# DMP pour le projet "Subversion immunitaire épigénétique dans les macrophages infectés par Leishmania"

Gerald F Späth, Hervé Lecoeur, Maria Sanchez-Gutierrez, Eric Prina

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Gerald F Späth, Hervé Lecoeur, Maria Sanchez-Gutierrez, Eric Prina. DMP pour le projet "Subversion immunitaire épigénétique dans les macrophages infectés par Leishmania". Institut Pasteur [Paris]. 2026. pasteur-05504750

HAL Id: pasteur-05504750

https://pasteur.hal.science/pasteur-05504750v1

Submitted on 11 Feb 2026

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<table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>1.</td><td>Information on the Data Management Plan (DMP)</td><td></td></tr><tr><td>1.1.</td><td>Author(s) of the DMP</td><td>Gerald F. Späth, gerald.spaeth@pasteur.fr</td></tr><tr><td>1.2.</td><td>Date of the first version of the DMP</td><td>July 05, 2024</td></tr><tr><td>1.3.</td><td>Current version of the DMP and date</td><td>Final version, February 2026</td></tr><tr><td>1.4.</td><td>Location of storage of the DMP</td><td>Initial and intermediate versions will be stored on an internal server and published on the ANR portal. The final version will be made accessible upon request after publication</td></tr><tr><td>2.</td><td>Information on the project</td><td></td></tr><tr><td>2.1.</td><td>Name of the project funder and funding programme</td><td>The project is funded by the ANR PRCI program</td></tr><tr><td>2.2.</td><td>Acronym of the project</td><td>ELATION (ANR-21-CE15-0046)</td></tr><tr><td>2.3.</td><td>Title of the project</td><td>Epigenetic subversion in Leishmania macrophage infection</td></tr><tr><td>2.4.</td><td>Project ID</td><td>ANR-21-CE15-0046</td></tr><tr><td>2.5.</td><td>Coordinator of the project</td><td>Gerald F. Späth, gerald.spaeth@pasteur.fr</td></tr><tr><td>2.6.</td><td>Organization and unit of the coordinator</td><td>Institut Pasteur, Unit of molecular Parasitology and Signalling (Parsig)</td></tr><tr><td>2.7.</td><td>Start date of the project</td><td>01/05/2022</td></tr><tr><td>2.8.</td><td>End date of the project</td><td>31/05/2025</td></tr><tr><td>3.</td><td>Overview of the data</td><td></td></tr><tr><td>3.1.</td><td>What is the purpose of the data collection/generation?</td><td>Objective 1: Mapping infection- and LSD1-dependent changes of the macrophage epigenome. ATACseq, CUT&amp;RUN, ChemSeq and RNA seq data plus gene promoters list<br>Objective 2: Characterization of the role of macrophage LSD1 in intracellular Leishmania infection. siRNA sequences, compounds/protacs... structures, viability assays, anti-leishmanial assays, biochemical assays<br>Objective 3: Identification of LSD1-dependent, regulatory mechanisms required for parasite survival. Proteomic data, system level analyses, anti-leishmanial readouts</td></tr><tr><td>3.2.</td><td>How many dataset(s) will you generate during this project?</td><td>7</td></tr><tr><td>3.3.</td><td>What is the nature and format of generated/collected data?</td><td>Sequencing paired-end data in fastaq.gz format stored at the Biomics Institut Pasteur platform and in the NCBI public functional genomics data repository GEO (Gene Expression Omnibus) when available online for publication.<br>Microscopy images from confocal and classical fluorescence microscopes in proprietary, tiff and PNG formats are stored in internal servers and backcopied in external hard disks<br>The following data are stored on hard disks and internal servers:<br>*Real-time PCR data in .ixo format<br>*Excel, Word, Access and PDF documents<br>*Gel electrophoresis and WB images in jpeg, tiff and PNG formats<br>*Liquid chromatography-tandem mass spectrometry (LC-MS/MS) data processed using myProMS v3.10.0 (https://github.com/bioinfo-pf-curie/myproms)<br>*cytoscape data in PNG or JPEG</td></tr><tr><td>3.4.</td><td>Give the expected volume of generated data for this project</td><td>2 To</td></tr><tr><td>3.5.</td><td>Will you reuse existing data? If yes, specify their origin.</td><td>NO</td></tr><tr><td>4.</td><td>Resources needed for data management</td><td></td></tr><tr><td>4.1.</td><td>What hardware resources do you need to manage your data?</td><td>We rely on the Institut Pasteur's enterprise-grade IT infrastructure, utilizing secure internal server space for primary long-term storage and automated redundancy. Additionally, dedicated local high-capacity storage units are maintained to ensure immediate data availability and an extra layer of backup security.</td></tr><tr><td>4.2.</td><td>Who is in charge of data management during the research project?</td><td>Eric Prina, supervised by Gerald Spaeth in the Parsig Unit, is responsible for data collection, processing and analysis, as well as data storage, archiving and sharing.</td></tr><tr><td>4.3.</td><td>What training or support do you think is necessary to help you manage your data?</td><td>We have the support from the Institut Pasteur (training in data management), the Institut Pasteur BioHub platform that is specialized in big data storage and analysis (https://research.pasteur.fr/fr/team/bioinformatics-and-biostatistics-hub/) and the Imaging facilities platform that benefit from a specific data storage center with automatic backups</td></tr><tr><td>4.4.</td><td>What budget do you have for managing your data? How do you intend to cover these costs?</td><td>The host institute and host ParSig unit provide storage and maintenance of the data internally. The freely available target space is enough for our needs.</td></tr><tr><td>5.</td><td>Legal, ethical and security aspects</td><td></td></tr><tr><td>5.1.</td><td>Does your project include personal data?</td><td>No</td></tr><tr><td>5.2.</td><td>Does your project include other data subject to a contractual, regulatory or legal obligation? If so, what type?</td><td>No</td></tr><tr><td>5.3.</td><td>Is there any data that should be kept confidential during your project? If so, please specify what types of data are concerned and to whom it can be made accessible</td><td>No sensitive or personally identifiable data (e.g., human patient records) will be generated. However, to preserve scientific priority and intellectual property rights, the dataset will remain under a temporary embargo. During this period, access will be strictly limited to authorized project members and collaborators until the associated manuscript is accepted for publication.</td></tr><tr><td>5.4.</td><td>What security measures are implemented for data storage during the project?</td><td>Data security is guaranteed through the Institut Pasteur's enterprise-grade IT infrastructure. Measures include:<br>Access Control: Folder permissions are strictly managed via institutional authentication protocols (LDAP), ensuring access is limited solely to authorized project members.<br>Data Integrity: Automated daily backups and RAID storage systems are employed to prevent data loss.<br>Network Security: All data transfers occur within a protected firewall perimeter, with remote access requiring secure VPN authentication.</td></tr></tbody></table>
