Abstract
With High Throughput Sequencing (HTS) technologies, biology is experiencing a sequence data deluge. A single sequencing experiment currently yields 100 million short sequences, or reads, the analysis of which demands efficient and scalable sequence analysis algorithms. Diverse kinds of applications repeatedly need to query the sequence collection for the occurrence positions of a subword. Time can be saved by building an index of all subwords present in the sequences before performing huge numbers of queries. However, both the scalability and the memory requirement of the chosen data structure must suit the data volume. Here, we introduce a novel indexing data structure, called Gk arrays, and related algorithms that improve on classical indexes and state of the art hash tables.