DeePromoter: Robust Promoter Predictor Using Deep Learning

By Mhaned Oubounyt, Zakaria Louadi, Hilal Tayara, Kil To Chong. 2019

171 citations recorded by the source corpus.

The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Big Bird: Transformers for Longer Sequences (2020, methodology): “Multiple methods have been proposed to identify the promoter regions in a given DNA sequence [100, 59, 11, 99, 71], as it is an important ﬁrst step in understanding gene regulation.”
The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics (2024, methodology): “Following the DeePromoter method [24], we considered sequences of 300 base pairs (bp) genome-wide, selected to span 249bp upstream and 50bp downstream of transcription start sites.”
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution (2023, methodology): “For the promoter dataset, negative samples were not available, and had to be generated following the procedure described by [34].”
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome (2020, methodology): “We compared DNABERT-Prom-300 with DeePromoter (42) using −249 to 50 bp sequences around TSS as positive examples, randomly selected 300 bp-long, TATA-containing sequences as TATA negative examples, and dinucleotide-shuffled sequences as non-TATA negative examples (Supplementary Methods).”