<table><tr><td>5.5.</td><td>What security measures are implemented for data collection and exchange?</td><td>Data collection and exchange are conducted exclusively via Drive Pasteur, a secured institutional private cloud. The platform ensures data integrity and confidentiality through strong encryption protocols (both in transit and at rest) and restricted access controls.</td></tr><tr><td>6.</td><td>Data management during the project</td><td></td></tr><tr><td>6.1.</td><td>What is the storage location of your data during the project?</td><td>Data storage utilizes a hybrid model to ensure security and compliance. Raw, primary data is stored locally on the secure institutional infrastructure of each partner (Institut Pasteur Paris and Freiburg University). Only anonymized, summarized data (in PDF or Microsoft Office formats) is uploaded to the Drive Pasteur repository for collaborative purposes.</td></tr><tr><td>6.2.</td><td>Do you use a file classification scheme to manage your data files? Briefly indicate how it is organized.</td><td>Yes, we implement a standardized hierarchical classification scheme. Data is organized first by Methodology (e.g., Microscopy, Phenotyping, Sequencing), and then into sub-directories organized Chronologically (using ISO 8601 format: YYYY-MM-DD) and by Experiment ID to ensure traceability.</td></tr><tr><td>6.3.</td><td>What naming conventions do you use for your data? What rules do you use for clear versioning?</td><td>We apply a consistent naming convention using underscores as separators and ISO 8601 date formats for chronological sorting.<br/>*Naming Pattern: YYYYMMDD_ExperimentID_DataType_AuthorInitials (Example: 20231114_Exp05_Microscopy_JD.tif)<br/>*Versioning: We use incremental numbering (_v01, _v02) at the end of filenames for text documents and analyzed data. This prevents overwriting previous drafts and clearly distinguishes the current version from legacy files.</td></tr><tr><td>6.4.</td><td>What measures are in place to ensure the quality of the data?</td><td>In order to guarantee the quality of the data, various measures have been implemented:<br/>- Independent repetition of the experiments (biological and technical replicates)<br/>- Inherent quality controls<br/>- Standardization of data collection conditions<br/>- Regular review of data with PI, and result sharing in weekly lab meetings</td></tr><tr><td>7.</td><td>Data selection and long term preservation</td><td></td></tr><tr><td>7.1.</td><td>Are your data subject to preservation regulations? If yes, which ones?</td><td>There are no specific statutory regulations regarding the content of the data (e.g., no GDPR concerns). However, we adhere to the Rules of Good Scientific Practice and institutional policies, which require data preservation for a minimum of 5 years to ensure reproducibility and scientific integrity.</td></tr><tr><td>7.2.</td><td>Which datasets are of long-term value and should be preserved? What are the datasets to destroy?</td><td>Long-term preservation: All raw and processed RNA-seq data, mass spectrometry data, and associated metadata will be preserved indefinitely on Pasteur Institute's institutional data storage infrastructure (\\atlas.pasteur.fr\ParSig\1 - Team EP) and deposited in public repositories (NCBI GEO, ProteomeXchange). These datasets constitute the primary research outputs with long-term scientific value for future reanalysis and validation.<br/>Data destruction: Preliminary/intermediate analysis files, unsuccessful experimental attempts, and redundant technical replicates will be destroyed after 5 years following publication, in accordance with institutional data retention policies.</td></tr><tr><td>7.3.</td><td>On which platform(s) or in which repository(s) will the datasets to be preserved be archived in the long term (after the end of the project)?</td><td>Data sharing strategy: We will follow a tiered approach: (1) Minimum compliance: meet specific journal data availability requirements for target publications; (2) Best practice: deposit all primary data in public repositories with open access; (3) Institutional backup: maintain copies on Pasteur Institute secure infrastructure for long-term preservation. We commit to the most open sharing model compatible with publication venue and ethical constraints.</td></tr><tr><td>7.4.</td><td>Specify the formats chosen for archiving.</td><td>The following formats will be used: PDF, TIFF, PNG, BAM, FASTQ.gz, CSV, IXO, FCS, and Microsoft Office (Excel, Word, Powerpoint, Access).</td></tr><tr><td>7.5.</td><td>How long will the data be preserved?</td><td>The data will be kept for an unlimited period of time as long as the space allocated within the Institut Pasteur is available.</td></tr><tr><td>7.6.</td><td>What is the expected volume of archived data?</td><td>max 3 Tb</td></tr><tr><td>7.7.</td><td>If a long term preservation is needed, how do you intend to cover these costs?</td><td>Long-term preservation costs are covered by the Institut Pasteur as part of its core institutional support (in-kind contribution). The necessary storage infrastructure and maintenance are provided by the IT department's central budget, requiring no specific allocation from the project funds.</td></tr><tr><td>8.</td><td>Dataset 1</td><td></td></tr><tr><td>8.1.</td><td>Data description</td><td></td></tr><tr><td>8.1.1.</td><td>Title of the dataset</td><td>Transcriptomic profiling (RNA-seq) of Leishmania -infected macrophages</td></tr><tr><td>8.1.2.</td><td>Who is the provider or producer of the data?</td><td>Primary biological samples and associated metadata are produced by the project team under the scientific responsibility of Dr. G.F. Spaeth (Principal Investigator) at Institut Pasteur (Parasitologie moléculaire et Signalisation).<br/>For transcriptomic analyses, samples are transferred to the Biomics Core Facility at the Institut Pasteur, where sequencing data are generated.</td></tr><tr><td>8.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>The single-/paired-end sequencing will produce Short read (Illumina) sequencing data files:<br/>- Sequence Alignment Map (SAM), a text-based format originally for storing biological sequences aligned to a reference sequence.<br/>- Binary Alignment Map (BAM), which consists of the lossless, compressed binary representation of the Sequence Alignment Map-files.<br/>- FASTQ, a text-based format for storing both a biological sequence (usually nucleotide sequence) and its corresponding quality scores.</td></tr><tr><td>8.1.4.</td><td>Describe in more detail the data in this dataset</td><td>The dataset consists of Bulk RNA-Seq data derived from Mus musculus primary macrophages. The experimental design is a 2x2 factorial setup comprising:<br/>*Biological Material: Primary macrophages (uninfected vs. infected).<br/>*Treatment: Treated with an epigenetic inhibitor vs. untreated (vehicle control).<br/>*Data Type: The dataset includes raw sequencing reads (FASTQ format) and processed aligned data/count matrices (BAM/TXT format).</td></tr><tr><td>8.1.5.</td><td>Describe the method of data collection and/or generation</td><td>The dataset is generated and processed via the following pipeline:<br/>*Acquisition: Illumina Hiseq2500 platform; single-end 65 bp reads with strand specificity.<br/>*Pre-processing: Reads cleaned with cutadapt (v1.11); sequences &lt;25 nt removed.<br/>*Alignment: Mapped to Reference Genome GRCm38 (Ensembl 94) using STAR (v2.5.0a).<br/>*Quantification: Gene counting via featureCounts (v1.4.6-p3) using parameters -t gene -s 0.<br/>*Availability: Publicly accessible via the NCBI Gene Expression Omnibus (GEO) repository (ongoing Superseries submission).</td></tr><tr><td>8.1.6.</td><td>Describe your dataset with keywords</td><td>RNA-seq, Transcriptomics, Mus musculus, Primary macrophages, Epigenetics, Host-pathogen interaction, Gene expression, Innate immunity, Leishmania amazonensis</td></tr><tr><td>8.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>not available at this stage of the project, the identifier will be indicated upon publication of the dataset</td></tr><tr><td>8.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>1 To</td></tr><tr><td>8.2.</td><td>Making data openly accessible</td><td></td></tr></table>
