biostudies-literatureCarazo FEusko JaurlaritzaProvincial Council of Gipuzkoa521https://www.ebi.ac.uk/biostudies/studies/S-EPMC6592009biostudies-literatureUnknown20(1)<h4>Background</h4>Splicing is a genetic process that has important implications in several diseases including cancer. Deciphering the complex rules of splicing regulation is crucial to understand and treat splicing-related diseases. Splicing factors and other RNA-binding proteins (RBPs) play a key role in the regulation of splicing. The specific binding sites of an RBP can be measured using CLIP experiments. However, to unveil which RBPs regulate a condition, it is necessary to have a priori hypotheses, as a single CLIP experiment targets a single protein.<h4>Results</h4>In this work, we present a novel methodology to predict context-specific splicing factors from transcriptomic data. For this, we systematically collect, integrate and analyze more than 900 CLIP experiments stored in four BMC genomicsIntegration of CLIP experiments of RNA-binding proteins: a novel approach to predict context-dependent splicing factors from transcriptomic data.PMC6592009MINEDRUG project: “Predicting therapy response in oncology using Big Data analysis&quot;MINEDRUG project: "Predicting therapy response in oncology using Big Data analysis"PRE_2017_2_0033Ferrer-Bonsoms JARubio ACarazo FGimeno MfalseIntegration of CLIP experiments of RNA-binding proteins: a novel approach to predict context-dependent splicing factors from transcriptomic data.<h4>Background</h4>Splicing is a genetic process that has important implications in several diseases including cancer. Deciphering the complex rules of splicing regulation is crucial to understand and treat splicing-related diseases. Splicing factors and other RNA-binding proteins (RBPs) play a key role in the regulation of splicing. The specific binding sites of an RBP can be measured using CLIP experiments. However, to unveil which RBPs regulate a condition, it is necessary to have a priori hypotheses, as a single CLIP experiment targets a single protein.<h4>Results</h4>In this work, we present a novel methodology to predict context-specific splicing factors from transcriptomic data. For this, we systematically collect, integrate and analyze more than 900 CLIP experiments stored in four 2019-01-01T00:00:00Z2019 Jun2025-05-18T11:02:44.26Z2025-05-18T11:02:44.26ZS-EPMC65920093123888410.1186/s12864-019-5900-1