pmultiqc 0.0.4

Python package for quality control of proteomics datasets, based on multiqc package

pmultiqc

A library for proteomics QC report based on MultiQC framework. The library generates a QC report for the proteomicsLFQ pipeline. The library read the input of the proteomicsLFQ pipeline, with the following structure:

• consensus_ids : Identification results from ConsesusId tool in OpenMS
• dbs : Database used for the peptide/protein identification step.
• ids : Identification results from each search engine.
• logs : Log files for each independent step
• pipeline_info : Pipeline info.
• proteomics_lfq : Final results of the pipeline
  • out.consensusXML : Feature map output of OpenMS including non-id features.
  • out.mzTab : mzTab with results of the identification
  • out_msstats.csv : Input of MSstats software
  • out_triqler.tsv : Input of Triqler software
• raw_ids : Identification results from search + percolator

An example report can be found in multiqc_report.html

Most of the metrics are compute based on the out.mzTab and the consensus_ids which contains the filtered peptides and protein identifications.

Metrics

General report

• First we show the experimental design of the dataset project http://bigbio.xyz/pmultiqc/shared-peptides-star-align-stricter-pep-protein-FDR/multiqc_report.html#proteomicslfq_exp_design . This is a translation from the SDRF proteomics standard to OpenMS standard configuration.

• Summary Table: shows the number of spectra, % of identified spectra, total peptide count, total identified proteins (including protein groups - if two proteins are identified by the same peptide the two proteins are count) http://bigbio.xyz/pmultiqc/shared-peptides-star-align-stricter-pep-protein-FDR/multiqc_report.html#proteomicslfq_summary_table

• Number of peptides per Protein: Includes an histogram with the number of peptides per proteins http://bigbio.xyz/pmultiqc/shared-peptides-star-align-stricter-pep-protein-FDR/multiqc_report.html#num_of_pep_per_prot

Results tables

Two tables are shown to the user with the first 500 peptides in the mzTab and the first 500 PSMs. This tables enable to show some of the most relevant peptide and PSMs in the experiment.

Identification Statistics

A table called Spectra Tracking summarize the Identification results by mzML file. The table capture the following numbers:

• MS1_num: Number of MS1 in the mzML
• MS2_num: Number of MS2 in the mzML
• MSGF: Number of Peptides identified using the MSGF+ search engine
• Comet: Number of Peptides identified using the Comet search engine
• Final result of Spectra: Final number of PSMs reported in the mzTab?
• Final result of Peptides: Final number of Peptides identified in the mzTab

Peak Intensity Distribution

The Peak Intensity Distribution aims to show the Peak instensity in the MS2 spectra for all the experiment but also for the identified spectra. The plot split the intesity in chunks of 0-10, 10-100, 100-300, ... 6k-10k, >10k.

This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra. Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.