{"title":"Cell Decoder: decoding cell identity with multi-scale explainable deep learning.","authors":"Jun Zhu, Zeyang Zhang, Yujia Xiang, Beini Xie, Xinwen Dong, Linhai Xie, Peijie Zhou, Rongyan Yao, Xiaowen Wang, Yang Li, Fuchu He, Wenwu Zhu, Ziwei Zhang, Cheng Chang","doi":"10.1186/s13059-025-03832-y","DOIUrl":"10.1186/s13059-025-03832-y","url":null,"abstract":"<p><strong>Background: </strong>Cells are the fundamental units of life, and understanding their diversity and functionality requires detailed characterization. The rise of single-cell omics data enables this, yet current deep learning approaches lack multi-scale interpretability.</p><p><strong>Results: </strong>We introduce Cell Decoder, a model that integrates biological prior knowledge to provide a multi-scale representation of cells. Using automated machine learning and post hoc analysis, Cell Decoder decodes cell identity and outperforms existing methods. It offers multi-view interpretability and facilitates data integration.</p><p><strong>Conclusions: </strong>Applied to human bone and mouse embryonic data, Cell Decoder reveals the multi-scale heterogeneity of cell identities, providing a powerful framework for advancing our understanding of cellular diversity.</p>","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"26 1","pages":"359"},"PeriodicalIF":10.1,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12536527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145336684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2025-10-20DOI: 10.1186/s13059-025-03771-8
Martin Hemberg, Federico Marini, Shila Ghazanfar, Ahmad Al Ajami, Najla Abassi, Benedict Anchang, Bérénice A Benayoun, Yue Cao, Ken Chen, Yesid Cuesta-Astroz, Zachary DeBruine, Calliope A Dendrou, Iwijn De Vlaminck, Katharina Imkeller, Ilya Korsunsky, Alex R Lederer, Jessica Jingyi Li, Pieter Meysman, Clint L Miller, Kerry A Mullan, Uwe Ohler, Pratibha Panwar, Nikolaos Patikas, Jonas Schuck, Jacqueline H Y Siu, Timothy J Triche, Alex Tsankov, Sander W van der Laan, Masanao Yajima, Jean Yang, Fabio Zanini, Ivana Jelic
{"title":"Insights, opportunities, and challenges provided by large cell atlases.","authors":"Martin Hemberg, Federico Marini, Shila Ghazanfar, Ahmad Al Ajami, Najla Abassi, Benedict Anchang, Bérénice A Benayoun, Yue Cao, Ken Chen, Yesid Cuesta-Astroz, Zachary DeBruine, Calliope A Dendrou, Iwijn De Vlaminck, Katharina Imkeller, Ilya Korsunsky, Alex R Lederer, Jessica Jingyi Li, Pieter Meysman, Clint L Miller, Kerry A Mullan, Uwe Ohler, Pratibha Panwar, Nikolaos Patikas, Jonas Schuck, Jacqueline H Y Siu, Timothy J Triche, Alex Tsankov, Sander W van der Laan, Masanao Yajima, Jean Yang, Fabio Zanini, Ivana Jelic","doi":"10.1186/s13059-025-03771-8","DOIUrl":"10.1186/s13059-025-03771-8","url":null,"abstract":"<p><p>The field of single-cell biology is growing rapidly, generating large amounts of data from a variety of species, disease conditions, tissues, and organs. Coordinated efforts such as CZI CELLxGENE, HuBMAP, Broad Institute Single Cell Portal, and DISCO allow researchers to access large volumes of curated datasets, including more than just scRNA-seq data. These resources have created an opportunity to build and expand the computational biology ecosystem to develop tools necessary for data reuse and for extracting novel biological insights. We highlight achievements made so far, areas where further development is needed, and specific challenges that need to be overcome.</p>","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"26 1","pages":"358"},"PeriodicalIF":10.1,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12536537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145336746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2025-10-20DOI: 10.1186/s13059-025-03706-3
Matthew P Williams, Christian D Huber
{"title":"Publisher Correction: The genomic footprints of migration: how ancient DNA reveals our history of mobility.","authors":"Matthew P Williams, Christian D Huber","doi":"10.1186/s13059-025-03706-3","DOIUrl":"10.1186/s13059-025-03706-3","url":null,"abstract":"","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"26 1","pages":"357"},"PeriodicalIF":10.1,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12536517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145336772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome BiologyPub Date : 2025-10-16DOI: 10.1186/s13059-025-03827-9
Yuzhuo Ma, He Xu, Ying Li, Hyesung Kim, Lin-lin Xu, Lin Miao, Peng Xu, Fengbiao Mao, Xu-jie Zhou, Wei Zhou, Seunggeun Lee, Ji-Feng Zhang, Peipei Zhang, Wenjian Bi
