Dataset Information

PromoSer: A large-scale mammalian promoter and transcription start site identification service.

ABSTRACT: Proximal promoters have a major impact on transcriptional regulation. Studies of the sequence-based nature of this regulation usually require collection of proximal promoter sequences for large sets of co-regulated genes. We report a newly implemented web service that facilitates extraction of user specified regions around the transcription start site of all annotated human, mouse or rat genes. The transcription start sites have been identified computationally by considering alignments of a large number of partial and full-length mRNA sequences to genomic DNA, with provision for alternative promoters. The service is publicly available at http://biowulf.bu.edu/zlab/PromoSer/.

SUBMITTER: Halees AS

PROVIDER: S-EPMC168956 | biostudies-literature | 2003 Jul

REPOSITORIES: biostudies-literature

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PromoSer: A large-scale mammalian promoter and transcription start site identification service.

Halees Anason S AS Leyfer Dmitriy D Weng Zhiping Z

Nucleic acids research 20030701 13

Proximal promoters have a major impact on transcriptional regulation. Studies of the sequence-based nature of this regulation usually require collection of proximal promoter sequences for large sets of co-regulated genes. We report a newly implemented web service that facilitates extraction of user specified regions around the transcription start site of all annotated human, mouse or rat genes. The transcription start sites have been identified computationally by considering alignments of a larg ...[more]

PMID: 12824364

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Project description:Recognizing transcription start sites is key to gene identification. Several approaches have been employed in related problems such as detecting translation initiation sites or promoters, many of the most recent ones based on machine learning. Deep learning methods have been proven to be exceptionally effective for this task, but their use in transcription start site identification has not yet been explored in depth. Also, the very few existing works do not compare their methods to support vector machines (SVMs), the most established technique in this area of study, nor provide the curated dataset used in the study. The reduced amount of published papers in this specific problem could be explained by this lack of datasets. Given that both support vector machines and deep neural networks have been applied in related problems with remarkable results, we compared their performance in transcription start site predictions, concluding that SVMs are computationally much slower, and deep learning methods, specially long short-term memory neural networks (LSTMs), are best suited to work with sequences than SVMs. For such a purpose, we used the reference human genome GRCh38. Additionally, we studied two different aspects related to data processing: the proper way to generate training examples and the imbalanced nature of the data. Furthermore, the generalization performance of the models studied was also tested using the mouse genome, where the LSTM neural network stood out from the rest of the algorithms. To sum up, this article provides an analysis of the best architecture choices in transcription start site identification, as well as a method to generate transcription start site datasets including negative instances on any species available in Ensembl. We found that deep learning methods are better suited than SVMs to solve this problem, being more efficient and better adapted to long sequences and large amounts of data. We also create a transcription start site (TSS) dataset large enough to be used in deep learning experiments.

Project description:BackgroundTranscription factors (TF) regulate expression by binding to specific DNA sequences. A binding event is functional when it affects gene expression. Functionality of a binding site is reflected in conservation of the binding sequence during evolution and in over represented binding in gene groups with coherent biological functions. Functionality is governed by several parameters such as the TF-DNA binding strength, distance of the binding site from the transcription start site (TSS), DNA packing, and more. Understanding how these parameters control functionality of different TFs in different biological contexts is a must for identifying functional TF binding sites and for understanding regulation of transcription.Methodology/principal findingsWe introduce a novel method to screen the promoters of a set of genes with shared biological function (obtained from the functional Gene Ontology (GO) classification) against a precompiled library of motifs, and find those motifs which are statistically over-represented in the gene set. More than 8,000 human (and 23,000 mouse) genes, were assigned to one of 134 GO sets. Their promoters were searched (from 200 bp downstream to 1,000 bp upstream the TSS) for 414 known DNA motifs. We optimized the sequence similarity score threshold, independently for every location window, taking into account nucleotide heterogeneity along the promoters of the target genes. The method, combined with binding sequence and location conservation between human and mouse, identifies with high probability functional binding sites for groups of functionally-related genes. We found many location-sensitive functional binding events and showed that they clustered close to the TSS. Our method and findings were tested experimentally.Conclusions/significanceWe identified reliably functional TF binding sites. This is an essential step towards constructing regulatory networks. The promoter region proximal to the TSS is of central importance for regulation of transcription in human and mouse, just as it is in bacteria and yeast.

Dataset Information

PromoSer: A large-scale mammalian promoter and transcription start site identification service.

Publications

PromoSer: A large-scale mammalian promoter and transcription start site identification service.

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