BUMHMM: Computational pipeline for computing probability of modification from structure probing experiment data

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 sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform 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

AuthorAlina Selega (alina.selega@gmail.com), Sander Granneman, Guido Sanguinetti
Bioconductor views Bayesian Classification Coverage FeatureExtraction GeneExpression GeneRegulation GeneticVariability Genetics HiddenMarkovModel ImmunoOncology RNASeq Regression Sequencing StructuralPrediction Transcription Transcriptomics
MaintainerAlina Selega <alina.selega@gmail.com>
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))


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BUMHMM documentation built on Nov. 8, 2020, 5:13 p.m.