Summary Characterizing biomarkers based on microbiome profiles has great potential for translational medicine and precision medicine. Here, we present microbiomeMarker, an R/Bioconductor package implementing commonly used normalization and differential analysis methods, and three supervised learning models to identify microbiome markers. microbiomeMarker also allows comparison of different methods of differential analysis and confounder analysis. It uses standardized input and output formats, which renders it highly scalable and extensible, and allows it to seamlessly interface with other microbiome packages and tools. In addition, the package provides a set of functions to visualize and interpret the identified microbiome markers. Availability and implementation microbiomeMarker is freely available from Bioconductor (https://www.bioconductor.org/packages/microbiomeMarker). Source code is available and maintained at GitHub (https://github.com/yiluheihei/microbiomeMarker). Supplementary information Supplementary data are available at Bioinformatics online.

BackgroundAll species live in complex ecosystems. The structure and complexity of a microbial community reflects not only diversity and function, but also the environment in which it occurs. However, traditional ecological methods can only be applied on a small scale and for relatively well-understood biological systems. Recently, a graph-theory-based algorithm called the reverse ecology approach has been developed that can analyze the metabolic networks of all the species in a microbial community, and predict the metabolic interface between species and their environment.ResultsHere, we present RevEcoR, an R package and a Shiny Web application that implements the reverse ecology algorithm for determining microbe–microbe interactions in microbial communities. This software allows users to obtain large-scale ecological insights into species’ ecology directly from high-throughput metagenomic data. The software has great potential for facilitating the study of microbiomes.ConclusionsRevEcoR is open source software for the study of microbial community ecology. The RevEcoR R package is freely available under the GNU General Public License v. 2.0 at http://cran.r-project.org/web/packages/RevEcoR/ with the vignette and typical usage examples, and the interactive Shiny web application is available at http://yiluheihei.shinyapps.io/shiny-RevEcoR, or can be installed locally with the source code accessed from https://github.com/yiluheihei/shiny-RevEcoR.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1088-4) contains supplementary material, which is available to authorized users.

Summary Heat acclimation (HA) is the best strategy to improve heat stress tolerance by inducing positive physiological adaptations. Evidence indicates that the gut microbiome plays a fundamental role in the development of HA, and modulation of gut microbiota can improve tolerance to heat exposure and decrease the risks of heat illness. In this study, for the first time, we applied 16S rRNA gene sequencing and untargeted liquid chromatography–mass spectrometry (LC‐MS) metabolomics to explore variations in the gut microbiome and faecal metabolic profiles in rats after HA. The gut microbiota of HA subjects exhibited higher diversity and richer microbes. HA altered the gut microbiota composition with significant increases in the genera Lactobacillus (a major probiotic) and Oscillospira alongside significant decreases in the genera Blautia and Allobaculum. The faecal metabolome was also significantly changed after HA, and among the 13 perturbed metabolites, (S)‐AL 8810 and celastrol were increased. Moreover, the two increased genera were positively correlated with the two upregulated metabolites and negatively correlated with the other 11 downregulated metabolites, while the correlations between the two decreased genera and the upregulated/downregulated metabolites were completely contrary. In summary, both the structure of the gut microbiome community and the faecal metabolome were improved after 28 days of HA. These findings provide novel insights regarding the improvement of the gut microbiome and its functions as a potential mechanism by which HA confers protection against heat stress.

Comparisons of gene expression signatures provide a way to explore functional connections among biological events in global aspects of cell response. GeneExpressionSignature is an R package developed for the large-scale analysis of gene expression signatures. The package implements two rank-merging algorithms and two similarity-scoring algorithms. The functions of GeneExpressionSignature provide a flexible solution for gene expression signature-based studies and hold great potential in biomedical research applications, such as drug repurposing. GeneExpressionSignature is released under GPL v2 within the Bioconductor project and is freely available at http://www.bioconductor.org/packages/release/bioc/html/GeneExpressionSignature.html .

Dependability of software, a major concern in many computer applications, can be improved through several means. But systematic approaches for its evaluation do not exist, which is the prerequisite for dependability control and improvement. Software dependability evaluation is an urgent problem to be solved. There is some subjectivity about weighting coefficient when applying fuzzy comprehensive evaluation model to evaluate software dependability. A new comprehensive evaluation model with combinational weight based on rough