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wombat-p / WOMBAT-Pipelines

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WOMBAT-P Pipelines

Summary

wombat-p pipelines is a bioinformatics analysis pipeline that bundles different workflow for the analysis of label-free proteomics data with the purpose of comparison and benchmarking. It allows using files from the proteomics metadata standard SDRF.

It aims both for experienced end-users that want to test different workflows and configurations and developers that want to e.g. test a new software in a workflow setting. Contributions to this project are most welcome.

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. We used one of the nf-core templates.

This work contains four major different workflows for the analysis or label-free proteomics data, originating from LC-MS experiments.

  1. MaxQuant + NormalyzerDE
  2. SearchGui + Proline + PolySTest
  3. Compomics tools + FlashLFQ + MSqRob
  4. Tools from the Trans-Proteomic Pipeline + ROTS

Initialization and parameterization of the workflows is based on tools from the SDRF pipelines, the ThermoRawFileParser with our own contributions and additional programs from the wombat-p organizaion [https://github.com/wombat-p/Utilities] as well as our fork. This includes setting a generalized set of data analysis parameters and the calculation of a multiple benchmarks.

Usage

Installation and testing

  1. Install Nextflow (>=23.0.4)

  2. Install Docker or Singularity (you can follow this tutorial) (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

  3. Download the pipeline and test it on a minimal dataset with a single command:

    wget https://github.com/wombat-p/WOMBAT-Pipelines
nextflow run main.nf -profile test,YOURPROFILE

    Substitute wget with curlor alike.

Configuration and execution

  1. Setup of system for running the analysis

Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

  • The pipeline comes with config profiles called docker, singularity and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
  • If you are using docker, your host system might need to set a parameters that should stop the mono-based programs from failing when running large data sets on multiple threads. For that please set sudo sysctl -w vm.max_map_count=262144
  • If you are using singularity, setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
  • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.
  1. Start running your own analysis!

For a detailed explanation of the parameters, see below. Not all parameters are needed.

nextflow run main.nf --sdrf experimental_metadata.sdrf --fasta your_fasta_file.fasta --parameters your_parameters_yaml --raws thermo_raw_files --exp_design simple_experimental_design --workflow [other more specific parameters] -profile <docker/singularity/conda>

Input options and parameters

WOMBAT-P can run workflows using different (minimal) input, such as 1) with SDRF file (raw files can be given as parameter or are download from the location specified in the sdrf file): a) SDRF file + fasta file b) SDRF file + fasta file + experimental design file (will overwrite experimental design in sdrf) c) SDRF file + fasta file + experimental design file + yaml parameter file (will overwrite default and sdrf parameters)

2) without SDRF file: a) Raw files + fasta file + yaml parameter file b) Raw file + fasta file + yaml parameter file + experimental design file

-profile Set the profile and environment as described above

--sdrf This is a tab-delimited file containng details about experimental design and can also include all paramters given in the --parameters yaml file. Several data sets on the PRIDE repository come with an sdrf file which is can then be found toghether with the other deposited files. For the PXD001819, this would be https://ftp.pride.ebi.ac.uk/pride/data/archive/2015/12/PXD001819/sdrf.tsv See also the URL for SDRF files: https://github.com/bigbio/proteomics-metadata-standard/tree/master/annotated-projects and the description of the extended SDRF including data analysis parameters: https://github.com/bigbio/proteomics-metadata-standard/blob/master/sdrf-proteomics/Data-analysis-metadata.adoc

--fasta You also need a fasta database to run the database search in the workflows. Standard databases can be downloaded from UniProt

--parameters When deviating from the standard settings, use a yaml file containing new parameters settings. For more details about the different parameters and an example file, see https://github.com/bigbio/proteomics-metadata-standard/blob/master/sdrf-proteomics/Data-analysis-metadata.adoc As not all of these parameters are available for all workflows, see this table for an overview

--raws Without given sdrf file containing the paths to the raw data files (Thermo raw format) or if you have the files already downloaded, specify the wildcard (e.g. "*" or "?") to access the files on your system. We recommend putting this parameters in 'single quotes' as you might run into an error when using wildcards.

--exp_design An experimental design is automatically calculated from differences in the samples in the SDRF file. Alternatively, provide a tab-separated file with the five columns _rawfile, _expcondition, biorep, fraction and techrep.
_rawfile: raw file names without path. Incorrect or incomplete names will lead to errors.
_expcondition: arbitrary names for the sample groups. Files with the same sample group name will be considered replicates.
biorep: biological replicate with numbering starting with 1.
fraction: fraction number with numbering starting with 1.
techrep: technical replicate with numbering starting with 1.
Note: The numbering needs to be consistent and each line needs to be unique in the combination of _expcondition, biorep, fraction and techrep. See example.

