Description Usage Arguments Details Author(s) See Also Examples

View source: R/detector_1rOrdre_diff.R

This detection algorithm is based on Information Theory. Specifically, entropy algorithm uses a parametric uncertainty measurement called Renyi entropy. This computational method evaluates the variation on the total Renyi entropy of a set of sequences when a candidate sequence is assumed to be a a true binding site belonging to the set.The measurement of the variation of the total redundancy when the candidate sequence is added to the set has been computed by using the difference between the redundancy profile. This technique assumes independecy between positions in the binding sequence.

1 | ```
detector_1rOrdre_diff(training.set, val.set, iicc)
``` |

`training.set` |
A set of aligned nucleotide sequences |

`val.set` |
A candidate sequence |

`iicc` |
A set of inicial conditions for the MEET-package: mode, method,background,alignment,threshold,parameters,Transcriptionfactor, nummotif,lenmotif,sentit,position,missing,vector,gapopen,maxiters,gapextend |

Options parameter has to contain the next arguments: maximum entropy (HXmax), correction entropy and redundancy from the Finite Sample Size Effect(correction, Redundancia_corregida, Herror and ErrorHX)

Joan Maynou <joan.maynouatupc.edu>

detector_2Ordre, MEME, MDscan, Q-residuals and MATCH

1 2 3 |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.

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