Background reduction software tools | CLIP sequencing data analysis
RNA regulation is key to understanding the rules that govern gene expression regulation and epigenetic changes. The systemic action of several RNA-binding proteins (RBPs) is one of the principal mechanisms of post-transcriptional gene regulation. Several experimental techniques was developed to determine RBPs binding sites were SELEX, RIP-chip and CLIP. In particular, CLIP-seq protocols combine the action of CLIP and next-generation sequencing (NGS) to derive a transcriptome-wide set of RBP binding sites. There are different CLIP-seq protocols; each one introduces experimental variations to improve the signal to noise ratio (iCLIP, PAR-CLIP and HITS-CLIP). The main challenge of these protocols is to improve the signal to noise ratio, hence to remove background and false positives.
A statistical and computational framework for PAR-CLIP data analysis. A sensitive transition-centered algorithm specifically designed to resolve protein binding sites at high resolution in PAR-CLIP data was developed. This method employes a Bayesian network approach to associate posterior log-odds with the observed transitions, providing an overall quantification of the confidence in RNA-protein interaction.
A toolbox for analysing PAR-CLIP data and detecting T-to-C substitutions induced following RNA-protein cross-linking. BMix uses a constrained three-component binomial mixture to account for the different sources of noise in PAR-CLIP data and distinguish T-to-C substitutions of different origins. Candidate binding sites are reported starting from the identified cross-link T-to-C substitutions.
Allows to precisely capture protein-RNA interaction footprints from iCLIP/eCLIP-seq data. PureCLIP provides a promising method to analyse datasets, also for proteins with lower binding affinities or proteins binding to low abundant RNAs, such as lncRNAs. It is able to incorporate RNA abundances and non-specific sequence biases. This method uses a nonhomogeneous Hidden Markov model (HMM) to incorporate additional factors into the model.
Identifies the presence of common RNA background in a PAR-CLIP dataset. We used the measured sets of non-specific RNA backgrounds to build a common background set. Each element from the common background set has a score that reflects its presence in several PAR-CLIP datasets. BackCLIP uses this score to identify the amount of common backgrounds present in a PAR-CLIP dataset, and we provide the user the option to use or remove it.
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