g-INFO portal Doan Trung Tung Aurlien BERNARD, Ana Lucia DA-COSTA, - PowerPoint PPT Presentation

g-INFO portal Doan Trung Tung Aurélien BERNARD, Ana Lucia DA-COSTA, Vincent BLOCH, Thanh-Hoa LE, Yannick LEGRE, Lydia MAIGNE, Jean SALZEMANN, Hong-Quang NGUYEN, Vincent BRETON 1

Outline  Introduction  Overview of g-INFO  Implementation of g-INFO  Conclusions and perspectives 2

Why g-INFO?  H5N1 (avian flu) 262 deaths 436 cases WHO - July 2009 287 deaths 486 cases WHO – March 2010 3

Influenza surveillance  Data collection  Data processing in batch mode  BioHealthBase  General phylogenetic pipelines  NCBI  Specific phylogenetic  LosAlamos pipelines g-INFO: Grid-based  Deployment of International phylogenetic tools on Network for Flu clusters / grids Oservation 4

Global Surveillance Network g-INFO’s overview 5

g-INFO’s goals  Integration of influenza virus data sources into a federation of databases  Automatic phylogenetic pipelines  Specific molecular epidemiology studies 6

Architecture of g-INFO system  Each data provider has its own server(s) to store his data  Data provider export only selected data to a data grid interface server  The data exported is integrated in a common schema on the interface servers  Providers can keep the privilege of granting access rights to their data 7

Architecture of g-INFO system  Epidemiologic pipelines will be deployed on the grid  BLAST  Alignment  Phylogenetic trees  Visualisation  ... and more 8

Phylogenetic workflow g-INFO’s implementation 9

Data collection >ABV25634 MKAILLVLLCAFAATNADTLCIGYHANNSTDTVDTVLEKNVTVTHSVNLLEDSHNGKLCRLGGIAPLQLG KCNIAGWLLGNPECDLLLTVSSWSYIVETSNSDNGTCYPGDFIDYEELREQLSSVSSFEKFEIFPKTSSW PNHETTRGVTAACPYAGASSFYRNLLWLVKKENSYPKLSKSYVNNKGKEVLVLWGVHHPPTSTDQQSLYQ NADAYVSVGSSKYDRRFTPEIAARPKVRGQAGRMNYYWTLLEPGDTITFEATGNLVAPRYAFALNRGSES GIITSDAPVHDCDTKCQTPHGAINSSLPFQNIHPVTIGECPKYVKSTKLRMVTGLRNIPSIQSRGLFGAI AGFIEGGWTGLIDGWYGYHHQNGQGSGYAADQKSTQNAIDGITNKVNSVIEKMNTQFTVVGKEFNNLERR IKNLNKKVDDGFLDVWTYNAELLVLLENERTLDFHDSNVKNLYEKARSQLRNNAKEIGNGCFEFYHKCDD ACMESVRNGTYDYPKYSEESKLNREEIDGVKLESMMVYQILAIYSTVASSLVLLVSLGAISFWMCSNGSL QCRICI Grid DB FTP NCBI Metadata Sequences Daily Protein, Nucleotide, updates Coding region IDs 10

WISDOM Production Environment 11

Integration of g-INFO into WPE g-INFO Data Manager Job Manager Data collection Job Submitter Amga Data service Task Manager WISDOM Information System MUSCLE Gblocks Amga PhyML BLAST 12

Automatic phylogenetic pipeline Task g-INFO Manager pipeline Job Job g-INFO g-INFO portal database Job Job Wisdom IS Manager 13

Automatic phylogenetic pipeline NCBI > Run daily a phylogenetic workflow on the grid Prepare Data in correct format AMGA Metadata Alignment + Curation Sequences ( Muscle + Gblocks ) Protein, Nucleotide, Coding region IDs Phylogenetic Visualisation tool Analysis ( PhyML ) Grid portal 14

Manual phylogenetic workflow MOTEUR MOTEUR Task desktop web Manager tool services Job Job g-INFO g-INFO portal database Job Job Wisdom IS Manager 15

Workflow execution example 16

Phylogenetic workflow g-INFO portal 17

g-INFO portal  Collaboration among IFI, IOIT and HPC:  IFI: web services to interact between the portal and the system  IOIT: design and develop the portal  HPC: visualization tool  Technologies:  JSF 2.0, Ajax, web services, Java aplet, … 18

g-INFO portal MOTEUR Task web Manager services Job Job g-INFO Intermediate g-INFO portal web services database Job JSF 2.0 Web services Ajax Job JDBC Wisdom IS Manager 19

g-INFO portal – home page 20

g-INFO portal - search 21

g-INFO portal – search results 22

g-INFO portal – search results 23

g-INFO portal – working sessions 24

g-INFO portal – define working session template 25

g-INFO portal – define working session template 26

g-INFO portal – define working session template 27

g-INFO portal – define working session template 28

g-INFO portal – run working session 29

g-INFO portal – run working session Pipeline01 result01 Pipeline02 result02 Input01 Input02 Pipeline03 result03 Input03 WorkingSession Pipeline04 result04 inputN PipelineN resultN 30

g-INFO portal – visualization 31

Conclusions  A success in terms of international collaboration  An example of developping grid application in Vietnam  A complementary service for the public health research community 32

Perspectives  Provide more tools and pipelines  Import other database resources  Improve system’s performance  We are expecting the research community to contribute with more useful tools  Can be applied for other emerging diseases 33

Thank you! 34