Genome Biology最新文献

{"title":"Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns","authors":"Nghia Millard, Jonathan H. Chen, Mukta G. Palshikar, Karin Pelka, Maxwell Spurrell, Colles Price, Jiang He, Nir Hacohen, Soumya Raychaudhuri, Ilya Korsunsky","doi":"10.1186/s13059-025-03479-9","DOIUrl":"https://doi.org/10.1186/s13059-025-03479-9","url":null,"abstract":"Spatial transcriptomics facilitates gene expression analysis of cells in their spatial anatomical context. Batch effects hinder visualization of gene spatial patterns across samples. We present the Crescendo algorithm to correct for batch effects at the gene expression level and enable accurate visualization of gene expression patterns across multiple samples. We show Crescendo’s utility and scalability across three datasets ranging from 170,000 to 7 million single cells across spatial and single-cell RNA sequencing technologies. By correcting for batch effects, Crescendo enhances spatial transcriptomics analyses to detect gene colocalization and ligand-receptor interactions and enables cross-technology information transfer.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"89 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"De novo basecalling of RNA modifications at single molecule and nucleotide resolution","authors":"Sonia Cruciani, Anna Delgado-Tejedor, Leszek P. Pryszcz, Rebeca Medina, Laia Llovera, Eva Maria Novoa","doi":"10.1186/s13059-025-03498-6","DOIUrl":"https://doi.org/10.1186/s13059-025-03498-6","url":null,"abstract":"RNA modifications influence RNA function and fate, but detecting them in individual molecules remains challenging for most modifications. Here we present a novel methodology to generate training sets and build modification-aware basecalling models. Using this approach, we develop the m6ABasecaller, a basecalling model that predicts m6A modifications from raw nanopore signals. We validate its accuracy in vitro and in vivo, revealing stable m6A modification stoichiometry across isoforms, m6A co-occurrence within RNA molecules, and m6A-dependent effects on poly(A) tails. Finally, we demonstrate that our method generalizes to other RNA and DNA modifications, paving the path towards future efforts detecting other modifications.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping snoRNA-target RNA interactions in an RNA-binding protein-dependent manner with chimeric eCLIP","authors":"Zhuoyi Song, Bongmin Bae, Simon Schnabl, Fei Yuan, Thareendra De Zoysa, Maureen V. Akinyi, Charlotte A. Le Roux, Karine Choquet, Amanda J. Whipple, Eric L. Van Nostrand","doi":"10.1186/s13059-025-03508-7","DOIUrl":"https://doi.org/10.1186/s13059-025-03508-7","url":null,"abstract":"Small nucleolar RNAs (snoRNAs) are non-coding RNAs that function in ribosome and spliceosome biogenesis, primarily by guiding modifying enzymes to specific sites on ribosomal RNA (rRNA) and spliceosomal RNA (snRNA). However, many orphan snoRNAs remain uncharacterized, with unidentified or unvalidated targets, and studies on additional snoRNA-associated proteins are limited. We adapted an enhanced chimeric eCLIP approach to comprehensively profile snoRNA-target RNA interactions using both core and accessory snoRNA-binding proteins as baits. Using core snoRNA-binding proteins, we confirmed most annotated snoRNA-rRNA and snoRNA-snRNA interactions in mouse and human cell lines and called novel, high-confidence interactions for orphan snoRNAs. While some of these interactions result in chemical modification, others may have modification-independent functions. We showed that snoRNA ribonucleoprotein complexes containing certain accessory proteins, like WDR43 and NOLC1, enriched for specific subsets of snoRNA-target RNA interactions with distinct roles in ribosome and spliceosome biogenesis. Notably, we discovered that SNORD89 guides 2′-O-methylation at two neighboring sites in U2 snRNA that fine-tune splice site recognition. Chimeric eCLIP of snoRNA-associating proteins enables a comprehensive framework for studying snoRNA-target interactions in an RNA-binding protein-dependent manner, revealing novel interactions and regulatory roles in RNA biogenesis.