Genome research最新文献

{"title":"Accurate reconstruction of spatial cell-type maps and characterization of domain-specific functions based on a gene-aware heterogeneous network.","authors":"Zilin Li, Zhaoyang Huang, Yan Li, Chenguang Zhao, Liang Yu","doi":"10.1101/gr.282246.126","DOIUrl":"10.1101/gr.282246.126","url":null,"abstract":"<p><p>Spatial transcriptomic (ST) profiles gene expression with spatial context, but most platforms capture multicellular spots containing mixed cell types, making accurate deconvolution essential. Existing reference-based methods using scRNA-seq often ignore spatial dependency and gene-level contribution, yielding fragmented maps and limited insight into domain-specific programs. Here, we propose a gene-aware heterogeneous graph attention network called STGnet for ST deconvolution and functional annotation. Leveraging a hybrid pseudospot generation strategy that captures realistic spatially enriched cell-type patterns, STGnet accurately integrates spatial adjacency, transcriptional similarity, and gene-spot associations within a unified heterogeneous network. Attention weights highlight domain-specific genes for interpretable domain annotation. Importantly, STGnet can characterize spatially ordered functional programs across domains that may be associated with disease progression. These insights may facilitate the discovery of spatial disease mechanisms and improve understanding of pathological tissue organization. Experiments on simulated and real data sets show that STGnet achieves the best overall performance compared with state-of-the-art methods.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"2104-2115"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148818231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CircExor enables interpretable prediction of circRNA localization into extracellular vesicles.","authors":"Yusa Zhang, Hanbo Lu, Pengfei Bao, Anhao Wang, Xiaohong Lyu, Yidong Zhou, Songjie Shen, Zhi John Lu","doi":"10.1101/gr.281656.125","DOIUrl":"10.1101/gr.281656.125","url":null,"abstract":"<p><p>Certain circular RNAs (circRNAs) are selectively enriched in extracellular vesicles (EVs), in which they contribute to intercellular communication and represent promising biomarkers, yet the sequence determinants of their sorting remain unclear. Existing computational predictors are optimized mainly for linear RNAs and rarely address circRNA localization into EVs. Here we introduce circExor, the first framework specifically designed for circRNA EV localization. We curate a dedicated benchmark data set of 2102 circRNAs and implement a variable-length end-to-end concatenation strategy together with <i>k</i>-mer frequency encoding to accommodate circular topology, long sequence length, and length heterogeneity. Using a tree-based classifier, circExor achieves superior performance compared with RNAlocate-v3 and ExoGRU, reaching an AUROC of 0.743 on the internal test set and an average AUROC of 0.680 on the held-out test set. SHAP-based analysis, sequence perturbation analysis, motif mapping, and cell-based experimental validation support the predicted EV tendency and identify YBX1, HNRNPK, HNRNPL, and NOVA2 as candidate RBPs potentially associated with circRNA sorting. CircExor therefore provides a predictive and interpretable framework that links in silico modeling to mechanistic hypotheses, and supports biomarker discovery and candidate prioritization for downstream studies of EV-associated circRNAs.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"2159-2171"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148989566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum: Profiling the long noncoding RNA interaction network in the regulatory elements of target genes by chromatin in situ reverse transcription sequencing","authors":"Shilin Zhang,Yichen Wang,Lin Jia,Xue Wen,Zhonghua Du,Cong Wang,Yajing Hao,Dehai Yu,Lei Zhou,Naifei Chen,Jingcheng Chen,Huiling Chen,Hui Zhang,Ilkay Celik,Günhan Gülsoy,Jianjun Luo,Baoming Qin,Xueling Cui,Zhonghui Liu,Songling Zhang,Miguel A. Esteban,Ferhat Ay,Wei Xu,Runsheng Chen,Wei Li,Andrew R. Hoffman,Ji-Fan Hu,Jiuwei Cui","doi":"10.1101/gr.282733.126","DOIUrl":"https://doi.org/10.1101/gr.282733.126","url":null,"abstract":"","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"14 1","pages":"2187-2187"},"PeriodicalIF":7.