Computational Performance Assessment of k-mer Counting Algorithms.

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.

Modeling the angiotensin-converting enzyme inhibitory activity of peptide mixtures obtained from cheese whey hydrolysates using concentration-response curves.

Three mathematical models, two logistic models (previously published in previous works) and one mechanistic, developed in this work and based on Michaelis-Menten kinetics, were compared to select the most adequate model in describing the angiotensin-converting enzyme (ACE)-inhibitory activity of bioactive peptide mixtures obtained from cheese whey protein. The significance of both the model and its parameters as well as the value of the regression coefficient was used as criteria to select the most adequate model for obtaining the IC(50) values corresponding to each bioactive peptides mixture. The best results were obtained with the Michaelis-Menten-based model because it provided the best fits and in addition the values for its parameters were always significant.