<table><tr><td>8.2.1.</td><td>Will this dataset be freely accessible?</td><td>This dataset will be made freely available at the time of pre-publication of the associated article.</td></tr><tr><td>8.2.2.</td><td>If this dataset cannot be freely disseminated, explain why.</td><td>Not applicable</td></tr><tr><td>8.2.3.</td><td>Which data repository did you choose to store and make accessible this dataset?</td><td>All raw sequencing data will be initially deposited in the Sequence Read Archive (SRA) and linked via a BioProject (NCBI, USA). Processed gene expression data will be archived in the Gene Expression Omnibus (GEO). Through the International Nucleotide Sequence Database Collaboration (INSDC) framework, these datasets will be automatically synchronized with and eventually made accessible through European counterparts, such as the European Nucleotide Archive (ENA) and ArrayExpress (EMBL-EBI).</td></tr><tr><td>8.2.4.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>Not applicable. There are no restrictions on the use or distribution of this non-human data. Upon publication of the results, the datasets will be made fully publicly available.<br/>Raw Data: Deposited in the Sequence Read Archive (SRA), accessible via the NCBI BioProject portal.<br/>Processed Data: Gene expression matrices will be available in the Gene Expression Omnibus (GEO).</td></tr><tr><td>8.2.5.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>The data can be used as described under 8.1.5. All pipelines are open source.</td></tr><tr><td colspan="3"><b>8.3. Making data findable</b></td></tr><tr><td>8.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>All datasets will be identified by an Accession Number provided by in Sequence Read Archive (SRA) and GEO repositories.</td></tr><tr><td>8.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>Metadata describes the sample that the RNA sequence was obtained from, e.g. the organism, the cell line, and the library-preparation method. This information will be included in the SRA and GEO repositories, which will specify experimental design, study title and abstract, sample description, organism, name of the library, instrument and settings, and sequencing run statistics. Metadata standards used by SRA/GEO are available at https://www.ncbi.nlm.nih.gov/sra/docs/submitmeta/ and https://www.ncbi.nlm.nih.gov/geo/info/overview.html.</td></tr><tr><td>8.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Yes. The keywords previously selected will be included as indexed metadata fields during the submission to the NCBI repositories (GEO and BioProject). This ensures that the dataset is discoverable through standard search engines (such as PubMed, Google Dataset Search, and NCBI Entrez) and properly categorized within the database structure.</td></tr><tr><td>8.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>Yes, an abstract is always associated with the dataset submitted to the SRA/GEO repositories that contextualizes and summarizes the analyses performed. This abstract is written by the experimenter at the time of data generation and will be shared along with the data files.</td></tr><tr><td colspan="3"><b>8.4. Making data interoperable</b></td></tr><tr><td>8.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes, the sequencing data are in FASTQ format. Tables accompanying the data are in CSV format. FASTQ and CSV formats are open formats and therefore interoperable.</td></tr><tr><td>8.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>8.4.3.</td><td>Specify whether you will be using standard vocabulary for your dataset, to allow semantic inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>Yes, to ensure semantic interoperability, we rely on standardized controlled vocabularies. Gene features are identified using Ensembl or NCBI RefSeq stable identifiers. Functional annotation and biological interpretation are mapped to the Gene Ontology (GO). Furthermore, experimental conditions and pathological contexts are described using terms from the Disease Ontology (DO) and Medical Subject Headings (MeSH).</td></tr><tr><td colspan="3"><b>8.5. Increase data reuse</b></td></tr><tr><td>8.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Once published, this dataset may be reused by the scientific community.</td></tr><tr><td>8.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>To ensure the widest reuse possible, the data will be deposited into NCBI SRA/GEO and dedicated to the Public Domain (No Rights Reserved, CC0). This aligns with NCBI's policy of placing no restrictions on the use or distribution of these data.</td></tr><tr><td>8.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>The dataset will be deposited prior to manuscript submission but will remain under a temporary embargo until the date of publication. This ensures the data is available for peer review (via secure access) while protecting the novelty of the findings. Upon publication, the dataset will be released immediately with no further restrictions.</td></tr><tr><td>8.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>Primary long-term preservation is guaranteed indefinitely through the chosen public repository (NCBI SRA/GEO). For redundancy, a copy will also be archived on Institut Pasteur's central servers and backed up according to institutional data security protocols for at least 10 years.</td></tr><tr><td colspan="3"><b>9 Dataset 2</b></td></tr><tr><td colspan="3"><b>9.1. Data description</b></td></tr><tr><td>9.1.1.</td><td>Title of the dataset</td><td>High Content Imaging assay for the antiparasitic activity profiling of chemical libraries, PROTACs, and Hytags.</td></tr><tr><td>9.1.2.</td><td>Who is the provider or producer of the data?</td><td>Primary biological samples and associated metadata are produced by the project team under the scientific responsibility of Dr. G.F. Spaeth (Principal Investigator) at Institut Pasteur, Parasitologie moléculaire et Signalisation, within the framework of the ANR-DFG project ELATION.</td></tr><tr><td>9.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>The dataset comprises high-throughput imaging data generated using the PerkinElmer Opera system. The data is available in its native .cxd format (containing the complete acquisition metadata structure) and has been converted into .tiff format to facilitate broad reuse across different image analysis platforms.</td></tr><tr><td>9.1.4.</td><td>Describe in more detail the data in this dataset</td><td>This dataset contains multi-channel confocal imagery generated via a PerkinElmer Opera Phenix platform. It documents a phenotypic screen of Leishmania-infected macrophages across 384-well microplates, capturing the morphological effects of treatment with various candidate compounds on both the host cells and the parasites.</td></tr><tr><td>9.1.5.</td><td>Describe the method of data collection and/or generation</td><td>Data collection was performed using the PerkinElmer Opera Phenix, an automated plate-reader high-content confocal microscopy system. Following acquisition, quantitative data generation was conducted using the Columbus Image Data Storage and Analysis System. Custom Acapella-based algorithms were utilized to segment cells/parasites and extract single-cell phenotypic features. The final dataset includes both the raw image files and the derived quantitative results. Adobe Photoshop was used solely for final figure assembly and display adjustments (e.g., brightness/contrast applied uniformly), in accordance with standard scientific publishing guidelines.</td></tr><tr><td>9.1.6.</td><td>Describe your dataset with keywords</td><td>Leishmania, macrophages, HCA, chemical compounds, PROTACs, Hytags, LSD1 inhibitors</td></tr></table>
