KCMBT: ak-mer Counter based on Multiple Burst Trees

Abdullah-Al Mamun, Soumitra Pal, Sanguthevar Rajasekaran
2016 Bioinformatics  
Motivation: 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
more » ... ons. Results: 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. Availability and Implementation: KCMBT is freely available on GitHub: (https://github.com/abdul
doi:10.1093/bioinformatics/btw345 pmid:27283950 pmcid:PMC5939891 fatcat:bxsnlxx775ddvawcusuevzsfgm