INSTITUT COCHIN ENTITY DMP - PROTEOM'IC
Core Facility

Emilie-Fleur Gautier, Thomas Guilbert

► To cite this version:

Emilie-Fleur Gautier, Thomas Guilbert. INSTITUT COCHIN ENTITY DMP - PROTEOM'IC Core Facility. Institut Cochin, Inserm U1016, CNRS UMR 8104, Université Paris Cité, F-75014 Paris, France. 2024, https://institutcochin.fr/plateformes/proteomic. hal-04789284v3

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Submitted on 18 Dec 2024

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# INSTITUT COCHIN ENTITY DMP - PROTEOM'IC

Plan de gestion de données créé à l'aide de DMP OPIDoR, basé sur le modèle "Science Europe: structured template for research entities" fourni par DMP OPIDoR.

## Plan Details

**Plan title** INSTITUT COCHIN ENTITY DMP - PROTEOM'IC

**Deliverable**

**Version** First version

**Plan purpose/scope** The aim of this DMP is to describe as precisely as possible the various research products produced by the services offered by the core facility. This DMP is part of a wider framework, that of making available all the DMPs of the 9 Institut Cochin core facilities (MOUSET'IC, PROTEOM'IC, PIV, PIME, METABOL'IC, CYBIO, IMAG'IC, GENOM'IC and HISTIM) as well as the DMP of the CID database dedicated to the transfer, storage, consultation and sometimes processing of most of the data produced by the Institut Cochin's core facilities. The purpose of this DMP entity is to provide drafting assistance for researchers wishing to draw up a DMP for their research projects and wishing to call on the expertise of our core facilities.

**Fields of science and technology (from OECD classification)** 1.6 Biological sciences

**Language** eng

**Creation date** 2024-07-15

**Last modification date** 2024-12-18

**Identifier** https://doi.org/10.57889/proteom_icCF

**Identifier type** DOI

**License**

**Name** Creative Commons Attribution 4.0 International

**URL** http://spdx.org/licenses/CC-BY-4.0.json

## Structure Details

**Entity's name** Institut Cochin Core Facility - PROTEOM'IC

**Acronym** PROTEOM'IC

**Identifier** https://institutcochin.fr/plateformes/proteomic

**Identifier type** URL

**Description** The Proteom'IC core-facility offers proteomic analysis using from protein extraction to statistical analysis of the data obtained, as well as multi-omics functional analyses.

**Creation date** 2006-01-01

**Research outputs :**
<table><tr><td>Marjorie - 0000-0002- 4049-3976</td><td>Institut Cochin - 051sk4035</td><td><ul><li>Data Manager (Stat soft, Stat soft, Stat soft, Stat soft, Stat soft, Stat soft, Stat soft, Stat soft, PROT DATA)</li><li>Data Processor (from GDPR) (PROT DATA)</li></ul></td></tr><tr><td>Proteom'IC</td><td>Institut Cochin - 051sk4035</td><td><ul><li>Contact Person (PROT DATA)</li></ul></td></tr><tr><td>Salnot Virginie - 0000-0002- 7530-4493</td><td>Institut Cochin - 051sk4035</td><td><ul><li>Data Collector (PROT DATA, PROT DATA)</li><li>Data Manager (PROT DATA)</li><li>Data Processor (from GDPR) (PROT DATA)</li></ul></td></tr></table>
Data description and collection or re-use of existing data

Mass Spectrometry-based Proteomic Analysis Data

Research output description

Name Mass Spectrometry-based Proteomic Analysis Data

Description Mass spec raw data are underprocesses data generated on the Proteom'IC platform using the U3000 RSLC nano-LC (Dionex) system, coupled either to a timsTOF Pro mass spectrometer (Bruker), an Orbitrap Fusion mass spectrometer (Thermo Scientific), or a timsTOF HT mass spectrometer (Bruker).
Protein identification and quantification will be performed using DIA-NN (Demichev et al., Nat Meth, 2020), MaxQuant (Cox J et al., Mol Cell Proteomics, 2014) and MASCOT softwares to process timsTOF .d and Orbitrap Fusion .raw data. Uniprot-Swissprot or TREMBL protein sequence databases and an extended list of contaminants will be used.
For statistical analysis, Perseus and R software will be employed and sent by e-mail.

