NPEST: a nonparametric method and a database for transcription start site prediction

Abstract

In this paper we present NPEST, a novel tool for the analysis of expressed sequence tags (EST) distributions and transcription start site (TSS) prediction. This method estimates an unknown probability distribution of ESTs using a maximum likelihood (ML) approach, which is then used to predict positions of TSS. Accurate identification of TSS is an important genomics task, since the position of regulatory elements with respect to the TSS can have large effects on gene regulation, and performance of promoter motif-finding methods depends on correct identification of TSSs. Our probabilistic approach expands recognition capabilities to multiple TSS per locus that may be a useful tool to enhance the understanding of alternative splicing mechanisms. This paper presents analysis of simulated data as well as statistical analysis of promoter regions of a model dicot plant Arabidopsis thaliana. Using our statistical tool we analyzed 16520 loci and developed a database of TSS, which is now publicly available at www.glacombio.net/NPEST.

Keywords

transcription start site (TSS) / nonparametric maximum likelihood

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ACKNOWLEDEMENTS

The authors thank Prof. Xiaoyu Zhang from Department of Plant Biology, University of Georgia for providing Pol II occupancy data and Prof. Yoshi Yamamoto from the Laboratory for Plant Molecular Physiology, Gifu University for access to the PlantPromoter database.

Support from NIH-NIGMS GM068968, NIH-NICHD HD070996, USC Center for High-Performance Computing and Communications, Fujitsu Lab Europe, and HPC Wales is gratefully acknowledged. We are grateful to the two anonymous reviewers for providing valuable feedback and suggestions for improvement.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg