ArticleProceedings of the National Academy of Sciences of the United States of America2021
A machine learning-based framework for modeling transcription elongation.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- RNA polymerase III transcription-associated polyadenylation promotes the accumulation of noncoding retrotransposons during infection.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Probabilistic and machine-learning methods for predicting local rates of transcription elongation from nascent RNA sequencing data.Nucleic acids research · 2025Article
- RNA Polymerase II Activity Control of Gene Expression and Involvement in Disease.Journal of molecular biology · 2025Review
- Deciphering HIV-1 Transcription Initiation and Elongation from Single-Molecule Imaging Data.Research (Washington, D.C.) · 2025Article
- Predicting RNA polymerase II transcriptional elongation pausing and associated histone code.Briefings in bioinformatics · 2024Article
- DNA-sequence and epigenomic determinants of local rates of transcription elongation.bioRxiv : the preprint server for biology · 2023Article
- Stochastic modeling of the mRNA life process: A generalized master equation.Biophysical journal · 2023Article
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
- Predictive model of transcriptional elongation control identifies trans regulatory factors from chromatin signatures.Nucleic acids research · 2023Article
- Article
- Roles of Physicochemical and Structural Properties of RNA-Binding Proteins in Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.International journal of molecular sciences · 2022Article
- Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science.Entropy (Basel, Switzerland) · 2021Review
- Review
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Authors and funding
8 authors.
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Abstract
RNA polymerase II (Pol II) generally pauses at certain positions along gene bodies, thereby interrupting the transcription elongation process, which is often coupled with various important biological functions, such as precursor mRNA splicing and gene expression regulation. Characterizing the transcriptional elongation dynamics can thus help us understand many essential biological processes in eukaryotic cells. However, experimentally measuring Pol II elongation rates is generally time and resource consuming. We developed PEPMAN (polymerase II elongation pausing modeling through attention-based deep neural network), a deep learning-based model that accurately predicts Pol II pausing sites based on the native elongating transcript sequencing (NET-seq) data. Through fully taking advantage of the attention mechanism, PEPMAN is able to decipher important sequence features underlying Pol II pausing. More importantly, we demonstrated that the analyses of the PEPMAN-predicted results around various types of alternative splicing sites can provide useful clues into understanding the cotranscriptional splicing events. In addition, associating the PEPMAN prediction results with different epigenetic features can help reveal important factors related to the transcription elongation process. All these results demonstrated that PEPMAN can provide a useful and effective tool for modeling transcription elongation and understanding the related biological factors from available high-throughput sequencing data.
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