General Information
| Description | A k-mer Counter based on Multiple Burst Trees |
| Homepage | https://github.com/abdullah009/kcmbt_mt |
Publications
KCMBT: a k-mer Counter based on Multiple Burst Trees
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 applications.
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/abdullah009/kcmbt_mt).
Contact:
rajasek@engr.uconn.edu
Supplementary information:
Supplementary data are available at Bioinformatics online.
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