BUMHMM
This is the released version of BUMHMM; for the devel version, see BUMHMM.
Computational pipeline for computing probability of modification from structure probing experiment data
Bioconductor version: Release (3.23)
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("BUMHMM")):
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M (2015). "Orchestrating high-throughput genomic analysis with Bioconductor." Nature Methods, 12(2), 115–121. doi:10.1038/nmeth.3252.
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J (2004). "Bioconductor: open software development for computational biology and bioinformatics." Genome Biology, 5(10), R80. doi:10.1186/gb-2004-5-10-r80.
Installation
To install this package, start R (version "4.6") 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.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("BUMHMM")| An Introduction to the BUMHMM pipeline | R Script | |
| Reference Manual | ||
| NEWS | Text | |
| LICENSE | Text |
Details
| biocViews | Bayesian, Classification, Coverage, FeatureExtraction, GeneExpression, GeneRegulation, GeneticVariability, Genetics, HiddenMarkovModel, ImmunoOncology, RNASeq, Regression, Sequencing, Software, StructuralPrediction, Transcription, Transcriptomics |
| Version | 1.36.0 |
| In Bioconductor since | BioC 3.5 (R-3.4) (9.5 years) |
| License | GPL-3 |
| Depends | R (>= 3.5.0) |
| Imports | devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges |
| System Requirements | |
| URL |
See More
| Suggests | testthat, knitr, BiocStyle |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | BUMHMM_1.36.0.tar.gz |
| Windows Binary (x86_64) | BUMHMM_1.36.0.zip |
| macOS Binary (big-sur-x86_64) | BUMHMM_1.36.0.tgz |
| macOS Binary (sonoma-arm64) | BUMHMM_1.36.0.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/BUMHMM |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/BUMHMM |
| Bioc Package Browser | https://code.bioconductor.org/browse/BUMHMM/ |
| Package Short Url | https://bioconductor.org/packages/BUMHMM/ |
| Package Downloads Report | Download Stats |