recipe bioconductor-bumhmm

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 bioconductor-bumhmm

(downloads) docker_bioconductor-bumhmm



Required By


With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-bumhmm

and update with:

conda update bioconductor-bumhmm

or use the docker container:

docker pull<tag>

(see bioconductor-bumhmm/tags for valid values for <tag>)