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Pipeline Catalog: Proteomics

Uploading Data

Spectral data generated from proteomics experiments can be uploaded in either mzML or RAW file format. In addition, the experimental design structure which describes that set of files should be uploaded as a samplesheet.csv for each dataset.

Note: The path for each of the spectra files in the samplesheet.csv should not include any parent folders. For example, instead of /path/to/dataset/Sample1.mzML, just use Sample1.mzML.

Reference Data - Genome

The set of protein targets used for analyzing peptide spectra must be provided as a Reference Genome (FASTA) on the References page.

Note: If the protein FASTA provided as a reference does not already contain 'decoy' sequences, then the "Add Decoys" option must be selected when running the analysis.

Data-Dependent Acquisition – Label Free Quantitation (DDA-LFQ)

Analysis of DDA-LFQ data is performed using the nf-core/quantms pipeline.

Input Data:

Input files can be processed from either the .mzML or .raw file formats. The input for this workflow should be uploaded as either the "Proteomics (RAW)" or "Proteomics (mzML)" dataset type. Include a full batch of samples to be processed as a single dataset for optimal processing.

Sample Metadata:

In addition to the spectral data files, metadata may be provided to annotate specific details of the experimental design. To provide sample-level metadata appropriately, upload a samplesheet.csv as part of the dataset.

The metadata included in the samplesheet.csv mirrors the Sample and Data Relationship (SDRF) Format for Proteomics, with the important distinction that the samplesheet.csv must be comma-delimited (and not tab-delimited as expected for SDRF).

Metadata Fields

Required Metadata Fields:

When providing a samplesheet.csv the columns sample and file must be provided. Every sample in the dataset must be listed on its own line. If there are multiple replicate files from the same sample, use additional columns file_2, file_3, etc.

Note: The file column should list the relative path of the file within the uploaded folder, not the full URI.

The information in those two columns is used to automatically populate the columns:

  • source name (from sample)
  • comment[file uri] (from file)
  • comment[data file] (from file)

If no samplesheet.csv is provided the following fields will be populated with default values:

  • characteristics[organism]: Homo sapiens
  • characteristics[organism part]: not applicable
  • characteristics[disease]: not applicable
  • characteristics[cell type]: not applicable
  • characteristics[biological replicate]: not applicable
  • comment[fraction identifier]: 1
  • comment[technical replicate]: (autoincrement files with the same sample)
  • comment[cleavage agent details]: NT=Trypsin
  • comment[instrument]: NT=LTQ Orbitrap XL
  • comment[label]: NT=label free sample

Optional Metadata Fields:

The following fields may also be provided as columns in the samplesheet.csv, with the values being passed along to the workflow for processing:

  • characteristics[cell line]
  • characteristics[ancestry category]
  • characteristics[age]
  • characteristics[sex]
  • characteristics[developmental stage]
  • characteristics[individual]
  • material type
  • technology type
  • comment[flow rate chromatogram]
  • comment[gradient time]
  • comment[fractionation method]
  • comment[modification parameters]
  • comment[modification parameters]
  • comment[modification parameters]
  • comment[fragment mass tolerance]
  • comment[precursor mass tolerance]
  • factor value[flow rate chromatogram]
  • factor value[gradient time]

Analyze Subset of Files:

If you wish to analyze only a portion of the files in a dataset, use the "Analyze Subset" dropdown menu when launching the workflow. If no files are selected (the default), then everything present in the dataset will be analyzed. If any files are selected, then only those files will be analyzed.

Workflow Overview:

nf-core/quantms workflow overview

While the nf-core/quantms workflow supports a variety of data types (DDA-LFQ, DDA-Isobaric, and DIA-LFQ), each of those approaches is provided as a distinct pipeline within Cirro.

For more details on the parameters available, see the full workflow documentation.

Note: The DDA-LFQ pipeline in Cirro is run with the parameters: acquisition_method: "dda", labelling_type: "label free sample"