G-quadruplexes (G4s) are non-canonical secondary nucleic acid structures. Sequences with the potential to form G4s are abundant in regulatory regions of the genome including telomeres, promoters and 5′ non-coding regions, indicating they fulfill important genome regulatory functions. Generally, G4s perform various biological functions by interacting with proteins. In recent years, an increasing number of G-quadruplex-binding proteins have been identified with biochemical experiments. G4-binding proteins are involved in vital cellular processes such as telomere maintenance, DNA replication, gene transcription, mRNA processing. Therefore, G4-binding proteins are also associated with various human diseases. An intensive study of G4-protein interactions provides an attractive approach for potential therapeutics and these proteins can be considered as drug targets for novel medical treatment. In this review, we present biological functions and structural properties of G4-binding proteins, and discuss how to exploit G4-protein interactions to develop new therapeutic targets.

G-quadruplex (G4) structures are critical epigenetic regulatory elements, which usually form in guanine-rich regions in DNA. However, predicting the formation of G4 structures within living cells remains a challenge. Here, we present an ultra-robust machine learning method, G4Beacon, which utilizes the Gradient-Boosting Decision Tree (GBDT) algorithm, coupled with the ATAC-seq data and the surrounding sequences of in vitro G4s, to accurately predict the formation ability of these in vitro G4s in different cell types. As a result, our model achieved excellent performance even when the test set was extremely skewed. Besides this, G4Beacon can also identify the in vivo G4s of other cell lines precisely with the model built on a special cell line, regardless of the experimental techniques or platforms. Altogether, G4Beacon is an accurate, reliable, and easy-to-use method for the prediction of in vivo G4s of various cell lines.

Background: G-quadruplexes (G4s) are non-canonical higher-order nucleic acid structures that form at guanine-rich motifs, with features spanning both secondary and tertiary structural levels. These dynamic structures play pivotal roles in diverse cellular processes. Endogenous G4s (eG4s) function through their dynamically formed structures, prompting the hypothesis that their thermostability, as a key structural property, may critically influence their functionality. This study investigates the relationship between G4 stability and other functional genomic signals within eG4 regions and examines its broader impact on chromatin organization and transcriptional regulation. Methods: We developed a mapping strategy to associate in vitro-derived thermostability metrics and multi-omics functional signals with eG4 regions. A stability-centric analytical framework combining correlation analysis and causal inference using the Bayesian networks was applied to decipher causal relationships between G4 stability and the other related signals. We further analyzed the association between the stability of transcription start site (TSS)-proximal eG4s and the biological functions of their downstream genes. Results: Our analyses demonstrate that G4 thermostability exerts causal effects on epigenetic states and transcription factor binding, thereby influencing chromatin and transcription regulation. We further show distinct network architectures for G4-binding versus non-binding transcription factors. Additionally, we find that TSS-proximal eG4s are enriched in genes involved in core proliferation and stress-response pathways, suggesting that eG4s may serve as regulatory elements facilitating rapid stress responses through genome-wide coordination. Conclusions: These findings establish thermostability—though measured in vitro—as an intrinsic property that shapes eG4 functionality. Our study not only provides novel insights into the functional relevance of G4 thermostability but also introduces a generalizable framework for high-throughput G4 data interpretation, significantly advancing the functional decoding of eG4s across biological contexts.

G-quadruplexes are the non-canonical nucleic acid structures that are preferentially formed in G-rich regions. This structure has been shown to be associated with many biological functions. Regardless of the broad efforts on DNA G-quadruplexes, we still have limited knowledge on RNA G-quadruplexes, especially in a transcriptome-wide manner. Herein, by integrating the DMS-seq and the bioinformatics pipeline, we profiled and depicted the RNA G-quadruplexes in the human transcriptome. The genes that contain RNA G-quadruplexes in their specific regions are significantly related to immune pathways and the COVID-19-related gene sets. Bioinformatics analysis reveals the potential regulatory functions of G-quadruplexes on miRNA targeting at the scale of the whole transcriptome. In addition, the G-quadruplexes are depleted in the putative, not the real, PAS-strong poly(A) sites, which may weaken the possibility of such sites being the real cleaved sites. In brief, our study provides insight into the potential function of RNA G-quadruplexes in post-transcription.

G-quadruplex (G4) ChIP-Seq data are critical for studying the roles of G4 structures in various biological processes, yet their reproducibility remains systematically uncharacterized. In this study, we evaluated the consistency of in vivo G4 peaks across multiple replicates in three publicly available datasets. We observed considerable heterogeneity in peak calls, with only a minority of peaks shared across all replicates. To address this challenge, we compared three computational methods—IDR, MSPC, and ChIP-R—for assessing reproducibility and found that MSPC is the optimal solution in reconciling inconsistent signals in G4 ChIP-Seq data. We further demonstrated that employing at least three replicates significantly improved detection accuracy compared to conventional two-replicate designs, while four replicates proved sufficient to achieve reproducible outcomes, with diminishing returns beyond this number. Moreover, we showed that the reproducibility-aware analytical strategies can partially mitigate the adverse effects of low sequencing depth, though they do not fully substitute for high-quality data. Based on our findings, we recommend 10 million mapped reads as a minimum standard for G4 ChIP-Seq experiments, with 15 million or more reads being preferable for optimal results. Our study provides practical guidelines for experimental design and data analysis in G4 studies, emphasizing the importance of replication and robust bioinformatic strategies to enhance the reliability of genome-wide G4 mapping.

DNA G-quadruplexes (G4) are non-canonical DNA structures that play key roles in various biological processes. Antibody-dependent sequencing is an important tool for identifying intracellularly formed DNA G4s, and peak calling is a crucial step in processing the sequencing data. As the applicability of existing peak calling algorithms to intracellular G4 data has not been previously assessed, we systematically compared and evaluated these algorithms to determine those best suited for G4 detection. We selected seven representative candidates from 43 published peak calling algorithms for detailed evaluation. The performance of each candidate on six published intracellular G4 sequencing datasets (GSE107690, GSE145090, GSE133379, GSE178668ChIP-seq, GSE178668CUT&Tag, GSE221437) were assessed by precision and recall against customized benchmarks integrating results from multiple algorithms, as well as consistency with known G4 information (pG4 predicted by pqsfinder, oG4 from GSE63874, and multi-cell-line conserved G4s) and epigenetic signals. We identified MACS2, PeakRanger, and GoPeaks as the most effective algorithms for analyzing intracellular G4 sequencing data, and attributed their superior performance partially to the distribution model of sequencing reads/fragments used in the hypothesis testing step of the peak calling procedures. These findings provide guidance and rationale for selecting peak callers appropriate for intracellular G4 data.