Regulus, a transcriptional regulatory networks inference tool based on Semantic Web technologies

Abstract

MotivationTranscriptional regulation -a major field of investigation in life science-is performed by binding of specialized proteins called transcription factors (TF) to DNA in specific, context-dependent regulatory regions, leading to either activation or inhibition of gene expression. Relations between TF, regions and genes can be described as regulatory networks, which are basically knowledge graphs containing the relationships between the different entities. Current methods of transcriptional regulatory networks inference rarely use information about TF binding or regulatory regions, often require a large number of samples and most of time do not indicate if the TF-gene relation is an activation or an inhibition. The resulting networks may then contain inconsistent relations and the methods are not applicable for common experimental or clinical settings, where the number of samples is limited. Therefore, based on our previous experience of formalizing the Regulatory Circuits data-sets with Semantic Web Technologies, we decided to create a new tool for transcriptional networks inference, that could solve these issues. Results: Our tool, Regulus, provides candidate signed TF-gene relations computed from gene expressions, regulatory region activities and TF binding sites data, together with the genomic location of all entities. After creating expressions and activities patterns, data are integrated into a RDF endpoint. A dedicated SPARQL query retrieves all potential TF-region relations for a given gene expression pattern. These ternary TF-region-gene pattern relations are then filtered and signed using a logical consistency check translated from biological knowledge. Regulus compares favorably to its closest network inference method, provides signs which are consistent with public databases and, when applied to real biological data, identifies both known and potential new regulators. We also provide several means to more stringently filter the output regulators. Altogether, we propose a new tool devoted to transcriptional network inference in settings where samples are scarce and cell populations may be closely related.AvailabilityThe Regulus package is available at https://gitlab.com/gcollet/regulus