AIMC Topic: Nucleotide Motifs

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Deep generalizable prediction of RNA secondary structure via base pair motif energy.

Nature communications Jul 1, 2025
Deep learning methods have demonstrated great performance for RNA secondary structure prediction. However, generalizability is a common unsolved issue on unseen out-of-distribution RNA families, which hinders further improvement of the accuracy and r...
Deep Learning Base Pairing RNA Nucleotide Motifs Computational Biology Nucleic Acid Conformation Thermodynamics
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GINClus: RNA structural motif clustering using graph isomorphism network.

NAR genomics and bioinformatics Apr 26, 2025
Ribonucleic acid (RNA) structural motif identification is a crucial step for understanding RNA structure and functionality. Due to the complexity and variations of RNA 3D structures, identifying RNA structural motifs is challenging and time-consuming...
Nucleic Acid Conformation Nucleotide Motifs Cluster Analysis RNA Algorithms Deep Learning Software
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Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.

Science advances Jan 1, 2025
DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory varian...
Humans Brain Young Adult Deep Learning Genome-Wide Association Study Genetic Predisposition to Disease Adult Male Nucleotide Motifs Models, Biological Inheritance Patterns Alzheimer Disease CpG Islands Transcription Factors DNA Methylation
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Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7-Methlguanosine Site Prediction.

Journal of chemical information and modeling Jul 16, 2024
N-7methylguanosine (m7G) modification plays a crucial role in various biological processes and is closely associated with the development and progression of many cancers. Accurate identification of m7G modification sites is essential for understandin...
RNA Nucleotide Motifs Guanosine Deep Learning
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TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.

Genome biology Jul 10, 2024
Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence fea...
Nucleotide Motifs Chromatin Immunoprecipitation Sequencing Humans Machine Learning Transcription Factors Binding Sites Protein Binding
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Interpretable deep learning reveals the role of an E-box motif in suppressing somatic hypermutation of AGCT motifs within human immunoglobulin variable regions.

Frontiers in immunology May 28, 2024
INTRODUCTION: Somatic hypermutation (SHM) of immunoglobulin variable (V) regions by activation induced deaminase (AID) is essential for robust, long-term humoral immunity against pathogen and vaccine antigens. AID mutates cytosines preferentially wit...
AICDA (Activation-Induced Cytidine Deaminase) Nucleotide Motifs Amino Acid Motifs Humans Cytidine Deaminase Somatic Hypermutation, Immunoglobulin Immunoglobulin Variable Region Deep Learning Mutation
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Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.

Genome biology Aug 4, 2023
We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and m...
RNA Datasets as Topic Binding Sites Nucleotide Motifs Base Sequence RNA, Viral Alleles Nucleotides Humans RNA Splice Sites Mutation Consensus Sequence Internet RNA, Messenger RNA-Binding Proteins Computer Simulation Bias Deep Learning
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NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America Apr 6, 2023
Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding the mechanisms of gene expression regulation. Although deep convolutional neural networks (CNNs) have achieved great success in predicting cis-regulator...
Nucleotide Motifs Regulatory Sequences, Nucleic Acid Neural Networks, Computer Algorithms Databases, Factual
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A Method for Predicting DNA Motif Length Based On Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2023
A DNA motif is a sequence pattern shared by the DNA sequence segments that bind to a specific protein. Discovering motifs in a given DNA sequence dataset plays a vital role in studying gene expression regulation. As an important attribute of the DNA ...
Algorithms Deep Learning Nucleotide Motifs Sequence Analysis, DNA Neural Networks, Computer
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Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators.

PLoS computational biology Jul 7, 2022
It is well-established that neural networks can predict or identify structural motifs of non-coding RNAs (ncRNAs). Yet, the neural network based identification of RNA structural motifs is limited by the availability of training data that are often in...
Nucleotide Motifs RNA, Untranslated Humans Neural Networks, Computer
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