Mar 17, 2016

Computational Performance Assessment of k-mer Counting Algorithms

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Nelson PérezNelson Vera


This article is about the assessment of several tools for k-mer counting, with the purpose to create a reference framework for bioinformatics researchers to identify computational requirements, parallelizing, advantages, disadvantages, and bottlenecks of each of the algorithms proposed in the tools. The k-mer counters evaluated in this article were BFCounter, DSK, Jellyfish, KAnalyze, KHMer, KMC2, MSPKmerCounter, Tallymer, and Turtle. Measured parameters were the following: RAM occupied space, processing time, parallelization, and read and write disk access. A dataset consisting of 36,504,800 reads was used corresponding to the 14th human chromosome. The assessment was performed for two k-mer lengths: 31 and 55. Obtained results were the following: pure Bloom filter-based tools and disk-partitioning techniques showed a lesser RAM use. The tools that took less execution time were the ones that used disk-partitioning techniques. The techniques that made the major parallelization were the ones that used disk partitioning, hash tables with lock-free approach, or multiple hash tables.

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Mentioned in this Paper

Chromosomes, Human, Pair 14
Research Personnel
Chromosomes, Human
Anatomical Space Structure
Computer Programs and Programming
Cyanea capillata preparation
Filter - Medical Device

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