A conservative parametric approach to motif significance analysis.
We suggest a novel, parametric, approach to estimating the significance of the output of motif finders. Specifically, we rely on the good fit we observe between the 3-parameters Gamma family and the null distribution of motif scores. This fit was observed across multiple motif finders, background models and scoring functions. Under this parametric assumption we compute and show the utility of a conservative confidence interval for the p-value of the observed score. Since our method relies on the 3-parameters Gamma fit it should be applicable to a variety of finders.
Keich
Uri
U
Department of Computer Science, Cornell University, Ithaca, NY, USA.
Ng
Patrick
P
D000465:Algorithms; D020816:Amino Acid Motifs; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D017434:Protein Structure, Tertiary; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
D014157:Transcription Factors