LearnNonCoding {DECIPHER} | R Documentation

## Learn a Non-Coding RNA Model

### Description

Learns a compact representation of patterns representing a set of non-coding RNAs belonging to the same family.

### Usage

    LearnNonCoding(myXStringSet,
                   threshold = 0.3,
                   weight = NA,
                   maxLoopLength = 500,
                   maxPatterns = 20,
                   scoreDependence = FALSE,
                   structure = NULL,
                   processors = 1)

### Arguments

`myXStringSet` | A `DNAStringSet` or `RNAStringSet` object of aligned sequence representatives belonging to the same non-coding RNA family.
`threshold` | Numeric specifying the minimum relative frequency of patterns to consider during learning.
`weight` | Either a numeric vector of weights for each sequence, a single number implying equal weights, or `NA` (the default) to automatically calculate sequence weights based on `myXStringSet`.
`maxLoopLength` | Numeric giving the maximum length of conserved hairpin loops to consider.
`maxPatterns` | A numeric vector of length two specifying the maximum number of motifs and hairpins, respectively, or a single numeric giving the maximum for each.
`scoreDependence` | Logical determining whether to record a log-odds score for dependencies between patterns. The default (`FALSE`) is recommended for most non-coding RNA families.
`structure` | Either a character string providing the consensus secondary structure in dot bracket notation, a matrix of paired positions in the first two columns, or `NULL` (the default) to predict the consensus secondary structure with `PredictDBN`.
`processors` | The number of processors to use, or `NULL` to automatically detect and use all available processors.

### Details

Non-coding RNAs belonging to the same family typically have conserved sequence motifs, secondary structure elements, and k-mer frequencies that can be used to identify members of the family. `LearnNonCoding` identifies these conserved patterns and determines which are best for identifying the non-coding RNA relative to a random sequence background. Sequence motifs and hairpins are defined relative to their distance from the start or end of the non-coding RNA, allowing the precise and rapid identification of the boundaries of any matches to the non-coding RNA in a genome.

### Value

An object of class `NonCoding`.

### Author(s)

Erik Wright eswright@pitt.edu

### See Also

`FindNonCoding`, `NonCoding-class`

### Examples

    # import a family of non-coding RNAs
    fas_path <- system.file("extdata",
    	"IhtA.fas",
    	package="DECIPHER")
    rna <- readRNAStringSet(fas_path)
    rna

    # align the sequences
    RNA <- AlignSeqs(rna)
    RNA

    y <- LearnNonCoding(RNA)
    y
    y[["motifs"]]
    y[["hairpins"]]
    head(y[["kmers"]])

[Package DECIPHER version 2.20.0 Index]