Metaproteomic Workflow (v1.2.1)

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 from GitHub uses all the listed docker images to run all third-party tools. 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:

  1. A mapper list “metapro.mapper_list” containing an array of JSON objects for mapping paths of .raw files, FASTA, GFF, and additional metadata a. “genome_dir”: The NMDC Identifier for the parent omics processing record, used when generating NMDC metadata. This input will be renamed in the future. b. “dataset_name”: The name of the mass spec data file without extension. This input will be removed in the future. c. “annotation_name”: The FASTA file name without extension. This input will be removed in the future. d. “raw_file_loc”: The local filepath to the mass spec data file. e. “dataset_id”: The NMDC Identifier of the mass spec data file, used when generating NMDC metadata. f. “faa_file_loc”: The local filepath to the FASTA file. g. “faa_file_id”: The NMDC Identifier of the FASTA file, used when generating NMDC metadata. h. “gff_file_loc”: The local filepath to the GFF file. i. “gff_file_id”: The NMDC Identifier of the GFF file, used when generating NMDC metadata.

  2. Path to a MS-GF+ parameter file, example file

  3. Path to a MASIC parameter file, example file

  4. Path to a contaminant FASTA file

  5. The Q-Value threshold

  6. A study name, used when generating NMDC metadata.

  7. The name of the execution resource this workflow was ran on, used when generating NMDC metadata.

  8. The base URL used to construct the location for the workflows output reports, used when generating NMDC metadata.

  9. The NMDC Identifier of the MASIC parameter file, used when generating NMDC metadata.

  10. The NMDC Identifier of the MS-GF+ parameter file, used when generating NMDC metadata.

  11. The NMDC Identifier of the contaminant FASTA file, used when generating NMDC metadata.

  12. A boolean value indicating whether Kaiko should be used or not.

  13. Path to a Kaiko configuration file.

An example input JSON file is shown below:

{
"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"
}

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

  • 1.2.1

  • 1.2.0

  • 1.0.0

Point of contact

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