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust annotation and discovery of novel cell types in single-cell ATAC-seq data through cross-modal reference alignment.","authors":"Lan Cao, Wenhao Zhang, Feng Zhou, Yushuang He, Yongyu Long, Shengquan Chen, Ying Wang","doi":"10.1101/gr.281981.126","DOIUrl":"10.1101/gr.281981.126","url":null,"abstract":"<p><p>Accurate cell-type annotation is essential for revealing the dynamic, cell-type-specific accessibility of regulatory elements from single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data. However, unlike the more mature single-cell RNA-sequencing (scRNA-seq) cell-type annotation workflows, scATAC-seq cell-type annotation remains challenging owing to extreme sparsity, high dimensionality, the scarcity of labeled scATAC references, and pronounced batch effects across data sets. To enable annotation without relying on extensive scATAC labels, we introduce CARA, a cross-modal Bayesian framework that transfers cell-type knowledge from scRNA-seq to scATAC-seq. CARA simultaneously enables cell-type annotation, batch correction, and detection of cell types absent from the reference by combining pretraining on scRNA-seq data and semisupervised learning on target scATAC-seq data, along with distribution alignment, dynamic class weighting, and novel cell-type detection. Across diverse benchmark data sets, CARA consistently outperforms baseline methods, remaining robust to batch effects. CARA's cross-modal alignment and robust annotation strategy preserve fine-grained lineage structure, enabling reconstruction of the hematopoietic differentiation trajectory. Through multidimensional uncertainty and latent-space clustering, CARA identifies novel, rare, or aberrant populations outside the reference cell-type space, providing candidates for further biological validation and perturbation. Using an omic-specific generative framework and distribution alignment, CARA delivers accurate knowledge transfer and detects novel cell types in single-cell DNA methylation data, demonstrating generalization capability to gene-centric modalities. Ultimately, CARA offers a powerful and flexible solution for cross-modal cell-type annotation in complex single-cell settings, facilitating the discovery of novel cell types and mechanistic insight into cell-type-specific regulation across diverse analyses.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"2037-2052"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"T2T genomes of <i>Caenorhabditis nigoni</i> and <i>Caenorhabditis briggsae</i> reveal divergence in satellite DNA abundance.","authors":"Ryan Pellow, Manitejus Kotikalapudi, Ofer Rog","doi":"10.1101/gr.281988.126","DOIUrl":"10.1101/gr.281988.126","url":null,"abstract":"<p><p>The two closely related nematode species, <i>Caenorhabditis nigoni</i> and <i>Caenorhabditis briggsae</i>, are commonly used to study the evolution of reproductive modes in animals, with the self-fertile <i>C. briggsae</i> and outcrossing <i>C. nigoni</i> sharing a common ancestor ∼3.5 million years ago. Earlier genomic analyses revealed that selfing <i>Caenorhabditis</i> species have smaller genomes and proposed that at least some gene loss in <i>C. briggsae</i> is adaptive. However, the incomplete <i>C. nigoni</i> reference genome has limited most comparative analyses to genic regions. Here, we leverage long-read sequencing to generate and annotate telomere-to-telomere (T2T) assemblies for the <i>C. nigoni</i> strain JU1422 and the <i>C. briggsae</i> strain AF16. This new 139 Mb <i>C. nigoni</i> genome resolves 57 gaps and 149 unassigned scaffolds from the previous genome assembly. A major driver of the size difference with the 107 Mb T2T <i>C. briggsae</i> genome is the abundance of satellite DNA, which accounts for 12.8 Mb (9.2%) in <i>C. nigoni</i> and only 3.2 Mb (3.0%) in <i>C. briggsae</i> Notably, the <i>C. nigoni</i> X Chromosome is 13.4 Mb larger than in the previous assembly, making it 60% larger than the <i>C. briggsae</i> X Chromosome compared with 18%-26% difference for the autosomes. We also document a surprising degree of plasticity in the ribosomal DNA, with the <i>C. nigoni</i> X Chromosome harboring a second 45S rDNA array that is absent in <i>C. briggsae</i> The hitherto undocumented divergence in the abundance of repetitive DNA elements makes the new genomes an invaluable resource for genomic analysis.