--workflow Instead of running 'all' workflows (default), run only one of 'maxquant', 'proline', 'compomics' or 'tpp'

other parameters:

--comps (only maxquant workflow): Provide contrasts (specific comparisons) for the statistical tests. This is a list of comma-separated group names, e.g. "B-A,C-A" when having the three sample groups A, B and C

--proline_engine (only proline workflow): Define the search engine for the database search. Can be one or multiples of "xtandem", "msgf", "ms-amanda", "tide", "comet", "myrimatch", "meta_morpheus" and "andromeda". Note that not all engines necessarily work well with each data set.

You can add other NextFlow parameters as described extensively here

Valid data analysis parameters per workflow

The following parameters can be provided via the parameter yaml file (--parameters flag)

As not all parameters are available for each workflow, the last columns describe their applicability. Here, TRUE means that the parameter is available and can be modified.

parameter type sdrf name default maxquant proline compomics tpp
fixed_mods ontology modification parameters NT=Carbamidomethyl;TA=C;MT=fixed;AC=UNIMOD:4 TRUE TRUE TRUE TRUE
variable mods ontology modification parameters NT=oxidation;MT=variable;TA=M;AC=UNIMOD:35 TRUE TRUE TRUE TRUE
precursor_mass_tolerance string precursor mass tolerance 30 ppm TRUE TRUE TRUE TRUE
fragment_mass_tolerance string fragment mass tolerance 0.05 Da TRUE TRUE TRUE TRUE
enzyme ontology cleavage agent details Trypsin TRUE TRUE TRUE TRUE
fions class forward ions b FALSE TRUE TRUE TRUE
rions class reverse ions y FALSE TRUE TRUE TRUE
isotope_error_range integer isotope error range 0 FALSE TRUE TRUE TRUE
add_decoys boolean add decoys true FALSE TRUE TRUE TRUE
num_hits integer num peptide hits 1 FALSE FALSE FALSE FALSE
allowed_miscleavages integer allowed miscleavages 1 TRUE TRUE TRUE TRUE
min_precursor_charge integer minimum precursor charge 2 FALSE TRUE TRUE TRUE
max_precursor_charge integer maximum precursor charge 3 TRUE TRUE TRUE TRUE
min_peptide_length integer minimum peptide length 8 TRUE TRUE TRUE TRUE
max_peptide_length integer maximum peptide length 12 FALSE TRUE TRUE TRUE
max_mods integer maximum allowed modification 4 TRUE TRUE TRUE TRUE
ident_fdr_psm float fdr on psm level 0.01 TRUE TRUE TRUE TRUE
ident_fdr_peptide float fdr on peptide level 0.01 TRUE TRUE TRUE TRUE
ident_fdr_protein float fdr on protein level 0.01 TRUE TRUE Not clear Not clear
match_between_runs boolean run match between runs true TRUE FALSE TRUE Not available
protein_inference class protein inference method unique TRUE FALSE TRUE TRUE
quantification_method class quantification method intensity FALSE FALSE FALSE FALSE
summarization_proteins class summarization of proteins method sum_abs FALSE FALSE FALSE FALSE
min_num_peptides integer minimum number of peptides per protein 2 TRUE TRUE TRUE TRUE
summarization_psms class summarization of psms method sum_abs FALSE FALSE FALSE FALSE
quant_transformation class transformation of quantitative values log FALSE FALSE FALSE FALSE
normalization_method class normalization method median TRUE FALSE FALSE FALSE
run_statistics boolean run statistical tests true TRUE TRUE TRUE TRUE
fdr_method class method for correction of multiple testing benjamini-hochberg FALSE FALSE FALSE FALSE
fdr_threshold float threshold for statistical test fdr 0.01 By filtering the results By filtering the results By filtering the results By filtering the results

Workflow output

Intermediate and final files are provided in the results folder or the folder specified via the outdir parameter.

On top of the workflow-specific output, a standardized tabular format on both peptide (stand_pep_quant_merged.csv) and protein (stand_prot_quant_merged.csv) level is given.

