HPC4BD 2017 Workshop Foreword and Program Committee

[No abstract available

HPC4BD 2015 Workshop Foreword

[No abstract available

Metagenomic insights into the carbohydrate-active enzymes carried by the microorganisms adhering to solid digesta in the rumen of cows

The ruminal microbial community is a unique source of enzymes that underpin the conversion of cellulosic biomass. In this study, the microbial consortia adherent on solid digesta in the rumen of Jersey cattle were subjected to an activity-based metagenomic study to explore the genetic diversity of carbohydrolytic enzymes in Jersey cows, with a particular focus on cellulases and xylanases. Pyrosequencing and bioinformatic analyses of 120 carbohydrate-active fosmids identified genes encoding 575 putative Carbohydrate-Active Enzymes (CAZymes) and proteins putatively related to transcriptional regulation, transporters, and signal transduction coupled with polysaccharide degradation and metabolism. Most of these genes shared little similarity to sequences archived in databases. Genes that were predicted to encode glycoside hydrolases (GH) involved in xylan and cellulose hydrolysis (e. g., GH3, 5, 9, 10, 39 and 43) were well represented. A new subfamily (S-8) of GH5 was identified from contigs assigned to Firmicutes. These subfamilies of GH5 proteins also showed significant phylum-dependent distribution. A number of polysaccharide utilization loci (PULs) were found, and two of them contained genes encoding Sus-like proteins and cellulases that have not been reported in previous metagenomic studies of samples from the rumens of cows or other herbivores. Comparison with the large metagenomic datasets previously reported of other ruminant species (or cattle breeds) and wallabies showed that the rumen microbiome of Jersey cows might contain differing CAZymes. Future studies are needed to further explore how host genetics and diets affect the diversity and distribution of CAZymes and utilization of plant cell wall materials

Parkway 2.0: A Parallel Multilevel Hypergraph Partitioning Tool

Algorithms for serial hypergraph partitioning have been studied extensively [9,2, 14] and tool support exists (e.g. hMeTiS [13] and PaToH [4]). However, thes