This is the released version of BUMHMM; for the devel version, see BUMHMM.
Computational pipeline for computing probability of modification from structure probing experiment dataBioconductor version: Release (3.21)
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 (alina.selega@gmail.com), Sander Granneman, Guido Sanguinetti
Maintainer: Alina Selega <alina.selega at gmail.com>
Citation (from within R, entercitation("BUMHMM")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("BUMHMM")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("BUMHMM")Follow Installation instructions to use this package in your R session.
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