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 (firstname.lastname@example.org), Sander Granneman, Guido Sanguinetti|
|Bioconductor views||Bayesian Classification Coverage FeatureExtraction GeneExpression GeneRegulation GeneticVariability Genetics HiddenMarkovModel ImmunoOncology RNASeq Regression Sequencing StructuralPrediction Transcription Transcriptomics|
|Maintainer||Alina Selega <email@example.com>|
|Package repository||View on GitHub|
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