This is a probabilistic modelling pipeline for computing per nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage and sequencedependent biases. The model utilises the measure of a "dropoff rate" for each nucleotide, which is compared between replicates through a logratio (LDR). The LDRs between control replicates define a null distribution of variability in dropoff rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical pvalues (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a BetaUniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment.
Package details 


Author  Alina Selega ([email protected]), Sander Granneman, Guido Sanguinetti 
Bioconductor views  Bayesian Classification Coverage FeatureExtraction GeneExpression GeneRegulation GeneticVariability Genetics HiddenMarkovModel RNASeq Regression Sequencing StructuralPrediction Transcription Transcriptomics 
Maintainer  Alina Selega <[email protected]> 
License  GPL3 
Version  1.2.0 
Package repository  View on Bioconductor 
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