Section: Overall Objectives

Optimization of genomic data processing

The first objective of GenScale is the design of scalable, optimized and parallel algorithms for processing the mass of genomic data provided by today biotechnologies. More specifically, our research activities focus on the optimization of the following treatments:

• Processing of HTS data (High Throughput Sequencing) generated by sequencers of 2nd and 3rd generation. These machines generate billions of short DNA fragments (called reads) requiring treatments such as read compression, read correction, genome assembly (contig generation, scaffolding) and detection of variants (Single Nucleotide Polymorphism (SNP), insertion, deletion, inversion, etc.).

• Comparison of large genomic or metagenomic data sets. This fundamental bioinformatics task, due to the steadily increasing of genomic data, is still a bottleneck in many treatments such as taxonomic assignation, functional assignation, genome annotation, etc. Furthermore, the data analysis of large metagenomic projects does not scale with standard sequence comparison methods. New strategies must be investigated.

• 3D protein structure. Functionalities of proteins are mainly supported by their three dimensional structures. Determining these structures from Nuclear Magnetic Resonance (NMR) data or classifying them based on their 3D structures into families require the development of highly optimized algorithms.

Optimization is addressed both in terms of memory space and computation time. Space optimization aims to lower the memory footprint of the algorithms. This is done by the design of innovative data structures. Time optimization aims to provide algorithms with short computation time. Two main ways are followed: combinatorial optimization and multilevel parallelism.