e-In Infrastructure for th the Multi- Sc Scale Complex Genomics VRE Jose Josep Ll Ll. . Gelp Gelpí BSC BSC - UB UB https://vre.multiscalegenomics.eu DI4R Brussels 30 Nov This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 676556.
Why MuG: The two Genomics worlds Structural Biology Biochemistry Biophysics,… Output structures ensembles Chem. mechanisms Molecular Biology Cell Biology,… Output Sequences Images Functions 2
Why MuG: The dream 3
The Problems Interoperability Fast-evolving->immature Biologists Usability Tools HPC facilities Methods developers MuG Lack of standards No FAIR data VRE Formats Data Visualization & Types Unfriendly Undigested Disconnected 4
Users need to forget about the infrastructure … Friendly Integrated environment. State-of-the-art Hidden (and Stable and workspace. Known interfaces. Tools and scalable) Sustainable Data and tools in workflows infrastructure ecosystem Only scientific a single portal decisions needed 5
Design Guidelines Flexible and easy to deploy research platform Software scheduler(s) to manage infrastructure and tools Multi-scale execution (Cluster to HPC) Web & Programmatic access, easy user access and support Procedure to integrate analysis, simlation, and visualization tools Compatible with European e-infrastructures First release of MuG VRE 15th November 6
User perspective: Authentication 7
User perspective: the Workspace Intuitive Toolkits for File system layout Data mngt. Rich set of Analysis Data types and Visualization Formats 8
User perspective: Tools CG NA Simulation Prot DNA Complexes Binding sites Nucleosome positioning HiC analysis ChipSeq Analysis Genome indexes 9
User perspective: Visualizing data 10
MuG VRE Backend Users Web Access Web Services Galaxy interface User support Programming Model Data Access Data Access SGE Execution Service API API Tools Stand- Apps COMPSs Repository User User alone apps. Workspace Workspace Resource Management Public Repos HPC metadata 11
Tool execution life cycle (metadata driven) 12
Tool execution life cycle Execution scheduler Python wrappers to enclose tools 13
Data management (aim) ? Single virtual data space Tool’s execution Public Repos 14
Linking to e-infrastructures EBI Repos Shared storage MuG @ MuG BSC/IRB Repos User interface 15 15
The MuG’s team Laia Codo (BSC) Genis Bayarri (IRB) Marco Pasi (UNot) Mark McDowall (EBI) https://youtu.be/DRtf7b6M9WY 16
Register now!! 17
Thank you! www.multiscalegenomics.eu irbinfo.mug@irbbarcelona.org @MuG_genomics This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 676556.
Recommend
More recommend
