Add support to handle (tdf) archives

.github/workflows/ci.yml

@@ -4,6 +4,7 @@ on:
   push:
     branches:
       - dev
+      - master
   pull_request:
   release:
     types: [published]
@@ -74,3 +75,37 @@ jobs:
       - name: Run pipeline with profile ${{ matrix.tests }}
         run: |
           nextflow run ${GITHUB_WORKSPACE} -profile ${{ matrix.tests }},docker --max_memory '6.GB' --max_cpus 2 --spectrum_batch_size 5000 --outdir ./results
+
+  # Define a second workflow only against main with additional tests: test_timstof and test_full
+  profile_main:
+    name: Run timsTOF profile tests
+    if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' && github.repository == 'nf-core/mhcquant' }}
+    runs-on: ubuntu-latest
+    env:
+      NXF_VER: ${{ matrix.nxf_ver }}
+      NXF_ANSI_LOG: false
+    strategy:
+      matrix:
+        include:
+          # Test pipeline minimum Nextflow version
+          - NXF_VER: "23.04.0"
+            NXF_EDGE: ""
+          # Test latest edge release of Nextflow
+          - NXF_VER: ""
+            NXF_EDGE: "1"
+        tests: ["test_timstof"]
+    steps:
+      - name: Check out pipeline code
+        uses: actions/checkout@v2
+      - name: Install Nextflow
+        env:
+          NXF_VER: ${{ matrix.NXF_VER }}
+          # Uncomment only if the edge release is more recent than the latest stable release
+          # See https://github.com/nextflow-io/nextflow/issues/2467
+          # NXF_EDGE: ${{ matrix.NXF_EDGE }}
+        run: |
+          wget -qO- get.nextflow.io | bash
+          sudo mv nextflow /usr/local/bin/
+      - name: Run pipeline with profile ${{ matrix.tests }}
+        run: |
+          nextflow run ${GITHUB_WORKSPACE} -profile ${{ matrix.tests }},docker --max_memory '6.GB' --max_cpus 2 --spectrum_batch_size 5000 --outdir ./results

CHANGELOG.md

@@ -8,6 +8,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### `Added`
 
 - Added MS²Rescore module with the underlying python CLI [#293](https://github.com/nf-core/mhcquant/pull/293)
+- Added support for handling various archive formats: `d|d.tar.gz|d.tar|d.zip|mzML.gz|raw|RAW|mzML` [#323](https://github.com/nf-core/mhcquant/pull/323)
+- Added test for timsTOF data [#323](https://github.com/nf-core/mhcquant/pull/323)
 - Added new flag `ms2pip_model_dir`, which allows specifying a cache directory for ms2pip models [#322](https://github.com/nf-core/mhcquant/pull/322)
 
 ### `Fixed`
@@ -22,6 +24,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Template update 2.13.1 [#313](https://github.com/nf-core/mhcquant/pull/313)
 - Template update 2.14.1 [#320](https://github.com/nf-core/mhcquant/pull/320)
 
+### `Changed`
+
+- Set identifcation mode as default and rename `--skip_quantification` to `--quantify` [#323](https://github.com/nf-core/mhcquant/pull/323)
+
 ### `Deprecated`
 
 - Removed MS²PIP and DeepLC modules. These feature generators are now called via the MS²Rescore framework [#293](https://github.com/nf-core/mhcquant/pull/293)

README.md

@@ -23,7 +23,7 @@
 
 It was specifically designed to analyse immunopeptidomics data, which deals with the analysis of affinity purified, unspecifically cleaved peptides that have recently been discussed intensively in [the context of cancer vaccines](https://www.nature.com/articles/ncomms13404).
 
