Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 3,845

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data

Open access
Pluskal, Tomáš, Castillo, Sandra, Villar-Briones, Alejandro, Orešič, Matej·2010·BMC Bioinformatics

Abstract

BACKGROUND: Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. RESULTS: A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. CONCLUSIONS: MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/1471-2105-11-395

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Research works printed but not linked: 23

  1. Orchard S, Hoogland C, Bairoch A, Eisenacher M, Kraus HJ, Binz PA: Managing the data explosion. A report on the HUPO-PSI Workshop. August 2008, Amsterdam, The Netherlands. Proteomics 2009, 9(3):499–501. 10.1002/pmic.200800838 · DOI
  2. Katajamaa M, Miettinen J, Oresic M: MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 2006, 22(5):634–636. 10.1093/bioinformatics/btk039 · DOI · Paper page
  3. Katajamaa M, Oresic M: Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics 2005, 6: 179. 10.1186/1471-2105-6-179 · DOI · Paper page
  4. Laaksonen R, Katajamaa M, Paiva H, Sysi-Aho M, Saarinen L, Junni P, Lutjohann D, Smet J, Van Coster R, Seppanen-Laakso T, Lehtimäki T, Soini J, Oresic M: A systems biology strategy reveals biological pathways and plasma biomarker candidates for potentially toxic statin-induced changes in muscle. PLoS One 2006, 1: e97. 10.1371/journal.pone.0000097 · DOI · Paper page
  5. Gopalacharyulu PV, Velagapudi VR, Lindfors E, Halperin E, Oresic M: Dynamic network topology changes in functional modules predict responses to oxidative stress in yeast. Mol Biosyst 2009, 5(3):276–287. 10.1039/b815347g · DOI
  6. Medina-Gomez G, Gray SL, Yetukuri L, Shimomura K, Virtue S, Campbell M, Curtis RK, Jimenez-Linan M, Blount M, Yeo GS, Lopez M, Seppänen-Laakso T, Ashcroft FM, Oresic M, Vidal-Puig A: PPAR gamma 2 prevents lipotoxicity by controlling adipose tissue expandability and peripheral lipid metabolism. PLoS Genet 2007, 3(4):e64. 10.1371/journal.pgen.0030064 · DOI · Paper page
  7. Kind T, Tolstikov V, Fiehn O, Weiss RH: A comprehensive urinary metabolomic approach for identifying kidney cancer. Anal Biochem 2007, 363(2):185–195. 10.1016/j.ab.2007.01.028 · DOI · Paper page
  8. Timischl B, Dettmer K, Kaspar H, Thieme M, Oefner PJ: Development of a quantitative, validated capillary electrophoresis-time of flight-mass spectrometry method with integrated high-confidence analyte identification for metabolomics. Electrophoresis 2008, 29(10):2203–2214. 10.1002/elps.200700517 · DOI
  9. Lange E, Tautenhahn R, Neumann S, Gropl C: Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. BMC Bioinformatics 2008, 9(1):375. 10.1186/1471-2105-9-375 · DOI · Paper page
  10. Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E: The Chemistry Development Kit (CDK): an open-source Java library for chemo- and bioinformatics. J Chem Inf Comput Sci 2003, 43(2):493–500. · DOI · Paper page
  11. Pluskal T, Nakamura T, Villar-Briones A, Yanagida M: Metabolic profiling of the fission yeast S. pombe: quantification of compounds under different temperatures and genetic perturbation. Mol Biosyst 2010, 6(1):182–198. 10.1039/b908784b · DOI
  12. Tautenhahn R, Bottcher C, Neumann S: Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 2008, 9: 504. 10.1186/1471-2105-9-504 · DOI · Paper page
  13. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH: PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009, (37 Web Server):W623–633. 10.1093/nar/gkp456 · DOI · Paper page
  14. Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28(1):27–30. 10.1093/nar/28.1.27 · DOI · Paper page
  15. Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G: METLIN: a metabolite mass spectral database. Ther Drug Monit 2005, 27(6):747–751. 10.1097/01.ftd.0000179845.53213.39 · DOI · Paper page
  16. Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, Hau DD, Psychogios N, Dong E, Bouatra S, Mandal R, Sinelnikov I, Xia J, Jia L, Cruz JA, Lim E, Sobsey CA, Shrivastava S, Huang P, Liu P, Fang L, Peng J, Fradette R, Cheng D, Tzur D, Clements M, Lewis A, De Souza A, Zuniga A, Dawe M, Xiong Y, Clive D, Greiner R, Nazyrova A, Shaykhutdinov R, Li L, Vogel HJ, Forsythe I: HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 2009, (37 Database):D603–610. 10.1093/nar/gkn810 · DOI · Paper page
  17. Perkins DN, Pappin DJ, Creasy DM, Cottrell JS: Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20(18):3551–3567. 10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2 · DOI · Paper page
  18. Cleveland WS, Devlin SJ: Locally weighted regression - an approach to regression-analysis by local fitting. J Am Stat Assoc 1988, 83(403):596–610. 10.2307/2289282 · DOI · Paper page
  19. Benton HP, Wong DM, Trauger SA, Siuzdak G: XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization. Anal Chem 2008, 80(16):6382–6389. 10.1021/ac800795f · DOI · Paper page
  20. Keller A, Eng J, Zhang N, Li XJ, Aebersold R: A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol 2005, 1: 2005.0017. 10.1038/msb4100024 · DOI · Paper page
  21. Gehlenborg N, Yan W, Lee IY, Yoo H, Nieselt K, Hwang D, Aebersold R, Hood L: Prequips--an extensible software platform for integration, visualization and analysis of LC-MS/MS proteomics data. Bioinformatics 2009, 25(5):682–683. 10.1093/bioinformatics/btp005 · doi:10.1093/bioinformatics/btp005
  22. Kohlbacher O, Reinert K, Gropl C, Lange E, Pfeifer N, Schulz-Trieglaff O, Sturm M: TOPP--the OpenMS proteomics pipeline. Bioinformatics 2007, 23(2):e191–197. 10.1093/bioinformatics/btl299 · DOI · Paper page
  23. Oresic M, Seppanen-Laakso T, Yetukuri L, Backhed F, Hanninen V: Gut microbiota affects lens and retinal lipid composition. Exp Eye Res 2009, 89(5):604–607. 10.1016/j.exer.2009.06.018 · DOI

Cited by

See all 3,845

Metadata

Authors
Pluskal, T., Castillo, S., Villar-Briones, A., & Orešič, M.
Venue
BMC Bioinformatics
Published
2010-07-23
Volume
11
Issue
1
Pages
395-395
ISSN
1471-2105 (ISSN-L)
OpenAlex
W1989299822
Cited by
3,845
References
4
FWCI
26.70
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/2.0 (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. https://app.cochat.ai/papers/10.1186/1471-2105-11-395
The paper
Pluskal, T., Castillo, S., Villar-Briones, A., & Orešič, M. (2010). MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-11-395

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.