Diet alters both the structure and taxonomy of the ovine gut microbial ecosystem.

We surveyed the ruminal metagenomes of 16 sheep under two different diets using Illumina pair-end DNA sequencing of raw microbial DNA extracted from rumen samples. The resulting sequence data were bioinformatically mapped to known prokaryotic 16S rDNA sequences to identify the taxa present in the samples and then analysed for the presence of potentially new taxa. Strikingly, the majority of the microbial individuals found did not map to known taxa from 16S sequence databases. We used a novel statistical modelling approach to compare the taxonomic distributions between animals fed a forage-based diet and those fed concentrated grains. With this model, we found significant differences between the two groups both in the dominant taxa present in the rumen and in the overall shape of the taxa abundance curves. In general, forage-fed animals have a more diverse microbial ecosystem, whereas the concentrate-fed animals have ruminal systems more heavily dominated by a few taxa. As expected, organisms from methanogenic groups are more prevalent in forage-fed animals. Finally, all of these differences appear to be grounded in an underlying common input of new microbial individuals into the rumen environment, with common organisms from one feed group being present in the other, but at much lower abundance.

Accelerating large-scale protein structure alignments with graphics processing units.

BACKGROUND: Large-scale protein structure alignment, an indispensable tool to structural bioinformatics, poses a tremendous challenge on computational resources. To ensure structure alignment accuracy and efficiency, efforts have been made to parallelize traditional alignment algorithms in grid environments. However, these solutions are costly and of limited accessibility. Others trade alignment quality for speedup by using high-level characteristics of structure fragments for structure comparisons. FINDINGS: We present ppsAlign, a parallel protein structure Alignment framework designed and optimized to exploit the parallelism of Graphics Processing Units (GPUs). As a general-purpose GPU platform, ppsAlign could take many concurrent methods, such as TM-align and Fr-TM-align, into the parallelized algorithm design. We evaluated ppsAlign on an NVIDIA Tesla C2050 GPU card, and compared it with existing software solutions running on an AMD dual-core CPU. We observed a 36-fold speedup over TM-align, a 65-fold speedup over Fr-TM-align, and a 40-fold speedup over MAMMOTH. CONCLUSIONS: ppsAlign is a high-performance protein structure alignment tool designed to tackle the computational complexity issues from protein structural data. The solution presented in this paper allows large-scale structure comparisons to be performed using massive parallel computing power of GPU.