-The workflow is based on the OpenMS C++ framework for computational mass spectrometry. RAW files (mzML) serve as inputs and a database search (Comet) is performed based on a given input protein database. FDR rescoring is applied using Percolator based on a competitive target-decoy approach (reversed decoys). For label free quantification all input files undergo identification based retention time alignment (MapAlignerIdentification), and targeted feature extraction matching ids between runs (FeatureFinderIdentification). In addition, a variant calling file (vcf) can be specified to translate variants into proteins that will be included in the database search and binding predictions on specified alleles (alleles.tsv) using MHCFlurry (Class 1) or MHCNugget (Class 2) can be directly run on the output peptide lists. Moreover, if a vcf file was specified, neoepitopes will automatically be determined and binding predictions can also directly be predicted for them.
+The workflow is based on the OpenMS C++ framework for computational mass spectrometry. RAW files (mzML) serve as inputs and a database search (Comet) is performed based on a given input protein database. FDR rescoring is applied using Percolator based on a competitive target-decoy approach (reversed decoys). For label free quantification all input files undergo identification based retention time alignment (MapAlignerIdentification), and targeted feature extraction matching ids between runs (FeatureFinderIdentification).
 
 The pipeline is built using [Nextflow](https://www.nextflow.io), 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](https://www.nextflow.io/docs/latest/dsl2.html) 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](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!
 

conf/modules.config

@@ -29,8 +29,32 @@ process {
 process {
 
     withName: 'THERMORAWFILEPARSER' {
-        ext.args = "-f 2"
+        ext.args   = "-f 2"
         ext.prefix = {"${raw.baseName}"}
+        publishDir = [
+            path: {"${params.outdir}"},
+            mode: params.publish_dir_mode,
+            enabled: false
+        ]
+    }
+
+    withName: 'UNTAR' {
+        publishDir  = [
+            path: {"${params.outdir}"},
+            mode: params.publish_dir_mode,
+            enabled: false
+        ]
+    }
+
+    withName: 'UNZIP' {
+        publishDir  = [
+            path: {"${params.outdir}"},
+            mode: params.publish_dir_mode,
+            enabled: false
+        ]
+    }
+
+    withName: 'GUNZIP' {
         publishDir  = [
             path: {"${params.outdir}"},
             mode: params.publish_dir_mode,
@@ -108,7 +132,7 @@ process {
 }
 
 process {
-    if (!params.skip_quantification) {
+    if (params.quantify) {
         withName: 'NFCORE_MHCQUANT:MHCQUANT:QUANT:OPENMS_IDSCORESWITCHER' {
             ext.args   = [
                 "-new_score COMET:xcorr",

conf/test.config

@@ -23,6 +23,4 @@ params {
     input = params.pipelines_testdata_base_path + 'mhcquant/testdata/HepG2_sample_sheet.tsv'
     fasta = params.pipelines_testdata_base_path + 'mhcquant/testdata/UP000005640_9606.fasta'
 
-    // Don't do quantification since this step needs a larger test dataset (-> test quantification using test_full)
-    skip_quantification = true
 }

conf/test_full.config

@@ -21,4 +21,7 @@ params {
     // Test multiple modifications
     fixed_mods    = 'Oxidation (M),Carbamidomethyl (C)'
     variable_mods = 'Oxidation (M),Carbamidomethyl (C)'
+
+    // Pipeline settings
+    quantify = true
 }

conf/test_ionannotator.config

@@ -20,11 +20,10 @@ params {
     max_time   = '6.h'
 
     // Input data
-    fasta = 'https://raw.githubusercontent.com/nf-core/test-datasets/mhcquant/testdata/UP000005640_9606.fasta'
-    input = 'https://raw.githubusercontent.com/nf-core/test-datasets/mhcquant/testdata/HepG2_sample_sheet.tsv'
+    input = params.pipelines_testdata_base_path + 'mhcquant/testdata/HepG2_sample_sheet.tsv'
+    fasta = params.pipelines_testdata_base_path + 'mhcquant/testdata/UP000005640_9606.fasta'
 
-    // Don't do quantification since this step needs a larger test dataset (-> test quantification using test_full)
-    skip_quantification = true
+    // Pipeline settings
     annotate_ions       = true
     use_x_ions          = true
     use_z_ions          = true

conf/test_mokapot.config

@@ -20,10 +20,9 @@ params {
     max_time   = '6.h'
 
     // Input data
-    fasta = 'https://raw.githubusercontent.com/nf-core/test-datasets/mhcquant/testdata/UP000005640_9606.fasta'
-    input = 'https://raw.githubusercontent.com/nf-core/test-datasets/mhcquant/testdata/HepG2_sample_sheet.tsv'
+    input = params.pipelines_testdata_base_path + 'mhcquant/testdata/HepG2_sample_sheet.tsv'
+    fasta = params.pipelines_testdata_base_path + 'mhcquant/testdata/UP000005640_9606.fasta'
 
