GenScale Scalable, Optimized and Parallel Algorithms for Genomics. Dominique LAVENIER

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1 GenScale Scalable, Optimized and Parallel Algorithms for Genomics Dominique LAVENIER

2 Context New Sequencing Technologies - NGS Exponential growth of genomic data Drastic decreasing of costs Emergence of new applications Personalized medicine Metagenomic Bioinformatics Methods / Algorithms for processing NGS data

3 GenScale objectives 1. Design of algorithms/methodologies for NGS Scaling issue : genes ( ) genomes ( ) Time optimization (algorithm, parallelism) Space optimization (data structures) 2. Design of tools matching with real needs Maintain strong links with life science teams Housing bio competences inside GenScale Long term common projects Validation and test of our software by end-users Immediate feedback

4 Implementation Selection of NGS domains Parallelism Data Structure Optimization Project α Life Science Community Project β Sequence comparison Comparative metagenomic Genome assembly Variant detection Protein structure comparison Design/acceleration of workflows Algorithms & Methodologies GenOuest Platform Computin g Resources ---- Banks ---- Software Bioinformatics competences Computer Science Competences SOFTWARE

5 Sequence Comparison Bank to bank comparison Increasing need from the metagenomic field Challenges How Taxonomic assignation, discovering metabolic pathway, reads vs. protein banks One order of magnitude faster than BLAST: same sensitivity Two orders faster with minimal lost of sensitivity Optimally exploit the parallel nature of today and tomorrow processors (SIMD instruction, multi & many cores) New indexing strategies (minimize memory access)

6 Sequence Comparison Current activity Development of PLAST: Parallel Local Alignment Search Tool X5 vs BLASTN - X8 vs TBLASTX Transfer to Korilog company KLAST Partnership with CLCbio and Knime companies Scalability test Kalray & Cray companies Next: adaptation to NGS Process much higher volume of data Release sensitivity (speed/sensitivity tradeoff) Exploit redundancy of NGS data set Explore new index structures

7 Comparative metagenomic de novo approach Evaluate similarity between two samples Sample A Sample B 10 8 reads Challenge Process NGS metagenomic data in a reasonable amount of time Size of intersection similarity How Time (seconds) 100M 10M 1M 100K New similarity metric 10K Fast and low memory footprint 1K indexing structures New algorithms 1 BLAST-like approach 10 days 270 years 1K 10K 100K 1M 10 M 100M 1G Data Base size (# reads)

8 Comparative metagenomic Current activity Approximate similarity Two reads are considered to be similar if they share N non overlapped k-mers Probabilistic indexing structures Bloom filter + fast hash DNA specific functions Tara Ocean project Next Heat map based on Compareads Scaling methodology for processing thousands of samples Include statistical strategies Compareads software 2 samples of 10 8 reads Time: 10 hours Memory: 4 Go

9 de novo Genome Assembly Reconstruct genome text of unknown species Current bottleneck: Challenge Memory ~512 Go RAM for complex genomes Time days of computation Fast assembler with low memory footprint How Efficient and low foot print memory structures Parallel algorithms Assembly Process

10 de novo Genome Assembly Current activity Efficient representation of de-bruijn graph Probabilistic data structure (Cascading Bloom Filter) Assembly Genomic Tool Box Aim: Customize assembly Minia pipeline Human genome : < 8 Gbytes / 48 hours Target assembly INRA Collaboration : pest genome analysis Next Scaffolding Combinatorial optimization approach Parallel assembly algorithms Correction Contiger Scaffolding Gap filling Assembly process

11 Variant detection Types of variants Single Nucleotide Polymorphism (SNP) Insertion / deletion Structural variants (Inversion, transposition,...) Challenge How Detect variants without assembly No mapping step no reference genome Process directly de-bruijn graphs Detection of representative patterns ACGACTCG GACTCGAT Set of reads ACG CGA GAC ACT CTC TCG GAT

12 Variant detection Current activity Reference free detection of SNPs 1 to N read sets Better than classical approaches Detection of inversions Detection of long insertions INRA Collab: ape & tick genome analysis Next Enhance de-bruijn graph Mate-pair information Detection of other types of variants Transposition, translocation Merge assembly + de novo variant detection Disc Snp

13 Protein Structure Comparison Comparison of 3D structures Global : Classification / Clustering Local : Docking Challenges Automatic hierarchical classification Reference database: SCOP, CATH Flexible comparison How : alignment graph Cliques represent local alignments of 3D structures Alignment graph Advance combinatorial optimization technics for solving NP-hard problems Search of maximal clique

14 Protein Structure Comparison Current activity Superfamily prediction/recognition Detection of structural repeats Parallelization of algorithms Collaboration : INRA MIG Next New structural metric distance New algorithms to detect community in the protein structure space

15 Design of Workflows NGS workflows Large datasets Time consuming Challenge Simplify capture Automate parallelization How Abstract level execution level Data flow parallelism

16 GenScale team Research Scientists D. Lavenier (DR CNRS) P. Peterlongo (CR Inria) C. Lemaitre (CR Inria) Faculty Member R. Andonov (Prof. Univ. Rennes 1) A. Mucherino (Ass. Prof. Univ. Rennes 1) External Collaborator G. Rizk (AlgoRizk Company) Post-Doctoral Fellows D. Concalves (CNRS) Engineers C. Deltel (Inria) F. Legeai (Inra) E. Drezen (Inria, ANR GATB) A. Andrieux (Inria, ADT Mapsembler) S. Alves-Carvalho (Inra, Peapol project) A. Gouin (Inra, ANR SpeciAphid) PhD Students F. Moreews (Inra) N. Maillet (Inria / ANR Mappi) G. Chapuis (MESR) M. Leboudic-Jamin (MESR) Administrative Assistant M.N. Georgeault (Inria)

17 GenScale Scalable, Optimized and Parallel Algorithms for Genomics Thank You!