Type Dataset

Workpackage Raw data will be analyzed directly without any preprocessing.

Keywords (free-text) mass spectrometry, nLC-MS/MS, proteomics, bottom up

Language eng

Issued Date

Persistent identifier Data are uploaded on PRIDe website with Project PXD ID on demand.

Identifier type Identifiant local

May contain personal data? No

May contain sensible data? No

May take ethical issues into account? Yes

Will existing data be reused?

Justification These data are likely to be reused in a research context.

How new data will be collected or produced?
**Name of the method** Mass spectrometry

**Description** Proteom'IC core-facility will extract proteins from samples and then digest proteins into peptides. Peptides will be separated on their hydrophobic properties on a C18 reverse phase column. All along their elution from the column, peptides will be ionized and analyzed by mass spectrometry.

Generated data are .d for timsTOF data (Bruker format) and .raw for Orbitrap Fusion format (Thermo Fisher). These data are readable by DataAnalysis software (Bruker) and Qual Browser (Thermo) or Free Style (Thermo).

The raw data will be processed using Maxquant, DIA-NN or Mascot software in the aim to identify and eventually quantify proteins. The generated output data format are .txt (Maxquant), .tsv (DIA-NN), .dat (Mascot).

These data will be stored during 10 years on an Institut Cochin owned servor.
These data will be deposited in the PRIDE reference repositories on demands.

**Data Nature** Experimental Data

**Equipments, technical platforms used**

* PROTEOM'IC : https://cat.opidor.fr/index.php/PROTEOM'IC

## Functional data files

### Research output description

**Name** Functional data files

**Description** Functional data files correspond to export excel files of processed statistical data from proteomics, transcriptomics or metabolomics studies using different softwares depending on the needs for the study (Ingenuity Pathway Analysis (IPA), GSEA, Webgestalt, REPRINT AP-MS, DAVID, Panther, STRING ...).
These data are stored on a server owned by Institut Cochin.

**Type** Dataset

**Workpackage** Excel data .tsv data will be directly analyzed with software.

**Keywords (free-text)** Gene Ontology, Enrichment, Over representation Analysis, Network

**Language** eng

**Issued Date**

**Persistent identifier** 4-letters_request-date

**Identifier type** Identifiant local

**May contain personal data?** No

**May contain sensible data?** No

**May take ethical issues into account?** Yes

**Will existing data be reused?**
## How new data will be collected or produced?

<table><tr><td>Name of the method</td><td>BioAnalysis</td></tr><tr><td>Description</td><td>Omics data can be analyzed using 3 differents methods. Both of them are enrichment analyzes (ORA & GSEA) an the third is a topology-based method (NTA).<br/>ORA : Over-Representation-Analysis examines if there is a significant number of molecules involved in a specific biological process among a list of differential transcripts, proteins or metabolites. Among the softwares performing ORA : IPA, STRING, PANTHER, DAVID, Webgestalt.<br/>GSEA : Gene Set Enrichment Analysis is a ranked based method. All molecules of the omic dataset are pre-ranked according to a chosen metric (fold change, t...). Next GSEA tests whether molecules involved in a same term (pathways, functions, disease, subcellular location...) tend to cluster together at the top or at the bottom of the ranked list .<br/>Among the softwares performing GSEA : GSEA, STRING, PANTHER, Webgestalt.<br/>NTA : Network Topology Analysis integrates scores measuring molecules positions within a network and molecule-molecule interactions into the enrichment tests. STRING perfoms NTA.</td></tr><tr><td>Data Nature</td><td>Experimental Data</td></tr><tr><td>Equipments, technical platforms used</td><td>• PROTEOM'IC : https://cat.opidor.fr/index.php/PROTEOM'IC</td></tr></table>