{"title":"SPAmix: a scalable, accurate, and universal analysis framework for large-scale genetic association studies in admixed populations","authors":"Yuzhuo Ma, He Xu, Ying Li, Hyesung Kim, Lin-lin Xu, Lin Miao, Peng Xu, Fengbiao Mao, Xu-jie Zhou, Wei Zhou, Seunggeun Lee, Ji-Feng Zhang, Peipei Zhang, Wenjian Bi","doi":"10.1186/s13059-025-03827-9","DOIUrl":"https://doi.org/10.1186/s13059-025-03827-9","url":null,"abstract":"Inclusion of individuals with diverse or admixed genetic ancestries is crucial to discover novel findings that may be missed by genomics analyses rooted solely in European population. Here, we present an analysis framework, SPAmix, which is scalable to a large-scale biobank data analysis including hundreds of thousands of admixed individuals and is universally applicable to various types of complex traits including quantitative traits, time-to-event traits, ordinal traits, and longitudinal traits. Since no alternative model is fitted, SPAmix primarily focuses on association p values. For each genetic variant, SPAmix uses genotype data and genetic principal components to estimate individual-specific allele frequency, which is subsequently used to calibrate p values via a retrospective analysis. A hybrid strategy including saddlepoint approximation (SPA) can greatly increase the accuracy to analyze rare genetic variants, especially if the phenotypic distribution is unbalanced or extremely unbalanced. We also propose SPAmixlocal to incorporate local ancestry to calculate ancestry-specific p values. To maximize the statistical powers, SPAmixCCT is proposed to combine the p values of SPAmix and SPAmixlocal via Cauchy combination. The SPAmix-based approaches are more accurate than Tractor to address phenotypic variance heterogeneity among ancestries when analyzing quantitative traits and to address an unbalanced case–control ratio when analyzing binary traits. SPAmixCCT is an optimal unified approach for various cross-ancestry genetic architectures. Extensive simulation studies and real data analyses of 369,314 UK Biobank individuals from multiple ancestries demonstrated that SPAmix is scalable and can discover novel hits while controlling type I error rates well.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"11 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295942","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}
Genome BiologyPub Date : 2025-10-15DOI: 10.1186/s13059-025-03761-w
Marius F. Maurstad, Siv Nam Khang Hoff, José Cerca, Mark Ravinet, Ian Bradbury, Kjetill S. Jakobsen, Kim Præbel, Sissel Jentoft
{"title":"Reference genome bias in light of species-specific chromosomal reorganization and translocations","authors":"Marius F. Maurstad, Siv Nam Khang Hoff, José Cerca, Mark Ravinet, Ian Bradbury, Kjetill S. Jakobsen, Kim Præbel, Sissel Jentoft","doi":"10.1186/s13059-025-03761-w","DOIUrl":"https://doi.org/10.1186/s13059-025-03761-w","url":null,"abstract":"Whole-genome sequencing efforts, have during the past decade, unveiled the central role of genomic rearrangements—such as chromosomal inversions—in evolutionary processes, including local adaptation in a wide range of taxa. However, employment of reference genomes from distantly or even closely related species for mapping and the subsequent variant calling can lead to errors and/or biases in the datasets generated for downstream analyses. Here, we capitalize on the recently generated chromosome-anchored genome assemblies for Arctic cod (Arctogadus glacialis), polar cod (Boreogadus saida), and Atlantic cod (Gadus morhua) to evaluate the extent and consequences of reference bias on population sequencing datasets (approx. 15–20 × coverage) for both Arctic cod and polar cod. Our findings demonstrate that the choice of reference genome impacts the mapping statistics, including mapping depth and mapping quality, as well as core population genetic estimates, such as heterozygosity levels, nucleotide diversity (π), and cross-species genetic divergence (DXY). Furthermore, using a more distantly related reference genome can lead to inaccurate detection and characterization of chromosomal inversions, i.e., in terms of size (length) and location (position), due to inter-chromosomal reorganizations between species. Additionally, we observe that some of the verified species-specific inversions are split across multiple genomic regions when mapped against a heterospecific reference. Inaccurate identification of chromosomal rearrangements as well as biased population genetic measures could potentially lead to erroneous interpretation of species-specific genomic diversity, impede the resolution of local adaptation, and thus, impact predictions of their genomic potential to respond to climatic and other environmental perturbations.