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quantms

Quantitative mass spectrometry workflow. Currently supports proteomics experiments with complex experimental designs for DDA-LFQ, DDA-Isobaric and DIA-LFQ quantification.

https://github.com/bigbio/quantms

Science Score: 67.0%

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Keywords

lfq mass-spectrometry proteogenomics proteomics tmt
Last synced: 6 months ago · JSON representation ·

Repository

Quantitative mass spectrometry workflow. Currently supports proteomics experiments with complex experimental designs for DDA-LFQ, DDA-Isobaric and DIA-LFQ quantification.

Basic Info
  • Host: GitHub
  • Owner: bigbio
  • License: mit
  • Language: Nextflow
  • Default Branch: master
  • Homepage: https://quantms.org
  • Size: 25.1 MB
Statistics
  • Stars: 53
  • Watchers: 7
  • Forks: 44
  • Open Issues: 60
  • Releases: 4
Topics
lfq mass-spectrometry proteogenomics proteomics tmt
Created over 4 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

bigbio/quantms bigbio/quantms

GitHub Actions CI Status GitHub Actions Linting Status AWS CI Cite with Zenodo nf-test

Nextflow run with docker run with singularity Launch on Seqera Platform

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Introduction

bigbio/quantms is a bioinformatics best-practice analysis pipeline for Quantitative Mass Spectrometry (MS). Currently, the workflow supports three major MS-based analytical methods: (i) Data dependant acquisition (DDA) label-free and Isobaric quantitation (e.g. TMT, iTRAQ); (ii) Data independent acquisition (DIA) label-free quantification (for details see our in-depth documentation on quantms).

bigbio/quantms workflow overview

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website. This gives you a hint on which reports and file types are produced by the pipeline in a standard run. The automatic continuous integration tests on every pull request evaluate different workflows, including peptide identification, quantification for LFQ, LFQ-DIA, and TMT test datasets.

Pipeline summary

bigbio/quantms allows uses to perform analyses of three main types of analytical mass spectrometry-based quantitative methods: DDA-LFQ, DDA-ISO, DIA-LFQ. Each of these workflows share some processes but also includes their own steps. In summary:

DDA-LFQ (data-dependent label-free quantification)

  1. RAW file conversion to mzML (thermorawfileparser)
  2. Peptide identification using comet and/or msgf+
  3. (Optional) Add extra PSM features using ms2rescore
  4. Re-scoring peptide identifications percolator
  5. Peptide identification FDR openms fdr tool
  6. Modification localization luciphor
  7. Quantification: Feature detection proteomicsLFQ
  8. Protein inference and quantification proteomicsLFQ
  9. QC report generation pmultiqc
  10. Normalization, imputation, significance testing with MSstats

DDA-ISO (data-dependent quantification via isobaric labelling)

  1. RAW file conversion to mzML (thermorawfileparser)
  2. Peptide identification using comet and/or msgf+
  3. (Optional) Add extra PSM features using ms2rescore
  4. Re-scoring peptide identifications percolator
  5. Peptide identification FDR openms fdr tool
  6. Modification localization luciphor
  7. Extracts and normalizes isobaric labeling IsobaricAnalyzer
  8. Protein inference ProteinInference or Epifany for bayesian inference.
  9. Protein Quantification ProteinQuantifier
  10. QC report generation pmultiqc
  11. Normalization, imputation, significance testing with MSstats

DIA-LFQ (data-independent label-free quantification)

  1. RAW file conversion to mzML when RAW as input(thermorawfileparser)
  2. Performing an optional step: Converting .d to mzML when bruker data as input and set convert_dotd to true
  3. DIA-NN analysis dia-nn
  4. Generation of output files (msstats)
  5. QC reports generation pmultiqc

Functionality overview

A graphical overview of suggested routes through the pipeline depending on context can be seen below.

bigbio/quantms metro map

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, find or create a sample-to-data relationship file (SDRF). Have a look at public datasets that were already annotated here. Those SDRFs should be ready for one-command re-analysis and you can just use the URL to the file on GitHub, e.g., https://raw.githubusercontent.com/bigbio/proteomics-sample-metadata/master/annotated-projects/PXD000396/PXD000396.sdrf.tsv. If you create your own, please adhere to the specifications and point the pipeline to your local folder or a remote location where you uploaded it to.

The second requirement is a protein sequence database. We suggest downloading a database for the organism(s)/proteins of interest from Uniprot.

Now, you can run the pipeline using:

bash nextflow run bigbio/quantms \ -profile <docker/singularity/.../institute> \ --input project.sdrf.tsv \ --database database.fasta \ --outdir <OUTDIR>

[!NOTE] Conda is no longer supported in this pipeline. Please use Docker, Singularity, or other container-based profiles.

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Additional documentation and tutorial

The bigbio/quantms pipeline comes with a stand-alone full documentation including examples, benchmarks, and detailed explanation about the data analysis of proteomics data using quantms.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

bigbio/quantms was originally written by: Chengxin Dai (@daichengxin), Julianus Pfeuffer (@jpfeuffer) and Yasset Perez-Riverol (@ypriverol).

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #quantms channel (you can join with this invite).

How to cite

If you use bigbio/quantms for your analysis, please cite it using the following citation:

quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data

Chengxin Dai, Julianus Pfeuffer, Hong Wang, Ping Zheng, Lukas Käll, Timo Sachsenberg, Vadim Demichev, Mingze Bai, Oliver Kohlbacher & Yasset Perez-Riverol

Nat Methods. 2024 July 4. doi: 10.1038/s41592-024-02343-1.

How to cite nf-core

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

Owner

  • Name: BigBio Stack
  • Login: bigbio
  • Kind: organization
  • Email: proteomicsstack@gmail.com
  • Location: Cambridge, UK

Provide big data solutions Bioinformatics

Citation (CITATIONS.md) 

# bigbio/quantms: Citations

## [Pipeline](https://www.researchsquare.com/article/rs-3002027/v1)

> Dai C, Pfeuffer J, Wang H, Zheng P, Käll L, Sachsenberg T, Demichev V, Bai M, Kohlbacher O, Perez-Riverol Y. quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data. Nat Methods. 2024 Jul 4. doi: 10.1038/s41592-024-02343-1. Epub ahead of print. PMID: 38965444.

## Pipeline research manuscripts

- [proteogenomics](https://pubmed.ncbi.nlm.nih.gov/34904638/)

  > Umer HM, Audain E, Zhu Y, Pfeuffer J, Sachsenberg T, Lehtiö J, Branca RM, Perez-Riverol Y. Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides. Bioinformatics. 2022 Feb 7;38(5):1470-1472. doi: 10.1093/bioinformatics/btab838. PMID: 34904638; PMCID: PMC8825679.

  > Wang, Dong, Robbin Bouwmeester, Ping Zheng, Chengxin Dai, Aniel Sanchez Puente, Kunxian Shu, Mingze Bai, Husen M. Umer, and Yasset Perez-Riverol. "Proteogenomics analysis of human tissues using pangenomes." bioRxiv (2024): 2024-05.

- [lfq dda benchmark](https://pubmed.ncbi.nlm.nih.gov/37220883/)

  > Bai M, Deng J, Dai C, Pfeuffer J, Sachsenberg T, Perez-Riverol Y. LFQ-Based Peptide and Protein Intensity Differential Expression Analysis. J Proteome Res. 2023 Jun 2;22(6):2114-2123. doi: 10.1021/acs.jproteome.2c00812. Epub 2023 May 23. PMID: 37220883; PMCID: PMC10243145.

- [tissue absolute expression](https://pubmed.ncbi.nlm.nih.gov/37488995/)

  > Wang H, Dai C, Pfeuffer J, Sachsenberg T, Sanchez A, Bai M, Perez-Riverol Y. Tissue-based absolute quantification using large-scale TMT and LFQ experiments. Proteomics. 2023 Jul 24:e2300188. doi: 10.1002/pmic.202300188. Epub ahead of print. PMID: 37488995.

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

- [thermorawfileparser](https://pubmed.ncbi.nlm.nih.gov/31755270/)

  > Hulstaert N, Shofstahl J, Sachsenberg T, Walzer M, Barsnes H, Martens L, Perez-Riverol Y. ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion. J Proteome Res. 2020 Jan 3;19(1):537-542. doi: 10.1021/acs.jproteome.9b00328. Epub 2019 Dec 6. PMID: 31755270; PMCID: PMC7116465.

- [sdrf-pipelines](https://pubmed.ncbi.nlm.nih.gov/34615866/)

  > Dai C, Füllgrabe A, Pfeuffer J, Solovyeva EM, Deng J, Moreno P, Kamatchinathan S, Kundu DJ, George N, Fexova S, Grüning B, Föll MC, Griss J, Vaudel M, Audain E, Locard-Paulet M, Turewicz M, Eisenacher M, Uszkoreit J, Van Den Bossche T, Schwämmle V, Webel H, Schulze S, Bouyssié D, Jayaram S, Duggineni VK, Samaras P, Wilhelm M, Choi M, Wang M, Kohlbacher O, Brazma A, Papatheodorou I, Bandeira N, Deutsch EW, Vizcaíno JA, Bai M, Sachsenberg T, Levitsky LI, Perez-Riverol Y. A proteomics sample metadata representation for multiomics integration and big data analysis. Nat Commun. 2021 Oct 6;12(1):5854. doi: 10.1038/s41467-021-26111-3. PMID: 34615866; PMCID: PMC8494749.

- [OpenMS](https://pubmed.ncbi.nlm.nih.gov/27312411/)

  > Röst HL., Sachsenberg T., Aiche S., Bielow C., Weisser H., Aicheler F., Andreotti S., Ehrlich HC., Gutenbrunner P., Kenar E., Liang X., Nahnsen S., Nilse L., Pfeuffer J., Rosenberger G., Rurik M., Schmitt U., Veit J., Walzer M., Wojnar D., Wolski WE., Schilling O., Choudhary JS, Malmström L., Aebersold R., Reinert K., Kohlbacher O. (2016). OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nature methods, 13(9), 741–748. doi: 10.1038/nmeth.3959. PubMed PMID: 27575624; PubMed Central PMCID: PMC5617107.

- [DIA-NN](https://pubmed.ncbi.nlm.nih.gov/31768060/)

  > Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods. 2020 Jan;17(1):41-44. doi: 10.1038/s41592-019-0638-x. Epub 2019 Nov 25. PMID: 31768060; PMCID: PMC6949130.

- [MSstats](https://www.ncbi.nlm.nih.gov/pubmed/24794931/)

  > Choi M., Chang CY., Clough T., Broudy D., Killeen T., MacLean B., Vitek O. (2014). MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics (Oxford, England), 30(17), 2524–2526. doi: 10.1093/bioinformatics/btu305. PubMed PMID: 24794931.

- [Comet](https://www.ncbi.nlm.nih.gov/pubmed/23148064/)

  > Eng JK., Jahan TA., Hoopmann MR. (2013). Comet: an open-source MS/MS sequence database search tool. Proteomics, 13(1), 22–24. doi: 10.1002/pmic.201200439. PubMed PMID: 23148064

- [MS-GF+](https://www.ncbi.nlm.nih.gov/pubmed/25358478/)

  > Kim S., Pevzner PA. (2014). MS-GF+ makes progress towards a universal database search tool for proteomics. Nature communications, 5, 5277. doi: 10.1038/ncomms6277. PubMed PMID: 25358478; PubMed Central PMCID: PMC5036525

- [Sage](https://pubmed.ncbi.nlm.nih.gov/37819886/)

  > Lazear MR. Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale. J Proteome Res. 2023 Oct 11. doi: 10.1021/acs.jproteome.3c00486. Epub ahead of print. PMID: 37819886.

- [Epifany](https://pubmed.ncbi.nlm.nih.gov/31975601/)

  > Pfeuffer J, Sachsenberg T, Dijkstra TMH, Serang O, Reinert K, Kohlbacher O. EPIFANY: A Method for Efficient High-Confidence Protein Inference. J Proteome Res. 2020 Mar 6;19(3):1060-1072. doi: 10.1021/acs.jproteome.9b00566. Epub 2020 Feb 13. PMID: 31975601; PMCID: PMC7583457.

- [Triqler](https://pubmed.ncbi.nlm.nih.gov/30482846/)

  > The M, Käll L. Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics. Mol Cell Proteomics. 2019 Mar;18(3):561-570. doi: 10.1074/mcp.RA118.001018. Epub 2018 Nov 27. PMID: 30482846; PMCID: PMC6398204.

- [luciphor](https://pubmed.ncbi.nlm.nih.gov/23918812/)

  > Fermin D, Walmsley SJ, Gingras AC, Choi H, Nesvizhskii AI. LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach. Mol Cell Proteomics. 2013 Nov;12(11):3409-19. doi: 10.1074/mcp.M113.028928. Epub 2013 Aug 5. PMID: 23918812; PMCID: PMC3820951.

- [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)

  > Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.

## Software packaging/containerisation tools

- [Anaconda](https://anaconda.com)

  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)

  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

- [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

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Dependencies

.github/workflows/awsfulltest.yml actions
  • actions/upload-artifact v3 composite
  • nf-core/tower-action v3 composite
.github/workflows/awstest.yml actions
  • actions/upload-artifact v3 composite
  • nf-core/tower-action v3 composite
.github/workflows/branch.yml actions
  • mshick/add-pr-comment v1 composite
.github/workflows/ci.yml actions
  • actions/checkout v2 composite
  • actions/upload-artifact v1 composite
  • conda-incubator/setup-miniconda v2 composite
  • nf-core/setup-nextflow v1 composite
.github/workflows/fix-linting.yml actions
  • actions/checkout v3 composite
  • actions/setup-node v2 composite
.github/workflows/linting.yml actions
  • actions/checkout v2 composite
  • actions/setup-node v2 composite
  • actions/setup-python v3 composite
  • actions/upload-artifact v2 composite
  • mshick/add-pr-comment v1 composite
  • nf-core/setup-nextflow v1 composite
  • psf/black stable composite
.github/workflows/linting_comment.yml actions
  • dawidd6/action-download-artifact v2 composite
  • marocchino/sticky-pull-request-comment v2 composite