BACKGROUND: Sequencing of marker genes amplified from environmental samples, known as amplicon sequencing, allows us to resolve some of the hidden diversity and elucidate evolutionary relationships and ecological processes among complex microbial communities. The analysis of large numbers of samples at high sequencing depths generated by high throughput sequencing technologies requires efficient, flexible, and reproducible bioinformatics pipelines. Only a few existing workflows can be run in a user-friendly, scalable, and reproducible manner on different computing devices using an efficient workflow management system. RESULTS: We present Natrix, an open-source bioinformatics workflow for preprocessing raw amplicon sequencing data. The workflow contains all analysis steps from quality assessment, read assembly, dereplication, chimera detection, split-sample merging, sequence representative assignment (OTUs or ASVs) to the taxonomic assignment of sequence representatives. The workflow is written using Snakemake, a workflow management engine for developing data analysis workflows. In addition, Conda is used for version control. Thus, Snakemake ensures reproducibility and Conda offers version control of the utilized programs. The encapsulation of rules and their dependencies support hassle-free sharing of rules between workflows and easy adaptation and extension of existing workflows. Natrix is freely available on GitHub ( https://github.com/MW55/Natrix ) or as a Docker container on DockerHub ( https://hub.docker.com/r/mw55/natrix ). CONCLUSION: Natrix is a user-friendly and highly extensible workflow for processing Illumina amplicon data.

MOTIVATION: Integrative network analysis methods provide robust interpretations of differential high-throughput molecular profile measurements. They are often used in a biomedical context-to generate novel hypotheses about the underlying cellular processes or to derive biomarkers for classification and subtyping. The underlying molecular profiles are frequently measured and validated on animal or cellular models. Therefore the results are not immediately transferable to human. In particular, this is also the case in a study of the recently discovered interleukin-17 producing helper T cells (Th17), which are fundamental for anti-microbial immunity but also known to contribute to autoimmune diseases. RESULTS: We propose a mathematical model for finding active subnetwork modules that are conserved between two species. These are sets of genes, one for each species, which (i) induce a connected subnetwork in a species-specific interaction network, (ii) show overall differential behavior and (iii) contain a large number of orthologous genes. We propose a flexible notion of conservation, which turns out to be crucial for the quality of the resulting modules in terms of biological interpretability. We propose an algorithm that finds provably optimal or near-optimal conserved active modules in our model. We apply our algorithm to understand the mechanisms underlying Th17 T cell differentiation in both mouse and human. As a main biological result, we find that the key regulation of Th17 differentiation is conserved between human and mouse. AVAILABILITY AND IMPLEMENTATION: xHeinz, an implementation of our algorithm, as well as all input data and results, are available at http://software.cwi.nl/xheinz and as a Galaxy service at http://services.cbib.u-bordeaux2.fr/galaxy in CBiB Tools. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: High-throughput molecular data provide a wealth of information that can be integrated into network analysis. Several approaches exist that identify functional modules in the context of integrated biological networks. The objective of this study is 2-fold: first, to assess the accuracy and variability of identified modules and second, to develop an algorithm for deriving highly robust and accurate solutions. RESULTS: In a comparative simulation study accuracy and robustness of the proposed and established methodologies are validated, considering various sources of variation in the data. To assess this variation, we propose a jackknife resampling procedure resulting in an ensemble of optimal modules. A consensus approach summarizes the ensemble into one final module containing maximally robust nodes and edges. The resulting consensus module identifies and visualizes robust and variable regions by assigning support values to nodes and edges. Finally, the proposed approach is exemplified on two large gene expression studies: diffuse large B-cell lymphoma and acute lymphoblastic leukemia.

MOTIVATION: Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules. RESULTS: The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization. AVAILABILITY: The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de.