KCMBT: a k-mer Counter based on Multiple Burst Trees.
A massive number of bioinformatics applications require counting of k-length substrings in genetically important long strings. A k-mer counter generates the frequencies of each k-length substring in genome sequences. Genome assembly, repeat detection, multiple sequence alignment, error detection and many other related applications use a k-mer counter as a building block. Very fast and efficient algorithms are necessary to count k-mers in large data sets to be useful in such applications.



We propose a novel trie-based algorithm for this k-mer counting problem. We compare our devised algorithm k-mer Counter based on Multiple Burst Trees (KCMBT) with available all well-known algorithms. Our experimental results show that KCMBT is around 30% faster than the previous best-performing algorithm KMC2 for human genome dataset. As another example, our algorithm is around six times faster than Jellyfish2. Overall, KCMBT is 20-30% faster than KMC2 on five benchmark data sets when both the algorithms were run using multiple threads.



KCMBT is freely available on GitHub: (https://github.com/abdullah009/kcmbt_mt).



rajasek@engr.uconn.edu



Supplementary data are available at Bioinformatics online.
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.
Abdullah-Al
AA
Mamun
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.
Soumitra
S
Pal
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.
Sanguthevar
S
Rajasekaran
D000465:Algorithms; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods; D016678:Genome; D006801:Humans; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D012984:Software