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.

Gene Self-Expressive Networks as a Generalization-Aware Tool to Model Gene Regulatory Networks.

Self-expressiveness is a mathematical property that aims at characterizing the relationship between instances in a dataset. This property has been applied widely and successfully in computer-vision tasks, time-series analysis, and to infer underlying network structures in domains including protein signaling interactions and social-networks activity. Nevertheless, despite its potential, self-expressiveness has not been explicitly used to infer gene networks.

A comparative study revealed hyperspectral imaging as a potential standardized tool for the analysis of cuticle tanning over insect development.

Cereal-feeding beetles are a major risk for cereal crop maintenance. Cereal weevils such as have symbiotic intracellular bacteria that provide essential aromatic amino acid to the host for the biosynthesis of their cuticle building blocks. Their cuticle is an important protective barrier against biotic and abiotic stresses, providing high resistance from insecticides.

GReNaDIne: A Data-Driven Python Library to Infer Gene Regulatory Networks from Gene Expression Data.

Inferring gene regulatory networks (GRN) from high-throughput gene expression data is a challenging task for which different strategies have been developed. Nevertheless, no ever-winning method exists, and each method has its advantages, intrinsic biases, and application domains. Thus, in order to analyze a dataset, users should be able to test different techniques and choose the most appropriate one.