Computational efforts in the last decade have led to two different types of approaches for inference of gene regulatory networks (GRNs) given large-scale gene expression data, namely unsupervised and supervised. I will present an approach based on regularized regression for unsupervised learning of GRNs, with applications to data sets from different organisms. In addition, I will discuss findings from a recently developed approach based on network representation of transcriptomics data to learn GRNs in a supervised fashion. Emphasis will be placed on the accuracy of predictions and means for their improvements by considering other types of data (e.g. DAP-seq, ChIP-seq, motif binding). Finally, I will present preliminary results of applying these approaches to transcriptomics data from Chlamydomonas reinhardtii to discern factors contributing to regulating the response to high light.

Invited by Dimitrios Petroutsos du Laboratoire Physiologie Cellulaire et Végétale