2016/07/22 by Marius Erbert, Erbert, Marius, Steffen Rechner +4
Biochemistry, Genetics and Molecular Biology · Computer Science · #Algorithms and Data Compression #DNA and Biological Computing #Genomics and Phylogenetic Studies #cs.DS #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1607.06618
A short version of this paper will appear in the proceedings of WABI 2016
arxiv created 2016/07/22 · arxiv updated 2016/07/25
A basic task in bioinformatics is the counting of k-mers in genome strings. The k-mer counting problem is to build a histogram of all substrings of length k in a given genome sequence. We present the open source k-mer counting software Gerbil that has been designed for the efficient counting of k-mers for k≥32. Given the technology trend towards long reads of next-generation sequencers, support for large k becomes increasingly important. While existing k-mer counting tools suffer from excessive memory resource consumption or degrading performance for large k, Gerbil is able to efficiently support large k without much loss of performance. Our software implements a two-disk approach. In the first step, DNA reads are loaded from disk and distributed to temporary files that are stored at a working disk. In a second step, the temporary files are read again, split into k-mers and counted via a hash table approach. In addition, Gerbil can optionally use GPUs to accelerate the counting step. For large k, we outperform state-of-the-art open source k-mer counting tools for large genome data sets.