m5C Sites Research Articles

Structured Sparse Regularization based Random Vector Functional Link Networks for DNA N4-methylcytosine sites prediction

As an epigenetic modification that plays an important role in modifying gene function and controlling gene expression during cell development, DNA N4-methylcytosine (4mC) is still lack of researching. It is therefore necessary to accurately predict the 4mC sites to make fully aware of its mechanism and function. In this paper, we propose a novel model which is called Structural Sparse Regularized Random Vector Functional Link Network (SSR-RVFL) for predicting 4mC sites. Compared with other state-of-the-art methods, SSR-RVFL performs better and achieves higher prediction accuracy. There are total six benchmark datasets used in the experiments, namely C.elegans, D.elanogaster, E.coli, A.thaliana G.subterraneus and G.pickeringii. Our model improves the accuracy by 0.42%, 0.45%, 0.48%, 0.91%, 0.66% and 0.7% on these six benchmark datasets respectively, so it can be regarded as a more effective prediction tool.

THOC3 interacts with YBX1 to promote lung squamous cell carcinoma progression through PFKFB4 mRNA modification

The THO complex (THOC) is ubiquitously involved in RNA modification and various THOC proteins have been reported to regulate tumor development. However, the role of THOC3 in lung cancer remains unknown. In this study, we identified that THOC3 was highly expressed in lung squamous cell carcinoma (LUSC) and negatively associated with prognosis. THOC3 knockdown inhibited LUSC cell growth, migration, and glycolysis. THOC3 expression was regulated by TRiC proteins, such as CCT8 and CCT6A, which supported protein folding. Furthermore, THOC3 could form a complex with YBX1 to promote PFKFB4 transcription. THOC3 was responsible for exporting PFKFB4 mRNA to the cytoplasm, while YBX1 ensured the stability of PFKFB4 mRNA by recognizing m5C sites in its 3′UTR. Downregulation of PFKFB4 suppressed the biological activities of LUSC. Collectively, these findings suggest that THOC3, folded by CCT proteins can collaborate with YBX1 to maintain PFKFB4 expression and facilitate LUSC development. Therefore, THOC3 could be considered as a novel promising therapeutic target for LUSC.

Multi-correntropy fusion based fuzzy system for predicting DNA N4-methylcytosine sites

The identification of DNA N4-methylcytosine (4mC) sites is an important field of bioinformatics. Statistical learning methods and deep learning have been applied in this direction. The previous methods focused on feature representation and feature selection, and did not take into account the deviation of noise samples for recognition. Moreover, these models were not established from the perspective of prediction error distribution. To solve the problem of complex error distribution, we propose a maximum multi-correntropy criterion based kernelized higher-order fuzzy inference system (MMC-KHFIS), which is constructed with multi-correntropy fusion. There are 6 4mC and 8 UCI data sets are employed to evaluate our model. The MMC-KHFIS achieves better performance in the experiment.

XGBoost framework with feature selection for the prediction of RNA N5-methylcytosine sites

5-methylcytosine (m5C) is indeed a critical post-transcriptional alteration that is widely present in various kinds of RNAs and is crucial to the fundamental biological processes. By correctly identifying the m5C-methylation sites on RNA, clinicians can more clearly comprehend the precise function of these m5C-sites in different biological processes. Due to their effectiveness and affordability, computational methods have received greater attention over the last few years for the identification of methylation sites in various species. To precisely identify RNA m5C locations in five different species including Homo sapiens, Arabidopsis thaliana, Mus musculus, Drosophila melanogaster, and Danio rerio, we proposed a more effective and accurate model named m5C-pred. To create m5C-pred, five distinct feature encoding techniques were combined to extract features from the RNA sequence, and then we used SHapley Additive exPlanations to choose the best features among them, followed by XGBoost as a classifier. We applied the novel optimization method called Optuna to quickly and efficiently determine the best hyperparameters. Finally, the proposed model was evaluated using independent test datasets, and we compared the results with the previous methods. Our approach, m5C- pred, is anticipated to be useful for accurately identifying m5C sites, outperforming the currently available state-of-the-art techniques.

DRSN4mCPred: accurately predicting sites of DNA N4-methylcytosine using deep residual shrinkage network for diagnosis and treatment of gastrointestinal cancer in the precision medicine era.

