Metaproteomics Workflow (v2.0.2)

Metaproteomics workflow

Workflow Overview

The NMDC metaproteomics workflow is an end-to-end data processing pipeline for studying the proteomes of complex communities using data dependent LC-MS/MS data. The workflow matches the sequences from a metagenome annotation to fragmentation spectra (MS2) in the mass spectrometry data to identify peptide sequences, which are fragments of proteins contained in a processed proteomic sample. The metagenome annotation can either come from genomic sequencing that has been processed using the NMDC metagenomics pipeline, or for the more recent development that removes the paired NMDC metagenome requirement, by utilizing a neural network model to identify peptide sequences de novo from the fragmentation spectra (MS2). This sequence information is then used to identify model organisms with genome sequences archived on UniProt. The matched organisms genomes are then concatenated to create a pseudo-metagenome for in-depth peptide identification. Relative abundance measurements are derived by taking the area under the LC elution curve for the peptide from the intact spectra (MS1). Metaproteomics analyses identify the active organisms/species in a microbial community through the identification and relative quantification of protein expression.

Workflow Availability

The workflow is available in GitHub, https://github.com/microbiomedata/metaPro.
The corresponding Docker images are available in DockerHub:

Requirements for Execution

(recommendations are in bold):

  • WDL-capable Workflow Execution Tool (Cromwell)

  • Container Runtime that can load Docker images (Docker v2.1.0.3 or higher)

Hardware Requirements

  • 250 GB of disk space for static files, results, and intermediate files

  • Recommended to run with 32 GB of RAM or more

Workflow Dependencies

Sample Dataset

A sample dataset is forthcoming.

Input

A JSON file containing the following information:

  • metapro.mapper_list: List of datasets to process. Each dataset should contain:

    • genome_dir: Path to genome index directory (optional if running metagenome-free).

    • dataset_name: Name of dataset.

    • raw_file_loc: Path to the raw file.

    • dataset_id: Unique dataset identifier.

    • faa_file_loc: Path to translated protein FASTA file (optional if metagenome-free, see below).

    • faa_file_id: ID of the translated FASTA file (use “kaiko” if metagenome-free).

    • gff_file_loc: Path to GFF annotation file (optional if metagenome-free, see below).

    • gff_file_id: ID of the GFF file (use “kaiko” if metagenome-free).

    • analysis_id: Optional analysis identifier.

  • metapro.MASIC_PARAM_FILE_LOC: Path to MASIC parameter file.

  • metapro.MSGFPLUS_PARAM_FILE_LOC: Path to MS-GF+ parameter file.

  • metapro.CONTAMINANT_FILE_LOC: Path to contaminant FASTA file.

  • metapro.QVALUE_THRESHOLD: Q-value threshold (e.g. 0.05).

  • metapro.STUDY: Study name.

  • metapro.EXECUTION_RESOURCE: Execution resource (e.g. EMSL).

  • metapro.DATA_URL: Data URL for result publication.

  • metapro.MASIC_PARAM_FILE_ID: ID for MASIC parameter file.

  • metapro.MSGFPLUS_PARAM_FILE_ID: ID for MS-GF+ parameter file.

  • metapro.CONTAMINANT_FILE_ID: ID for contaminant FASTA file.

  • metapro.KAIKO_PARAM_FILE_LOC: Path to Kaiko parameter file (required if running metagenome-free).

  • metapro.METAGENOME_FREE: false to run with a matched metagenome.

If metapro.METAGENOME_FREE: true, faa_file_loc and gff_file_loc should be empty, and faa_file_id / gff_file_id should be set to any text string value, but a value must be present, and example is shown below.

An example input JSON file is shown below when running with a matched metagenome:

{
"metapro.mapper_list": [
   {
      "genome_dir": "nmdc_omprc-11-wfzppa38",
      "dataset_name": "SpruceW_P4_15A_22Jun17_Pippin_17-04-06",
      "annotation_name": "nmdc_wfmgan-11-pmh0a992.1",
      "raw_file_loc": "/mnt/d/NMDC/nmdc_bsm-13-bgefg837/SpruceW_P4_15A_22Jun17_Pippin_17-04-06.raw",
      "dataset_id": "nmdc_dobj-11-9gcej008",
      "faa_file_loc": "/mnt/d/NMDC/nmdc_bsm-13-bgefg837/nmdc_wfmgan-11-pmh0a992.1_proteins.faa",
      "faa_file_id": "nmdc_dobj-11-j5mh8584",
      "gff_file_loc": "/mnt/d/NMDC/nmdc_bsm-13-bgefg837/nmdc_wfmgan-11-pmh0a992.1_functional_annotation.gff",
      "gff_file_id": "nmdc_dobj-11-jq8ct440"
   }
],
"metapro.MASIC_PARAM_FILE_LOC": "/mnt/d/NMDC/reprocessing_spruce_2/LTQ-FT_10ppm_2014-08-06.xml",
"metapro.MSGFPLUS_PARAM_FILE_LOC": "/mnt/d/NMDC/reprocessing_spruce_2/MSGFPlus_Tryp_NoMods_20ppmParTol.txt",
"metapro.CONTAMINANT_FILE_LOC": "/mnt/d/NMDC/reprocessing_spruce_2/Tryp_Pig_Bov.fasta",
"metapro.QVALUE_THRESHOLD": "0.05",
"metapro.STUDY": "spruce",
"metapro.EXECUTION_RESOURCE": "EMSL",
"metapro.DATA_URL": "https://nmdcdemo.emsl.pnnl.gov/proteomics/results/",
"metapro.MASIC_PARAM_FILE_ID": "nmdc_dobj-11-hfx93f93",
"metapro.MSGFPLUS_PARAM_FILE_ID": "nmdc_dobj-11-h9637w90",
"metapro.CONTAMINANT_FILE_ID": "nmdc_dobj-11-sprrem27",
"metapro.METAGENOME_FREE": false,
"metapro.KAIKO_PARAM_FILE_LOC": "/mnt/d/NMDC/parameters/kaiko_defaults.yaml"
}

An example input JSON file is shown below when running with Kaiko, metagenome-free mode:

{
"metapro.mapper_list": [
   {
      "genome_dir": "nmdc_omprc-11-mkam5c55",
      "dataset_name": "Froze_Core_2015_N3_40_50_6_QE_26May16_Pippin_16-03-39",
      "raw_file_loc": "/mnt/d/NMDC/stegen/Froze_Core_2015_N3_40_50_6_QE_26May16_Pippin_16-03-39.raw",
      "dataset_id": "nmdc_dobj-11-hp2vmj63",
      "faa_file_loc": "",
      "faa_file_id": "kaiko",
      "gff_file_loc": "",
      "gff_file_id": "kaiko",
      "analysis_id": ""
   }
],
"metapro.MASIC_PARAM_FILE_LOC": "/mnt/d/NMDC/reprocessing_stegen_15/LTQ-FT_10ppm_2014-08-06.xml",
"metapro.MSGFPLUS_PARAM_FILE_LOC": "/mnt/d/NMDC/reprocessing_stegen_15/MSGFPlus_Tryp_NoMods_20ppmParTol.txt",
"metapro.CONTAMINANT_FILE_LOC": "/mnt/d/NMDC/reprocessing_stegen_15/Tryp_Pig_Bov.fasta",
"metapro.QVALUE_THRESHOLD": "0.05",
"metapro.STUDY": "stegen",
"metapro.EXECUTION_RESOURCE": "EMSL",
"metapro.DATA_URL": "https://nmdcdemo.emsl.pnnl.gov/proteomics/results/",
"metapro.MASIC_PARAM_FILE_ID": "nmdc_dobj-11-hfx93f93",
"metapro.MSGFPLUS_PARAM_FILE_ID": "nmdc_dobj-11-h9637w90",
"metapro.CONTAMINANT_FILE_ID": "nmdc_dobj-11-sprrem27",
"metapro.KAIKO_PARAM_FILE_LOC": "/mnt/d/NMDC_C/storage/parameters/kaiko_defaults.yaml",
"metapro.METAGENOME_FREE": true
}

Output

The workflow will produce four results files:

  • <dataset_id_faa_file_id>_Peptide_Report.tsv

  • <dataset_id_faa_file_id>_Protein_Report.tsv

  • <dataset_id_faa_file_id>_QC_metrics.tsv

  • <dataset_id_faa_file_id>_msgfplus_syn_PlusSICStats.txt

  • If METAGENOME_FREE is true, the FASTA file generated by Kaiko

  • If METAGENOME_FREE is true, the GFF file generated by Kaiko

Version History

  • 2.0.2

  • 2.0.1

  • 2.0.0

  • 1.2.1

  • 1.2.0

  • 1.0.0

Point of contact

Package maintainer: Cam Giberson <cameron.giberson@pnnl.gov>