<table><tr><td>9.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>Not available at this stage of the project, the identifier will be indicated upon publication of the dataset.</td></tr><tr><td>9.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>1 To</td></tr><tr><td>9.2.</td><td>Making data openly accessible</td><td></td></tr><tr><td>9.2.1.</td><td>Will this dataset be freely accessible?</td><td>Yes. The dataset will be made fully and freely accessible immediately upon the publication of the associated scientific article. There will be no access fees or login requirements for the public.</td></tr><tr><td>9.2.2.</td><td>If this dataset cannot be freely disseminated, explain why.</td><td>NA</td></tr><tr><td>9.2.3.</td><td>Which data repository did you choose to store and make accessible this dataset?</td><td>During the project, Columbus was chosen as the main data repository for storing and organizing all high-content screening images generated through the Opera Phenyx system. Columbus is running on the servers of the Institut Pasteur and is accessible upon request at eric.prina@pasteur.fr. At the time of manuscript submission, to ensure the widest reuse possible, the High Content Analysis (HCA) dataset will be deposited in the BioImage Archive (EMBL-EBI).</td></tr><tr><td>9.2.4.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>During the project, the dataset is stored on the servers of the Institut Pasteur and is accessible upon request at eric.prina@pasteur.fr. Upon publication of the dataset, the datasets will be made fully publicly available on BioImage Archive</td></tr><tr><td>9.2.5.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>Columbus is required for managing images acquired with the Opera Phenix. TIFF images will be deposit on BioImage Archive.</td></tr><tr><td>9.3.</td><td>Making data findable</td><td></td></tr><tr><td>9.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>A DOI (Digital Object Identifier) will be assigned upon deposition in the repository BioImage Archive.</td></tr><tr><td>9.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>For our High Content Assays, the metadata provided for images acquired on microscopes will include detailed information about the imaging acquisition details, microscope settings, image parameters, environmental conditions, image processing Information, and sample Information in associated files or within the image file headers, depending on the microscope type and data export format used, Metadata approach: We use the OME (Open Microscopy Environment) metadata standard natively supported by the Opera Phenix platform. Default OME-XML captures all technical acquisition parameters (instrument settings, wavelengths, exposure, magnification, pixel dimensions). We supplement this with experimental metadata using standardized templates including: sample identifiers, treatment conditions, biological replicates, time points, and plate layouts. All metadata are machine-readable (OME-TIFF + CSV formats) and version-controlled alongside raw images.</td></tr><tr><td>9.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Yes, the dataset is described by a comprehensive set of keywords to ensure findability. Key terms include the Methodology (High Content Screening, HCS, Fluorescence Microscopy), the Compounds (PROTACs, Hytags, Small Molecules), and the Biological System (Leishmania amazonensis, Mouse, Macrophages, Host-pathogen interaction).</td></tr><tr><td>9.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>Yes, a README file is associated with the dataset to contextualize it and summarize the analyses performed. This file is written by the experimenter at the time of data generation and will be shared along with the data files.</td></tr><tr><td>9.4.</td><td>Making data interoperable</td><td></td></tr><tr><td>9.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes. Although raw images are maintained in their native proprietary format to preserve data integrity, they are fully interoperable via the Bio-Formats library. This ensures they can be opened and analyzed using standard open-source tools like ImageJ/Fiji, CellProfiler, or QuPath. Images are provided as TIFF/OME-TIFF, and extracted numerical data is provided as CSV or TXT files, removing any dependency on proprietary commercial software.</td></tr><tr><td>9.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>9.4.3.</td><td>Specify whether you will be using standard vocabulary for your dataset, to allow semantic inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>Yes, to ensure semantic interoperability, we rely on standardized controlled vocabularies. Gene features are identified using Ensembl or NCBI RefSeq stable identifiers. Functional annotation and biological interpretation are mapped to the Gene Ontology (GO). Furthermore, experimental conditions and pathological contexts are described using terms from the Disease Ontology (DO) and Medical Subject Headings (MeSH).</td></tr><tr><td>9.5.</td><td>Increase data reuse</td><td></td></tr><tr><td>9.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Once published, this dataset may be reused by the scientific community.</td></tr><tr><td>9.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>To ensure the widest reuse possible, the High Content Analysis (HCA) dataset will be deposited in the BioImage Archive (EMBL-EBI). The data will be assigned a CC0 (Public Domain).</td></tr><tr><td>9.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>The dataset will be deposited prior to manuscript submission but will remain under a temporary embargo until the date of publication. This ensures the data is available for peer review (via secure access) while protecting the novelty of the findings. Upon publication, the dataset will be released immediately with no further restrictions.</td></tr><tr><td>9.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>Primary long-term preservation is guaranteed indefinitely through the BioImage Archive (EMBL-EBI). To ensure redundancy, a backup copy will be maintained on the Institut Pasteur's central infrastructure for a minimum of 10 years following the project's completion, ensuring availability even in the event of external repository maintenance.</td></tr><tr><td>10.</td><td>Dataset 3</td><td></td></tr><tr><td>10.1.</td><td>Data description</td><td></td></tr><tr><td>10.1.1.</td><td>Title of the dataset</td><td>Resazurin-based cell viability assay for anti-leishmanial activity screening on Leishmania promastigotes</td></tr><tr><td>10.1.2.</td><td>Who is the provider or producer of the data?</td><td>Primary biological samples and associated metadata are produced by the project team under the scientific responsibility of Dr. G.F. Spaeth (Principal Investigator) at Institut Pasteur, Parasitologie moléculaire et Signalisation, within the framework of the ANR-DFG project ELATION.</td></tr><tr><td>10.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>The dataset consists of quantitative fluorescence readings provided in open CSV format, alongside the original XLSX output from the instrument.</td></tr><tr><td>10.1.4.</td><td>Describe in more detail the data in this dataset</td><td>This dataset includes raw fluorescence values and metadata including fluorescence reading settings</td></tr></table>
<table><tbody><tr><td>10.1.5.</td><td>Describe the method of data collection and/or generation</td><td>Fluorescence values are acquired using a TECAN [Insert Model] plate reader measuring Resazurin reduction at specific excitation/emission wavelengths (530-560/590 nm).</td></tr><tr><td>10.1.6.</td><td>Describe your dataset with keywords</td><td>Leishmania, promastigotes, Resazurin assay</td></tr><tr><td>10.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>To be determined. A DOI (Digital Object Identifier) will be assigned upon deposition in the repository (Recherche Data Gouv).</td></tr><tr><td>10.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>&lt; 100 Mb</td></tr><tr><td><b>10.2.</b></td><td><b>Making data openly accessible</b></td><td></td></tr><tr><td>10.2.1.</td><td>Will this dataset be freely accessible?</td><td>This dataset will be made freely available at the time of pre-publication of the associated article.</td></tr><tr><td>10.2.2.</td><td>If this dataset cannot be freely disseminated, explain why.</td><td>NA</td></tr><tr><td>10.2.3.</td><td>Which data repository did you choose to store and make accessible this dataset?</td><td>The dataset will be deposited in the general-purpose public repository Recherche Data Gouv. A duplicate copy is secured on Institut Pasteur's central servers.</td></tr><tr><td>10.2.4.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>Not applicable. Once published, the data will be directly downloadable from the repository without restriction.