BACKGROUND: Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network.
RESULTS: Here, we present an R package for calculating the weighted topological overlap (wTO), that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. To graphically inspect the resulting networks, the R package contains a visualization tool, which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of more than 20,000 genes in under two hours. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set.
CONCLUSIONS: In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL -2 Open Source License ( https://cran.r-project.org/web/packages/wTO/ ).

Protein production can be improved if methods for soluble protein expression are developed. Interferon consensus (IFN-con) is used to treat hepatitis C. IFN-con has superior activity compared to other clinically used interferon α subtypes. However IFN-con is a challenging protein to produce in a soluble form using an Escherichia coli expression system. Here we describe the expression of soluble and active recombinant IFN-con in E. coli. The IFN-con gene sequence was optimised for expression in E. coli, which was then cloned into the Champion™ pET SUMO expression vector downstream of the SUMO fusion protein and under strong T7lac promoter. The SUMO-IFN-con fusion protein was efficiently expressed using the SHuffle™ E. coli strain and existed in soluble form as 86-88% of the total IFN-con. After removal of the SUMO fusion partner, approximately 50mg of recombinant IFN-con of at least 98% purity (by RP-HPLC) was obtained from a 1L fermentation culture. Using an A549/EMCV antiviral assay, the specific activity of the recombinant IFN-con was determined to be 960×10(6) IU/mg as calculated to NIBSC standard for IFN-con (3×10(5)pfu/mL virus titre). Comparison of the antiviral activity of the produced IFN-con to IFN α-2a showed that IFN-con displays 2.8 times greater activity, which is in good agreement with what has been reported in the literature for pure protein. IFN-con expression in a soluble form from E. coli allowed us to use a simple, two-step purification process to yield highly pure and active IFN-con which is more efficient than obtaining IFN-con from inclusion bodies.