GPUDePiCt: A Parallel Implementation of a Clustering Algorithm for Computing Degenerate Primers on Graphics Processing Units

A Method for Degenerate Primer Design Based on Artificial Bee Colony Algorithm

Aiming to address the complex degenerate primer design problem in the biological field, in this paper, we design a degenerate primer optimization model considering primer coverage and degeneracy that allows a small number of base mismatches, and propose a global optimization method based on the artificial bee colony algorithm. The proposed algorithm combines the idea of the ant colony algorithm with the optimization process of the artificial bee colony algorithm, overcomes the disadvantage of the uncertain candidate solution length of the artificial bee colony algorithm in solving discrete optimization problems, designs the search space model according to the construction process of candidate solution in ant colony optimization algorithm, and redesigns various bee foraging strategies according to the optimization process information. In the comparative experiments on DNA template sequences of different scales, the degenerate primer designed by the proposed algorithm is superior to the existing methods in terms of stability, specificity, coverage and degeneracy.

Gene expression patterns in transgenic mouse models of hypertrophic cardiomyopathy caused by mutations in myosin regulatory light chain

Using microarray and bioinformatics, we examined the gene expression profiles in transgenic mouse hearts expressing mutations in the myosin regulatory light chain shown to cause hypertrophic cardiomyopathy (HCM). We focused on two malignant RLC-mutations, Arginine 58→Glutamine (R58Q) and Aspartic Acid 166 → Valine (D166V), and one benign, Lysine 104 → Glutamic Acid (K104E)-mutation. Datasets of differentially expressed genes for each of three mutants were compared to those observed in wild-type (WT) hearts. The changes in the mutant vs. WT samples were shown as fold-change (FC), with stringency FC ≥ 2. Based on the gene profiles, we have identified the major signaling pathways that underlie the R58Q-, D166V- and K104E-HCM phenotypes. The correlations between different genotypes were also studied using network-based algorithms. Genes with strong correlations were clustered into one group and the central gene networks were identified for each HCM mutant. The overall gene expression patterns in all mutants were distinct from the WT profiles. Both malignant mutations shared certain classes of genes that were up or downregulated, but most similarities were noted between D166V and K104E mice, with R58Q hearts showing a distinct gene expression pattern. Our data suggest that all three HCM mice lead to cardiomyopathy in a mutation-specific manner and thus develop HCM through diverse mechanisms.