</td></tr><tr><td>10.2.5.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>No specific software required. Data is provided as CSV (Comma-Separated Values) files, which are interoperable with any text editor, spreadsheet software (Excel, LibreOffice), or programming language (R, Python).</td></tr><tr><td><b>10.3.</b></td><td><b>Making data findable</b></td><td></td></tr><tr><td>10.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>Yes. The chosen repository (e.g., Recherche Data Gouv) will automatically assign a DOI (Digital Object Identifier) to the dataset upon publication, ensuring it is citable.</td></tr><tr><td>10.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>Experimental metadata (plate layout, drug names, concentrations, time points) is embedded within the column headers of the CSV files and detailed in an accompanying ReadMe file.</td></tr><tr><td>10.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Yes, this dataset is described with 3 keywords minimum</td></tr><tr><td>10.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>Yes, a Readme file or a Powerpoint/Excel file is associated with the dataset to contextualize it and summarize the analyses performed. This file is written by the experimenter at the time of data generation and will be shared along with the data files.</td></tr><tr><td><b>10.4.</b></td><td><b>Making data interoperable</b></td><td></td></tr><tr><td>10.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes, technical interoperability is ensured by the use of non-proprietary text-based formats (CSV).</td></tr><tr><td>10.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>10.4.3.</td><td>Specify whether you will be using standard vocabulary for your dataset, to allow semantic inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>We rely on standard scientific terminology for species (Linnean taxonomy) and chemical compounds (IUPAC names). Specific abbreviations are defined in the associated documentation</td></tr><tr><td><b>10.5.</b></td><td><b>Increase data reuse</b></td><td></td></tr><tr><td>10.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Yes. Once published, the dataset may be reused by the scientific community. It will be licensed under CC-BY-NC 4.0 (Attribution-NonCommercial), allowing reuse with credit but preventing commercial exploitation.</td></tr><tr><td>10.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>It will be licensed under CC-BY-NC 4.0 (Attribution-NonCommercial), allowing reuse with credit but preventing commercial exploitation.</td></tr><tr><td>10.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>Data will be available for reuse within one year following manuscript submission and publication</td></tr><tr><td>10.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>Data will be preserved for a minimum of 10 years on institutional servers, in accordance with the Institut Pasteur's research integrity policy.</td></tr><tr><td><b>11.</b></td><td><b>Dataset 4</b></td><td></td></tr><tr><td><b>11.1.</b></td><td><b>Data description</b></td><td></td></tr><tr><td>11.1.1.</td><td>Title of the dataset</td><td>Raw Western Blot and CETSA imaging dataset on macrophage samples</td></tr><tr><td>11.1.2.</td><td>Who is the provider or producer of the data?</td><td>Primary biological samples and associated metadata are produced by the project team under the scientific responsibility of Dr. G.F. Spaeth (Principal Investigator) at Institut Pasteur, Parasitologie moléculaire et Signalisation, within the framework of the ANR-DFG project ELATION.</td></tr><tr><td>11.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>The dataset comprises raw proprietary files (ImageQuant format) and exported high-resolution TIFF images (lossless compression) to ensure accessibility.</td></tr><tr><td>11.1.4.</td><td>Describe in more detail the data in this dataset</td><td>Includes uncropped, full-membrane scans for cell lysates and Cellular Thermal Shift Assay (CETSA), including molecular weight marker overlays.</td></tr><tr><td>11.1.5.</td><td>Describe the method of data collection and/or generation</td><td>Images acquired using an Amersham ImageQuant 800 system. Detection modes include Chemiluminescence (for IP/WB) and Fluorescence/Colorimetry (for markers/specific targets).</td></tr><tr><td>11.1.6.</td><td>Describe your dataset with keywords</td><td>Leishmania, Western Blots, CETSA, Immunoprecipitation, Macrophages</td></tr><tr><td>11.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>To be determined. A DOI (Digital Object Identifier) will be generated automatically upon deposition in the repository (Recherche Data Gouv).</td></tr><tr><td>11.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>20 Go</td></tr><tr><td><b>11.2.</b></td><td><b>Making data openly accessible</b></td><td></td></tr><tr><td>11.2.1.</td><td>Will this dataset be freely accessible?</td><td>This dataset will be made freely available at the time of pre-publication of the associated article.</td></tr><tr><td>11.2.2.</td><td>If this dataset cannot be freely disseminated, explain why.</td><td>NA</td></tr><tr><td>11.2.3.</td><td>Which data repository did you choose to store and make accessible this dataset?</td><td>The dataset will be deposited in Recherche Data Gouv repository. A duplicate copy is archived on Institut Pasteur's central servers for redundancy.</td></tr><tr><td>11.2.4.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>Not applicable. Upon publication of the article, the dataset will be fully open and accessible without restriction.</td></tr></tbody></table>
<table><tr><td>11.2.5.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>Proprietary files require ImageQuant TL. However, exported TIFF images can be analyzed using open-source software such as ImageJ/Fiji.</td></tr><tr><td>11.3.</td><td><strong>Making data findable</strong></td><td></td></tr><tr><td>11.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>Yes. The dataset will be assigned a unique DOI by the Recherche Data Gouv repository to facilitate citation and linking to the publication.</td></tr><tr><td>11.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>Data will be deposited in Recherche Data Gouv repository. We will follow the metadata standard for western blots that includes experimental details, sample information, gel and blotting information, antibody information, detection and imaging, data analysis, experimental conditions, image information, and replication</td></tr><tr><td>11.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Leishmania, Western Blot, CETSA, Immunoprecipitation, ImageQuant, Thermal Shift, Protein interaction.</td></tr><tr><td>11.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>A PowerPoint/Excel file is associated with the dataset to provide context and summarize the analyses performed. This file, created by the experimenter at the time of data generation, will be shared along with the data files.</td></tr><tr><td>11.4.</td><td><strong>Making data interoperable</strong></td><td></td></tr><tr><td>11.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes. By providing images in standard TIFF format, the data is independent of the proprietary ImageQuant software.</td></tr><tr><td>11.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>11.4.3.</td><td>Specify whether you will be using standard vocabulary for your dataset, to allow semantic inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>Standard nomenclature is used for protein targets (Uniprot IDs where possible) and reagents.</td></tr><tr><td>11.5.</td><td><strong>Increase data reuse</strong></td><td></td></tr><tr><td>11.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Once published, this dataset may be reused by the scientific community, with the restriction that the data may not be used for commercial purposes (a CC-BY-NC 4.0 license will be associated with the dataset).</td></tr><tr><td>11.