add CITATION.cff file (very first version)

CITATION.cff

+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+title: CCQTL
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Remi
+    family-names: Planel
+    email: remi.planel@pasteur.fr
+    affiliation: >-
+      Université Paris Cité, Bioinformatics and
+      Biostatistics Hub, F-75015 Paris, France
+    orcid: 'https://orcid.org/0000-0002-7826-3316'
+  - given-names: Victoire
+    family-names: Baillet
+    email: victoire.baillet@pasteur.fr
+    affiliation: >-
+      Institut Pasteur, Université Paris Cité,
+      Bioinformatics and Biostatistics Hub, F-75015 Paris,
+      France
+    orcid: 'https://orcid.org/0000-0002-1081-2921'
+  - given-names: Vincent
+    family-names: Guillemot
+    orcid: 'https://orcid.org/0000-0002-7421-0655'
+    email: vincent.guillemot@pasteur.fr
+    affiliation: >-
+      Institut Pasteur, Université Paris Cité,
+      Bioinformatics and Biostatistics Hub, F-75015 Paris,
+      France
+  - given-names: Pascal
+    family-names: Campagne
+    email: pascal.campagne@pasteur.fr
+    affiliation: >-
+      Institut Pasteur, Université Paris Cité,
+      Bioinformatics and Biostatistics Hub, F-75015 Paris,
+      France
+    orcid: 'https://orcid.org/0000-0001-7018-1896'
+identifiers:
+  - type: swh
+    value: 'swh:1:dir:2acedd6f278157b5b0112245f0febc23a45937ef'
+    description: >-
+      Software Heritage identifier for version 1.0.0 of the
+      work.
+repository-code: 'https://gitlab.pasteur.fr/cc-qtl/cc-qtl-db'
+abstract: >-
+  Quantitative Trait Locus (QTL) mapping in mapping
+  populations and Genome-Wide Association Studies (GWAS) in
+  natural populations are complementary approaches for
+  dissecting the genetic architecture of complex traits.
+  While GWAS are typically carried out by statistical
+  genetics groups well-versed in quantitative environments
+  and code management, experimental geneticists performing
+  QTL mapping focus on labor-intensive phenotyping
+  experiments thus requiring further support, both for code
+  and statistics, to benefit from best practices in the
+  field.
+
+  We present CCQTL, a comprehensive platform for QTL mapping
+  in the Collaborative Cross (CC), an increasingly used
+  mouse mapping population. CCQTL features an intuitive
+  graphical user interface (GUI) for seamless end-to-end QTL
+  mapping analysis, from data transformation to candidate
+  gene identification. It also includes a robust database
+  structure ensuring secure, organized storage of phenotypic
+  data, accompanied by an advanced permissions system.
+
+  CCQTL's analytical component leverages R/qtl2 tools
+  integrated into preconfigured Galaxy workflows designed
+  explicitly for the CC. This setup facilitates one-click,
+  reproducible analyses. The platform's interface (GUI,
+  database, and analytics) is containerized using Docker,
+  enabling straightforward deployment and scalability. While
+  primarily designed to empower non-specialists in
+  conducting their own data analyses, CCQTL's Galaxy-brought
+  reproducibility and sophisticated database permission
+  system also renders it valuable for experienced users
+  seeking streamlined solutions.
+keywords:
+  - QTL mapping
+  - Collaborative Cross
+  - Web interface
+  - Database
+  - Galaxy
+license: GPL-3.0
+version: 1.0.0
+date-released: '2023-07-07'