ArticleNature genetics2022
Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types.
Article in Nature genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 56 citations in OpenAlex.
- DeepGeSeq: deep learning library for genomic sequence modeling and analysis.Bioinformatics (Oxford, England) · 2026Article
- Decoding sequence determinants of gene expression in diverse cellular and disease states.Nature methods · 2026Article
- Refining sequence-to-expression modelling with chromatin accessibility.Bioinformatics (Oxford, England) · 2026Article
- Evolutionary conservation and divergence of phagocytic and coagulation programs across bilaterian circulating immune cells.Molecular biology and evolution · 2026Article
- Robust integration and annotation of single-cell and spatial omics data using interpretable gene programs.Cell genomics · 2026Article
- Construction of a Multitissue Cell Atlas Reveals Cell-Type-Specific Regulation of Molecular and Complex Phenotypes in Pigs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The evolution of gene regulation in mammalian cerebellum development.Science (New York, N.Y.) · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Comparative single-cell transcriptomic map reveals divergence in leaves between two cotton species at cell type resolution.Journal of advanced research · 2026Article
- Single-cell analysis reveals cellular heterogeneity and molecular features during postnatal pig testis development.BMC genomics · 2025Article
- Omnireg-gpt: a high-efficiency foundation model for comprehensive genomic sequence understanding.Nature communications · 2025Article
- scooby: modeling multimodal genomic profiles from DNA sequence at single-cell resolution.Nature methods · 2025Article
- UNICORN: Towards universal cellular expression prediction with a multi-task learning framework.Nature communications · 2025Article
- Review
- Dissecting cross-lineage tumourigenesis under p53 inactivation through single-cell multi-omics and spatial transcriptomics.Clinical and translational medicine · 2025Article
- Article
- scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution.bioRxiv : the preprint server for biology · 2025Article
- iVAE: an interpretable representation learning framework enhances clustering performance for single-cell data.BMC biology · 2025Article
- The Transcriptomic Signature of Donkey Ovarian Tissue Revealed by Cross-Species Comparative Analysis at Single-Cell Resolution.Animals : an open access journal from MDPI · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
Corrections and comments
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Authors and funding
29 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila and earthworm. We then integrated cell landscapes from eight representative metazoan species to study gene regulation across evolution. Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in vertebrates and invertebrates. Our work provides a valuable resource and offers a new strategy for studying regulatory grammar in diverse biological systems.
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Registered trials
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 OpenQuestion graph.