About
ARTbio is the bioinformatics platform of the Institut de Biologie Paris Seine, a 600-members research institute located in the campus Jussieu of Sorbonne University. ARTbio is open to all biologists and doctors from IBPS, Sorbonne University, any academic structure, and private companies. The main objective of ARTbio is to help Biologist and medical doctors in their computational approaches required to analyse big data in genomics, medical research, neurobiology, imaging, etc. This objective is tackled through 3 lines: Services (Services in analysis or collaborative projects, online web services), Teaching (Companionship, training days, seminars, documentation) and Development (tools, workflows, analysis environments, storage, etc).
Galaxy
ARTbio works with Galaxy, a collaborative open source platform dedicated to scientific and biomedical research. The Galaxy servers enable computational data analyses with a user-friendly interface. They provide both reproducibility and transparency of the analyses which can be shared for collaboration and publication.
We have an extensive knowledge of the Galaxy software and its use, from raw data acquisition to publication.
We integrate software and methods into the Galaxy framework and build workflows for complex computational treatments.
Expertise
We have a core expertise high throughput sequencing approaches to study genetics and gene regulation, epigenetics, small RNA biology and viruses metagenomics.
We use statistics for significance testing and classification using probabilistic, bayesian and artificial intelligence approaches.
We are currently focusing on single-cell RNAseq and rare genetic variant analyses.
We ensure project quality and optimal interaction with users by using Continuous Integration technologies and the AGILE guidelines.
Technologies
We use the R, Python and Bash programming languages, the Git and Mercurial revision control software, and continuous integration tools such as Planemo and Travis CI.
We implement virtualization and container technologies (e.g. Docker) in order to make analyses reproducible in any hardware infrastructure.
We code our service deployments using Ansible.
We can increase our computing resources on demand thanks to advance knowledge in cloud computing.