The DNA N4-methylcytosine (4mC) site levels of those suffering from digestive system cancers were higher, and the pathogenesis of digestive system cancers may also be related to the changes in DNA 4mC levels. Identifying DNA 4mC sites is a very important step in studying the analysis of biological function and cancer prediction. Extracting accurate features from DNA sequences is the key to establishing a prediction model of effective DNA 4mC sites. This study sought to develop a new predictive model, DRSN4mCPred, which aimed to improve the performance of the predicting DNA 4mC sites. The model adopted multi-scale channel attention to extract features and used attention feature fusion (AFF) to fuse features. In order to capture features information more accurately and effectively, this model utilized Deep Residual Shrinkage Network with Channel-Wise thresholds (DRSN-CW) to eliminate noise-related features and achieve a more precise feature representation, thereby, distinguishing the sites in DNA with 4mC and non-4mC. Additionally, the predictive model incorporated an inverted residual block, a Multi-scale Channel Attention Module (MS-CAM), a Bi-directional Long Short Term Memory Network (Bi-LSTM), AFF, and DRSN-CW. The results indicated the predictive model DRSN4mCPred had extremely good performance in predicting the DNA 4mC sites across different species. This paper will potentially provide support for the diagnosis and treatment of gastrointestinal cancer based on artificial intelligence in the precise medical era.

No evidence for epitranscriptomic m5C modification of SARS-CoV-2, HIV and MLV viral RNA.

The addition of chemical groups to cellular RNA to modulate RNA fate and/or function is summarized under the term epitranscriptomic modification. More than 170 different modifications have been identified on cellular RNA, such as tRNA, rRNA and, to a lesser extent, on other RNA types. Recently, epitranscriptomic modification of viral RNA has received considerable attention as a possible additional mechanism regulating virus infection and replication. N6-methyladenosine (m6A) and C5-methylcytosine (m5C) have been most broadly studied in different RNA viruses. Various studies, however, reported varying results with regard to number and extent of the modification. Here we investigated the m5C methylome of SARS-CoV-2, and we reexamined reported m5C sites in HIV and MLV. Using a rigorous bisulfite-sequencing protocol and stringent data analysis, we found no evidence for the presence of m5C in these viruses. The data emphasize the necessity for optimizing experimental conditions and bioinformatic data analysis.

Dynamics of RNA m5C modification during brain development

Post-transcriptional RNA modifications have been recognized as key regulators of neuronal differentiation and synapse development in the mammalian brain. While distinct sets of 5-methylcytosine (m5C) modified mRNAs have been detected in neuronal cells and brain tissues, no study has been performed to characterize methylated mRNA profiles in the developing brain. Here, together with regular RNA-seq, we performed transcriptome-wide bisulfite sequencing to compare RNA cytosine methylation patterns in neural stem cells (NSCs), cortical neuronal cultures, and brain tissues at three postnatal stages. Among 501 m5C sites identified, approximately 6% are consistently methylated across all five conditions. Compared to m5C sites identified in NSCs, 96% of them were hypermethylated in neurons and enriched for genes involved in positive transcriptional regulation and axon extension. In addition, brains at the early postnatal stage demonstrated substantial changes in both RNA cytosine methylation and gene expression of RNA cytosine methylation readers, writers, and erasers. Furthermore, differentially methylated transcripts were significantly enriched for genes regulating synaptic plasticity. Altogether, this study provides a brain epitranscriptomic dataset as a new resource and lays the foundation for further investigations into the role of RNA cytosine methylation during brain development.

A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4-Methylcytosine Using Deep Learning Approach.

DNA (Deoxyribonucleic Acid) N4-methylcytosine (4mC), a kind of epigenetic modification of DNA, is important for modifying gene functions, such as protein interactions, conformation, and stability in DNA, as well as for the control of gene expression throughout cell development and genomic imprinting. This simply plays a crucial role in the restriction-modification system. To further understand the function and regulation mechanism of 4mC, it is essential to precisely locate the 4mC site and detect its chromosomal distribution. This research aims to design an efficient and high-throughput discriminative intelligent computational system using the natural language processing method "word2vec" and a multi-configured 1D convolution neural network (1D CNN) to predict 4mC sites. In this article, we propose a grid search-based multi-layer dynamic ensemble system (GS-MLDS) that can enhance existing knowledge of each level. Each layer uses a grid search-based weight searching approach to find the optimal accuracy while minimizing computation time and additional layers. We have used eight publicly available benchmark datasets collected from different sources to test the proposed model's efficiency. Accuracy results in test operations were obtained as follows: 0.978, 0.954, 0.944, 0.961, 0.950, 0.973, 0.948, 0.952, 0.961, and 0.980. The proposed model has also been compared to 16 distinct models, indicating that it can accurately predict 4mC.

