Eukaryotic promoter recognition.

By J. Fickett, Artemis G. Hatzigeorgiou. 1997

Only recently has it become common to determine eukaryotic genomic sequences large enough to contain several genes. With these data comes a new problem for gene finding programs: to partition a set of exons correctly among several genes. One line of development in eukaryotic gene identification begins with coding region identification by statistical means and adds pattern recognition for sites of transcriptional, splicing, and translational control to produce algorithms capable of suggesting overall gene structure (for review, see Gelfand 1995; Fickett 1996a). To date, most development effort has focused on integration of the various kinds of pattern information in the relatively simple case where a single complete gene is present in the input sequence. In this case, current algorithms usually suggest a putative protein translation similar to that in the literature, though there is still significant room for improvement (Burset and Guigo 1996). The extension of these algorithms to deal with a sequence containing multiple or partial genes is just beginning (Burge and Karlin 1997; http://gnomic.stanford.edu/∼chris/GENSCANW.html). Because the signals that control the start and stop of transcription and translation, and the location of splicing, are still not very well understood, it is not uncommon for a gene-finding algorithm to confuse internal with initial and terminal exons, thus wrongly partitioning the exons. The problem is compounded by our incomplete understanding of alternative splicing control elements. Another line of development in gene identification is based on homology (e.g., Gish and States 1993; Gelfand et al. 1996). If there is a close homolog in the databases to one of the genes in the sequence under analysis, sequence similarity will usually group the exons for this gene correctly. Still, in many cases there is no close homolog and no guarantee when there is some homolog that the encoded protein lacks insertions/deletions. Clearly, some means of recognizing the beginnings of genes, probably via the promoter, or the ends, probably by means of the polyadenylation signal or translation termination signal (e.g., Kondrakhin et al. 1994; Wahle and Keller 1996; Dalphin et al. 1997; Solovyev and Salamov 1997), would enable a major advance. The promoter seems to be a much richer signal than the 38 processing signals, though, as we shall see below, it is not easy to take advantage of the information in the promoter.

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Typically algorithms will recognize only half of all promoters, with an accompanying high false positive rate [175].”
Application of a Time-delay Neural Network to Promoter Annotation in the Drosophila Melanogaster Genome (2001, background): “The higher classification rate in the Adh region might be due to the small number of promoters or the difference in composition in the Fickett and Hatzigeorgiou (1997) dataset.”
Transcriptional regulatory elements in the human genome. (2006, unknown): “In addition, because promoters can contain any one of a number of combinations of core promoter elements [and, conversely, many promoters have only one or no such elements (68)], simply searching for the co-occurrence of known core promoter motifs has had only limited success (58).”
Computational prediction of transcription-factor binding site locations (2003, background): “…start sites can be kilobases away from the translational start sites [53], so identification of the start site is an important task in order that searches of upstream sequence can be focused on non-coding sequence upstream of 5 (cid:1) untranslated regions (UTRs; for reviews see [51,54]).”
Applied bioinformatics for the identification of regulatory elements (2004, methodology): “Early bioinformatics algorithms that were used to pinpoint exact locations for TSSs were plagued by false prediction”
The State of the Art of Mammalian Promoter Recognition (2003, background): “Most methods would work only in small regions of DNA and even there with an unacceptable high amount of false positive matches as reviewed in 1997 by Fickett and Hatzigeorgiou.(3) For example, most of the methods came up with one match every 1,000 bp even under favourable conditions (much higher pro”
Artificial neural networks for computer-based molecular design. (1998, background): “Several studies of promotor sequences revealed species-speci®c sequences (Fickett and Hatzigeorgiou, 1997).”
Human-mouse genome comparisons to locate regulatory sites (2000, background): “can suggest possible transcription-factor binding sites and regulatory modules, but false-positive prediction rates are hig”
Highly specific localization of promoter regions in large genomic sequences by PromoterInspector: a novel context analysis approach. (2000, methodology): “There are 13 known and six candidate genes in the sequence (Chen et al., 1996) 219,447 11 D87675 Homo sapiens DNA for amyloid precursor protein 301,692 1 AF017257 Homo sapiens chromosome 21-derived BAC containing erythroblastosis virus oncogene homolog 2 protein (ets-2) gene 101,569 1 AF146793 Mouse”
Computational analysis of core promoters in the Drosophila genome (2002, background): “Prediction and analysis of core promoters have been active areas of research in computational biology [6], with several recent publications on prediction of human promoters [7-10].”
Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks (2016, background): “Fickett and Hatzigeorgiou [7] presented one of the first reviews of eukaryotic promoter prediction programs.”