MOTIVATION: Several freely available tools perform analysis using algorithms developed to identify significant variation of gene expression individually. The transcriptogramer R package uses protein-protein interaction to perform differential expression of functionally associated genes. The software assesses expression profile of entire genetic systems and reveals which biological systems are significantly altered in case-control designed transcriptome experiments. RESULTS: R/Bioconductor transcriptogramer package projects expression values on an ordered gene list to perform topological analysis, differential expression and gene ontology enrichment analysis, independently of data platform or operating system. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/transcriptogramer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysis by Transcriptogram was developed as a solution to reduce the noise in the micro array measuring technique of the Transcriptome, and has demonstrated potential to be applied as a method of disease diagnostics. The noise reduction in the measurement is achieved by ordering the proteins of a given protein interaction network in a linear way, allowing gene expression analysis in whole genome scale. The ordering process uses a seriation technique, which models a protein-protein association network into an undirected graph. So far, the viability of the diagnosis method was hindered by the high runtime of the ordering algorithm, since the Homo sapiens network can have around 30 thousand proteins. This paper presents some optimizations applied to the seriation problem, which lead to a significant reduction of execution time and the problem complexity analysed. Results show that the algorithm produces good results in reasonable time, e.g. Order an undirected network of 9684 nodes in about 35 minutes, which is faster if compared with other seriation techniques evaluated.

UNLABELLED: ViaComplex is an open-source application that builds landscape maps of gene expression networks. The motivation for this software comes from two previous publications (Nucleic Acids Res., 35, 1859-1867, 2007; Nucleic Acids Res., 36, 6269-6283, 2008). The first article presents a network-based model of genome stability pathways where we defined a set of genes that characterizes each genetic system. In the second article we analyzed this model by projecting functional information from several experiments onto the gene network topology. In order to systematize the methods developed in these articles, ViaComplex provides tools that may help potential users to assess different high-throughput experiments in the context of six core genome maintenance mechanisms. This model illustrates how different gene networks can be analyzed by the same algorithm. AVAILABILITY: (http://lief.if.ufrgs.br/pub/biosoftwares/viacomplex).