Genome BiologyPub Date : 2025-09-22DOI: 10.1186/s13059-025-03768-3
Marion Pitz, Jutta A. Baldauf, Hans-Peter Piepho, Peng Yu, Heiko Schoof, Annaliese S. Mason, Guoliang Li, Frank Hochholdinger
{"title":"Regulation of heterosis-associated gene expression complementation in maize hybrids","authors":"Marion Pitz, Jutta A. Baldauf, Hans-Peter Piepho, Peng Yu, Heiko Schoof, Annaliese S. Mason, Guoliang Li, Frank Hochholdinger","doi":"10.1186/s13059-025-03768-3","DOIUrl":"https://doi.org/10.1186/s13059-025-03768-3","url":null,"abstract":"Classical genetic concepts to explain heterosis attribute the superiority of F1-hybrids over their homozygous parents to the complementation of unfavorable by beneficial alleles (dominance) or to heterozygote advantage (overdominance). Here we analyze 112 intermated B73xMo17 recombinant inbred lines of maize and their backcrosses to their original parents B73 and Mo17 to obtain hybrids with an average heterozygosity of ~ 50%. This genetic architecture allows studying the influence of homozygous and heterozygous genomic regions on gene expression in hybrids. We demonstrate that single parent expression (SPE) complementation explains between − 8% and 29% of the mid-parent heterotic variance in these hybrids. In this expression pattern, consistent with dominance, genes are active in only one parent and in the hybrid, thus increasing the number of expressed genes in hybrids. Furthermore, we establish that eQTL regulating SPE genes are predominantly located in heterozygous regions of the genome. Finally, we identify an SPE gene that regulates lateral root density in hybrids. Remarkably, the activity of this gene depends on the presence of a Mo17 allele in an eQTL that regulates this gene. Here we show that dominance of SPE genes influences the number of active genes in hybrids, while heterozygosity is instrumental for the regulation of these genes. This finding supports the notion that the genetic constitution of distant regulatory elements is instrumental for the activity of heterosis-associated genes. In summary, our results connect genetic variation at regulatory loci and the degree of heterozygosity with phenotypic variation of heterosis via SPE complementation.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"195 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103684","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":"SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis","authors":"Wenlin Li, Maocheng Zhu, Yucheng Xu, Mengqian Huang, Ziyi Wang, Jing Chen, Hao Wu, Xiaobo Sun","doi":"10.1186/s13059-025-03748-7","DOIUrl":"https://doi.org/10.1186/s13059-025-03748-7","url":null,"abstract":"Spatial transcriptomics (ST) integrates spatial information into genomics, yet methods for generating spatially-informed gene representations are limited and computationally intensive. We present SIGEL, a cost-effective framework that derives gene manifolds from ST data by exploiting spatial genomic context. The resulting SIGEL-generated gene representations (SGRs) are context-aware, biologically meaningful, and robust across samples, making them highly effective for key downstream tasks, including imputing missing genes, detecting spatial expression patterns, identifying disease-related genes and interactions, and improving spatial clustering. Extensive experiments across diverse ST datasets validate SIGEL’s effectiveness and highlight its potential in advancing spatial genomics research.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"78 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103801","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 : 2025-09-22DOI: 10.1186/s13059-025-03783-4
Linh Nguyen, Jae Young Choi
{"title":"Topsicle: a method for estimating telomere length from whole genome long-read sequencing data","authors":"Linh Nguyen, Jae Young Choi","doi":"10.1186/s13059-025-03783-4","DOIUrl":"https://doi.org/10.1186/s13059-025-03783-4","url":null,"abstract":"Telomeres protect chromosome ends and their length varies significantly between organisms. Because telomere length variation is associated with various biomedical and eco-evolutionary phenotypes, many biological fields are interested in understanding its biological significance. Here, we introduce Topsicle, a computational method that estimates telomere length from whole genome long-read sequencing data using k-mer and change-point detection analysis. Simulations show Topsicle is robust to sequencing errors and coverage. Application of Topsicle to plant and human cancer cells shows high accuracy and comparable results to direct telomere length measurements. We predict Topsicle will be a useful tool for studying telomere biology.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"2 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103682","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 : 2025-09-22DOI: 10.1186/s13059-025-03749-6
Zheng Su, Mingyan Fang, Andrei Smolnikov, Marcel E. Dinger, Emily C. Oates, Fatemeh Vafaee
