A demonstration of the use of Datagrid testbed and services for the biomedical community

Transcription

1 A demonstration of the use of Datagrid testbed and services for the biomedical community Biomedical applications work package V. Breton, Y Legré (CNRS/IN2P3) R. Météry (CS) Credits : C. Blanchet, T. Contamine, S. Gadras, M. Joubert, A.Minne, J. Montagnat

2 The Visual DataGrid Blast A graphical interface to enter query sequences and select the reference database A script to execute the algorithm on the grid A graphical interface to analyze results

3 WP10 When/Where do biologists use? (When?) The first step for analysing new sequences: to compare DNA or protein sequences to other ones: stored in personal or public databases (Where?) in a laboratory with an updated version of the genomics and post-genomics data banks Requires equipment to store databases and run algorithms Requires manpower for system & network maintenance and frequent update of databases Most biologists use integrated web portals for their genomics comparative analysis: no need to worry about the biological file format and the method arguments

4 Web portals for biologists under growing pressure Biologist enters sequences through web interface Pipelined execution of bio-informatics algorithms Genomics comparative analysis Phylogenetics 2D, 3D molecular structure of proteins The algorithms are executed on a local cluster Big labs have big clusters But growing pressure More and more biologists compare larger and larger sequences (whole genomes) to more and more genomes with fancier and fancier algorithms!!

5 UI JDL Executing on the grid Input Sandbox : Input sequences Replica Catalog Job Submit Event Output Sandbox : result Resource Broker Job Submission Service Information Service Job Status Logging & Bookkeeping Computing Element Storage Element Credit : Fabio Hernandez

6 WP10 WP10 WP10 Actual demonstration Seq1 > dcdcdsc bscvbfvbvfbvbvbhvbh svbhdvbhfdbvfd Seq2 > bvdfvfdvhbdfvb bhvdsvbhvbhdvrefghefgdscgdf gcsdycgdkcsqkc Seqn > bvdfvfdvhbdfvb bhvdsvbhvbhdvrefghefgdscgdf gcsdycgdkcsqkchdsqhfduhdhd hqedezhhezldhezhfehflezfzejfv RESULT vbfvbvfbvbvbhvb hsvbhdvbhfdbvfdbvdfvfdvhbdfvbh dbhvdsvbhvbhdvrefghefgdscgdfgc sdycgdkcsqkcqhdsqhfduhdhdhqede zhdhezldhezhfehflezfzeflehfhezfhe hfezhflezhflhfhfelhfehflzlhfzdjazsl zdhfhfdfezhfehfizhflqfhduhsdslchl kchudcscscdscdscdscsddzdzeqvnvq vnq! Vqlvkndlkvnldwdfbwdfbdbd wdfbfbndblnblkdnblkdbdfbwfdbfn UI Computing element Computing element Input file Computing element Seq1 > dsbcbjbd fndfjvbndfbnbnfb jnbjxbnxbjk:nxbf Seq2 > dsbcbjbd fndfjvbndfbnbnfb jnbjxbnxbjk:nxbf Seqn > dsbcbjbd fndfjvbndfbnbnfb jnbjxbnxbjk:nxbf

7 The Grid impact on computing Swissprot vs Swissprot ( sequences) Running time on one CPU : 228 hours Tests at Institut de Biologie et Chimie des Protéines (quadripro) : ~49 hours Tests on DataGrid (cc-in2p3) : 3 hours Impacts : Reduced pressure on local computing Ability to handle very large jobs

8 The grid impact on data handling DataGrid will allow mirroring of databases An alternative to the current costly replication mechanism Allowing web portals on the grid to access updated databases Trembl(EBI) Biomedical Replica Catalog Swissprot (Geneva)

9 This demo illustrates how grids can bring a revolution to genomics Grids expand the performances of genomics web portals Distributed execution of bio-informatics algorithms, Even the ones requiring huge amount of CPU Maintenance of up-to-date biological databases over the network Grids open new perspectives in large scale genomics analysis Complete genome annotation Cross-genomes analysis Data mining on distributed databases Pipelining of huge automatic bio-informatics analysis