ArticlePLoS computational biology2021
DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An updated overview of experimental and computational approaches to identify non-canonical DNA/RNA structures with emphasis on G-quadruplexes and R-loops.Briefings in bioinformatics · 2022Pooled it
- Non-B DNA structures and their contributions to genetic diversity, aging, and disease.Nucleic acids research · 2026Review
- Stability of non-canonical nucleic acid structure as a potential modulator of cell fate.Nucleic acids research · 2026Review
- G4mer: An RNA language model for transcriptome-wide identification of G-quadruplexes and disease variants from population-scale genetic data.Nature communications · 2025Article
- GQ-DNABERT reveals GQ proximal enhancer-promoter interactions associated with tissue-specific transcription.Nucleic acids research · 2025Article
- Mammalian conservation of endogenous G-quadruplex reveals their associations with complex traits.Genome biology · 2025Article
- Multi-objective computational optimization of human 5' UTR sequences.Briefings in bioinformatics · 2025Article
- Machine learning-based prediction of DNA G-quadruplex folding topology with G4ShapePredictor.Scientific reports · 2024Article
- Prediction of DNA i-motifs via machine learning.Nucleic acids research · 2024Article
- EndoQuad: a comprehensive genome-wide experimentally validated endogenous G-quadruplex database.Nucleic acids research · 2024Article
- Detection of alternative DNA structures and its implications for human disease.Molecular cell · 2023Review
- Prediction of G4 formation in live cells with epigenetic data: a deep learning approach.NAR genomics and bioinformatics · 2023Article
- Deep statistical modelling of nanopore sequencing translocation times reveals latent non-B DNA structures.Bioinformatics (Oxford, England) · 2023Article
- Dynamic alternative DNA structures in biology and disease.Nature reviews. Genetics · 2023Review
- G4mismatch: Deep neural networks to predict G-quadruplex propensity based on G4-seq data.PLoS computational biology · 2023Article
- G4Beacon: An In Vivo G4 Prediction Method Using Chromatin and Sequence Information.Biomolecules · 2023Article
- Toward a Better Understanding of G4 Evolution in the 3 Living Kingdoms.Evolutionary bioinformatics online · 2023Article
- G4Boost: a machine learning-based tool for quadruplex identification and stability prediction.BMC bioinformatics · 2022Article
- Deep Learning in RNA Structure Studies.Frontiers in molecular biosciences · 2022Review
- Decoding regulatory associations of G-quadruplex with epigenetic and transcriptomic functional components.Frontiers in genetics · 2022Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
DNA is a complex molecule carrying the instructions an organism needs to develop, live and reproduce. In 1953, Watson and Crick discovered that DNA is composed of two chains forming a double-helix. Later on, other structures of DNA were discovered and shown to play important roles in the cell, in particular G-quadruplex (G4). Following genome sequencing, several bioinformatic algorithms were developed to map G4s in vitro based on a canonical sequence motif, G-richness and G-skewness or alternatively sequence features including k-mers, and more recently machine/deep learning. Recently, new sequencing techniques were developed to map G4s in vitro (G4-seq) and G4s in vivo (G4 ChIP-seq) at few hundred base resolution. Here, we propose a novel convolutional neural network (DeepG4) to map cell-type specific active G4 regions (e.g. regions within which G4s form both in vitro and in vivo). DeepG4 is very accurate to predict active G4 regions in different cell types. Moreover, DeepG4 identifies key DNA motifs that are predictive of G4 region activity. We found that such motifs do not follow a very flexible sequence pattern as current algorithms seek for. Instead, active G4 regions are determined by numerous specific motifs. Moreover, among those motifs, we identified known transcription factors (TFs) which could play important roles in G4 activity by contributing either directly to G4 structures themselves or indirectly by participating in G4 formation in the vicinity. In addition, we used DeepG4 to predict active G4 regions in a large number of tissues and cancers, thereby providing a comprehensive resource for researchers. Availability: https://github.com/morphos30/DeepG4.
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