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).

<p align="center"> <img src="docs/images/quantms.png" alt="bigbio/quantms workflow overview" width="60%"> </p>

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 quantms-rescoring
4. Re-scoring peptide identifications percolator
5. Peptide identification FDR openms fdr tool
6. Modification localization onsite
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 quantms-rescoring
4. Re-scoring peptide identifications percolator
5. Peptide identification FDR openms fdr tool
6. Modification localization onsite
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.

<p align="center"> <img src="docs/images/quantms_metro.png" alt="bigbio/quantms metro map" width="70%"> </p>

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.

Supported file formats

The pipeline supports the following mass spectrometry data file formats:

• .raw - Thermo RAW files (automatically converted to mzML)
• .mzML - Open standard mzML files
• .d - Bruker timsTOF files (optionally converted to mzML when --convert_dotd is set)
• .dia - DIA-NN native binary format (passed through without conversion)

Compressed variants are supported for .raw, .mzML, and .d formats:

• .gz (gzip compressed)
• .tar (tar archive)
• .tar.gz or .tgz (tar gzip compressed)
• .zip (zip compressed)

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:

nextflow run bigbio