Regulus infers signed regulatory relations from few samples' information using discretization and likelihood constraints.
Marine LouarnGuillaume ColletÈve BarréThierry FestOlivier DameronAnne SiegelFabrice ChatonnetPublished in: PLoS computational biology (2024)
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 (Resource Description Framework) endpoint. A dedicated SPARQL (SPARQL Protocol and RDF Query Language) query retrieves all potential relations between expressed TF and genes involving active regulatory regions. These TF-region-gene relations are then filtered using biological likelihood constraints allowing to qualify them as activation or inhibition. Regulus provides signed relations consistent with public databases and, when applied to biological data, identifies both known and potential new regulators. Regulus is devoted to context-specific transcriptional circuits inference in human settings where samples are scarce and cell populations are closely related, using discretization into patterns and likelihood reasoning to decipher the most robust regulatory relations.
Keyphrases
- genome wide
- transcription factor
- genome wide identification
- copy number
- electronic health record
- big data
- healthcare
- endothelial cells
- randomized controlled trial
- single cell
- genome wide analysis
- gene expression
- stem cells
- machine learning
- magnetic resonance imaging
- artificial intelligence
- computed tomography
- oxidative stress
- health information
- social media
- deep learning
- cell therapy
- pluripotent stem cells