A de novo MS1 feature detector for the Bruker timsTOF Pro.
Identification of peptides by analysis of data acquired by the two established methods for bottom-up proteomics, DDA and DIA, relies heavily on the fragment spectra. In DDA, peptide features detected in mass spectrometry data are identified by matching their fragment spectra with a peptide database....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0277122 |
| _version_ | 1797978088915599360 |
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| author | Daryl Wilding-McBride Andrew I Webb |
| author_facet | Daryl Wilding-McBride Andrew I Webb |
| author_sort | Daryl Wilding-McBride |
| collection | DOAJ |
| description | Identification of peptides by analysis of data acquired by the two established methods for bottom-up proteomics, DDA and DIA, relies heavily on the fragment spectra. In DDA, peptide features detected in mass spectrometry data are identified by matching their fragment spectra with a peptide database. In DIA, a peptide's fragment spectra are targeted for extraction and matched with observed spectra. Although fragment ion matching is a central aspect in most peptide identification strategies, the precursor ion in the MS1 data reveals important characteristics as well, including charge state, intensity, monoisotopic m/z, and apex in retention time. Most importantly, the precursor's mass is essential in determining the potential chemical modification state of the underlying peptide sequence. In the timsTOF, with its additional dimension of collisional cross-section, the data representing the precursor ion also reveals the peptide's peak in ion mobility. However, the availability of tools to survey precursor ions with a wide range of abundance in timsTOF data across the full mass range is very limited. Here we present a de novo feature detector called three-dimensional intensity descent (3DID). 3DID can detect and extract peptide features down to a configurable intensity level, and finds many more features than several existing tools. 3DID is written in Python and is freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). The dataset used for validation of the algorithm is publicly available (ProteomeXchange identifier PXD030706). |
| first_indexed | 2024-04-11T05:17:29Z |
| format | Article |
| id | doaj.art-8a512c28af964d948473dd82e467b57d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T05:17:29Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-8a512c28af964d948473dd82e467b57d2022-12-24T05:32:32ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011711e027712210.1371/journal.pone.0277122A de novo MS1 feature detector for the Bruker timsTOF Pro.Daryl Wilding-McBrideAndrew I WebbIdentification of peptides by analysis of data acquired by the two established methods for bottom-up proteomics, DDA and DIA, relies heavily on the fragment spectra. In DDA, peptide features detected in mass spectrometry data are identified by matching their fragment spectra with a peptide database. In DIA, a peptide's fragment spectra are targeted for extraction and matched with observed spectra. Although fragment ion matching is a central aspect in most peptide identification strategies, the precursor ion in the MS1 data reveals important characteristics as well, including charge state, intensity, monoisotopic m/z, and apex in retention time. Most importantly, the precursor's mass is essential in determining the potential chemical modification state of the underlying peptide sequence. In the timsTOF, with its additional dimension of collisional cross-section, the data representing the precursor ion also reveals the peptide's peak in ion mobility. However, the availability of tools to survey precursor ions with a wide range of abundance in timsTOF data across the full mass range is very limited. Here we present a de novo feature detector called three-dimensional intensity descent (3DID). 3DID can detect and extract peptide features down to a configurable intensity level, and finds many more features than several existing tools. 3DID is written in Python and is freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). The dataset used for validation of the algorithm is publicly available (ProteomeXchange identifier PXD030706).https://doi.org/10.1371/journal.pone.0277122 |
| spellingShingle | Daryl Wilding-McBride Andrew I Webb A de novo MS1 feature detector for the Bruker timsTOF Pro. PLoS ONE |
| title | A de novo MS1 feature detector for the Bruker timsTOF Pro. |
| title_full | A de novo MS1 feature detector for the Bruker timsTOF Pro. |
| title_fullStr | A de novo MS1 feature detector for the Bruker timsTOF Pro. |
| title_full_unstemmed | A de novo MS1 feature detector for the Bruker timsTOF Pro. |
| title_short | A de novo MS1 feature detector for the Bruker timsTOF Pro. |
| title_sort | de novo ms1 feature detector for the bruker timstof pro |
| url | https://doi.org/10.1371/journal.pone.0277122 |
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