-    // Don't do quantification since this step needs a larger test dataset (-> test quantification using test_full)
-    skip_quantification = true
+    // Pipeline settings
     rescoring_engine    = 'mokapot'
 }

conf/test_percolator.config

@@ -20,10 +20,9 @@ params {
     max_time   = '6.h'
 
     // Input data
-    fasta = 'https://raw.githubusercontent.com/nf-core/test-datasets/mhcquant/testdata/UP000005640_9606.fasta'
-    input = 'https://raw.githubusercontent.com/nf-core/test-datasets/mhcquant/testdata/HepG2_sample_sheet.tsv'
+    input = params.pipelines_testdata_base_path + 'mhcquant/testdata/HepG2_sample_sheet.tsv'
+    fasta = params.pipelines_testdata_base_path + 'mhcquant/testdata/UP000005640_9606.fasta'
 
-    // Don't do quantification since this step needs a larger test dataset (-> test quantification using test_full)
-    skip_quantification = true
+    // Pipeline settings
     rescoring_engine    = 'percolator'
 }

conf/test_timstof.config

@@ -0,0 +1,36 @@
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    Nextflow config file for running minimal tests on timsTOF data with  MS²Rescore and Percolator
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    Defines input files and everything required to run a fast and simple pipeline test.
+
+    Use as follows:
+        nextflow run nf-core/mhcquant -profile test_timstof,<docker/singularity> --outdir <OUTDIR>
+
+----------------------------------------------------------------------------------------
+*/
+
+params {
+    config_profile_name        = 'Test timsTOF profile'
+    config_profile_description = 'Minimal test dataset to check pipeline function with timsTOF data'
+
+    // Limit resources so that this can run on GitHub Actions
+    max_cpus   = 2
+    max_memory = '6.GB'
+    max_time   = '6.h'
+
+    // Input data
+    input = params.pipelines_testdata_base_path + 'mhcquant/testdata/sample_sheet_timstof.tsv'
+    fasta = params.pipelines_testdata_base_path + 'mhcquant/testdata/UP000005640_9606.fasta'
+
+    // Search settings
+    activation_method        = 'CID'
+    prec_charge              = '1:4'
+    precursor_mass_tolerance = 20
+    fragment_mass_tolerance  = 0.01
+    spectrum_batch_size      = 1000
+
+    // MS²Rescore settings
+    feature_generators       = 'ms2pip'
+    ms2pip_model             = 'timsTOF'
+}

docs/output.md

@@ -21,9 +21,12 @@ MzTab files contain many columns and annotate the most important information - h
 PEP  sequence  accession  best_search_engine_score[1]  retention_time  charge  mass_to_charge  peptide_abundance_study_variable[1]
 ```
 
-Most important to know is that in this format we annotated the Comet XCorr of each peptide identification in the `best_search_engine_score[1]` column and peptide quantities in the `peptide_abundance_study_variable` columns. If `--skip_quantification` is specified the `best_search_engine_score[1]` holds the percolator q-value.
+By default (only identification) the `best_search_engine_score[1]` holds the percolator q-value. If `--quantify` is specified we annotated the Comet XCorr of each peptide identification in the `best_search_engine_score[1]` column and peptide quantities in the `peptide_abundance_study_variable` columns.
 
-The TSV output file is an alternative output of [OpenMS](https://www.openms.de/) comprising similar information to the mzTab output. A brief explanation of the structure is listed below. See documentation of the format or PSI documentation for more information about [annotated scores and format](https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/TOPP_TextExporter.html).
+The TSV output file is an alternative output of [OpenMS](https://www.openms.de/) comprising similar information to the mzTab output. The TSV output of identification runs is a simple tab-delimited file holding information about FDR-filtered peptides and currently all values produced by `MS²Rescore`. The TSV file in quantification mode (by using `--quantify`) is more complex and described in more detail below
+
+<details markdown="1">
+<summary>TSV Quant</summary
 
 MAP contains information about the different mzML files that were provided initially
 
@@ -61,6 +64,10 @@ PEPTIDE contains information about peptide hits that were identified and corresp
 #PEPTIDE        rt      mz      score   rank    sequence        charge  aa_before       aa_after        score_type      search_identifier       accessions      FFId_category   fea
 ```
 
