IRFinder-S: a comprehensive suite to discover and explore intron retention
Claudio Lorenzi, Sylvain Barrière, Katharina Arnold, Reini F. Luco, Andrew Oldfield, William Ritchie
Abstract
Accurate quantification and detection of intron retention levels require specialized software. Building on our previous software, we create a suite of tools called IRFinder-S, to analyze and explore intron retention events in multiple samples. Specifically, IRFinder-S allows a better identification of true intron retention events using a convolutional neural network, allows the sharing of intron retention results between labs, integrates a dynamic database to explore and contrast available samples, and provides a tested method to detect differential levels of intron retention.
Topics & Concepts
BiologySuiteGenome BiologyHuman geneticsIntronComputational biologyEvolutionary biologyData scienceGenomicsGeneticsGenomeComputer scienceGeneArchaeologyHistoryRNA and protein synthesis mechanismsRNA modifications and cancerMachine Learning in Bioinformatics