Functional partnerships between proteins are at the core of complex cellular phenotypes, and the networks formed by interacting proteins provide researchers with crucial scaffolds for modeling, data reduction and annotation. STRING is a database and web resource dedicated to protein–protein interactions, including both physical and functional interactions. It weights and integrates information from numerous sources, including experimental repositories, computational prediction methods and public text collections, thus acting as a meta-database that maps all interaction evidence onto a common set of genomes and proteins. The most important new developments in STRING 8 over previous releases include a URL-based programming interface, which can be used to query STRING from other resources, improved interaction prediction via genomic neighborhood in prokaryotes, and the inclusion of protein structures. Version 8.0 of STRING covers about 2.5 million proteins from 630 organisms, providing the most comprehensive view on protein–protein interactions currently available. STRING can be reached at http://string-db.org/.

With the increasing availability of various ‘omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at http://eggnog.embl.de) that derives nonsupervised orthologous groups (NOGs) from complete genomes, and then applies a comprehensive characterization and analysis pipeline to the resulting gene families. Compared with the previous version, we have more than tripled the underlying species set to cover 3686 organisms, keeping track with genome project completions while prioritizing the inclusion of high-quality genomes to minimize error propagation from incomplete proteome sets. Major technological advances include (i) a robust and scalable procedure for the identification and inclusion of high-quality genomes, (ii) provision of orthologous groups for 107 different taxonomic levels compared with 41 in eggNOGv3, (iii) identification and annotation of particularly closely related orthologous groups, facilitating analysis of related gene families, (iv) improvements of the clustering and functional annotation approach, (v) adoption of a revised tree building procedure based on the multiple alignments generated during the process and (vi) implementation of quality control procedures throughout the entire pipeline. As in previous versions, eggNOGv4 provides multiple sequence alignments and maximum-likelihood trees, as well as broad functional annotation. Users can access the complete database of orthologous groups via a web interface, as well as through bulk download.

Summary: Clann has been developed in order to provide methods of investigating phylogenetic information through the application of supertrees. Availability: Clann has been precompiled for Linux, Apple Macintosh and Windows operating systems and is available from http://bioinf.may.ie/software/clann. Source code is available on request from the authors. Supplementary information: Clann has been written in the C programming language. Source code is available on request. Contact:chris.creevey@may.ie

Background: The liver is central to most economically important metabolic processes in cattle. However, the changes in expression of genes that drive these processes remain incompletely characterised. RNA-seq is the new gold standard for whole transcriptome analysis but so far there are no reports of its application to analysis of differential gene expression in cattle liver. We used RNA-seq to study differences in expression profiles of hepatic genes and their associated pathways in individual cattle in either mild negative energy balance (MNEB) or severe negative energy balance (SNEB). NEB is an imbalance between energy intake and energy requirements for lactation and body maintenance. This aberrant metabolic state affects high-yielding dairy cows after calving and is of considerable economic importance because of its negative impact on fertility and health in dairy herds. Analysis of changes in hepatic gene expression in SNEB animals will increase our understanding of NEB and contribute to the development of strategies to circumvent it.Results: RNA-seq analysis was carried out on total RNA from liver from early post partum Holstein Friesian cows in MNEB (n = 5) and SNEB (n = 6). 12,833 genes were deemed to be expressed (>4 reads per gene per animal), 413 of which were shown to be statistically significantly differentially expressed (SDE) at a false discovery rate (FDR) of 0.1% and 200 of which were SDE (FDR of 0.1%) with a ≥2-fold change between MNEB and SNEB animals. GOseq/KEGG pathway analysis showed that SDE genes with ≥2- fold change were associated (P<0.05) with 9 KEGG pathways. Seven of these pathways were related to fatty acid metabolism and unexpectedly included ‘Steroid hormone biosynthesis’, a process which mainly occurs in the reproductive organs rather than the liver.Conclusions: RNA-seq analysis showed that the major changes at the level of transcription in the liver of SNEB cows were related to fat metabolism. 'Steroid hormone biosynthesis', a process that normally occurs in reproductive tissue, was significantly associated with changes in gene expression in the liver of SNEB cows. Changes in gene expression were found in this pathway that have not been previously been identified in SNEB cows.

The rumen is a complex ecosystem. It is the primary site for microbial fermentation of ingested feed allowing conversion of a low nutritional feed source into high quality meat and milk products. However, digestive inefficiencies lead to production of high amounts of environmental pollutants; methane and nitrogenous waste. These inefficiencies could be overcome by development of forages which better match the requirements of the rumen microbial population. Although challenging, the application of meta-proteomics has potential for a more complete understanding of the rumen ecosystem than sequencing approaches alone. Here, we have implemented a meta-proteomic approach to determine the association between taxonomies of microbial sources of the most abundant proteins in the rumens of forage-fed dairy cows, with taxonomic abundances typical of those previously described by metagenomics. Reproducible proteome profiles were generated from rumen samples. The most highly abundant taxonomic phyla in the proteome were Bacteriodetes, Firmicutes and Proteobacteria, which corresponded with the most abundant taxonomic phyla determined from 16S rRNA studies. Meta-proteome data indicated differentiation between metabolic pathways of the most abundant phyla, which is in agreement with the concept of diversified niches within the rumen microbiota.