LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data
Abstract Background Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of bio...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-07-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1744-3 |
| _version_ | 1818266736510631936 |
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| author | Jeremy P. Koelmel Nicholas M. Kroeger Candice Z. Ulmer John A. Bowden Rainey E. Patterson Jason A. Cochran Christopher W. W. Beecher Timothy J. Garrett Richard A. Yost |
| author_facet | Jeremy P. Koelmel Nicholas M. Kroeger Candice Z. Ulmer John A. Bowden Rainey E. Patterson Jason A. Cochran Christopher W. W. Beecher Timothy J. Garrett Richard A. Yost |
| author_sort | Jeremy P. Koelmel |
| collection | DOAJ |
| description | Abstract Background Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology. Results We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode. Conclusions LipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules. |
| first_indexed | 2024-12-12T20:11:26Z |
| format | Article |
| id | doaj.art-24d0b49e7e954a8da7e3dbc92d2574d8 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-12T20:11:26Z |
| publishDate | 2017-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-24d0b49e7e954a8da7e3dbc92d2574d82022-12-22T00:13:29ZengBMCBMC Bioinformatics1471-21052017-07-0118111110.1186/s12859-017-1744-3LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry dataJeremy P. Koelmel0Nicholas M. Kroeger1Candice Z. Ulmer2John A. Bowden3Rainey E. Patterson4Jason A. Cochran5Christopher W. W. Beecher6Timothy J. Garrett7Richard A. Yost8Department of Chemistry, University of FloridaCollege of Engineering, University of Florida, 412Department of Chemistry, University of FloridaNational Institute of Standards and Technology, Hollings Marine LaboratoryDepartment of Chemistry, University of FloridaCollege of Engineering, University of Florida, 412Clinical and Translational Science Institute, University of FloridaDepartment of Chemistry, University of FloridaDepartment of Chemistry, University of FloridaAbstract Background Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology. Results We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode. Conclusions LipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.http://link.springer.com/article/10.1186/s12859-017-1744-3LipidomicsData-independent analysisData-dependent analysisMass spectrometryHigh resolution mass spectrometryTandem mass spectrometry |
| spellingShingle | Jeremy P. Koelmel Nicholas M. Kroeger Candice Z. Ulmer John A. Bowden Rainey E. Patterson Jason A. Cochran Christopher W. W. Beecher Timothy J. Garrett Richard A. Yost LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data BMC Bioinformatics Lipidomics Data-independent analysis Data-dependent analysis Mass spectrometry High resolution mass spectrometry Tandem mass spectrometry |
| title | LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data |
| title_full | LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data |
| title_fullStr | LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data |
| title_full_unstemmed | LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data |
| title_short | LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data |
| title_sort | lipidmatch an automated workflow for rule based lipid identification using untargeted high resolution tandem mass spectrometry data |
| topic | Lipidomics Data-independent analysis Data-dependent analysis Mass spectrometry High resolution mass spectrometry Tandem mass spectrometry |
| url | http://link.springer.com/article/10.1186/s12859-017-1744-3 |
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