Eukaryotic Promoter Recognition

Fickett, James W.; Hatzigeorgiou, Artemis G.

doi:10.1101/gr.7.9.861

Cited by 294 publications

(208 citation statements)

References 122 publications

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“…This compares well with the results described in the comparison of promoter recognition algorithms in vertebrate DNA (Fickett and Hatzigeorgiou 1997), especially considering the smaller amount of available training data for the organism of D. melanogaster.…”

Section: Resultssupporting

confidence: 86%

“…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.…”

mentioning

confidence: 99%

“…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.…”

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confidence: 99%

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Promoter Prediction on a Genomic Scale—The Adh Experience

Ohler¹

2000

Genome Res.

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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 ...

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Section: Resultssupporting

confidence: 86%

mentioning

confidence: 99%

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confidence: 99%

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Promoter Prediction on a Genomic Scale—The Adh Experience

Ohler¹

2000

Genome Res.

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“…In-silico eukaryotic promoter recognition is known to be a difficult problem [1]. Objective statements about the current state of the art in terms of promoter recognition are complicated by the wide selection of metrics used for assessing performance.…”

Section: Introductionmentioning

confidence: 99%

Promoter Prediction Using Physico-Chemical Properties of DNA

Uren

Cameron-Jones

Sale

2006

Computational Life Sciences II

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Abstract. The ability to locate promoters within a section of DNA is known to be a very difficult and very important task in DNA analysis. We document an approach that incorporates the concept of DNA as a complex molecule using several models of its physico-chemical properties. A support vector machine is trained to recognise promoters by their distinctive physical and chemical properties. We demonstrate that by combining models, we can improve upon the classification accuracy obtained with a single model. We also show that by examining how the predictive accuracy of these properties varies over the promoter, we can reduce the number of attributes needed. Finally, we apply this method to a real-world problem. The results demonstrate that such an approach has significant merit in its own right. Furthermore, they suggest better results from a planned combined approach to promoter prediction using both physicochemical and sequence based techniques.

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“…More methods to detect unknown elements within funtionally related sequences are availible (for a review, see [11]), most of which, such as the consensus [12] and the Gibbs sampler [13], are based upon well difined biological models. The type of signals that can be detected are generally limited; it is difficult for them to detect multiple signals.…”

Section: Introductionmentioning

confidence: 99%

Statistically significant strings are related to regulatory elements in the promoter regions of Saccharomyces cerevisiae

Wang

2001

Physica A: Statistical Mechanics and its Applications

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Finding out statistically significant words in DNA and protein sequences forms the basis for many genetic studies. By applying the maximal entropy principle, we give one systematic way to study the nonrandom occurrence of words in DNA or protein sequences. Through comparison with experimental results, it was shown that patterns of regulatory binding sites in Saccharomyces cerevisiae(yeast) genomes tend to occur significantly in the promoter regions. We studied two correlated gene family of yeast. The method successfully extracts the binding sites varified by experiments in each family. Many putative regulatory sites in the upstream regions are proposed. The study also suggested that some regulatory sites are active in both directions, while others show directional preference.

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Eukaryotic Promoter Recognition

Cited by 294 publications

References 122 publications

Promoter Prediction on a Genomic Scale—The Adh Experience

Promoter Prediction on a Genomic Scale—The Adh Experience

Promoter Prediction Using Physico-Chemical Properties of DNA

Statistically significant strings are related to regulatory elements in the promoter regions of Saccharomyces cerevisiae

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