arXiv subjects

Ting-He Zhang

Publications and source records attributed to Ting-He Zhang.

3 recordsLinked to original sources

arXiv2026

m6A-FORM: An m6A-focused Foundation Model for Decoding m6A Regulatory Function

N6-methyladenosine (m6A) regulates mRNA fate through site-specific methylation, reader recognition and downstream effects on RNA stability and decay. However, current computational approaches focus mainly on site prediction, leaving unresolved the broader challenge of inferring m6A regulatory context and function from epitranscriptomic profiles. Here we present m6A-FORM, an m6A-focused foundation model for for regulatory discovery. Pretrained on 24.9 million RNA sequence windows from 22.5 million MeRIP-seq regions across 143 human studies, m6A-FORM learns reusable representations of m6A-associated transcript contexts. We adapt this encoder to single-nucleotide m6A discovery, regulator-binding prediction, YTHDF2-associated decay prediction and tissue-scale epitranscriptomic mapping. m6A-FORM predicts binding of 19 m6A readers, writers and erasers and identifies sequence and RBP-context features associated with YTHDF2-mediated RNA degradation. Applied to 67 datasets from 24 human tissues, it identifies tissue-conserved m6A sites linked to stronger methylation, reader binding, RBP occupancy and decay propensity.

q-bio.GN↗

arXiv2024

Understanding YTHDF2-mediated mRNA Degradation By m6A-BERT-Deg

N6-methyladenosine (m6A) is the most abundant mRNA modification within mammalian cells, holding pivotal significance in the regulation of mRNA stability, translation, and splicing. Furthermore, it plays a critical role in the regulation of RNA degradation by primarily recruiting the YTHDF2 reader protein. However, the selective regulation of mRNA decay of the m6A-methylated mRNA through YTHDF2 binding is poorly understood. To improve our understanding, we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated degradation of m6A-methylated mRNAs. We meticulously assembled a high-quality training dataset by integrating multiple data sources for the HeLa cell line. To overcome the limitation of small training samples, we employed a pre-training-fine-tuning strategy by first performing a self-supervised pre-training of the model on 427,760 unlabeled m6A site sequences. The test results demonstrated the importance of this pre-training strategy in enabling m6A-BERT-Deg to outperform other benchmark models. We further conducted a comprehensive model interpretation and revealed a surprising finding that the presence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated mRNA degradation, subsequently enhancing mRNA stability. We also extended our analyses to the HEK293 cell line, shedding light on the context-dependent YTHDF2-mediated mRNA degradation.

q-bio.MN↗

arXiv2021

Deep learning tackles single-cell analysis A survey of deep learning for scRNA-seq analysis

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioning as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here we present a processing pipeline of single-cell RNA-seq data, survey a total of 25 DL algorithms and their applicability for a specific step in the processing pipeline. Specifically, we establish a unified mathematical representation of all variational autoencoder, autoencoder, and generative adversarial network models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative use of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.

q-bio.GN↗