## Statistic softwares

### Research output description
<table><tr><td>Name</td><td>Statistic softwares</td></tr><tr><td>Description</td><td>The tools developed by Proteom'IC core-facillity allows statistical analysis of proteomics data. The RML DIANN MaxQprocess2.6.Rmd R script processes DIA-NN or Maxquant output and generates word and excel reports containing metadata, histograms of quantified proteins, PCA, heatmaps of Euclidean and Pearson correlations of sample proteomes, heatmaps of quantified proteins, table of the number of statistical differentially expressed proteins for each comparison, volcanoplots. The RML shiny PROTEOM_IC 1.14.R Shiny script processes the .rds file generated by the previous script and allows to custom provided plots and doing further investigations. https://github.com/MarjorieLeduc</td></tr><tr><td>Type</td><td>Software</td></tr><tr><td>Workpackage</td><td>.r : for R script ; .rmd : (r markdown)</td></tr><tr><td>Keywords (free-text)</td><td>Statistical analysis, proteomics, R script, Shiny, heatmaps, volcano plot, sample correlations.</td></tr><tr><td>Language</td><td>eng</td></tr><tr><td>Issued Date</td><td></td></tr><tr><td>Persistent identifier</td><td>not taken into account for now</td></tr><tr><td>May contain personal data?</td><td>No</td></tr><tr><td>May contain sensible data?</td><td>No</td></tr><tr><td>May take ethical issues into account?</td><td>Yes</td></tr></table>

### Will existing data be reused?

<table><tr><td>Justification</td><td>Our scripts are in open-access and can be modified to be adapted to the needs. https://github.com/MarjorieLeduc</td></tr></table>

### How new data will be collected or produced?

<table><tr><td>Name of the method</td><td>Programmation</td></tr><tr><td>Description</td><td>R is widely used for statistical computing and data analysis, it's free and open-source. https://github.com/MarjorieLeduc</td></tr><tr><td>Data Nature</td><td>Simulation</td></tr><tr><td>Equipments, technical platforms used</td><td><ul><li>PROTEOM'IC : https://cat.opidor.fr/index.php/PROTEOM'IC</li></ul></td></tr></table>

### Related references

* R Project : https://www.r-project.org/
# Documentation and data quality

## Mass Spectrometry-based Proteomic Analysis Data

**What metadata and documentation (for example way of organising data) will accompany the data?**

**Description**

The naming of the data directly derives from the name of the project : 4 letters encoding the requesting team name, followed by the date of sample reception (in the format year/month/day), followed by the project title. The project is registered in a LIMS (Laboratory Information Management System) specific to the platform.

For DIA-NN and Maxquant analysis, statistical analysis are done and the return .docx result report contains metadata (mass spectrometer used for the analysis, nLC time duration, version of the software used for the identification and quantification of proteins, variable and fixed modifications, digestion enzyme, the number of missed cleavages allowed during peptides identification, if a normalization and a match between runs was applied). Additional metadata are specified during data deposition on PRIDE repository.

**Related references**

* HUPO_PSI : https://github.com/HUPO-PSI/psi-ms-CV

* PRIDE : https://www.ebi.ac.uk/pride/

**Metadata/data standards**

* SwissProt :

**Metadata language code**

eng

**What methods will be used to ensure their scientific quality?**

**Description**

The quality of the data is verified by :

* Running 1D SDS PAGE to check sample quality

* nLC-MS/MS analysis of peptides from HELA lysates before and after a project analysis and during sample analysis if the number of injected samples is high.

* vizualizing generated nLC-MS/MS data at the end of runs by using specific softwares (Data Analysis software for Bruker instruments and Qual Browser or Fresstyle softwares for Thermo).