A deep multiple kernel learning-based higher-order fuzzy inference system for identifying DNA N4-methylcytosine sites

N4-methylcytosine (4mC), as a DNA modification, plays a crucial role in epigenetic regulation. However, the existing experimentation methods for accurately identifying 4mC sites are inefficient and highly consumable, making them difficult to implement. Although a variety of new identification methods are continuously being proposed, existing techniques are not yet fully mature. Compared to traditional 4mC site predictors, based on support vector machine or convolutional neural network, we present an alternative computational approach. In this study, we propose a method based on a kernelized higher-order fuzzy inference system (KHFIS) and deep multiple kernel learning, called DMKL-HFIS, to improve the accuracy of 4mC site identification DNA sequences. We use PSTNP to process the benchmark datasets, and then apply KHFIS to obtain multiple fuzzy kernel matrices. A deep neural network is used to fuse multiple fuzzy kernel matrices. Finally, the predicted value is derived from the fused matrix. Our approach was compared with existing mainstream computational methods. On the benchmark datasets (G. subterraneus, D. melanogaster, E. coli, A. thaliana, and C. elegans), the accuracy of our approach exceeded that of a state-of-the-art method by 0.4%, 0.44%, 1.51%, 0.55%, and 0.25%, respectively. Compared to mainstream methods, our approach exhibits a higher level of accuracy and can therefore be considered an effective prediction tool.

A novel method for predicting DNA N4-methylcytosine sites based on deep forest algorithm.

N4-methyladenosine (4mC) methylation is an essential epigenetic modification of deoxyribonucleic acid (DNA) that plays a key role in many biological processes such as gene expression, gene replication and transcriptional regulation. Genome-wide identification and analysis of the 4mC sites can better reveal the epigenetic mechanisms that regulate various biological processes. Although some high-throughput genomic experimental methods can effectively facilitate the identification in a genome-wide scale, they are still too expensive and laborious for routine use. Computational methods can compensate for these disadvantages, but they still leave much room for performance improvement. In this study, we develop a non-NN-style deep learning-based approach for accurately predicting 4mC sites from genomic DNA sequence. We generate various informative features represented sequence fragments around 4mC sites, and subsequently implement them into a deep forest (DF) model. After training the deep model using 10-fold cross-validation, the overall accuracies of 85.0%, 90.0%, and 87.8% were achieved for three representative model organisms, A. thaliana, C. elegans, and D. melanogaster, respectively. In addition, extensive experiment results show that our proposed approach outperforms other existing state-of-the-art predictors in the 4mC identification. Our approach stands for the first DF-based algorithm for the prediction of 4mC sites, providing a novel idea in this field.

MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction

N4-methylcytosine (4mC) is an important epigenetic mechanism, which regulates many cellular processes such as cell differentiation and gene expression. The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitation of techniques, precise detection of 4mC is still a challenging task. In this paper, we presented a multi-scale convolution neural network (CNN) and adaptive embedding-based computational method for predicting 4mC sites in mouse genome, which was referred to as MultiScale-CNN-4mCPred. The MultiScale-CNN-4mCPred used adaptive embedding to encode nucleotides, and then utilized multi-scale CNNs as well as long short-term memory to extract more in-depth local properties and contextual semantics in the sequences. The MultiScale-CNN-4mCPred is an end-to-end learning method, which requires no sophisticated feature design. The MultiScale-CNN-4mCPred reached an accuracy of 81.66% in the 10-fold cross-validation, and an accuracy of 84.69% in the independent test, outperforming state-of-the-art methods. We implemented the proposed method into a user-friendly web application which is freely available at: http://www.biolscience.cn/MultiScale-CNN-4mCPred/.

