detector_1rOrdre_diff: Detection of Transcription Factor Binding Sites Through...

In MEET: MEET: Motif Elements Estimation Toolkit

Description

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.

Usage

detector_1rOrdre_diff(training.set, val.set, iicc)

Arguments

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

Details

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)

Author(s)

Joan Maynou <joan.maynouatupc.edu>

See Also

detector_2Ordre, MEME, MDscan, Q-residuals and MATCH

Examples

require("MEET")
data(iicc)
test<-detector_1rOrdre_diff(training.set=iicc$Transcriptionfactor,val.set=NULL,iicc) 

MEET documentation built on May 2, 2019, 1:45 p.m.