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"2172-2186"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13621871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nuclear mitochondrial sequences in great ape telomere-to-telomere genomes.","authors":"Edmundo Torres-González, Marzia A Cremona, Jessica M Storer, Mario Ventura, Rachel J O'Neill, Kateryna D Makova","doi":"10.1101/gr.280875.125","DOIUrl":"10.1101/gr.280875.125","url":null,"abstract":"<p><p>Mitochondrial sequences have integrated into the nuclear genome since the origin of eukaryotes. Recent insertions that retain homology with extant mitochondrial DNA (mtDNA), termed NUMTs, confound mtDNA sequence analysis. Here, we use great ape telomere-to-telomere (T2T) genomes to study NUMTs in bonobo, chimpanzee, human, gorilla, and Bornean and Sumatran orangutans. A phylogeny based on shared and lineage-specific NUMTs accurately recapitulates the great ape species tree topology. NUMTs are enriched at nonfunctional nonrepetitive regions of the nuclear genome and depleted within enhancers and coding sequences, suggesting negative selection. We validate the presence of a 76-kb-long heterozygous NUMT in chimpanzee, which is larger than any other NUMT observed in great apes, and find that dozens of NUMTs on the <i>Pan</i> Y Chromosome expanded together with palindromes. Finally, by analyzing intra-specific variation, we confirm that the vast majority of species-specific NUMTs identified in T2T assemblies are fixed or present at high frequencies in each species. Our study highlights NUMTs as a dynamic evolutionary force contributing to shaping ape genomes and is valuable for characterizing mtDNA in great apes.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1953-1965"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13629686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148792112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical genome mapping identifies clinically relevant somatic structural variation in epilepsy-affected brain tissue","authors":"Anthony R Miller,James J Anderson,Maria Elena Hernandez Gonzalez,Lakshmi Prakruthi Rao Venkata,Eileen Stonerock,Lauren Mashburn-Warren,Allison Daley,Jeffrey Leonard,Jonathan Pindrik,Ammar Shaikhouni,Daniel R Boue,Diana L Thomas,Christopher R Pierson,Adam P Ostendorf,Elaine R Mardis,Daniel C Koboldt,Katherine E Miller,Tracy A Bedrosian","doi":"10.1101/gr.281418.125","DOIUrl":"https://doi.org/10.1101/gr.281418.125","url":null,"abstract":"Somatic variants are a prominent cause of epilepsy-associated cortical malformations, but about half of patients undergoing genetic testing have no finding due partly to limitations in variant detection. Most studies have focused on single-nucleotide variants or small indels that are accessible to short-read sequencing technologies, but somatic structural variants are also emerging as important contributors despite their unique detection challenges. Optical genome mapping (OGM) is a promising methodology for the detection of structural variants, but requires high quality, high molecular weight DNA from clinical specimens. Here we successfully optimize a protocol for OGM of surgically-resected patient brain tissue which yields ~450× effective coverage suitable for detecting somatic variants at low allele fractions. We apply this approach to brain specimens from four patients with epilepsy. OGM identifies large and complex structural variants, most of which are not captured by short-read exome sequencing of the same specimen. In one patient with a known germline DEPDC5 variant, OGM reveals a somatic variant, a 13.2kb deletion in DEPDC5 at approximately 20% VAF, consistent with the established two-hit model in DEPDC5 -associated lesional epilepsies. By resolving the breakpoints in PacBio HiFi sequencing data, we identify a mechanism for this somatic deletion, mediated by recombination of two Alu elements flanking the region. Our findings demonstrate that OGM is a robust and complementary tool for detecting somatic structural variation in human brain tissue, with potential to improve diagnostic yield and refine genotype-phenotype correlations in neurological disorders.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"4 1","pages":"gr.281418.125"},"PeriodicalIF":7.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of differential topologically associating domains from low sequencing depth and pseudobulk chromatin contact maps.","authors":"Junping Li, Han Xu, Hebing Chen, Jiadong Lin, Yusen Ye, Lin Gao","doi":"10.1101/gr.281535.125","DOIUrl":"10.1101/gr.281535.125","url":null,"abstract":"<p><p>Topologically associating domains (TADs) are fundamental units of 3D genome architecture that shape gene regulation. Comparative analyses of TADs across biological conditions have revealed their involvement in development and disease. However, accurately identifying differential TADs from low sequencing depth and pseudobulk chromatin contact maps remains challenging. Here, we present HiDT, a graph neural network-based algorithm with an attention-based, edge-enhanced layer to capture structural differences between TADs. HiDT integrates a depth-specific normalization module and is trained across a wide range of sequencing depths, enabling robust detection of differential TADs under low sequencing depth