15 research outputs found
Using cascading Bloom filters to improve the memory usage for de Brujin graphs
De Brujin graphs are widely used in bioinformatics for processing
next-generation sequencing data. Due to a very large size of NGS datasets, it
is essential to represent de Bruijn graphs compactly, and several approaches to
this problem have been proposed recently. In this work, we show how to reduce
the memory required by the algorithm of [3] that represents de Brujin graphs
using Bloom filters. Our method requires 30% to 40% less memory with respect to
the method of [3], with insignificant impact to construction time. At the same
time, our experiments showed a better query time compared to [3]. This is, to
our knowledge, the best practical representation for de Bruijn graphs.Comment: 12 pages, submitte
CLIIQ: Accurate Comparative Detection and Quantification of Expressed Isoforms in a Population
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