{"title":"GeneRAIN: multifaceted representation of genes via deep learning of gene expression networks","authors":"Zheng Su, Mingyan Fang, Andrei Smolnikov, Marcel E. Dinger, Emily C. Oates, Fatemeh Vafaee","doi":"10.1186/s13059-025-03749-6","DOIUrl":"https://doi.org/10.1186/s13059-025-03749-6","url":null,"abstract":"We develop GeneRAIN, a suite of Transformer-based models that learn gene expression relationships from 410 K human bulk RNA-seq samples. Featuring a novel Binning-By-Gene normalization technique, our models capture diverse biological information beyond expression. We introduce GeneRAIN-vec, a multifaceted vectorized gene representation that outperforms those from existing models. We demonstrate knowledge transfer from protein-coding genes to Make 62.5 million biological attribute predictions for 13,030 long noncoding RNAs. This work advances Transformer and self-supervised deep learning applications to expression data, enhancing biological exploration.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"18 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103799","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 : 2025-09-22DOI: 10.1186/s13059-025-03758-5
Siyuan Wu, Ulf Schmitz
{"title":"ScIsoX: a multidimensional framework for measuring isoform-level transcriptomic complexity in single cells","authors":"Siyuan Wu, Ulf Schmitz","doi":"10.1186/s13059-025-03758-5","DOIUrl":"https://doi.org/10.1186/s13059-025-03758-5","url":null,"abstract":"Single-cell isoform analysis enables high-resolution characterization of transcript expression, yet analytical frameworks to systematically measure transcriptomic complexity are lacking. Here, we introduce ScIsoX, a computational framework that integrates a novel hierarchical data structure, a suite of complexity metrics, and dedicated visualization tools for isoform-level analysis. ScIsoX supports systematic exploration of global and cell-type-specific isoform expression patterns arising from alternative splicing, revealing multidimensional complexity signatures across diverse datasets—insights often missed by conventional gene-level approaches. We demonstrate the utility of ScIsoX across multiple real-world single-cell isoform sequencing datasets, showcasing its potential as a general framework for transcriptomic complexity analysis.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"48 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103798","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":"Systematic benchmarking of computational methods to identify spatially variable genes","authors":"Zhijian Li, Zain M.Patel, Dongyuan Song, Sai Nirmayi Yasa, Robrecht Cannoodt, Guanao Yan, Jingyi Jessica Li, Luca Pinello","doi":"10.1186/s13059-025-03731-2","DOIUrl":"https://doi.org/10.1186/s13059-025-03731-2","url":null,"abstract":"Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes (SVGs). Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we systematically evaluate 14 methods using 96 spatial datasets and 6 metrics. We compare the methods regarding gene ranking and classification based on real spatial variation, statistical calibration, and computation scalability and investigate the impact of identified SVGs on downstream applications such as spatial domain detection. Finally, we explore the applicability of the methods to spatial ATAC-seq data to examine their effectiveness in identifying spatially variable peaks (SVPs). Overall, SPARK-X outperforms other benchmarked methods and Moran’s I achieves a competitive performance, representing a strong baseline for future method development. Moreover, our results reveal that most methods are poorly calibrated, and more specialized algorithms are needed to identify spatially variable peaks. Our benchmarking provides a detailed comparison of SVG detection methods and serves as a reference for both users and method developers.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"18 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145077421","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 : 2025-09-18DOI: 10.1186/s13059-025-03745-w
Elizabeth A. Hemenway, Mary Gehring
{"title":"The 5-methylcytosine DNA glycosylase ROS1 prevents paternal genome hypermethylation in Arabidopsis endosperm","authors":"Elizabeth A. Hemenway, Mary Gehring","doi":"10.1186/s13059-025-03745-w","DOIUrl":"https://doi.org/10.1186/s13059-025-03745-w","url":null,"abstract":"DNA methylation patterning is a consequence of opposing activities of DNA methyltransferases and DNA demethylases. In many plant and animal species, reproduction is a period of significant epigenome lability. In flowering plants, two distinct female gametes, the egg cell and the central cell, are fertilized to produce the embryo and the endosperm of the seed. The endosperm is an unusual tissue, exemplified by triploidy and reduced DNA methylation. In Arabidopsis thaliana, a 5-methylcytosine DNA glycosylase, DME, demethylates regions of the central cell genome, leading to methylation differences between maternally- and paternally-inherited endosperm genomes after fertilization. Expression of DME in the central cell is required for gene imprinting, or parent-of-origin specific gene expression, in endosperm. DME is part of a four member gene family in Arabidopsis that includes ROS1, DML2, and DML3. It is unknown whether any of the other DNA glycosylases are required for endosperm methylation patterning. Using whole-genome methylation profiling, we identify ROS1 target regions in the endosperm. We show that ROS1 prevents hypermethylation of paternally-inherited alleles in the endosperm at regions that lack maternal or paternal allele methylation in wild-type endosperm. Additionally, we demonstrate that at many ROS1 target regions the maternal alleles are demethylated by DME. ROS1 promotes epigenetic symmetry between parental genomes in the endosperm by preventing CG methylation gain on the paternal genome. We conclude that ROS1 and DME act in a parent-of-origin-specific manner at shared endosperm targets, and consider possible implications for the evolution of imprinting mechanisms.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"82 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145077420","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 : 2025-09-18DOI: 10.1186/s13059-025-03737-w