* During statistical analysis of the data

## Functional data files

**What metadata and documentation (for example way of organising data) will accompany the data?**
**Description**

The naming of the data directly derives from the name of the projet : 4 letters encoding the requesting team name, followed by the date of request (in the format year/month/day). The project is registered in a LIMS (Laboratory Information Management System) specific to the core-facility.

**Related references**

* GO term : https://geneontology.org/docs/GO-term-elements

* HUGO (HGNC) : https://www.genenames.org/

* Uniprot : https://www.uniprot.org/ :

**Metadata language code**

eng

**Documentation software**

**What methods will be used to ensure their scientific quality?**

**Description**

Data are curated manually by Qiagen or by database owners.

**Statistic softwares**

**What metadata and documentation (for example way of organising data) will accompany the data?**

**Description**

The code presentations are available on:
https://github.com/MarjorieLeduc

**Related references**

* GitHub : https://github.com/MarjorieLeduc

**Metadata language code**

eng

**What methods will be used to ensure their scientific quality?**

**Description**

Scripts are tested internally before being released on Github.
Users can provide feedback and review our code.

**Related references**

* GitHub : https://github.com/MarjorieLeduc

**Legal and ethical requirements, codes of conduct**
## Mass Spectrometry-based Proteomic Analysis Data

**How will other legal issues, such as intellectual property rights and ownership, be managed?**

**What legislation is applicable?**

**Description** The data belong to the PROTEOM'IC core-facility until their publication, at which point they become public.

**What ethical issues and codes of conduct are there, and how will they be taken into account?**

**Description** Any establishment that uses animals for scientific purposes must hold an accreditation. This is issued by a prefectural decree, following inspections by veterinary inspectors from the Departmental Directorate for Social Cohesion and Population Protection (DDPP or DDCSPP). To begin, any research project involving animals must obtain authorization from the Ministry of Research. The authorization is granted for a maximum duration of 5 years, provided that the project has received a favorable evaluation from the ethics committee of the establishment where it will be carried out.

The core facility manager must ensure that the applicant complies with the legislation (Decree No. 2013-118 of February 1, 2013, concerning the protection of animals used for scientific purposes). The core facility manager can assist in drafting the project authorization request (APAFiS Platform on the website of the French Ministry of Higher Education and Research).

**Related references**

* JORF n°0032 du 7 février 2013 :
https://www.legifrance.gouv.fr/loda/id/JORFTEXT000027038013

## Functional data files

**How will other legal issues, such as intellectual property rights and ownership, be managed?**

**What legislation is applicable?**

**What ethical issues and codes of conduct are there, and how will they be taken into account?**

**Description** None.

## Statistic softwares

**How will other legal issues, such as intellectual property rights and ownership, be managed?**
## What legislation is applicable?

**Description** Script are open source, author and core-facility must be cited.
CC-BY-SA 4.0

## What ethical issues and codes of conduct are there, and how will they be taken into account?

**Description** CC-BY-SA 4.0

# Data processing and analysis

## Mass Spectrometry-based Proteomic Analysis Data

### How and with what resources will the data be processed / analyzed?

**Description** Proteom'IC core-facility will extract proteins from samples and then digest proteins into peptides. Peptides will be separated on their hydrophobic properties on a C18 reverse phase column. All along their elution from the column, peptides will be ionized and analyzed by mass spectrometry.
Generated data are .d for timsTOF data (Bruker format) and .raw for Orbitrap Fusion format (Thermo Fisher). These data are readable by DataAnalysis software (Bruker) and Qual Browser (Thermo) or Free Style (Thermo). No open source software exist to open all raw formats to our knowledge.
The raw data will be processed using Maxquant, DIA-NN or Mascot software in the aim to identify and eventually quantify proteins. The generated output data format are .txt (Maxquant), .tsv (DIA-NN), .dat (Mascot).
These data will be stored during 10 years on an Institut Cochin owned servor. These data will be deposited in the PRIDE reference repositories on demands.