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"39 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transposable elements may enhance antiviral resistance in HIV-1 elite controllers","authors":"Manvendra Singh, Sabrina M. Leddy, Luis Pedro Iñiguez, Matthew L. Bendall, Douglas F. Nixon, Cédric Feschotte","doi":"10.1186/s13059-025-03484-y","DOIUrl":"https://doi.org/10.1186/s13059-025-03484-y","url":null,"abstract":"Less than 0.5% of people living with HIV-1 are elite controllers (ECs)—individuals who maintain undetectable plasma viremia without antiretroviral therapy, despite having replication-competent viral reservoirs. While EC CD4+ T cells have been investigated for gene expression signatures associated with HIV-1 resistance, the expression and regulatory activity of transposable elements (TEs) remain unexplored. TEs can directly impact host immune responses to pathogens, including HIV-1, suggesting their activities could contribute to HIV-1 elite control. To begin testing this hypothesis, we conduct a TE-centric analysis of public multi-omics data from ECs and other populations. We find the CD4+ T cell transcriptome and retrotranscriptome of ECs are distinct from healthy controls, from people living with HIV-1 on antiretroviral therapy, and from viremic progressors. However, there is substantial transcriptomic heterogeneity among ECs. We categorize ECs into four clusters with distinct expression and chromatin accessibility profiles of TEs and antiviral factors. Several TE families with known immuno-regulatory activity are differentially expressed among ECs. Their expression positively correlates with their chromatin accessibility in ECs and negatively correlates with the expression of their KRAB zinc-finger (KZNF) repressors. This coordinated, locus-level variation forms a network of putative cis-regulatory elements for genes involved in HIV-1 restriction. We propose that the EC phenotype is driven in part by reduced KZNF-mediated repression of specific TE-derived cis-regulatory elements for antiviral genes, heightening their resistance against HIV-1. Our study reveals heterogeneity in the EC CD4+ T cell transcriptome, including variable expression of TEs and their KZNF controllers, that must be considered when deciphering HIV-1 control mechanisms.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"17 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic signals of ecogeographic adaptation in a wild relative are associated with improved wheat performance under drought stress","authors":"Moses Nyine, Dwight Davidson, Elina Adhikari, Marshall Clinesmith, Huan Wang, Alina Akhunova, Allan Fritz, Eduard Akhunov","doi":"10.1186/s13059-025-03500-1","DOIUrl":"https://doi.org/10.1186/s13059-025-03500-1","url":null,"abstract":"Prioritizing wild relative diversity for improving crop adaptation to emerging drought-prone environments is challenging. Here, we combine the genome-wide environmental scans (GWES) in wheat diploid ancestor Aegilops tauschii (Ae. tauschii) with allele testing in the genetic backgrounds of adapted cultivars to identify diversity for improving wheat adaptation to water-limiting conditions. We evaluate the adaptive allele effects in Ae. tauschii-wheat introgression lines phenotyped for multiple traits under irrigated and water-limiting conditions using both unmanned aerial system-based imaging and conventional approaches. The GWES show that climatic gradients alone explain more than half of genomic variation in Ae. tauschii, with many alleles associated with climatic factors in Ae. tauschii being linked with improved performance of introgression lines under water-limiting conditions. We find that the most significant GWES signals associated with temperature annual range in the wild relative are linked with reduced canopy temperature in introgression lines and increased yield. Our results suggest that introgression of climate-adaptive alleles from Ae. tauschii has the potential to improve wheat performance under water-limiting conditions, and that variants controlling physiological processes responsible for maintaining leaf temperature are likely among the targets of adaptive selection in a wild relative. Adaptive variation uncovered by GWES in wild relatives has the potential to improve climate resilience of crop varieties.