HPC in Bioinformatics and Genomics. Daniel Kahn, Clément Rezvoy and Frédéric Vivien Lyon 1 University & INRIA HELIX team LIP-ENS & INRIA GRAAL team
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1 HPC in Bioinformatics and Genomics Daniel Kahn, Clément Rezvoy and Frédéric Vivien Lyon 1 University & INRIA HELIX team LIP-ENS & INRIA GRAAL team
2 Moore s law in genomics Exponential increase Doubling time ~20 months
3 New high-throughput technologies Pyrosequencing (Roche 454 GS FLX) Mb per run (1 day) Long reads (up to 400 bp) ~15 Gb raw data Illumina Genome Analyzer 1,500 Mb per run (3 days) Short reads (35 bp) ~1 Tb raw data Applied Biosystems SOLID sequencer 3,000 Mb per run (5 days) Short reads (35 bp) ~15 Tb raw data
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5 Uses of high throughput sequencing Population genomics For instance, 1000 human genome project Individual sequencing Metagenomics Comprehensive appraisal of microbial communities and gene repertoires in various environments Phylogenomics Resolving the history of genes and species. As many computing challenges
6 Large scale protein sequence analysis All vs. all The challenge of protein modularity Most proteins are combinatorial arrangements of conserved modules (domains) GerE LuxR FixJ OmpR SpoOA NtrC NifA
7 The ProDom project Need for an automated process in order to allow for comprehensive analysis Automatically decompose proteins into domains and cluster domain families, using MKDOM2 Generate multiple alignments and trees for all families Automatically generate mutually consistent representations for all proteins
8 Resolving combinatorial proteins
9 i th iteration DB query The MKDOM2 program no internal repeat detection yes query PSI-BLAST no match matches repeat matches (i+1) th iteration DB DB changes remove newly found domains split modified sequences sort by size query
10 Drawbacks of sequential MKDOM2 Greedy algorithm Scales quadratically Data follow Moore s law no more tractable!
11 Parallelization of MKDOM2 Parallelization of the main loop Distribute sequences for independent family construction Difficulties: Heterogeneous run times for the main loop Possible dependencies between families Precalculate an all vs. all comparison in order to select independent queries Send batches of independent sequences before worker nodes are idle Verify family independence a posteriori
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18 Speed-up on medium scale test set 32 Archaeal genomes 21.5 M aminoacids
19 Large-scale test set 263 genomes 950,216 protein sequences 339 M aminoacids Run on GRID 5000 (150 nodes) Half of the data set processed in only 20 hours
20 Database crunching
21 Increasing query sizes
22 Variable sizes of domain families
23 Heterogeneous run times ~1000-fold range
24 Large result queue
25 yet efficient node usage 86% processor usage
26 Full-scale protein domain analysis To be scaled-up 7-fold for full processing of UniProt today! Will require stable MPI usage of ~1000 processors over the grid Appropriate infrastructure not yet identified Other program MPI_MKDOM3 envisioned to make full use of precalculated all vs. all comparison required in order to further cope with Moore s law
27 LIP-ENS Lyon Clément REZVOY Frédéric VIVIEN INRA Toulouse Emmanuel COURCELLE Daniel KAHN Lyon 1 University INRIA HELIX project Aurélie LAUGRAUD Lauranne DUQUENNE Daniel KAHN Support - PRABI - EU (EMBRACE & IMPACT) - IN2P3 - GRID 5000
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