+See documentation of the format or PSI documentation for more information about [annotated scores and format](https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/TOPP_TextExporter.html).
+
+</details>
+
 ### Intermediate results
 
 <details  markdown="1">

modules.json

@@ -5,6 +5,11 @@
         "https://github.com/nf-core/modules.git": {
             "modules": {
                 "nf-core": {
+                    "gunzip": {
+                        "branch": "master",
+                        "git_sha": "3a5fef109d113b4997c9822198664ca5f2716208",
+                        "installed_by": ["modules"]
+                    },
                     "multiqc": {
                         "branch": "master",
                         "git_sha": "b7ebe95761cd389603f9cc0e0dc384c0f663815a",

modules/local/untar/environment.yml

@@ -0,0 +1,11 @@
+name: untar
+
+channels:
+  - conda-forge
+  - bioconda
+  - defaults
+
+dependencies:
+  - conda-forge::grep=3.11
+  - conda-forge::sed=4.7
+  - conda-forge::tar=1.34

modules/local/untar/main.nf

@@ -0,0 +1,54 @@
+process UNTAR {
+    tag "$archive"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
+        'https://depot.galaxyproject.org/singularity/ubuntu:20.04' :
+        'nf-core/ubuntu:20.04' }"
+
+    input:
+    tuple val(meta), path(archive)
+
+    output:
+    tuple val(meta), path("*.d"), emit: untar
+    path "versions.yml"         , emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def args  = task.ext.args ?: ''
+    def args2 = task.ext.args2 ?: ''
+    def prefix = task.ext.prefix ?: archive.baseName.replaceAll(/\.tar(\.gz)?$/, '')
+
+    """
+    mkdir $prefix
+    depth=\$(tar -tf "${archive}" | grep '\\.d/\$' | head -n 1 | tr -cd '/' | wc -c)
+
+    tar \\
+        -C $prefix \\
+        -xavf \\
+        $args \\
+        $archive \\
+        --strip-components=\$depth \\
+        $args2
+
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        untar: \$(echo \$(tar --version 2>&1) | sed 's/^.*(GNU tar) //; s/ Copyright.*\$//')
+    END_VERSIONS
+    """
+
+    stub:
+    prefix    = task.ext.prefix ?: ( meta.id ? "${meta.id}" : archive.toString().replaceFirst(/\.[^\.]+(.gz)?$/, ""))
+    """
+    mkdir $prefix
+    touch ${prefix}/file.txt
+
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        untar: \$(echo \$(tar --version 2>&1) | sed 's/^.*(GNU tar) //; s/ Copyright.*\$//')
+    END_VERSIONS
+    """
+}

modules/local/untar/meta.yml

@@ -0,0 +1,46 @@
+name: untar
+description: Extract files.
+keywords:
+  - untar
+  - uncompress
+  - extract
+tools:
+  - untar:
+      description: |
+        Extract tar.gz files.
+      documentation: https://www.gnu.org/software/tar/manual/
+      licence: ["GPL-3.0-or-later"]
+input:
+  - meta:
+      type: map
+      description: |
+        Groovy Map containing sample information
+        e.g. [ id:'test', single_end:false ]
+  - archive:
+      type: file
+      description: File to be untar
+      pattern: "*.{tar}.{gz}"
+output:
+  - meta:
+      type: map
+      description: |
+        Groovy Map containing sample information
+        e.g. [ id:'test', single_end:false ]
+  - untar:
+      type: directory
+      description: Directory containing contents of archive
+      pattern: "*/"
+  - versions:
+      type: file
+      description: File containing software versions
+      pattern: "versions.yml"
+authors:
+  - "@joseespinosa"
+  - "@drpatelh"
+  - "@matthdsm"
+  - "@jfy133"
+maintainers:
+  - "@joseespinosa"
+  - "@drpatelh"
+  - "@matthdsm"
+  - "@jfy133"

modules/local/unzip/environment.yml

@@ -0,0 +1,7 @@
+name: unzip
+channels:
+  - conda-forge
+  - bioconda
+  - defaults
+dependencies:
+  - conda-forge::p7zip=16.02