4mCBERT: A computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies

N4-methylcytosine (4mC) is an important DNA chemical modification pattern which is a new methylation modification discovered in recent years and plays critical roles in gene expression regulation, defense against invading genetic elements, genomic imprinting, and so on. Identifying 4mC site from DNA sequence segment contributes to discovering more novel modification patterns. In this paper, we present a model called 4mCBERT that encodes DNA sequence segments by sequence characteristics including one-hot, electron-ion interaction pseudopotential, nucleotide chemical property, word2vec and chemical information containing physicochemical properties (PCP), chemical bidirectional encoder representations from transformers (chemical BERT) and employs ensemble learning framework to develop a prediction model. PCP and chemical BERT features are firstly constructed and applied to predict 4mC sites and show positive contributions to identifying 4mC. For the Matthew's Correlation Coefficient, 4mCBERT significantly outperformed other state-of-the-art models on six independent benchmark datasets including A. thaliana, C. elegans, D. melanogaster, E. coli, G. Pickering, and G. subterraneous by 4.32 % to 24.39 %, 2.52 % to 31.65 %, 2 % to 16.49 %, 6.63 % to 35.15, 8.59 % to 61.85 %, and 8.45 % to 34.45 %. Moreover, 4mCBERT is designed to allow users to predict 4mC sites and retrain 4mC prediction models. In brief, 4mCBERT shows higher performance on six benchmark datasets by incorporating sequence- and chemical-driven information and is available at http://cczubio.top/4mCBERT and https://github.com/abcair/4mCBERT.

Laplacian Regularized Sparse Representation Based Classifier for Identifying DNA N4-Methylcytosine Sites via L2,1/2-Matrix Norm.

N4-methylcytosine (4mC) is one of important epigenetic modifications in DNA sequences. Detecting 4mC sites is time-consuming. The computational method based on machine learning has provided effective help for identifying 4mC. To further improve the performance of prediction, we propose a Laplacian Regularized Sparse Representation based Classifier with L2,1/2-matrix norm (LapRSRC). We also utilize kernel trick to derive the kernel LapRSRC for nonlinear modeling. Matrix factorization technology is employed to solve the sparse representation coefficients of all test samples in the training set. And an efficient iterative algorithm is proposed to solve the objective function. We implement our model on six benchmark datasets of 4mC and eight UCI datasets to evaluate performance. The results show that the performance of our method is better or comparable.

I4mC-CPXG: A Computational Model for Identifying DNA N4- methylcytosine Sites in Rosaceae Genome Using Novel Encoding Strategy

Background: N4-methylcytosine (4mC) is one of the most widespread DNA methylation modifications, which plays an important role in DNA replication and repair, epigenetic inheritance, gene expression levels and regulation of transcription. Although biological experiments can identify potential 4mC modification sites, they are limited due to the experimental environment and labor intensive. Therefore, it is crucial to construct a computational model to identify the 4mC sites. background: N4-methylcytosine (4mC) is one of the most widespread DNA methylation modifications, which plays an important role in DNA replication and repair, epigenetic inheritance, gene expression levels and regulation of transcription. Although biological experiment can identify potential 4mC modification sites, it’s limited due to the experimental environment and labor intensive. Therefore, it is crucial to construct a computational model to identify the 4mC sites. Objective: Although some computational methods have been proposed to identify the 4mC sites, some problems should not be ignored, such as: (1) a large number of unknown nucleotides exist in the biological sequence; (2) a large number of zeros exist in the previous encoding technologies; (3) sequence distribution information is important to identify 4mC sites. Considering these aspects, we propose a computational model based on a novel encoding strategy with position specific information to identify 4mC sites. Methods: We constructed an accurate computational model i4mC-CPXG based on extreme gradient boosting. Two aspects of feature vectors are extracted according to nucleotide information and position specific information. From the aspect of nucleotide information, we used prior information to identify the base type of unknown nucleotide and decrease the influence of invalid information caused by lots of zeros. From the aspect of position specific information, the vector was designed carefully to express the base distribution and arrangement. Then the feature vector fused by nucleotide information and position specific information was input into extreme gradient boosting to construct the model. method: We constructed an accurate computational model i4mC-CPXG based on extreme gradient boosting. Two aspects feature vectors are extracted according to nucleotide information and position specific information. From the aspect of nucleotide information, we used prior information to identify the base type of unknown nucleotide and decrease the influence of invalid information caused by lots of zeros. From the aspect of position specific information, the vector was designed carefully to express the base distribution and arrangement. Then the feature vector fused by nucleotide information and position specific information was input into extreme gradient boosting to construct model. Results: The accuracy of i4mC-CPXG is 82.49% on independent dataset. The result was better than model i4mC-w2vec which was the best model in the imbalanced dataset with the ratio of 1:15. Meanwhile, our model achieved good performance on other species. These results validated the effectiveness of i4mC-CPXG. Conclusion: Our method is effective to identify potential 4mC modification sites due to the proposed new encoding strategy fused position specific information. The satisfactory prediction results of balanced datasets, imbalanced datasets and other species datasets indicate that i4mC-CPXG is valuable to provide a reasonable supplement for biology research. other: The satisfactory prediction results of balanced datasets, imbalanced datasets and other species datasets indicate that i4mC-CPXG is valuable to provide a reasonable supplement for biology research.

