This project is to improve the coordination between the Beyond 1 Million Genomes project and the Federated Human Data / ELIXIR CONVERGE projects. All three projects are based around Federated EGA, or its technology, to support cross border access to human controlled access genetic and phenotypic data.

The functional annotation of enzymes is an interesting but nontrivial task requiring experimental data and scientists' manual revision for optimal results. Due to the increasing amount of structural and sequence data, it is more difficult to do the case- by-case analysis, and there is a high demand for automated solutions.

Machine Learning (ML) enables computers to assist humans in making sense of large and complex data sets. With the fall in the cost of high-throughput technologies, large amounts of omics data are being generated and made accessible to researchers. Analysing these complex high-volume data is not trivial, and the use of classical statistics cannot explore their full potential.

This collaborative project focuses on two issues related to bioinformatics.

The first issue concerns the availability of the virAnnot pipeline (doi:10.1094/PBIOMES-07-19-0037-A) that was developed in the Virology team (INRAE UMR 1332) for the CATI BARIC community and the collaborators of the Virology team. This pipeline, intended for everyone, is however only used by bioinformaticians as it can only be used on command line.

The European Genome-phenome Archive (EGA) is a repository for all types of sequence and genotype
experiments, including case-control, population, and family studies. The EGA will serve as a permanent archive
that will archive several levels of data including the raw data (which could, for example, be re-analysed in the
future by other algorithms) as well as the genotype calls provided by the submitters. In spite of EGA accepting
data from all Europe, due to regulations over data and other constraints, it is desirable that ELIXIR Nodes deploy

SMiLE-seq is a new effective experimental method for transcription factor (TF) binding site sequence inference. Still, some TFs are challenging to analyze. We hope to improve the method by using modern statistical and deep learning approaches in both experiment design and the subsequent data analysis.

Deliverables:

• a tool for inferring binding motifs that cover the sequence space representatively
• GUI for analysis and analysis improvement
• “denoisifier” – a tool to use prior to the HMM-based analysis

Milestones:

The OATEN staff exchange aims at familiarising Nodes with open access tools for effective ELIXIR Node management. Several workshops are planned for the spring, together with the CONVERGE WP2 ELITMa program, covering strategic management, financial management and project management.