Flynet: a genomic resource for Drosophila melanogaster transcriptional regulatory networks

Annotation of transcription factors, chromatin-associated factors, and basal transcription machinery in the pea aphid, Acyrthosiphon pisum, and development of the ATFdb database, a resource for studies of transcriptional regulation

The pea aphid, Acyrthosiphon pisum, is an emerging model system in functional and comparative genomics, in part due to the availability of new genomic approaches and the different sequencing and annotation efforts that the community has dedicated to this important crop pest insect. The pea aphid is also used as a model to study fascinating biological traits of aphids, such as their extensive polyphenisms, their bacteriocyte-confined nutritional symbiosis, or their adaptation to the highly unbalanced diet represented by phloem sap. To get insights into the molecular basis of all these processes, it is important to have an appropriate annotation of transcription factors (TFs), which would enable the reconstruction/inference of gene regulatory networks in aphids. Using the latest version of the A. pisum genome assembly and annotation, which represents the first chromosome-level pea aphid genome, we annotated the complete repertoire of A. pisum TFs and complemented this information by annotating genes encoding chromatin-associated and basal transcription machinery proteins. These annotations were done combining information from the model Drosophila melanogaster, for which we also provide a revisited list of these proteins, and de novo prediction. The comparison between the two model systems allowed the identification of major losses or expansions in each genome, while a deeper analysis was made of ZNF TFs (with certain families expanded in the pea aphid), and the Hox gene cluster (showing reorganization in gene position in the pea aphid compared to D. melanogaster). All annotations are available to the community through the Aphid Transcription Factors database (ATFdb), consolidating the various annotations we generated. ATFdb serves as a valuable resource for gene regulation studies in aphids.

Computational Prediction of Protein-Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.