### Equipments, technical platforms

* PROTEOM'IC : https://cat.opidor.fr/index.php/PROTEOM'IC

## Functional data files

### How and with what resources will the data be processed / analyzed?

**Description** Data are exported manually from software as excel files and formated with R scripts.

### Equipments, technical platforms

* PROTEOM'IC : https://cat.opidor.fr/index.php/PROTEOM'IC
## Statistic softwares

**How and with what resources will the data be processed / analyzed?**

**Description** Details are available on github : https://github.com/MarjorieLeduc

**Related references**
* GitHub : https://github.com/MarjorieLeduc

**Equipments, technical platforms**
* PROTEOM'IC : https://cat.opidor.fr/index.php/PROTEOM'IC

## Storage and backup during the research process

### Mass Spectrometry-based Proteomic Analysis Data

**How will data be stored and backed up during the research?**

**Storage needs**
The produced data are automatically backed up daily on a server managed by Inserm (double copy). The integrity of the copy is verified at the time of analysis.
Open CID is also a solution that will likely to be implemented as a storage and archiving solution.
Storage requirements are indicated for an average year of data generation.

**Estimated volume of data** 150

**Unit** TB

**Equipments, technical platforms**
* CID : https://cat.opidor.fr/index.php/CID

**Measures taken for data security**
All data access are allowed by login + password.

### Functional data files

**How will data be stored and backed up during the research?**
**Storage needs**

Data are stored on local server (Open CID : Cochin Image Data).
These data will stored during 10 years on an Institut Cochin owned servor.

**Estimated volume of data**

500

**Unit**

MB

**Measures taken for data security**

None.

## Statistic softwares

**How will data be stored and backed up during the research?**

**Storage needs**

Data are stored according to the principles in effect on GitHub.
Storage requirements are indicated for an average year of data generation.

**Estimated volume of data**

10

**Unit**

MB

**Equipments, technical platforms**

* CID : https://cat.opidor.fr/index.php/CID

**Measures taken for data security**

CC-BY-SA 4.0

## Data sharing and long-term preservation

### Mass Spectrometry-based Proteomic Analysis Data

**How will data be shared?**

**Modalities of sharing**

The results of statistical analysis are sent to the researcher team by e-mail.
RAW data are deposit on PRIDe.

**Reusability**

YES

**Data repository/catalogs**

* PRIDe : https://cat.opidor.fr/index.php/PRIDe (Entrepôt CoSO)
* CID : https://cat.opidor.fr/index.php/CID ()

**How will data be long-term preserved? Which data?**
**Justification**
Once the data are published in a scientific article, the raw data are deposited in the public PRIDe repository
https://www.ebi.ac.uk/pride/
Storage requirements are indicated for an average year of data generation.

**Estimated volume of data** 150

**Unit** TB

**Start date**

**End date**

**Final dispositions**

## Functional data files

**How will data be shared?**

**How will data be long-term preserved? Which data?**

**Justification**
Once the data are published in a scientific article, the editor make them available.
Storage requirements are indicated for an average year of data generation.

**Estimated volume of data** 500

**Unit** MB

**Start date**

**End date**

**Final dispositions**

## Statistic softwares

**How will data be shared?**

**Modalities of sharing**
Storage requirements are indicated for an average year of data generation.
https://github.com/MarjorieLeduc/

**Reusability** YES

**Data repository/catalogs**
* CID : https://cat.opidor.fr/index.php/CID ()
## How will data be long-term preserved? Which data?

<table><tr><td>Justification</td><td>This software is be available publicly.<br/><a href="https://github.com/MarjorieLeduc/">https://github.com/MarjorieLeduc/</a><br/>Storage requirements are indicated for an average year of data generation.</td></tr><tr><td>Estimated volume of data</td><td>10</td></tr><tr><td>Unit</td><td>MB</td></tr><tr><td>Start date</td><td></td></tr><tr><td>End date</td><td></td></tr><tr><td>Final dispositions</td><td></td></tr></table>