ArticleBioinformatics (Oxford, England)2023
UNADON: transformer-based model to predict genome-wide chromosome spatial position.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Deep learning enabled prediction of nuclear lamina-associated chromatin.Patterns (New York, N.Y.) · 2026Article
- Early feature extraction drives model performance in high-resolution chromatin accessibility prediction.Genome research · 2026Article
- Analysis of long-range contacts across cell types outlines a core sequence determinant of 3D genome organization.NAR genomics and bioinformatics · 2025Article
- The emerging sequence grammar of 3D genome organisation.Human genetics · 2025Review
- Computational methods for analysing multiscale 3D genome organization.Nature reviews. Genetics · 2024Review
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2 authors.
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Abstract
motivationThe spatial positioning of chromosomes relative to functional nuclear bodies is intertwined with genome functions such as transcription. However, the sequence patterns and epigenomic features that collectively influence chromatin spatial positioning in a genome-wide manner are not well understood.
resultsHere, we develop a new transformer-based deep learning model called UNADON, which predicts the genome-wide cytological distance to a specific type of nuclear body, as measured by TSA-seq, using both sequence features and epigenomic signals. Evaluations of UNADON in four cell lines (K562, H1, HFFc6, HCT116) show high accuracy in predicting chromatin spatial positioning to nuclear bodies when trained on a single cell line. UNADON also performed well in an unseen cell type. Importantly, we reveal potential sequence and epigenomic factors that affect large-scale chromatin compartmentalization in nuclear bodies. Together, UNADON provides new insights into the principles between sequence features and large-scale chromatin spatial localization, which has important implications for understanding nuclear structure and function. AVAILABILITY AND IMPLEMENTATION: The source code of UNADON can be found at https://github.com/ma-compbio/UNADON.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.