metID: an R package for automatable compound annotation for LC−MS-based data
Abstract Summary Accurate and efficient compound annotation is a long-standing challenge for LC–MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information use...
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| Published in | Bioinformatics Vol. 38; no. 2; pp. 568 - 569 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
03.01.2022
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Abstract
Summary
Accurate and efficient compound annotation is a long-standing challenge for LC–MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials.
Availability and implementation
https://jaspershen.github.io/metID.
Supplementary information
Supplementary data are available at Bioinformatics online. |
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| AbstractList | Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials.SUMMARYAccurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials.https://jaspershen.github.io/metID.AVAILABILITY AND IMPLEMENTATIONhttps://jaspershen.github.io/metID.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. Abstract Summary Accurate and efficient compound annotation is a long-standing challenge for LC–MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials. Availability and implementation https://jaspershen.github.io/metID. Supplementary information Supplementary data are available at Bioinformatics online. Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials. https://jaspershen.github.io/metID. Supplementary data are available at Bioinformatics online. |
| Author | Liang, Liang Wu, Si Shen, Xiaotao Contrepois, Kévin Chen, Songjie Snyder, Michael Zhu, Zheng-Jiang |
| AuthorAffiliation | 2 Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences , Shanghai 200032, China 1 Department of Genetics, Stanford University School of Medicine , Stanford, CA 94304, USA |
| AuthorAffiliation_xml | – name: 2 Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences , Shanghai 200032, China – name: 1 Department of Genetics, Stanford University School of Medicine , Stanford, CA 94304, USA |
| Author_xml | – sequence: 1 givenname: Xiaotao orcidid: 0000-0002-9608-9964 surname: Shen fullname: Shen, Xiaotao organization: Department of Genetics, Stanford University School of Medicine, Stanford, CA 94304, USA – sequence: 2 givenname: Si surname: Wu fullname: Wu, Si organization: Department of Genetics, Stanford University School of Medicine, Stanford, CA 94304, USA – sequence: 3 givenname: Liang surname: Liang fullname: Liang, Liang organization: Department of Genetics, Stanford University School of Medicine, Stanford, CA 94304, USA – sequence: 4 givenname: Songjie surname: Chen fullname: Chen, Songjie organization: Department of Genetics, Stanford University School of Medicine, Stanford, CA 94304, USA – sequence: 5 givenname: Kévin surname: Contrepois fullname: Contrepois, Kévin organization: Department of Genetics, Stanford University School of Medicine, Stanford, CA 94304, USA – sequence: 6 givenname: Zheng-Jiang surname: Zhu fullname: Zhu, Zheng-Jiang email: jiangzhu@sioc.ac.cn organization: Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China – sequence: 7 givenname: Michael surname: Snyder fullname: Snyder, Michael email: mpsnyder@stanford.edu organization: Department of Genetics, Stanford University School of Medicine, Stanford, CA 94304, USA |
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| References | Sumner (2023020108413540100_btab583-B5) 2007; 3 Fraga-Corral (2023020108413540100_btab583-B3) 2020; 10 Wishart (2023020108413540100_btab583-B7) 2016; 15 Tsugawa (2023020108413540100_btab583-B6) 2015; 12 Chaleckis (2023020108413540100_btab583-B1) 2019; 55 Contrepois (2023020108413540100_btab583-B2) 2020; 181 Shen (2023020108413540100_btab583-B4) 2019; 10 |
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Summary
Accurate and efficient compound annotation is a long-standing challenge for LC–MS-based data (e.g. untargeted metabolomics and exposomics).... Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts... |
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| SubjectTerms | Applications Notes Chromatography, Liquid Databases, Factual Metabolomics Software Tandem Mass Spectrometry |
| Title | metID: an R package for automatable compound annotation for LC−MS-based data |
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