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Bioinformatics. 2019 Sep 1;35(17):3133-3139. doi: 10.1093/bioinformatics/btz047.

The ReproGenomics Viewer: a multi-omics and cross-species resource compatible with single-cell studies for the reproductive science community.

Author information

1
Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes F-35000, France.
2
Department of Genetic Medicine and Development, University of Geneva, Geneva 1211, Switzerland.
3
Institute of Human Genetics, University of Münster, Münster, Germany.
4
Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA/INRIA) - GenOuest platform, Université de Rennes 1, Rennes F-35042, France.

Abstract

MOTIVATION:

Recent advances in transcriptomics have enabled unprecedented insight into gene expression analysis at a single-cell resolution. While it is anticipated that the number of publications based on such technologies will increase in the next decade, there is currently no public resource to centralize and enable scientists to explore single-cell datasets published in the field of reproductive biology.

RESULTS:

Here, we present a major update of the ReproGenomics Viewer, a cross-species and cross-technology web-based resource of manually-curated sequencing datasets related to reproduction. The redesign of the ReproGenomics Viewer's architecture is accompanied by significant growth of the database content including several landmark single-cell RNA-sequencing datasets. The implementation of additional tools enables users to visualize and browse the complex, high-dimensional data now being generated in the reproductive field.

AVAILABILITY AND IMPLEMENTATION:

The ReproGenomics Viewer resource is freely accessible at http://rgv.genouest.org. The website is implemented in Python, JavaScript and MongoDB, and is compatible with all major browsers. Source codes can be downloaded from https://github.com/fchalmel/RGV.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
30668675
DOI:
10.1093/bioinformatics/btz047
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