Genome Informatics
Online ISSN : 2185-842X
Print ISSN : 0919-9454
ISSN-L : 0919-9454
LINEAR REGRESSION MODELS PREDICTING STRENGTH OF TRANSCRIPTIONAL ACTIVITY OF PROMOTERS
TETSUSHI YADAKEIGO YOSHIDAMASAO MORITATAKEAKI TANIGUCHITAKUMA IRIEYUTAKA SUZUKI
Author information
JOURNALS FREE ACCESS

2011 Volume 25 Issue 1 Pages 53-60

Details
Abstract

We developed linear regression models which predict strength of transcriptional activity of promoters from their sequences. Intrinsic transcriptional strength data of 451 human promoter sequences in three cell lines (HEK293, MCF7 and 3T3), which were measured by systematic luciferase reporter gene assays, were used to build the models. The models sum up contributions of CG dinucleotide content and transcription factor binding sites (TFBSs) to transcriptional strength. We evaluated prediction accuracies of the models by cross validation tests and found that they have adequate ability for predicting transcriptional strength of promoters in spite of their simple formalization. We also evaluated statistical significance of the contributions and proposed a picture of regulatory code hidden in promoter sequences. That is, CG dinucleotide content and TFBSs mainly determine strength of transcriptional activity under ubiquitous and specific environments, respectively.

Information related to the author
© 2011 Japanese Society for Bioinformatics
Previous article
feedback
Top