title: Computational Performance Assessment of k-mer Counting Algorithms.
abstract: 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.
authors.affiliation: GICOGE, Universidad Distrital Francisco José de Caldas , Bogotá, Colombia .
authors.forename: Nelson
authors.initials: N
authors.lastname: Pérez
authors.affiliation: GICOGE, Universidad Distrital Francisco José de Caldas , Bogotá, Colombia .
authors.forename: Miguel
authors.initials: M
authors.lastname: Gutierrez
authors.affiliation: GICOGE, Universidad Distrital Francisco José de Caldas , Bogotá, Colombia .
authors.forename: Nelson
authors.initials: N
authors.lastname: Vera
mesh_terms: D000465:Algorithms; D002883:Chromosomes, Human, Pair 14 / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
keywords: computational performance assessment; data structures; k-mer counters