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"15 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"catGRANULE 2.0: accurate predictions of liquid-liquid phase separating proteins at single amino acid resolution","authors":"Michele Monti, Jonathan Fiorentino, Dimitrios Miltiadis-Vrachnos, Giorgio Bini, Tiziana Cotrufo, Natalia Sanchez de Groot, Alexandros Armaos, Gian Gaetano Tartaglia","doi":"10.1186/s13059-025-03497-7","DOIUrl":"https://doi.org/10.1186/s13059-025-03497-7","url":null,"abstract":"Liquid-liquid phase separation (LLPS) enables the formation of membraneless organelles, essential for cellular organization and implicated in diseases. We introduce catGRANULE 2.0 ROBOT, an algorithm integrating physicochemical properties and AlphaFold-derived structural features to predict LLPS at single-amino-acid resolution. The method achieves high performance and reliably evaluates mutation effects on LLPS propensity, providing detailed predictions of how specific mutations enhance or inhibit phase separation. Supported by experimental validations, including microscopy data, it predicts LLPS across diverse organisms and cellular compartments, offering valuable insights into LLPS mechanisms and mutational impacts. The tool is freely available at https://tools.tartaglialab.com/catgranule2 and https://doi.org/10.5281/zenodo.14205831 .","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"9 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"stDyer enables spatial domain clustering with dynamic graph embedding","authors":"Ke Xu, Yu Xu, Zirui Wang, Xin Maizie Zhou, Lu Zhang","doi":"10.1186/s13059-025-03503-y","DOIUrl":"https://doi.org/10.1186/s13059-025-03503-y","url":null,"abstract":"Spatially resolved transcriptomics (SRT) data provide critical insights into gene expression patterns within tissue contexts, necessitating effective methods for identifying spatial domains. We introduce stDyer, an end-to-end deep learning framework for spatial domain clustering in SRT data. stDyer combines Gaussian Mixture Variational AutoEncoder with graph attention networks to learn embeddings and perform clustering. Its dynamic graphs adaptively link units based on Gaussian Mixture assignments, improving clustering and producing smoother domain boundaries. stDyer’s mini-batch strategy and multi-GPU support facilitate scalability to large datasets. Benchmarking against state-of-the-art tools, stDyer demonstrates superior performance in spatial domain clustering, multi-slice analysis, and large-scale dataset handling.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"29 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A refined analysis of Neanderthal-introgressed sequences in modern humans with a complete reference genome","authors":"Shen-Ao Liang, Tianxin Ren, Jiayu Zhang, Jiahui He, Xuankai Wang, Xinrui Jiang, Yuan He, Rajiv C. McCoy, Qiaomei Fu, Joshua M. Akey, Yafei Mao, Lu Chen","doi":"10.1186/s13059-025-03502-z","DOIUrl":"https://doi.org/10.1186/s13059-025-03502-z","url":null,"abstract":"Leveraging long-read sequencing technologies, the first complete human reference genome, T2T-CHM13, corrects assembly errors in previous references and resolves the remaining 8% of the genome. While studies on archaic admixture in modern humans have so far relied on the GRCh37 reference due to the availability of archaic genome data, the impact of T2T-CHM13 in this field remains unexplored. We remap the sequencing reads of the high-quality Altai Neanderthal and Denisovan genomes onto GRCh38 and T2T-CHM13. Compared to GRCh37, we find that T2T-CHM13 significantly improves read mapping quality in archaic samples. We then apply IBDmix to identify Neanderthal-introgressed sequences in 2504 individuals from 26 geographically diverse populations using different reference genomes. We observe that commonly used pre-phasing filtering strategies in public datasets substantially influence archaic ancestry determination, underscoring the need for careful filter selection. Our analysis identifies approximately 51 Mb of Neanderthal sequences unique to T2T-CHM13, predominantly in genomic regions where GRCh38 and T2T-CHM13 assemblies diverge. Additionally, we uncover novel instances of population-specific archaic introgression in diverse populations, spanning genes involved in metabolism, olfaction, and ion-channel function. Finally, to facilitate the exploration of archaic alleles and adaptive signals in human genomics and evolutionary research, we integrate these introgressed sequences and adaptive signals across all reference genomes into a visualization database, ASH ( www.arcseqhub.com ). Our study enhances the detection of archaic variations in modern humans, highlights the importance of utilizing the T2T-CHM13 reference, and provides novel insights into the functional consequences of archaic hominin admixture.