{
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  "Package": "BUMHMM",
  "Type": "Package",
  "Title": "Computational pipeline for computing probability of modification\nfrom structure probing experiment data",
  "Version": "1.37.0",
  "Date": "2021-11-20",
  "Author": "Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido\nSanguinetti",
  "Maintainer": "Alina Selega <alina.selega@gmail.com>",
  "Description": "This is a probabilistic modelling pipeline for computing\nper- nucleotide posterior probabilities of modification from\nthe data collected in structure probing experiments. The model\nsupports multiple experimental replicates and empirically\ncorrects coverage- and sequence-dependent biases. The model\nutilises the measure of a \"drop-off rate\" for each nucleotide,\nwhich is compared between replicates through a log-ratio (LDR).\nThe LDRs between control replicates define a null distribution\nof variability in drop-off rate observed by chance and LDRs\nbetween treatment and control replicates gets compared to this\ndistribution. Resulting empirical p-values (probability of\nbeing \"drawn\" from the null distribution) are used as\nobservations in a Hidden Markov Model with a Beta-Uniform\nMixture model used as an emission model. The resulting\nposterior probabilities indicate the probability of a\nnucleotide of having being modified in a structure probing\nexperiment.",
  "License": "GPL-3",
  "VignetteBuilder": "knitr",
  "LazyData": "true",
  "biocViews": "ImmunoOncology, GeneticVariability, Transcription,\nGeneExpression, GeneRegulation, Coverage, Genetics,\nStructuralPrediction, Transcriptomics, Bayesian,\nClassification, FeatureExtraction, HiddenMarkovModel,\nRegression, RNASeq, Sequencing",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:44:48 UTC",
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      "page": "computeProbs",
      "title": "Function to compute posterior probabilities of modification with the BUM-HMM model for all nucleotides.",
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