EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era

By René Dreos, Giovanna Ambrosini, Rouaïda Cavin Périer, Philipp Bucher. 2012

174 citations recorded by the source corpus.

The Eukaryotic Promoter Database (EPD), available online at http://epd.vital-it.ch, is a collection of experimentally defined eukaryotic POL II promoters which has been maintained for more than 25 years. A promoter is represented by a single position in the genome, typically the major transcription start site (TSS). EPD primarily serves biologists interested in analysing the motif content, chromatin structure or DNA methylation status of co-regulated promoter subsets. Initially, promoter evidence came from TSS mapping experiments targeted at single genes and published in journal articles. Today, the TSS positions provided by EPD are inferred from next-generation sequencing data distributed in electronic form. Traditionally, EPD has been a high-quality database with low coverage. The focus of recent efforts has been to reach complete gene coverage for important model organisms. To this end, we introduced a new section called EPDnew, which is automatically assembled from multiple, carefully selected input datasets. As another novelty, we started to use chromatin signatures in addition to mRNA 5′tags to locate promoters of weekly expressed genes. Regarding user interfaces, we introduced a new promoter viewer which enables users to explore promoter-defining experimental evidence in a UCSC genome browser window.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Big Bird: Transformers for Longer Sequences (2020, methodology): “We use the dataset compiled by Oubounyt et al. [71] which was built from Eukaryotic Promoter Database (EPDnew) [24] 4 .”
The Eukaryotic Promoter Database: expansion of EPDnew and new promoter analysis tools (2014, methodology): “the old EPD database, we will briefly outline the main differences between the two resources (9).”
DeePromoter: Robust Promoter Predictor Using Deep Learning (2019, methodology): “It provides high-quality promoters compared to ENSEMBL promoter collection (Dreos et al., 2012) and it is publically accessible at https://epd.”
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome (2020, methodology): “To fairly compare with existing tools with different sequence length settings, we fine-tuned two models, named DNABERTProm-300 and DNABERT-Prom-scan, using human TATA and non-TATA promoters of 10,000 bp length, from Eukaryotic Promoter Database (EPDnew) (36).”