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"6 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143435020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles","authors":"Jacob Matthew Schreiber, Carles A. Boix, Jin wook Lee, Hongyang Li, Yuanfang Guan, Chun-Chieh Chang, Jen-Chien Chang, Alex Hawkins-Hooker, Bernhard Schölkopf, Gabriele Schweikert, Mateo Rojas Carulla, Arif Canakoglu, Francesco Guzzo, Luca Nanni, Marco Masseroli, Mark James Carman, Pietro Pinoli, Chenyang Hong, Kevin Y. Yip, Jefrey P. Spence, Sanjit Singh Batra, Yun S. Song, Shaun Mahony, Zheng Zhang, Wuwei Tan, Yang Shen, Yuanfei Sun, Minyi Shi, Jessika Adrian, Richard S. Sandstrom, Nina P. Farrell, Jessica M. Halow, Kristen Lee, Lixia Jiang, Xinqiong Yang, Charles B. Epstein, J. Seth Strattan, Bradley E. Bernstein, Michael P. Snyder, Manolis Kellis, William S. Noble, Anshul Bharat Kundaje","doi":"10.1186/s13059-025-03494-w","DOIUrl":"https://doi.org/10.1186/s13059-025-03494-w","url":null,"abstract":"<p><b>Correction</b><b>: </b><b>Genome Biol 24, 79 (2023)</b></p><p><b>https://doi.org/10.1186/s13059-023-02915-y</b></p><br/><p>Following publication of the original article [1], the authors identified that the affiliation 1 was incorrectly used for all authors. The correct affiliations are used in this correction article and the original article [1] has been corrected.</p><ol data-track-component=\"outbound reference\" data-track-context=\"references section\"><li data-counter=\"1.\"><p>Schreiber J, Boix C, wook Lee J, et al. The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles. Genome Biol. 2023;24:79. https://doi.org/10.1186/s13059-023-02915-y.</p><p>Article PubMed PubMed Central Google Scholar </p></li></ol><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><span>Author notes</span><ol><li><p>Jacob Matthew Schreiber and Carles Boix are co-first authors.</p></li></ol><h3>Authors and Affiliations</h3><ol><li><p>Department of Genetics, Stanford University, Stanford, CA, USA</p><p>Jacob Matthew Schreiber, Jin wook Lee, Jefrey P. Spence, Minyi Shi, Jessika Adrian, Lixia Jiang, Xinqiong Yang, J. Seth Strattan, Michael P. Snyder & Anshul Bharat Kundaje</p></li><li><p>Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA</p><p>Carles A. Boix & Manolis Kellis</p></li><li><p>Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA</p><p>Hongyang Li & Yuanfang Guan</p></li><li><p>Department of Research and Development, DeepSeq.AI, San Francisco, CA, USA</p><p>Chun-Chieh Chang</p></li><li><p>RIKEN Center for Integrative Medical Sciences, Yokohama, Japan</p><p>Jen-Chien Chang</p></li><li><p>Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Stuttgart, Germany</p><p>Alex Hawkins-Hooker, Bernhard Schölkopf & Mateo Rojas Carulla</p></li><li><p>School of Life Sciences, University of Dundee, Dundee, UK</p><p>Gabriele Schweikert</p></li><li><p>Department of Electronics, Information and Bioengineering, Politecnico Di Milano, Milan, Italy</p><p>Arif Canakoglu, Francesco Guzzo, Marco Masseroli, Mark James Carman & Pietro Pinoli</p></li><li><p>Department of Computational Biology, University of Lausanne, Lausanne, Switzerland</p><p>Luca Nanni</p></li><li><p>Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong</p><p>Chenyang Hong</p></li><li><p>Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA</p><p>Kevin Y. Yip</p></li><li><p>Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA</p><p>Sanjit Singh Batra & Yun S. Song</p></li><li><p>Department of Statistics, University of ","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"59 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the role of chromosome structure morphology on gene function through chromosome conformation analysis","authors":"Yuxiang Zhan, Asli Yildirim, Lorenzo Boninsegna, Frank Alber","doi":"10.1186/s13059-024-03472-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03472-8","url":null,"abstract":"Single-cell chromosome conformations vary significantly among individual cells. We introduce a two-step dimensionality reduction method for density-based, unsupervised clustering of single-cell 3D chromosome structures from simulations or multiplexed 3D-FISH imaging. Our method clusters up to half of all structures into 5–12 prevalent conformational states per chromosome. These states are distinguished by subdivisions into chromosome territory domains, whose boundary locations influence subnuclear positions and speckle associations of certain genes and establish long-range structural variations of more than 10 Mb. Territory domain boundaries are found at few sequence locations, shared among cell types and often situated at syntenic breakpoints.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"41 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}