For each of the workflows, WOMBAT-Pipelines calculated the same set of benchmarks for more systematic and thorough comparison between workflows and/or between different values of the data analysis parameters. For details about the benchmarks, see the following table:

Category Aspect Subgroup Name Name in JSON file Definition Value
Functionality Traceability Spectra Tracable spectra TraceableSpectra Results tracable to original spectra Y/N
Functionality Traceability Spectra Universal spectrum identifiers UniversalSpectumIdentifiers Workflow generates USIs (Universal Spectrum Identifier) Y/N
Functionality Traceability Spectra Peptide to spectra PeptideToSpectra Corresponding spectrum numbers/ids available from peptide level Y/N
Functionality Traceability Spectra Protein to spectra ProteinToSpectra Corresponding spectrum numbers/ids available from protein level Y/N
Functionality Traceability File names Results to raw files ResultsToRawFiles Raw input file names preserved in tables on PSM/peptide/protein level Y/N
Functionality Traceability File names Public raw files PublicRawFiles Raw files publicly available Y/N
Functionality Traceability Parameters Experimental design ExperimentalDesign Biological and technical replicates can be identified in results Y/N
Functionality Performance Identification PSM number PSMNumber Number of identified PSMs passing preset FDR Integer
Functionality Performance Identification Peptide number PeptideNumber Number of uniquely identified peptide identifications passing preset FDR Integer
Functionality Performance Identification Protein number ProteinNumber Number of uniquely identified protein identifications passing preset FDR Integer
Functionality Performance Identification Protein group number ProteinGroupNumber Number of different protein groups passing preset FDR Integer
Functionality Performance Identification Peptide coverage PeptideCoverage Percentage of peptides identified in all samples Double
Functionality Performance Identification Protein coverage ProteinCoverage Percentage of proteins identified in all samples Double
Functionality Performance Identification Peptides per protein PeptidesPerProtein Distribution of peptides per protein group Set of Integer
Functionality Performance Quantification Correlation peptides CorrelationPeptides Mean of Pearsson correlation of protein abundances between replicates (log2-scale) Double
Functionality Performance Quantification Correlation proteins CorrelationProteins Mean of Pearsson correlation of peptide abundances between replicates (log2-scale) Double
Functionality Performance Quantification Number peptides NumberOfPeptides Number of quantified peptides with at least 50% coverage Integer
Functionality Performance Quantification Number protein groups NumberOfProteinGroups Number of quantified proteins groups with at least 50% coverage Integer
Functionality Performance Quantification Dynamic peptide range DynamicPeptideRange Difference of peptide abundance (top 5% versus bottom 5% quantile) Double
Functionality Performance Quantification Dynamic protein range DynamicProteinRange Difference of protein abundance (top 5% versus bottom 5% quantile) Double
Functionality Performance Statistics Differentially regulated peptides 5% DifferentialRegulatedPeptides5Perc Number of differentially regulated peptides with FDR below 5% Set of Double
Functionality Performance Statistics Differentially regulated proteins 5% DifferentialRegulatedProteins5Perc Number of differentially regulated proteins with FDR below 5% Set of Double
Functionality Performance Statistics Differentially regulated peptides 1% DifferentialRegulatedPeptides1Perc Number of differentially regulated peptides with FDR below 1% Set of Double
Functionality Performance Statistics Differentially regulated proteins 1% DifferentialRegulatedProteins1Perc Number of differentially regulated proteins with FDR below 1% Set of Double
Functionality Performance Statistics Missing peptide values MissingPeptideValues Percentage of missing values in entire peptide set Double
Functionality Performance Statistics Missing protein values MissingProteinValues Percentage of missing values in entire protein set Double
Functionality Performance Digestion Digestion efficiency Efficiency Distribution of number of miscleavages Set of Double
Functionality Performance PTMs PTM Distribution PTMDistribution Percentage of peptides with PTM xyz Set of Double
Functionality Performance PTMs PTM Occupancy PTMOccupancy Distribution of peptides with 1,2,... PTMs Set of Double
Functionality Parameter Identification Database size DatabaseSize Number of entries in fasta file Integer
Functionality Parameter Identification Canonical sequences CanonicalSequences Database includes canonical sequences Y/N
Functionality Parameter identification PTM localization PTMLocalization Is PTM localization scoring software included in the workflow Y/N

Contribute

Contributions to change and modify the workflows are most welcome. For this, please create a fork and add your changes. We strongly recommend reaching out to us either via email or by creating an issue in this repository, as we then help adding the new implementation.

Credits

nf-core/wombat was originally written by the members of the ELIXIR Implementation study Comparison, benchmarking and dissemination of proteomics data analysis pipelines under the lead of Veit Schwämmle and major participation of David Bouyssié and Fredrik Levander.

Citations

Preprint available: https://www.biorxiv.org/content/10.1101/2023.10.02.560412v1

As the workflows are using an nf-core template, we refer to the publication:

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.