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DOI: 10.18129/B9.bioc.BUMHMM    

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

Bioconductor version: Release (3.5)

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.

Author: Alina Selega (, Sander Granneman, Guido Sanguinetti

Maintainer: Alina Selega <alina.selega at>

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PDF R Script An Introduction to the BUMHMM pipeline
PDF   Reference Manual
Text   NEWS


biocViews Bayesian, Classification, Coverage, FeatureExtraction, GeneExpression, GeneRegulation, GeneticVariability, Genetics, HiddenMarkovModel, RNASeq, Regression, Sequencing, Software, StructuralPrediction, Transcription, Transcriptomics
Version 1.0.0
In Bioconductor since BioC 3.5 (R-3.4) (0.5 years)
License GPL-3
Depends R (>= 3.4)
Imports devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges
Suggests testthat, knitr, BiocStyle
Depends On Me
Imports Me
Suggests Me
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