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"1 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289266","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":"sCCIgen: a high-fidelity spatially resolved transcriptomics data simulator for cell–cell interaction studies","authors":"Xiaoyu Song, Joselyn C. Chavez-Fuentes, Weiping Ma, Weijia Fu, Sujung Crystal Shin, Pei Wang, Guo-Cheng Yuan","doi":"10.1186/s13059-025-03762-9","DOIUrl":"https://doi.org/10.1186/s13059-025-03762-9","url":null,"abstract":"Spatially resolved transcriptomics (SRT) facilitates the study of cell–cell interactions within native tissue environments. To support method development and benchmarking, we introduce sCCIgen, a real-data-based simulator that generates high-fidelity synthetic SRT data with known interaction features. sCCIgen preserves transcriptomic and spatial characteristics and provides key interaction features, including cell colocalization, spatial dependence of gene expression, and gene–gene interactions between neighboring cells. It supports input from SRT data, single-cell expression data alone, and unpaired expression and spatial data. sCCIgen is interactive, user-friendly, reproducible, and well-documented for studying cellular interactions and spatial biology.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289393","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}
Genome BiologyPub Date : 2025-10-13DOI: 10.1186/s13059-025-03811-3
Xiaosa Xu, Huiqiong Lin, Junli Zhang, German Burguener, Francine Paraiso, Kun Li, Connor Tumelty, Chengxia Li, Yuchen Liu, Jorge Dubcovsky
{"title":"Spatial and single-cell expression analyses reveal complex expression domains in early wheat spike development","authors":"Xiaosa Xu, Huiqiong Lin, Junli Zhang, German Burguener, Francine Paraiso, Kun Li, Connor Tumelty, Chengxia Li, Yuchen Liu, Jorge Dubcovsky","doi":"10.1186/s13059-025-03811-3","DOIUrl":"https://doi.org/10.1186/s13059-025-03811-3","url":null,"abstract":"Wheat is important for global food security. Understanding the molecular mechanisms driving spike and spikelet development can benefit the development of more productive varieties. Here we integrate single-molecule fluorescence in situ hybridization (smFISH) and single-cell RNA sequencing (scRNA-seq) to generate an atlas of cell clusters and expression domains during the early stages of wheat spike development. We characterize spatiotemporal expression of 99 genes by smFISH in 48,225 cells at early transition (W1.5), late double ridge (W2.5), and floret primordia stages (W3.5). These cells are grouped into 21 different expression domains, including four in the basal region of the developing spikelets and three different meristematic regions, which are consistent across spikelets and sections. Using induced mutants, we reveal functional roles associated with the specific expression patterns of LFY in intercalary meristems, SPL14 in inflorescence meristems, and FZP in glume axillae. Complementary scRNA-seq profiling of 26,009 cells from W2.5 and W3.5 stages identifies 23 distinct cell clusters. We use the scRNA-seq information to impute the expression of 74,464 genes into the spatially anchored smFISH-labelled cells and generate a public website to visualize them. We then use experimental and imputed expression profiles, together with co-expression studies and correlation matrices, to annotate the scRNA-seq clusters. From co-expression analyses, we identify genes associated with boundary genes TCP24 and FZP, as well as the meristematic genes AGL6 and ULT1. The smFISH and scRNA-seq studies provide complementary tools for dissecting gene networks that regulate spike development and identifying new co-expressed genes for functional characterization.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"18 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145277467","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}
Genome BiologyPub Date : 2025-10-13DOI: 10.1186/s13059-025-03819-9
Wei Su, Yuhe Yang, Yafei Zhao, Shishi Yuan, Xueqin Xie, Yuduo Hao, Hongqi Zhang, Dongxin Ye, Hao Lyu, Hao Lin
{"title":"iPro-MP: a BERT-based model to predict multiple prokaryotic promoters","authors":"Wei Su, Yuhe Yang, Yafei Zhao, Shishi Yuan, Xueqin Xie, Yuduo Hao, Hongqi Zhang, Dongxin Ye, Hao Lyu, Hao Lin","doi":"10.1186/s13059-025-03819-9","DOIUrl":"https://doi.org/10.1186/s13059-025-03819-9","url":null,"abstract":"Promoters, as essential cis-regulatory elements in prokaryotes, govern gene expression by mediating RNA polymerase binding through core motifs and long-range regulatory interactions, playing a pivotal role in cell metabolism and environmental adaptation. Hence, accurate identification of prokaryotic promoters is vital for understanding their biological functions. However, the existing tools for predicting prokaryotic promoters are mainly concentrated on individual model organisms, and their prediction accuracy needs