Description Usage Arguments Details Value References Examples
Rank transcription factors according to changes in binding affinity by sequence polymorphisms like SNPs
1 2 | rank.factors(matrices, seq1, seq2, local = F, gev = T, snp.pos = NULL,
margins = NULL, empirical.offset = 1, both.strands = TRUE)
|
matrices |
list of transfac matrices |
seq1 |
sequence allele 1 |
seq2 |
sequence allele 2 |
local |
compute affinities locally in a sliding window or over the whole sequence |
gev |
use the fitted generalized extreme value distribution for p-values (if FALSE, empirical distribution over the rest of the sequence) |
snp.pos |
position of the SNP in the sequences (should always be the same and only needed for local or empirical p-values (gev == FALSE) |
margins |
compute the affinity in a margin around the SNP for empirical p-values and local == FALSE |
empirical.offset |
when using empirical p-values and local == FALSE the offset can be used to determine the spacing of windows to measure the affinities over regions of same size as the margin |
both.strands |
compute affinity to both strands |
Unify all cases of comparing affinities for a set of pairs of sequence possible design choices are - computing affinities for the two alleles - globally in a region around the SNP - locally in a sliding window of length w (length of each PWM) - computing p-values of the affinities - using gobally fitted GEV distributions - empirical p-values from the surounding sequence
A data.frame with the fields a1, a2, p1, p2, ratio, log.ratio, min.p, min.prod. Each row corresponds to a matrix, the a fields contain the affinities, the p fields the corresponding p-values of the matrix and the two sequences. Further the ratio of the p-values, the log10 ratio, the min and the product of the p-values are given. If local is true, the min is over all positions.
Roider, H. G.; Kanhere, A.; Manke, T. & Vingron, M. Predicting transcription factor affinities to DNA from a biophysical model. Bioinformatics, 2007, 23, 134-141
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