rseq {cosmo} | R Documentation

## Random generation of DNA sequence according to ZOOPS or TCM model

### Description

This function randomly generates a number of DNA sequences that contain a given motif accordint to the ZOOPS or TCM model. In the ZOOPS model, each sequence contains one or zero occurrences of the motif. In the TCM model, each sequence may contain an arbitrary number of motif occurrences.

### Usage

    rseq(numSeqs, seqLength, rate, pwm, transMats,
         model="ZOOPS", posOnly=FALSE)

### Arguments

`numSeqs` | `numeric` The number of sequences to be generated
`seqLength` | `numeric` The length of each sequence. This may be either a single number, in which case that number is taken to be the common length of all sequence, or a vector of sequence lengths.
`rate` | `numeric` In the ZOOPS model, this is the proportion of sequences containg a motif occurrence. In the TCM model, this the rate parameter lambda with which motifs are inserted into the sequences.
`pwm` | `numeric` Position-weight matrix of the motif to be inserted.
`transMats` | The transition matrices to use for the background Markov model. This is a list of matrices, with the first matrix given the transition probabilities for the 0th order Markov model, the second matrix giving the transition probabilities for a 1st order Markov model, and so on.
`model` | Either "ZOOPS" or "TCM"
`posOnly` | `logical` If TRUE, motifs are inserted only in the forwards orientation. Otherwise, motifs are inserted in either of the two possible orientations with equal probabilities.

### Details

### Value

`seqs` | A list with one element for each sequence in the file. The elements are in two parts, one the description and the second a character string of the biological sequence.
`motifs` | An "align" object summarizing the positions of the inserted motif occurrences.
`empPWM` | An object of class `pwm` representing the position weight matrix obtained by aligning the inserted motifs.

### Note

### Author(s)

Oliver Bembom, bembom@berkeley.edu

### References

### See Also

### Examples

    ## generate 20 sequences according to ZOOPS model
    ## with an expected number of 10 sequences containing a
    ## motif

    data(motifPWM)
    data(transMats)
    res <- rseq(20, 250, 0.5, motifPWM, transMats,"ZOOPS")

[Package cosmo version 1.2.0 Index]