to be further improved. To address these gaps, we develop iPro-MP, a transformer-based prokaryotic promoter prediction framework that we systematically evaluate across 23 phylogenetically diverse species, including both model and non-model organisms. iPro-MP utilizes a multi-head attention mechanism to capture textual information in DNA sequences and effectively learns the hidden patterns. Cross-species prediction demonstrates the necessity of constructing species-specific models. Through a series of experiments, iPro-MP shows outstanding performance, with the AUC exceeding 0.9 in 18 out of 23 species. Our novel approach to predicting prokaryotic promoters, iPro-MP, provides the superiority to other existing tools, especially in predicting non-model organisms. Finally, for the convenience of other researchers, the source code and datasets of iPro-MP are freely available at https://github.com/Jackie-Suv/iPro-MP .","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"12 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145277465","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}
Genome BiologyPub Date : 2025-10-10DOI: 10.1186/s13059-025-03810-4
Mengjie Chen
{"title":"A novel gene expression stability metric to unveil homeostasis and regulation","authors":"Mengjie Chen","doi":"10.1186/s13059-025-03810-4","DOIUrl":"https://doi.org/10.1186/s13059-025-03810-4","url":null,"abstract":"We explore the concept of gene expression stability within a homeostatic cell through the gene homeostasis Z-index, a measure that highlights genes under active regulation in response to internal and external stimuli. The Z-index uncovers distinct regulatory activities and patterns driven by a small subset of cells, providing insights that traditional mean-based methods often overlook. By capturing subtle regulatory dynamics, this approach highlights the importance of stability metrics in uncovering detailed gene regulation patterns that underpin cellular adaptation.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"1 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255724","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}
Genome BiologyPub Date : 2025-10-09DOI: 10.1186/s13059-025-03789-y
Jingyi Xu, Xiaodong Chen, Jianrong Ren, Jiawen Xu, Lei Zhang, Fang Yan, Tao Liu, Guijie Zhang, Sharon A. Huws, Junhu Yao, Shengru Wu
{"title":"Multi-omics insights into microbiome-rumen epithelium interaction mechanisms underlying subacute rumen acidosis tolerance in dairy goats","authors":"Jingyi Xu, Xiaodong Chen, Jianrong Ren, Jiawen Xu, Lei Zhang, Fang Yan, Tao Liu, Guijie Zhang, Sharon A. Huws, Junhu Yao, Shengru Wu","doi":"10.1186/s13059-025-03789-y","DOIUrl":"https://doi.org/10.1186/s13059-025-03789-y","url":null,"abstract":"To address rising demand for dairy products, dairy goats are often fed high-concentrate diets, which lead to subacute rumen acidosis (SARA). The mechanisms behind individual variation in SARA tolerance are not well understood. This study aims to elucidate roles of rumen microbiome-host interactions in SARA-susceptibility and tolerance. Goats susceptible or tolerant to SARA were selected by feeding diets with different levels of rumen degradable starch. SARA-susceptible goats present prolonged periods of rumen pH below 5.8 and volatile fatty acids (VFAs) accumulation. Metagenomic analysis reveals a decrease in cellulose- and hemicellulose-utilizing bacteria and enzymes, along with increased lysozymes, suggesting disrupted rumen homeostasis. Transcriptomic and single-nucleus transcriptome analyses reveal upregulated Th17 cells, IL-17 signalling, and inflammatory pathways in SARA-susceptible goats. In contrast, SARA-tolerant goats maintain stable pH levels and enhance VFAs absorption. Bifidobacterium adolescentis and other beneficial bacteria are enriched in the rumen of SARA-tolerant goats. These microbes are positively correlated with 3-methyl pyruvic acid, a key metabolite involved in branched-chain amino acid synthesis and epithelial cell proliferation. Both microbiome transplantation and B. adolescentis direct feeding experiments confirm the protective effects of SARA-tolerant microbiota including B. adolescentis, promoting rumen epithelial VFAs absorption and reducing ruminal inflammation. This study highlights the importance of Th17-mediated immune responses in ruminal inflammation and the role of B. adolescentis in regulating rumen epithelial VFAs absorption. Modulating VFAs absorption in the rumen epithelium represents a promising strategy for improving animal health and enhancing rumen fermentation efficiency.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"109 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246596","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}