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>It will be licensed under CC-BY-NC 4.0 (Attribution-NonCommercial), allowing reuse with credit but preventing commercial exploitation.</td></tr><tr><td>11.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>Data will be available for reuse within one year following manuscript submission and publication.</td></tr><tr><td>11.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>The dataset will be preserved indefinitely by the Recherche Data Gouv repository. A local copy will be retained at Institut Pasteur for a minimum of 10 years.</td></tr><tr><td>12.</td><td><strong>Dataset 5</strong></td><td></td></tr><tr><td>12.1.</td><td><strong>Data description</strong></td><td></td></tr><tr><td>12.1.1.</td><td>Title of the dataset</td><td>LC-MS/MS proteomic analysis of LSD1-immunoprecipitated complexes in macrophages</td></tr><tr><td>12.1.2.</td><td>Who is the provider or producer of the data?</td><td>Primary biological samples and associated metadata are produced by the project team under the scientific responsibility of Dr. G.F. Spaeth (Principal Investigator) at Institut Pasteur (Parasitologie moléculaire et Signalisation). For proteomic analyses, samples are transferred to the CurieCoreTech proteomics facility at the Institut Curie. Following sample processing, LC-MS/MS data are acquired and database searches are performed for protein identification. Identification results are subsequently integrated into the in-house web server myProMS, developed at the Institut Curie, for protein quantification and downstream data analysis. All data are produced within the framework of the ANR-DFG project ELATION and remain under the responsibility of the project partners.</td></tr><tr><td>12.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>The dataset comprises Raw mass spectrometry files (.RAW), Peak lists (.mgf), and Result files (identification and quantification tables in .txt/.xlsx).</td></tr><tr><td>12.1.4.</td><td>Describe in more detail the data in this dataset</td><td>Spectral data acquired on an Orbitrap Astral, chromatography profiles, and peptide/protein identification lists generated by myProMS.</td></tr><tr><td>12.1.5.</td><td>Describe the method of data collection and/or generation</td><td>Data are generated by coupling a Vanquish Neo LC system (Thermo Scientific) to an Orbitrap Astral mass spectrometer (Thermo Scientific). The resulting spectrometer experiment Data file is processed using myProMS v3.10.0 and analyzed using Prostar v1.32.1.</td></tr><tr><td>12.1.6.</td><td>Describe your dataset with keywords</td><td>Leishmania, Macrophage, co-immunoprecipitation, Proteomic data, LC-MS/MS, Orbitrap Astral, myProMS</td></tr><tr><td>12.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>To be determined. A PXD identifier (ProteomeXchange ID) will be assigned upon deposition in the PRIDE repository.</td></tr><tr><td>12.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>140 Gb</td></tr><tr><td>12.2.</td><td><strong>Making data openly accessible</strong></td><td></td></tr><tr><td>12.2.1.</td><td>Will this dataset be freely accessible?</td><td>This dataset will be made freely available at the time of pre-publication of the associated article.</td></tr><tr><td>12.2.2.</td><td>If this dataset cannot be freely disseminated, explain why.</td><td>NA</td></tr><tr><td>12.2.3.</td><td>Which data repository did you choose to store and make accessible this dataset?</td><td>The dataset will be deposited in the PRIDE archive (EMBL-EBI), which is part of the ProteomeXchange consortium. A local copy is archived at Institut Curie.</td></tr><tr><td>12.2.4.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>Not applicable. Upon publication, the dataset will be publicly accessible via PRIDE without restriction.</td></tr><tr><td>12.2.5.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>The raw data (.RAW) requires Thermo Xcalibur or the open-source MSConvert tool to be read. The processed data (identification lists) generated by myProMS are provided as standard tab-delimited text (.txt) or Excel (.xlsx) files, requiring only standard spreadsheet software.</td></tr><tr><td>12.3.</td><td><strong>Making data findable</strong></td><td></td></tr><tr><td>12.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>Yes. The dataset will be assigned a unique PXD identifier and, after publication, a DOI by the PRIDE repository.</td></tr></table>
<table><tr><td>12.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>We will adhere to the MIAPE (Minimum Information About a Proteomics Experiment) guidelines required by ProteomeXchange. Metadata includes instrument settings, protocols, and search parameters.</td></tr><tr><td>12.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Leishmania, Macrophage, Co-immunoprecipitation, LC-MS/MS, Orbitrap, Interactome, Proteomics, myProMS</td></tr><tr><td>12.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>Yes, PowerPoint/Excel files are associated with the dataset to provide context, summarize the analyses performed, and list the peptides/proteins obtained. These files, created by the experimenter at the time of data generation, will be shared along with the data files</td></tr><tr><td><b>12.4.</b></td><td><b>Making data interoperable</b></td><td></td></tr><tr><td>12.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes. We provide result files in standard text formats (.txt/csv) and raw files can be converted to open mzML format.</td></tr><tr><td>12.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>12.4.3.</td><td>Specify whether you will be using standard vocabulary for your dataset, to allow semantic inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>We utilize UniProtKB accession numbers for protein identification and standard Gene Ontology (GO) terms for functional annotation.</td></tr><tr><td><b>12.5.</b></td><td><b>Increase data reuse</b></td><td></td></tr><tr><td>12.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Yes. The data will be available under a CC0 (Public Domain) or CC-BY license (depending on PRIDE defaults), permitting reuse for re-analysis or meta-analysis.</td></tr><tr><td>12.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>The license will be determined according to the requirements of the journals where the project results will be published.</td></tr><tr><td>12.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>Data will be available for reuse within one year following manuscript submission and publication.</td></tr><tr><td>12.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>The dataset is preserved indefinitely by the PRIDE repository. Local backups are kept for a minimum of 10 years.</td></tr><tr><td><b>13.</b></td><td><b>Dataset 6</b></td><td></td></tr><tr><td><b>13.1.</b></td><td><b>Data description</b></td><td></td></tr><tr><td>13.1.1.</td><td>Title of the dataset</td><td>RT-qPCR validation of siRNA knockdown efficiency in Leishmania -infected macrophages</td></tr><tr><td>13.1.2.</td><td>Who is the provider or producer of the data?</td><td>Primary biological samples and associated metadata are produced by the project team under the scientific responsibility of Dr. G.F. Spaeth (Principal Investigator) at Institut Pasteur (Parasitologie moléculaire et Signalisation). ChIP sample preparation</td></tr><tr><td>13.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>The dataset consists of raw instrument files (Roche .ixo), exported amplification/melting curves (Excel/.txt), and analyzed Cq/Ct values (Excel/.csv).</td></tr><tr><td>13.1.4.</td><td>Describe in more detail the data in this dataset</td><td>Includes raw fluorescence data, amplification curves, melting peak analysis, and quantification cycles (Cq) for target genes and reference controls.</td></tr><tr><td>13.1.5.</td><td>Describe the method of data collection and/or generation</td><td>Data acquired on a Roche LightCycler® 480 system using SYBR Green or TaqMan chemistry. Raw fluorescence is measured over 40 amplification cycles. Tm from melting curves analyses are checked for specificity.