Searching similarities between sequences is a fundamental operation in bioinformatics, providing insight in biological functions as well as tools for high-throughput data. There is a need to have algorithms able to process efficiently billions of sequences. To look for approximate similarities, a common heuristic is to consider short words that appear exactly in both sequences, the seeds, then to try to extend this similarity to the neighborhoods of the seeds. The thesis focuses on this second stage of seed-based heuristics : how can we retrieve and compare efficiently the neighborhoods of the seeds ? The thesis proposes several solutions tailored for manycore processors such as today's GPUs. Such processors are making massively parallel computing more and more popular. The thesis proposes direct approaches (extension of bit-parallel Wu-Manber algorithm, published in PBC 2011, and binary search) and approaches with another index (with perfect hash functions). Each one of these solutions was conceived to obtain as much fine-grained parallelism as possible, requiring intensive but homogeneous computational operations. All proposed methods were implemented in OpenCL and benchmarked. Finally, the thesis presents MAROSE, a prototype parallel read mapper using these concepts. In some situations, MAROSE is more efficient than the existing read mappers with a comparable sensitivity.
