation, Statistical ; Databases, Factual ; False Positive Reactions ; Fourier Analysis ; Fourier transforms ; Genomes ; Genomics ; Identification ; Information science ; Ions ; Mass spectrometry ; Mass spectroscopy ; Metabolism ; Metabolites ; Metabolome ; Metabolomics ; Metabolomics - methods ; Methods ; Monte Carlo methods ; Monte Carlo simulation ; Oryza - metabolism ; Peptides ; Plant Biology/Agricultural Biotechnology ; Plant Proteins - metabolism ; Plant sciences ; Quality assessment ; Scientific imaging ; Searching ; Sequence Alignment - methods ; Sequence Analysis, Protein - methods ; Simulation ; Software ; Tandem Mass Spectrometry - methods ; Theoretical analysis ; Trends</subject><ispartof>PloS one, 2009-10, Vol.4 (10), p.e7490-e7490</ispartof><rights>COPYRIGHT 2009 Public Library of Science</rights><rights>2009 Matsuda et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Matsuda et al. 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c729t-3e37f3b4c8927eef30af4f990f7aafba22484364aa59fd2fff4da54c3768ff373</citedby><cites>FETCH-LOGICAL-c729t-3e37f3b4c8927eef30af4f990f7aafba22484364aa59fd2fff4da54c3768ff373</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2761541/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2761541/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19847304$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>El-Shemy, Hany A.</contributor><creatorcontrib>Matsuda, Fumio</creatorcontrib><creatorcontrib>Shinbo, Yoko</creatorcontrib><creatorcontrib>Oikawa, Akira</creatorcontrib><creatorcontrib>Hirai, Masami Yokota</creatorcontrib><creatorcontrib>Fiehn, Oliver</creatorcontrib><creatorcontrib>Kanaya, Shigehiko</creatorcontrib><creatorcontrib>Saito, Kazuki</creatorcontrib><title>Assessment of metabolome annotation quality: a method for evaluating the false discovery rate of elemental composition searches</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>In metabolomics researches using mass spectrometry (MS), systematic searching of high-resolution mass data against compound databases is often the first step of metabolite annotation to determine elemental compositions possessing similar theoretical mass numbers. However, incorrect hits derived from errors in mass analyses will be included in the results of elemental composition searches. To assess the quality of peak annotation information, a novel methodology for false discovery rates (FDR) evaluation is presented in this study. Based on the FDR analyses, several aspects of an elemental composition search, including setting a threshold, estimating FDR, and the types of elemental composition databases most reliable for searching are discussed.
The FDR can be determined from one measured value (i.e., the hit rate for search queries) and four parameters determined by Monte Carlo simulation. The results indicate that relatively high FDR values (30-50%) were obtained when searching time-of-flight (TOF)/MS data using the KNApSAcK and KEGG databases. In addition, searches against large all-in-one databases (e.g., PubChem) always produced unacceptable results (FDR >70%). The estimated FDRs suggest that the quality of search results can be improved not only by performing more accurate mass analysis but also by modifying the properties of the compound database. A theoretical analysis indicates that FDR could be improved by using compound database with smaller but higher completeness entries.
High accuracy mass analysis, such as Fourier transform (FT)-MS, is needed for reliable annotation (FDR <10%). In addition, a small, customized compound database is preferable for high-quality annotation of metabolome data.</description><subject>Accuracy</subject><subject>Analysis</subject><subject>Annotations</subject><subject>Arabidopsis</subject><subject>Arabidopsis - metabolism</subject><subject>Biotechnology</subject><subject>Chemical composition</subject><subject>Chromatography</subject><subject>Computational Biology - methods</subject><subject>Computational Biology/Metabolic Networks</subject><subject>Computer simulation</subject><subject>Data bases</subject><subject>Data Interpretation, Statistical</subject><subject>Databases, Factual</subject><subject>False Positive Reactions</subject><subject>Fourier Analysis</subject><subject>Fourier transforms</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Identification</subject><subject>Information science</subject><subject>Ions</subject><subject>Mass spectrometry</subject><subject>Mass spectroscopy</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolome</subject><subject>Metabolomics</subject><subject>Metabolomics - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Matsuda, Fumio</au><au>Shinbo, Yoko</au><au>Oikawa, Akira</au><au>Hirai, Masami Yokota</au><au>Fiehn, Oliver</au><au>Kanaya, Shigehiko</au><au>Saito, Kazuki</au><au>El-Shemy, Hany A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of metabolome annotation quality: a method for evaluating the false discovery rate of elemental composition searches</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2009-10-16</date><risdate>2009</risdate><volume>4</volume><issue>10</issue><spage>e7490</spage><epage>e7490</epage><pages>e7490-e7490</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>In metabolomics researches using mass spectrometry (MS), systematic searching of high-resolution mass data against compound databases is often the first step of metabolite annotation to determine elemental compositions possessing similar theoretical mass numbers. However, incorrect hits derived from errors in mass analyses will be included in the results of elemental composition searches. To assess the quality of peak annotation information, a novel methodology for false discovery rates (FDR) evaluation is presented in this study. Based on the FDR analyses, several aspects of an elemental composition search, including setting a threshold, estimating FDR, and the types of elemental composition databases most reliable for searching are discussed.
The FDR can be determined from one measured value (i.e., the hit rate for search queries) and four parameters determined by Monte Carlo simulation. The results indicate that relatively high FDR values (30-50%) were obtained when searching time-of-flight (TOF)/MS data using the KNApSAcK and KEGG databases. In addition, searches against large all-in-one databases (e.g., PubChem) always produced unacceptable results (FDR >70%). The estimated FDRs suggest that the quality of search results can be improved not only by performing more accurate mass analysis but also by modifying the properties of the compound database. A theoretical analysis indicates that FDR could be improved by using compound database with smaller but higher completeness entries.
High accuracy mass analysis, such as Fourier transform (FT)-MS, is needed for reliable annotation (FDR <10%). In addition, a small, customized compound database is preferable for high-quality annotation of metabolome data.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>19847304</pmid><doi>10.1371/journal.pone.0007490</doi><tpages>e7490</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Analysis Annotations Arabidopsis Arabidopsis - metabolism Biotechnology Chemical composition Chromatography Computational Biology - methods Computational Biology/Metabolic Networks Computer simulation Data bases Data Interpretation, Statistical Databases, Factual False Positive Reactions Fourier Analysis Fourier transforms Genomes Genomics Identification Information science Ions Mass spectrometry Mass spectroscopy Metabolism Metabolites Metabolome Metabolomics Metabolomics - methods Methods Monte Carlo methods Monte Carlo simulation Oryza - metabolism Peptides Plant Biology/Agricultural Biotechnology Plant Proteins - metabolism Plant sciences Quality assessment Scientific imaging Searching Sequence Alignment - methods Sequence Analysis, Protein - methods Simulation Software Tandem Mass Spectrometry - methods Theoretical analysis Trends |
| title | Assessment of metabolome annotation quality: a method for evaluating the false discovery rate of elemental composition searches |
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