Regulus infers signed regulatory networks in few samples from regions and genes activities

Motivation Transcriptional regulation is performed by transcription factors (TF) binding to DNA in context-dependent regulatory regions and determines the activation or inhibition of gene expression. Current methods of transcriptional regulatory networks inference, based on one or all of TF, regions and genes activity measurements require a large number of samples for ranking the candidate TF-gene regulation relations and rarely predict whether they are activations or inhibitions. We hypothesize that transcriptional regulatory networks can be inferred from fewer samples by (1) fully integrating information on TF binding, gene expression and regulatory regions accessibility, (2) reducing data complexity and (3) using biology-based logical constraints to determine the global consistency of the candidate TF-gene relations and qualify them as activations or inhibitions. Results We introduce Regulus , a method which computes TF-gene relations from gene expressions, regulatory region activities and TF binding sites data, together with the genomic locations of all entities. After aggregating gene expressions and region activities into patterns, data are integrated into a RDF endpoint. A dedicated SPARQL query retrieves all potential relations between expressed TF and genes involving active regulatory regions. These TF-region-gene relations are then filtered using a logical consistency check translated from biological knowledge, also allowing to qualify them as activation or inhibition. Regulus compares favorably to the closest network inference method, provides signed relations consistent with public databases and, when applied to biological data, identifies both known and potential new regulators. Altogether, Regulus is devoted to transcriptional network inference in settings where samples are scarce and cell populations are closely related. Regulus is available at https://gitlab.com/teamDyliss/regulus