Particle Swarm Optimization-Assisted Multilayer Ensemble Model to predict DNA 4mC sites

DNA methylation is an epigenetic modification that plays a crucial role in various biological processes, including gene expression regulation, cell differentiation, and the development of diseases such as cancer. Identifying DNA methylation patterns is essential for understanding its functional implications. Traditional experimental methods for detecting DNA methylation are costly, time-consuming, and inefficient for analyzing large-scale sequencing data. In this research, we explore the application of machine learning techniques to accurately identify DNA methylation sites. Our research aims to develop a Particle Swarm Optimization-Assisted Multilayer Ensemble Model (PSO-MEM) with several significant contributions. These include extracting semantic features from genetic sequences, optimizing feature dimensions to reduce classification errors, developing a multilayer dynamic approach that transfers learned information between layers during classification, and incorporating ensemble techniques for improved prediction and optimal results. To evaluate the performance of our proposed model, we compare it with existing models using eight publicly available datasets. The results demonstrate the efficacy of our approach, with AUC values of 91.99%, 92.80%, 90.28%, 91.03%, 93.09%, 90.79%, 90.68%, and 91.88% for the C. elegans, D. melanogaster, A. thaliana, E. coli, G. subterraneus, G. pickeringi, F. vesca, and R. chinensis datasets, respectively. The results highlight the potential of machine learning techniques for efficient and reliable identification of DNA methylation sites in large-scale genomic data, facilitating advancements in understanding epigenetic modifications and their functional implications.

MSNet-4mC: learning effective multi-scale representations for identifying DNA N4-methylcytosine sites.

N4-methylcytosine (4mC) is an essential kind of epigenetic modification that regulates a wide range of biological processes. However, experimental methods for detecting 4mC sites are time-consuming and labor-intensive. As an alternative, computational methods that are capable of automatically identifying 4mC with data analysis techniques become a reasonable option. A major challenge is how to develop effective methods to fully exploit the complex interactions within the DNA sequences to improve the predictive capability. In this work, we propose MSNet-4mC, a lightweight neural network building upon convolutional operations with multi-scale receptive fields to perceive cross-element relationships over both short and long ranges of given DNA sequences. With strong imbalances in the number of candidates in different species in mind, we compute and apply class weights in the cross-entropy loss to balance the training process. Extensive benchmarking experiments show that our method achieves a significant performance improvement and outperforms other state-of-the-art methods. The source code and models are freely available for download at https://github.com/LIU-CT/MSNet-4mC, implemented in Python and supported on Linux and Windows. Supplementary data are available at Bioinformatics online.

DNA and coding/non-coding RNA methylation analysis provide insights into tomato fruit ripening.

Ripening is the last, irreversible developmental stage during which fruit become palatable, thus promoting seed dispersal by frugivory. In Alisa Craig fruit, mRNAs with increasing m5C levels, such as STPK and WRKY 40, were identified as being involved in response to biotic and abiotic stresses. Furthermore, two mRNAs involved in cell wall metabolism, PG and EXP-B1, also presented increased m5C levels. In the Nr mutant, several m5C-modified mRNAs involved in fruit ripening, including those encoding WRKY and MADS-box proteins, were found. Targets of long non-coding RNAs and circular RNAs with different m5C sites were also found; these targets included 2-alkenal reductase, soluble starch synthase 1, WRKY, MADS-box, and F-box/ketch-repeat protein SKIP11. A combined analysis of changes in 5mC methylation and mRNA revealed many differentially expressed genes with differentially methylated regions encoding transcription factors and key enzymes related to ethylene biosynthesis and signal transduction; these included ERF084, EIN3, AP2/ERF, ACO5, ACS7, EIN3/4, EBF1, MADS-box, AP2/ERF, and ETR1. Taken together, our findings contribute to the global understanding of the mechanisms underlying fruit ripening, thereby providing new information for both fruit and post-harvest behavior.

Mouse4mC-BGRU: Deep learning for predicting DNA N4-methylcytosine sites in mouse genome.

