TITLE:
A distribution pattern assisted method of transcription factor binding site discovery for both yeast and filamentous fungi
AUTHORS:
Jinnan Hu, Chenxi Chen, Kun Huang, Thomas K. Mitchell
KEYWORDS:
Transcription Factor Binding Site Discover; Distribution Pattern; Saccharomyces cerevisiae; Magnaporthe oryzae; MoCRZ1
JOURNAL NAME:
Advances in Bioscience and Biotechnology,
Vol.4 No.4,
April
16,
2013
ABSTRACT: Transcription
factors (TFs) are the core sentinels of gene regulation functioning by binding
to highly specific DNA sequences to activate or repress the recruitment of
RNA polymerase. The ability to identify transcription factor binding sites
(TFBSs) is necessary to understand gene regulation and infer regulatory networks.
Despite the fact that bioinformatics tools have been developed for years to
improve computational identification of TFBSs, the accurate prediction still
remains changeling as DNA motifs recognized by TFs are typically short and
often lack obvious patterns. In this study we introduced a new attribute-motif
distribution pattern (MDP) to assist in TFBS prediction. MDP was developed
using a TF distribution pattern curve generated by analyzing 25 yeast TFs and
37 of their experimentally validated binding motifs, followed by calculating
a scoring value to quantify the reliability of each motif prediction. Finally,
MDP was tested using another set of 7 TFs with known binding sites to in silico validate the approach. The
method was further tested in a non-yeast system using the filamentous fungus Magnaporthe oryzae transcription factor
MoCRZ1. We demonstrate superior prediction reranking results using MDP over the
commonly used program MEME and the other four predictors. The data showed
significant improvements in the ranking of validated TFBS and provides a more
sensitive statistics based approach for motif discovery.