{"doi":"10.5281/zenodo.3742218","title":"Curation and ISA representation of a SARS-Cov2/Covid-19 Proteomics Dataset - PXD107710 - ISA representation","abstract":"Curation and ISA representation of a SARS-Cov2/Covid-19 Proteomics Dataset deposited in PRIDE database with accession number: PXD107710 ISA-Tab annotation for the \"SARS-CoV-2 infected host cell proteomics reveal potential therapy targets\" publication. Github repository: https://github.com/ISA-tools/PXD017710 This is part of an effort to (re-)annotate: https://dx.doi.org/10.21203/rs.3.rs-17218/v1 Additional work done as part of: https://github.com/virtual-biohackathons/covid-19-bh20 https://github.com/virtual-biohackathons/covid-19-bh20/wiki/FairData Proteomics data Available from PRIDE at https://www.ebi.ac.uk/pride/archive/projects/PXD017710
and [MassIVE/CCMS Maestro+MSstats reanalysis of MSV000085096 / PXD017710] ISA-Tab representation: Rationale: Demonstrate suitability of the ISA format for representing MS based protein profiling experiment with more granularity and details, thus providing a better representation of the experiment design.
The formatting and re-annotation are based on information extracted from:
- the original publication
- the supplementary tables available from the publishers site
- the 'filtered-results.csv' helper file as supplied to @sneumann during the HUPO-PSI meeting March 2020
Viewing the ISA-tab formatted and re-annotated PXD017710 with ISATab-Viewer Viewing the ISA-tab formatted and re-annotated PXD017710 locally, do the following: ```bash
python -m http.server 8000
``` Then point your browser to `http://0.0.0.0:8000/isaviewer-demo.html` Curation tasks performed: * initial structure of the study design in ISA format: * linkage of Proteome and Translatome data (supplementary material) to ISA assay tables (via Derived Data File) * processing the Proteome and Translatome data (supplementary material) with python pandas library to generate the following csv files: - proteome_intensities_long_table_ggplot2.txt
- proteome_diffanal_ratio_pvalue_long_table_ggplot2.txt
- translatome_intensities_long_table_ggplot2.txt
- translatome_diffanal_ratio_pvalue_long_table_ggplot2
The files are `long table` corresponding to a `melt` on the Excel file originally generated by the users and can be readily loaded in R ggplot2 library for graphical representation.
The statistical relevant elements have been annotated with the STATO ontology and the tables comply with a Frictionless.io Data Package.
The jupyter notebook for the transformation is available. * conversion of raw data to mzML format: detailed in https://github.com/ISA-tools/PXD017710 install docker:
```bash
>brew update
>brew install docker
``` sign in to docker
```bash
>docker start
>docker login
``` pull docker container for ProteoWizard:
```bash
>docker pull chambm/pwiz-i-agree-to-the-vendor-licenses
``` :warning: be sure to sign-up and login to https://hub.docker.com/ in order to be able to reach https://hub.docker.com/r/chambm/pwiz-skyline-i-agree-to-the-vendor-licenses
run the pwiz tool from the container over the raw data:
```bash
docker run -it --rm -e WINEDEBUG=-all -v /Users/Downloads/PXD017710/raw/:/data chambm/pwiz-skyline-i-agree-to-the-vendor-licenses wine msconvert /data/*.raw --mzML
```
* ontology markup for:
* declaration of independent variables as ISA Study Factors:{biological agent, dose, time point, replicate} ->OBI
* Taxonomic information (host cells and virus) -> NCBITaxonomy
* Cell line: CaCo-2 cells -> Cell Line Ontology
* Disease: Colon Cancer -> Human Phenotype Ontology
* MS specific aspect (TMT reagent, instrument ... ) -> PSI-MS
* Statistical Tests -> STATO
Unresolved curatorial issues: 1. ambiguities related to Tandem Mass Tag labelling protocol
- the publication mentions TMT11 (see Figure 2 in https://www.researchsquare.com/article/rs-17218/v1)
- the information available from PRIDE mentions TMT6 (https://www.ebi.ac.uk/pride/archive/projects/PXD017710)
This may require another round of annotation on the TMT agents and fractions in the ISA a_assay representation
2. SARS-Cov2 isolate: no clear NCBI Taxonomic anchoring and unclear origin: -> the markup is made to the parent class (as of 06.04.2020) Release and packaging as a BDBAG: The tgz file associated with this upload has been producing using https://github.com/fair-research/bdbag. It contains several manifest files detailing metadata and data files, providing md5 and sha256 checksums. Github repository: https://github.com/ISA-tools/PXD017710","journal":"Zenodo (CERN European Organization for Nuclear Research)","year":2020,"id":8096,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":0.0,"corpus_rank":10062,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9378,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":1,"downloads":53,"has_version_chain":false,"published_date":"2020-04-06","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":290,"name":"Susanna‐Assunta Sansone","orcid":"0000-0001-5306-5690","position":1,"is_corresponding":false},{"id":289,"name":"Rocca-Serra, Philippe","orcid":"0000-0001-9853-5668","position":0,"is_corresponding":false}],"reference_count":1,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":"green","license":"cc-by","views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}