</td></tr><tr><td>13.1.6.</td><td>Describe your dataset with keywords</td><td>Leishmania, Macrophage, siRNA, Lsd1, RT-qPCR, Gene Silencing, LightCycler 480</td></tr><tr><td>13.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>To be determined. A DOI will be assigned immediately upon deposition in the Recherche Data Gouv repository.</td></tr><tr><td>13.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>2 Go</td></tr><tr><td><b>13.2.</b></td><td><b>Making data openly accessible</b></td><td></td></tr><tr><td>13.2.1.</td><td>Will this dataset be freely accessible?</td><td>This dataset will be made freely available at the time of pre-publication of the associated article.</td></tr><tr><td>13.2.2.</td><td>If this dataset cannot be freely disseminated, explain why.</td><td>NA</td></tr><tr><td>13.2.3.</td><td>Which data repository did you choose to store and make accessible this dataset?</td><td>The dataset will be deposited in Recherche Data Gouv. A secure copy is maintained on Institut Pasteur servers.</td></tr><tr><td>13.2.4.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>Not applicable. Upon publication, the dataset will be fully accessible without restriction.</td></tr><tr><td>13.2.5.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>Raw .ixo files require LightCycler® 480 Software (v1.5.0). However, all data is exported as .txt/Excel files, allowing analysis with any spreadsheet software or R/Python.</td></tr><tr><td><b>13.3.</b></td><td><b>Making data findable</b></td><td></td></tr><tr><td>13.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>Yes. The dataset will be identified by a unique DOI provided by Recherche Data Gouv.</td></tr><tr><td>13.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>We will adhere to the MIQE guidelines (Minimum Information for Publication of Quantitative Real-Time PCR Experiments). Metadata includes primer sequences, amplicon lengths, and thermal cycling conditions.</td></tr><tr><td>13.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>RT-qPCR, siRNA, Gene Silencing, LightCycler 480, Leishmania, Macrophage, Knockdown validation.</td></tr><tr><td>13.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>Yes, PowerPoint and Excel files are associated with the dataset to provide context, summarize the analyses performed, and list the fold changes and melting temperatures of specific amplicons obtained. Created by the experimenter at the time of data generation, these files will be shared along with the dataset.</td></tr><tr><td><b>13.4.</b></td><td><b>Making data interoperable</b></td><td></td></tr><tr><td>13.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes. Interoperability is ensured by providing text-based exports (.txt/.csv) of the raw values, removing dependence on the Roche software.</td></tr></table>
<table><tr><td>13.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>13.4.3.</td><td>Specify whether you will be using standard vocabulary for your dataset, to allow semantic inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>We use standard gene nomenclature (Ensembl/GenBank) and standard qPCR terminology (Cq, Ct, Melting Temp) as defined by the MIQE guidelines.</td></tr><tr><td>13.5.</td><td>Increase data reuse</td><td></td></tr><tr><td>13.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Yes. The dataset will be licensed under CC-BY-NC 4.0, permitting academic reuse while protecting against commercial exploitation.</td></tr><tr><td>13.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>It will be licensed under CC-BY-NC 4.0 (Attribution-NonCommercial), allowing reuse with credit but preventing commercial exploitation.</td></tr><tr><td>13.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>Data will be available for reuse within one year following manuscript submission and publication.</td></tr><tr><td>13.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>Indefinite preservation via Recherche Data Gouv. Local backups at Institut Pasteur are retained for a minimum of 10 years.</td></tr><tr><td>14.</td><td>Dataset 7</td><td></td></tr><tr><td>14.1.</td><td>Data description</td><td></td></tr><tr><td>14.1.1.</td><td>Title of the dataset</td><td>Epigenomic profiling (ChIP-seq) of LSD1 in Leishmania amazonensis -infected and uninfected macrophages</td></tr><tr><td>14.1.2.</td><td>Who is the provider or producer of the data?</td><td>Primary biological samples and associated metadata are produced by the project team under the scientific responsibility of Dr. G.F. Spaeth (Principal Investigator) at Institut Pasteur (Parasitologie moléculaire et Signalisation). ChIP sample preparation (crosslinking, chromatin extraction and shearing, immunoprecipitation, DNA extraction) and library construction are performed in the ParSig unit. Prepared libraries are transferred to the Biomics Core Facility (Institut Pasteur) where sequencing is carried out.</td></tr><tr><td>14.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>Data type: ChIP-seq of LSD1 (KDM1A) with matched Input DNA controls from Mus musculus primary macrophages. Raw data: demultiplexed sequencing reads in FASTQ.gz (paired-end). Processed data:<ul><li>Aligned reads in BAM/BAI.</li><li>Normalized genome coverage tracks in bigWig (RPM/RPGC).</li><li>Peak calls in BED/narrowPeak (per-replicate and combined replicates peaks).</li><li>Count matrices (peak-by-sample read counts) in TSV/CSV.</li><li>Quality control reports (FastQC, MultiQC, phantompeakqualtools) in HTML/TXT.</li><li>Sample sheet and run metadata in CSV/TSV and a README.txt.</li></ul></td></tr><tr><td>14.1.4.</td><td>Describe in more detail the data in this dataset</td><td><ul><li>Biological material: Primary Mus musculus macrophages under two conditions: uninfected and Leishmania amazonensis-infected. Infection parameters (e.g., MOI, time post-infection, parasite strain) are recorded in metadata.</li><li>Experimental design: 1x2 factorial (condition = uninfected vs. infected). LSD1 ChIP-seq with n=2 biological replicates per condition. For each condition, matched negative controls include IgG ChIP and Input DNA. Optional technical replicates may be pooled after QC.</li><li>Targets and controls: Anti-LSD1 ChIP; IgG and Input controls. Antibody vendor, catalog number, lot, and RRID will be recorded.</li><li>Sequencing: Illumina NextSeq 2000, paired-end 2x150 bp.</li><li>Output files: as listed in 13.1.3 (e.g., raw FASTQ PE reads, processed alignments, peak calls, signal tracks, QC reports, and complete sample/experiment metadata).</li></ul></td></tr></table>
<table><tr><td>14.1.5.</td><td>Describe the method of data collection and/or generation</td><td><b>Wet-lab</b><br/>*Crosslinking: 1% formaldehyde, quenched with glycine (details recorded per batch).<br/>* Chromatin preparation: nuclei isolation and sonication to 200-500 bp fragments (e.g., Covaris or equivalent; fragment size distribution QC by Bioanalyzer/TapeStation).<br/>* Immunoprecipitation: validated anti-LSD1 antibody; protein A/G beads; washes and reversal of crosslinks; DNA purification.<br/>* Libraries: NEBNext Ultra II (or equivalent) DNA library prep for Illumina; dual-indexed adapters; size selection ~300-450 bp final library; qPCR quantification and size QC.<br/><b>Sequencing</b><br/>* Platform: Illumina NextSeq 2000.<br/>* Mode/read length: paired-end 2x150 bp (PE150); actual run configuration recorded in metadata.<br/>* Depth targets: ≥20-30 million uniquely mapped read pairs per ChIP replicate; ≥30 million for Inputs.<br/><b>Bioinformatics (open-source)</b><br/>* Demultiplexing: bcl2fastq (facility).<br/>* QC and trimming: FastQC v0.11.x; MultiQC v1.x; adapter/quality trimming with fastp v0.23.x.<br/>* Alignment: Bowtie2 v2.5.4 to Mus musculus mm9 (NCBI37); typical PE parameters: --very-sensitive -X 2000. Genome build is recorded in metadata.