Promoter Prediction on a Genomic Scale—The Adh Experience

Abstract

We describe our statistical system for promoter recognition in genomic DNA with which we took part in the Genome Annotation Assessment Project (GASP1). We applied two versions of the system: the first uses a region-based approach toward transcription start site identification, namely, interpolated Markov chains; the second was a hybrid approach combining regions and signals within a stochastic segment model. We compare the results of both versions with each other and examine how well the application on a genomic scale compares with the results we previously obtained on smaller data sets.Within the next year, the complete genomes of several eukaryotic organisms will be stored in the databases, and we must face the challenge that the annotation process is getting more and more complicated for higher eukaryotes such as Drosophila melanogaster. The first draft of the annotation of a newly sequenced genome is usually limited to the coding part of a gene, but a complete annotation should also contain the positions of the transcription start sites (TSSs), as most of the regulatory elements involved in gene expression are located in the promoter region upstream or close to the TSS.The untranslated region between transcription and translation start site, the 5Ј UTR region, can span up to several kilobases in higher eukaryotes-it is an average of almost 2000 bases for the TSS set compiled in the paper by Reese et al. (2000). Therefore, we cannot simply take the sequence upstream from the start codon. Methods that aim at the identification of regulatory elements in the upstream regions of coexpressed genes such as described by van Helden et al. (1998) have been shown to deliver promising results for the yeast genome, which has very short UTRs, but they will be hard to apply when the annotation only consists of the coding part of a gene. Of course, TSS identification is alleviated by full-length cDNA sequencing projects; but the sequencing always starts at the 3Ј end of a gene, and we need additional methods to confirm the 5Ј end of the sequences or to hunt for rarely expressed genes that are not contained in the libraries at all. We are in a desperate need to at least get a good guess where the TSS (and thus the promoter region) is located, or we will start looking for the needle in the wrong haystack.The only available evaluation of promoter prediction tools on genomic DNA was performed by Fickett and Hatzigeorgiou (1997). At that time, no extensive unstudied genomic sequences were available for complex eukaryotic organisms, and the authors performed their evaluation on a set of 18 newly released vertebrate sequences, the longest of which comprised <6000 bp. It was, therefore, a great challenge to see how well a recently developed promoter recognition program performs on a genomic scale and what we can conclude for the annotation of complex eukaryotic genomes. We will briefly review the two versions of our promoter recognition system that we applied, discuss in detail the results that were described in the paper of Reese ...

Generic eukaryotic core promoter prediction using structural features of DNA

“…The proliferation of genome sequencing projects has driven the search for fast ways of sequence-based structural annotation, which involves the identification of genes and the modeling of their correct gene structure (Claverie et al 1997;Mathé et al 2002;Zhang 2002;Wang et al 2004). Although great progress has been achieved in gene prediction, for instance by using comparative approaches (Wasserman et al 2000;Liu et al 2004;Jin et al 2006;Wang and Zhang 2006), one of the more difficult tasks in the annotation of whole genomes remains the accurate identification and delineation of promoters (Fickett and Hatzigeorgiou 1997;Ohler 2000Ohler , 2001Bajic et al 2004Bajic et al , 2006a. Nevertheless, the prediction of the regions that control the transcriptional activation of genes is important for various reasons (Smale 2001;Butler and Kadonaga 2002;Bajic et al 2004;Sonnenburg et al 2006).…”

A Biologist's View of the Drosophila Genome Annotation Assessment Project

“…This faith can only be justified if the results of sequence analysis are tested continually against reality. The Genome Annotation Assessment Project (GASP) experiment, which took place in May, 1999, was one such test (see, in this issue, Birney and Durbin 2000; Gaasterland et al 2000;Henikoff and Henikoff 2000;Krogh 2000;Ohler 2000;Parra et al 2000;Reese et al 2000a,b;Salamov and Solovyev 2000). GASP compared the interpretation of a 2.9-Mb sequence made by a mix of computation and human analysis done over a period of 2 years (Ashburner et al 1999) with those done by wholly computational procedures carried out over a period of 6 weeks.…”

Reduced hyperBF networks : practical optimization, regularization, and applications in bioinformatics.

“…They demonstrate that a number of these tools perform well, yet leave a lot of room for improving detection accuracy. Among the most successful tools identified were Eponine [70], McPromoter [73], FirstEF [74] and DragonGSF [75].…”

“…ARTS is able to achieve a high accuracy with the area under the ROC curve of 92.77% and 93.44% for genomic DNA chunk sizes of 50 and 500 respectively, demonstrating a superiority to Eponine [70], McPromoter [73] and FirstEF [74]. As part of the ARTS system, a large training and testing dataset was constructed along with measures for testing and evaluating promoter detection approaches in a consistent fashion.…”