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arXiv · 1306.4037

Hybrid Indexes for Repetitive Datasets

H. Ferrada·T. Gagie·T. Hirvola·S. J. Puglisi
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Abstract

Advances in DNA sequencing mean databases of thousands of human genomes will soon be commonplace. In this paper we introduce a simple technique for reducing the size of conventional indexes on such highly repetitive texts. Given upper bounds on pattern lengths and edit distances, we preprocess the text with LZ77 to obtain a filtered text, for which we store a conventional index. Later, given a query, we find all matches in the filtered text, then use their positions and the structure of the LZ77 parse to find all matches in the original text. Our experiments show this also significantly reduces query times.

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H. Ferrada, T. Gagie, T. Hirvola, S. J. Puglisi. 2013-06-17. Hybrid Indexes for Repetitive Datasets. https://doi.org/10.1098/rsta.2013.0137

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