conditions. Comprehensive benchmarking demonstrates that HiDT consistently outperforms existing methods at both the TAD and subTAD levels, maintaining accuracy even in data sets with only a few million contacts. We further apply it to multiple low sequencing depth and pseudobulk data sets that are challenging for existing methods, revealing TAD reorganization linked to oncogene dysregulation during tumor progression, capturing differential TADs associated with underlying transcriptional heterogeneity in single-cell Hi-C data, and identifying haplotype-specific TADs associated with allele-specific structural variations. Overall, HiDT provides a robust tool for differential TAD analysis and facilitates insights into chromatin structure-function relationships.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"2127-2140"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13629692/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-contrastive learning for spatial domain identification in spatial transcriptomics with STAMGC.","authors":"Zhuoyue Zhang, Qianmao Wen, Junlin Xu, Yajie Meng, Feifei Cui, Zilong Zhang","doi":"10.1101/gr.281999.126","DOIUrl":"10.1101/gr.281999.126","url":null,"abstract":"<p><p>Spatial transcriptomics (STs) have become a valuable approach for understanding the growth and development of organisms. Despite the recent emergence of numerous ST models, accurately identifying spatial domains remains challenging owing to the trade-off between preserving local details and reducing noise. Here, we introduce STAMGC, which is a dual-contrastive learning framework built upon graph convolutional networks. This model leverages regional and topological contrastive learning to jointly optimize the model, effectively reducing the noise in spatial domain identification and enhancing the extraction of detailed features. In this study, Gaussian smoothing, originally developed in the image processing field, is introduced to process ST data, providing a foundation for region-level contrastive learning by mitigating spatial discontinuities of gene expression signals. Experimental results indicate that STAMGC outperforms existing methods across multiple data sets according to comprehensive evaluations. Furthermore, STAMGC not only identifies finer structures in the mouse brain but also brings new discoveries for human breast cancer research.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"2091-2103"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148982246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A network of steroid receptor transcription factors regulates ovarian chromatin remodeling in the transition to ovulation.","authors":"Doan T Dinh, Rebecca L Robker, Darryl L Russell","doi":"10.1101/gr.282066.126","DOIUrl":"10.1101/gr.282066.126","url":null,"abstract":"<p><p>Steroid receptors are transcription factors activated by progesterone, androgen, and glucocorticoid that bind the same canonical DNA sequence to modulate genome function in response to steroid hormones. However, the mechanisms defining unique physiological roles of these conserved receptors within the same tissue context, including the ovary, remain elusive. Here, we describe the dynamic association between each steroid receptor cistrome in the mouse ovary responding to the hormonal switch from follicle development to ovulation and generate chromatin conformation maps to define steroid receptor roles in promoter-enhancer interactions and gene transcription. Ovulatory hormones trigger progesterone receptor (PGR) and glucocorticoid receptor (NR3C1 [also known as GR]) binding to novel chromatin sites, promoting transcriptional activation of genes that are required for ovulation, whereas AR-chromatin interactions and androgen receptor (AR)-associated genes are repressed. Integration of genomic and transcriptomic data illustrates two parallel modes of PGR-mediated gene activation. Unique cooperation between PGR and GR enables their recruitment to previously inaccessible promoters, increasing histone acetylation, chromatin accessibility, and transcription activation, with PGR being the indispensable component of this transcriptional complex. Alternatively, PGR tethered to enhancers interacting with preaccessible, AR/GR-bound promoters induces gene activation. Our findings illustrate the multifaceted steroid receptor interactions that translate progressive change in steroid environments to collectively reprogram granulosa cell genome function to switch from follicle development to ovulation.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1997-2010"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148818218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}