DOI: 10.18129/B9.bioc.BUMHMM  

This package is for version 3.16 of Bioconductor; for the stable, up-to-date release version, see BUMHMM.

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

Bioconductor version: 3.16

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>

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PDF R Script An Introduction to the BUMHMM pipeline
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biocViews Bayesian, Classification, Coverage, FeatureExtraction, GeneExpression, GeneRegulation, GeneticVariability, Genetics, HiddenMarkovModel, ImmunoOncology, RNASeq, Regression, Sequencing, Software, StructuralPrediction, Transcription, Transcriptomics
Version 1.22.0
In Bioconductor since BioC 3.5 (R-3.4) (6 years)
License GPL-3
Depends R (>= 3.5.0)
Imports devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges
Suggests testthat, knitr, BiocStyle
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