Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics.

Background: Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing.

A primer on high-throughput computing for genomic selection.

High-throughput computing (HTC) uses computer clusters to solve advanced computational problems, with the goal of accomplishing high-throughput over relatively long periods of time. In genomic selection, for example, a set of markers covering the entire genome is used to train a model based on known data, and the resulting model is used to predict the genetic merit of selection candidates. Sophisticated models are very computationally demanding and, with several traits to be evaluated sequentially, computing time is long, and output is low.

The role of integrase/recombinase xerD and monofunctional biosynthesis peptidoglycan transglycosylase genes in the pathogenicity of Brucella abortus infection in vitro and in vivo.

Brucella abortus clones identified previously using a green fluorescence protein reporter system after 4h macrophage infection provided insight regarding possible genes involved in early host-pathogen interaction. Among identified genes were an integrase/recombinase (xerD) gene involved in cell division, and a monofunctional biosynthesis peptidoglycan transglycosylase (mtgA) gene that catalyzes the final stages of the peptidoglycan membrane synthesis. Here, we evaluate the in vitro and in vivo survival of B.