DNA N4-methylcytosine (4mC) is an important DNA modification and plays a crucial role in a variety of biological processes. Accurate 4mC site identification is fundamental to improving the understanding of 4mC biological functions and mechanisms. However, lots of identification approaches are limited to traditional machine learning, which leads to weak learning ability and a complex feature extraction process. Here, we propose Mouse4mC-BGRU, an advanced deep learning model that utilizes adaptive embedding based on bidirectional gated recurrent units (BGRU). Benchmark results show that our model performs better than the state-of-the-art methods in the prediction of 4mC sites in the mouse genome. By using adaptive features to extract representation, Mouse4mC-BGRU can capture the latent biology information of input sequence, which effectively enhances model representation ability. In addition, we visualize the training process of Mouse4mC-BGRU with dim reduction tools and intuitively show the effectiveness of our model, demonstrating that Mouse4mC-BGRU has great potential to be a powerful and practically useful tool to accurately identify 4mC sites.

An improved residual network using deep fusion for identifying RNA 5-methylcytosine sites.

5-Methylcytosine (m5C) is a crucial post-transcriptional modification. With the development of technology, it is widely found in various RNAs. Numerous studies have indicated that m5C plays an essential role in various activities of organisms, such as tRNA recognition, stabilization of RNA structure, RNA metabolism and so on. Traditional identification is costly and time-consuming by wet biological experiments. Therefore, computational models are commonly used to identify the m5C sites. Due to the vast computing advantages of deep learning, it is feasible to construct the predictive model through deep learning algorithms. In this study, we construct a model to identify m5C based on a deep fusion approach with an improved residual network. First, sequence features are extracted from the RNA sequences using Kmer, K-tuple nucleotide frequency component (KNFC), Pseudo dinucleotide composition (PseDNC) and Physical and chemical property (PCP). Kmer and KNFC extract information from a statistical point of view. PseDNC and PCP extract information from the physicochemical properties of RNA sequences. Then, two parts of information are fused with new features using bidirectional long- and short-term memory and attention mechanisms, respectively. Immediately after, the fused features are fed into the improved residual network for classification. Finally, 10-fold cross-validation and independent set testing are used to verify the credibility of the model. The results show that the accuracy reaches 91.87%, 95.55%, 92.27% and 95.60% on the training sets and independent test sets of Arabidopsis thaliana and M.musculus, respectively. This is a considerable improvement compared to previous studies and demonstrates the robust performance of our model. The data and code related to the study are available at https://github.com/alivelxj/m5c-DFRESG.

A single natural RNA modification can destabilize a U•A-T-rich RNA•DNA-DNA triple helix.

Recent studies suggest noncoding RNAs interact with genomic DNA, forming RNA•DNA-DNA triple helices, as a mechanism to regulate transcription. One way cells could regulate the formation of these triple helices is through RNA modifications. With over 140 naturally occurring RNA modifications, we hypothesize that some modifications stabilize RNA•DNA-DNA triple helices while others destabilize them. Here, we focus on a pyrimidine-motif triple helix composed of canonical U•A-T and C•G-C base triples. We employed electrophoretic mobility shift assays and microscale thermophoresis to examine how 11 different RNA modifications at a single position in an RNA•DNA-DNA triple helix affect stability: 5-methylcytidine (m5C), 5-methyluridine (m5U or rT), 3-methyluridine (m3U), pseudouridine (Ψ), 4-thiouridine (s4U), N6-methyladenosine (m6A), inosine (I), and each nucleobase with 2′-O-methylation (Nm). Compared to the unmodified U•A-T base triple, some modifications have no significant change in stability (Um•A-T), some have ∼2.5-fold decreases in stability (m5U•A-T, Ψ•A-T, and s4U•A-T), and some completely disrupt triple helix formation (m3U•A-T). To identify potential biological examples of RNA•DNA-DNA triple helices controlled by an RNA modification, we searched RMVar, a database for RNA modifications mapped at single-nucleotide resolution, for lncRNAs containing an RNA modification within a pyrimidine-rich sequence. Using electrophoretic mobility shift assays, the binding of DNA-DNA to a 22-mer segment of human lncRNA Al157886.1 was destabilized by ∼1.7-fold with the substitution of m5C at known m5C sites. Therefore, the formation and stability of cellular RNA•DNA-DNA triple helices could be influenced by RNA modifications.