Reproducible mass spectrometry data processing and compound annotation in MZmine 3

. 2024 May 20 ; () : . [epub] 20240520

Status Publisher Jazyk angličtina Země Velká Británie, Anglie Médium print-electronic

Typ dokumentu časopisecké články, přehledy

Perzistentní odkaz   https://www.medvik.cz/link/pmid38769143

Grantová podpora
891397 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
U01CA235507 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
R03OD034493 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
R01DK136117 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
U01CA235507 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
2152526 National Science Foundation (NSF)

Odkazy
PubMed 38769143
DOI 10.1038/s41596-024-00996-y
PII: 10.1038/s41596-024-00996-y
Knihovny.cz E-zdroje

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Zobrazit více v PubMed

PubMed DOI PMC

PubMed DOI PMC

PubMed DOI

PubMed DOI

PubMed DOI

PubMed DOI

Aksenov, A. A., da Silva, R., Knight, R., Lopes, N. P. & Dorrestein, P. C. Global chemical analysis of biology by mass spectrometry. Nat. Rev. Chem. 1, 1–20 (2017). DOI

PubMed DOI

PubMed DOI PMC

PubMed DOI

PubMed DOI PMC

PubMed DOI PMC

Tautenhahn, R., Böttcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinforma. 9, 504 (2008). DOI

PubMed DOI

PubMed DOI

PubMed DOI

PubMed DOI PMC

Pluskal, T. et al. in Processing Metabolomics and Proteomics Data with Open Software 232–254 (Royal Society of Chemistry, 2020).

PubMed DOI

PubMed DOI PMC

PubMed DOI PMC

PubMed DOI

PubMed DOI

PubMed DOI PMC

PubMed DOI

PubMed DOI

Rew, R. & Davis, G. NetCDF: an interface for scientific data access. IEEE Comput. Graph. Appl. 10, 76–82 (1990). DOI

Lu, M., An, S., Wang, R., Wang, J. & Yu, C. Aird: a computation-oriented mass spectrometry data format enables a higher compression ratio and less decoding time. BMC Bioinforma. 23, 35 (2022). DOI

PubMed DOI

PubMed DOI PMC

PubMed DOI

PubMed DOI

PubMed DOI PMC

PubMed DOI PMC

PubMed DOI PMC

PubMed DOI PMC

PubMed DOI

PubMed DOI

Meier, F. et al. Online parallel accumulation–serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer. Mol. Cell. Proteom. 17, 2534–2545 (2018). DOI

PubMed DOI PMC

PubMed DOI PMC

PubMed DOI PMC

PubMed DOI

PubMed DOI

PubMed DOI PMC

PubMed DOI

PubMed DOI PMC

PubMed DOI

PubMed DOI

Zdouc, M. M. et al. FERMO: a dashboard for streamlined rationalized prioritization of molecular features from mass spectrometry data. Preprint at bioRxiv https://doi.org/10.1101/2022.12.21.521422 (2022).

PubMed DOI PMC

Pakkir Shah, A. K. The hitchhiker’s guide to statistical analysis of feature-based molecular networks from non-targeted metabolomics data. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv-2023-wwbt0 (2023).

PubMed DOI PMC

PubMed DOI

PubMed DOI

Du, X., Smirnov, A., Pluskal, T., Jia, W. & Sumner, S. in Computational Methods and Data Analysis for Metabolomics (ed. Li, S.) 25–48 (Springer, 2020).

PubMed DOI PMC

PubMed DOI

PubMed DOI PMC

PubMed DOI

PubMed DOI

PubMed DOI

PubMed DOI

PubMed DOI

PubMed DOI

PubMed DOI PMC

PubMed DOI

PubMed DOI PMC

PubMed DOI PMC

Najít záznam

Citační ukazatele

Nahrávání dat...

Možnosti archivace

Nahrávání dat...