EPSILON: an eQTL prioritization framework using similarity measures derived from local networks.

Motivation: When genomic data are associated with gene expression data, the resulting expression quantitative trait loci (eQTL) will likely span multiple genes. eQTL prioritization techniques can be used to select the most likely causal gene affecting the expression of a target gene from a list of candidates. As an input, these techniques use physical interaction networks that often contain highly connected genes and unreliable or irrelevant interactions that can interfere with the prioritization process.

PheNetic: network-based interpretation of unstructured gene lists in E. coli.

At the present time, omics experiments are commonly used in wet lab practice to identify leads involved in interesting phenotypes. These omics experiments often result in unstructured gene lists, the interpretation of which in terms of pathways or the mode of action is challenging. To aid in the interpretation of such gene lists, we developed PheNetic, a decision theoretic method that exploits publicly available information, captured in a comprehensive interaction network to obtain a mechanistic view of the listed genes.

Identification of a complex genetic network underlying Saccharomyces cerevisiae colony morphology.

When grown on solid substrates, different microorganisms often form colonies with very specific morphologies. Whereas the pioneers of microbiology often used colony morphology to discriminate between species and strains, the phenomenon has not received much attention recently. In this study, we use a genome-wide assay in the model yeast Saccharomyces cerevisiae to identify all genes that affect colony morphology.

Network-based functional modeling of genomics, transcriptomics and metabolism in bacteria.

Molecular entities present in a cell (mRNA, proteins, metabolites,…) do not act in isolation, but rather in cooperation with each other to define an organisms form and function. Their concerted action can be viewed as networks of interacting entities that are active under certain conditions within the cell or upon certain environmental signals. A main challenge in systems biology is to model these networks, or in other words studying which entities interact to form cellular systems or accomplish similar functions.

A comparative transcriptome analysis of Rhizobium etli bacteroids: specific gene expression during symbiotic nongrowth.

Rhizobium etli occurs either in a nitrogen-fixing symbiosis with its host plant, Phaseolus vulgaris, or free-living in the soil. During both conditions, the bacterium has been suggested to reside primarily in a nongrowing state. Using genome-wide transcriptome profiles, we here examine the molecular basis of the physiological adaptations of rhizobia to nongrowth inside and outside of the host.

Query-based biclustering of gene expression data using Probabilistic Relational Models.

Background: With the availability of large scale expression compendia it is now possible to view own findings in the light of what is already available and retrieve genes with an expression profile similar to a set of genes of interest (i.e., a query or seed set) for a subset of conditions.

Transcriptome analysis of the rhizosphere bacterium Azospirillum brasilense reveals an extensive auxin response.

The rhizosphere bacterium Azospirillum brasilense produces the auxin indole-3-acetic acid (IAA) through the indole-3-pyruvate pathway. As we previously demonstrated that transcription of the indole-3-pyruvate decarboxylase (ipdC) gene is positively regulated by IAA, produced by A. brasilense itself or added exogenously, we performed a microarray analysis to study the overall effects of IAA on the transcriptome of A.

Stress response regulators identified through genome-wide transcriptome analysis of the (p)ppGpp-dependent response in Rhizobium etli.

Background: The alarmone (p)ppGpp mediates a global reprogramming of gene expression upon nutrient limitation and other stresses to cope with these unfavorable conditions. Synthesis of (p)ppGpp is, in most bacteria, controlled by RelA/SpoT (Rsh) proteins. The role of (p)ppGpp has been characterized primarily in Escherichia coli and several Gram-positive bacteria.

Genome-wide detection of predicted non-coding RNAs in Rhizobium etli expressed during free-living and host-associated growth using a high-resolution tiling array.

Background: Non-coding RNAs (ncRNAs) play a crucial role in the intricate regulation of bacterial gene expression, allowing bacteria to quickly adapt to changing environments. In the past few years, a growing number of regulatory RNA elements have been predicted by computational methods, mostly in well-studied gamma-proteobacteria but lately in several alpha-proteobacteria as well. Here, we have compared an extensive compilation of these non-coding RNA predictions to intergenic expression data of a whole-genome high-resolution tiling array in the soil-dwelling alpha-proteobacterium Rhizobium etli.