<br/>* Post-alignment processing: samtools v1.21; fixmate/sort/markdup for duplicate handling; filtering with MAPQ ≥30; remove secondary/supplementary/unmapped reads; retain properly paired reads; optional removal of chrM.<br/>* Reproducibility metrics: fragment-length estimation and NSC/RSC with phantompeakqualtools v1.2.x.<br/>* Peak calling: MACS3 v3.0.1 (macs3 callpeak -t &lt;ChIP.bam&gt; -c &lt;Input.bam&gt; -f BAMPE -B -g mm -q 0.01), generating narrow peaks and summits for LSD1. Peaks are called per replicate and on combined replicates; bdgcmp and bdgdiff are used for fold-enrichment/score tracks and comparative signal as appropriate.<br/>* Signal tracks and annotation: bigWig coverage tracks from deepTools v3.x (bamCoverage; RPGC or CPM as noted); metagene profiles and peak annotation with ChIPSeeker v1.42.1.<br/>* Quantification and differential binding: DiffBind (Bioconductor), ongoing; peak-by-sample count matrix and differential results will be deposited when finalized.<br/>* Workflow and provenance: analysis scripted in Snakemake/Nextflow; exact software versions, parameter files, and containers (Docker/Singularity) will be archived with the dataset (Research Data Gouv).<br/>Raw reads to SRA (via BioProject). Processed files (BAM, bigWig, BED/narrowPeak, count matrices, QC reports) to GEO, with complete sample and experiment metadata.</td></tr><tr><td>14.1.6.</td><td>Describe your dataset with keywords</td><td>ChIP-seq, LSD1, Mus musculus, Primary macrophages, Epigenomics, Host-pathogen interaction, Leishmania amazonensis, Illumina HiSeq2500</td></tr><tr><td>14.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>Not available at this stage of the project, accession numbers (BioProject, SRA, GEO) will be indicated upon publication.</td></tr><tr><td>14.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>500 GB (raw FASTQ plus processed BAM/bigWig/peak files and QC reports).</td></tr><tr><td><b>14.2.</b></td><td><b>Making data openly accessible</b></td><td></td></tr><tr><td>14.2.1.</td><td>Will this dataset be freely accessible?</td><td>Yes. This dataset will be made freely available at the time of pre-publication/manuscript acceptance.</td></tr><tr><td>14.2.2.</td><td>If this dataset cannot be freely disseminated, explain why.</td><td>Not applicable</td></tr><tr><td>14.2.3.</td><td>Which data repository did you choose to store and make accessible this dataset?</td><td>All raw sequencing data will be initially deposited in the Sequence Read Archive (SRA) and linked via a BioProject (NCBI, USA). Processed gene expression data will be archived in the Gene Expression Omnibus (GEO). Through the International Nucleotide Sequence Database Collaboration (INSDC) framework, these datasets will be automatically synchronized with and eventually made accessible through European counterparts, such as the European Nucleotide Archive (ENA) and ArrayExpress (EMBL-EBI).</td></tr><tr><td>14.2.4.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>Not applicable. There are no restrictions on the use or distribution of this non-human data. Upon publication of the results, the datasets will be made fully publicly available.<br/>Raw Data: Deposited in the Sequence Read Archive (SRA), accessible via the NCBI BioProject portal.<br/>Processed Data: Gene expression matrices will be available in the Gene Expression Omnibus (GEO) database.</td></tr><tr><td>14.2.5.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td><b>Access/viewers:</b> IGV for BAM/bigWig; UCSC Genome Browser for bigWig/bed; samtools/bedtools for BAM/BED; standard decompression tools for FASTQ.gz.<br/><b>Analysis:</b> Bowtie2/BWA, MACS2, deepTools, DiffBind, FastQC/MultiQC (all open source).<br/><b>Documentation/workflows:</b> parameter files, environment files, and a reproducible workflow (Snakemake/Nextflow) will be provided with README and deposited to GitHub and archived to Research Data Gouv.</td></tr><tr><td><b>14.3.</b></td><td><b>Making data findable</b></td><td></td></tr><tr><td>14.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>All datasets will be identified by an Accession Number provided by in Sequence Read Archive (SRA) and GEO repositories. A DOI will be minted for the workflow and documentation via Research Data Gouv.</td></tr><tr><td>14.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>Metadata describes the sample that the DNA sequence was obtained from, e.g. the organism, the cell line, and the library-preparation method. This information will be included in the SRA and GEO repositories, which will specify experimental design, study title and abstract, sample description, organism, name of the library, instrument and settings, and sequencing run statistics. Metadata standards used by SRA/GEO are available at https://www.ncbi.nlm.nih.gov/sra/docs/submitmeta/ and https://www.ncbi.nlm.nih.gov/geo/info/overview.html.</td></tr><tr><td>14.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Yes. The keywords previously selected will be included as indexed metadata fields during the submission to the NCBI repositories (GEO and BioProject). This ensures that the dataset is discoverable through standard search engines (such as PubMed, Google Dataset Search, and NCBI Entrez) and properly categorized within the database structure.</td></tr><tr><td>14.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>Yes, an abstract is always associated with the dataset submitted to the SRA/GEO repositories that contextualizes and summarizes the analyses performed. This abstract is written by the experimenter at the time of data generation and will be shared along with the data files.</td></tr></table>
<table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>14.4.</td><td>Making data interoperable</td><td></td></tr><tr><td>14.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes, the sequencing data are in FASTQ format. Tables accompanying the data are in CSV format. FASTQ and CSV formats are open formats and therefore interoperable.</td></tr><tr><td>14.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>14.4.3.</td><td>Specify whether you will be using standard vocabulary for your dataset, to allow semantic inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>Yes, to ensure semantic interoperability, we rely on standardized controlled vocabularies. Gene features are identified using Ensembl or NCBI RefSeq stable identifiers. Functional annotation and biological interpretation are mapped to the Gene Ontology (GO). Furthermore, experimental conditions and pathological contexts are described using terms from the Disease Ontology (DO) and Medical Subject Headings (MeSH).</td></tr><tr><td>14.5.</td><td>Increase data reuse</td><td></td></tr><tr><td>14.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Once published, this dataset may be reused by the scientific community.</td></tr><tr><td>14.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>To ensure the widest reuse possible, the data will be deposited into NCBI SRA/GEO and dedicated to the Public Domain (No Rights Reserved, CC0). This aligns with NCBI's policy of placing no restrictions on the use or distribution of these data. Documentation and code will use a permissive open-source license (e.g., MIT/BSD) and receive a DOI via Recherche Data Gouv.</td></tr><tr><td>14.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>The dataset will be deposited prior to manuscript submission but will remain under a temporary embargo until the date of publication. This ensures the data is available for peer review (via secure access) while protecting the novelty of the findings. Upon publication, the dataset will be released immediately with no further restrictions.</td></tr><tr><td>14.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>Primary long-term preservation is guaranteed indefinitely through the chosen public repository (NCBI SRA/GEO). For redundancy, a copy will also be archived on Institut Pasteur's central servers and backed up according to institutional data security protocols for at least 10 years.</td></tr></tbody></table>