Victoria Harle, Victoria Offord, Birkan Gökbağ, Lazaros Fotopoulos, Thomas Williams, Diana Alexander, Ishan Mehta, Nicola A. Thompson, Rebeca Olvera-León, Stefan Peidli, Vivek Iyer, Emanuel Gonçalves, Narod Kebabci, Barbara De Kegel, Joris van de Haar, Lang Li, Colm J. Ryan, David J. Adams
{"title":"A compendium of synthetic lethal gene pairs defined by extensive combinatorial pan-cancer CRISPR screening","authors":"Victoria Harle, Victoria Offord, Birkan Gökbağ, Lazaros Fotopoulos, Thomas Williams, Diana Alexander, Ishan Mehta, Nicola A. Thompson, Rebeca Olvera-León, Stefan Peidli, Vivek Iyer, Emanuel Gonçalves, Narod Kebabci, Barbara De Kegel, Joris van de Haar, Lang Li, Colm J. Ryan, David J. Adams","doi":"10.1186/s13059-025-03737-w","DOIUrl":"https://doi.org/10.1186/s13059-025-03737-w","url":null,"abstract":"Synthetic lethal interactions are attractive therapeutic candidates as they enable selective targeting of cancer cells in which somatic alterations have disrupted one member of a synthetic lethal gene pair while leaving normal tissues untouched, thus minimising off-target toxicity. Despite this potential, the number of well-established and validated synthetic lethal gene pairs is modest. We generate a dual-guide CRISPR/Cas9 Library and analyse 472 predicted synthetic lethal pairs in 27 cancer cell Lines from melanoma, pancreatic and lung cancer Lineages. We report a robust collection of 117 genetic interactions within and across cancer types and explore their candidacy as therapeutic targets. We show that SLC25A28 is an attractive target since its synthetic lethal paralog partner SLC25A37 is homozygously deleted pan-cancer. We generate knockout mice for Slc25a28 revealing that, except for cataracts in some mice, these animals are normal; suggesting inhibition of SLC25A28 is unlikely to be associated with profound toxicity. We provide and validate an extensive collection of synthetic lethal interactions across cancer types.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"76 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145077751","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 : 2025-09-17DOI: 10.1186/s13059-025-03729-w
Lang Yang, Yanfeng Lin, Peihan Li, Kaiying Wang, Jinhui Li, Yuqi Liu, Xiaochen Bo, Ming Ni, Peng Li, Hongbin Song
{"title":"A comprehensive benchmarking of adaptive sampling tools for nanopore sequencing","authors":"Lang Yang, Yanfeng Lin, Peihan Li, Kaiying Wang, Jinhui Li, Yuqi Liu, Xiaochen Bo, Ming Ni, Peng Li, Hongbin Song","doi":"10.1186/s13059-025-03729-w","DOIUrl":"https://doi.org/10.1186/s13059-025-03729-w","url":null,"abstract":"Adaptive sampling is an emerging technology to enrich target reads while depleting unwanted reads during real-time nanopore sequencing. The application of different algorithms has spawned various tools for the determination of read rejection. However, an evaluation in conjunction with identifying the optimal enrichment performance for a specific task has yet to be conducted. This study aimed to evaluate the performance of six widely used tools for nanopore adaptive sampling. Three distinct types of tasks were selected for testing, including the intraspecies enrichment of COSMIC genes, the interspecies enrichment of Saccharomyces cerevisiae, and the depletion of human host DNA. All the tools show increases in coverage depths of targets varying from 1.50- to 4.86-fold. The combination of Guppy for base calling and minimap2 for read alignment emerged as the optimal read classification strategy with the highest accuracy. MinKNOW, Readfish, and BOSS-RUNS using this strategy show generally excellent enrichment or depletion performance. The deep learning method utilizing raw signals demonstrates higher accuracy and quicker read ejection compared to the conventional signal-based approach, also achieving top-class performance in host depletion. Our benchmarking study conducted a thorough comparison of current tools on various adaptive sampling occasions. The nucleotide-alignment-based approach is capable of handling diverse target references with broad application. The tools employing this strategy, especially MinKNOW, could be considered as a prior option for most adaptive sampling scenarios. The deep learning technique utilizing raw signals demonstrates remarkable classification efficiency and accuracy, warranting greater emphasis and exploration in future software development endeavors.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"36 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071925","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 : 2025-09-17DOI: 10.1186/s13059-025-03744-x
Yuxuan Song, Chengxin Zhang, Gilbert S. Omenn, Matthew J. O’Meara, Joshua D. Welch
{"title":"Predicting the structural impact of human alternative splicing","authors":"Yuxuan Song, Chengxin Zhang, Gilbert S. Omenn, Matthew J. O’Meara, Joshua D. Welch","doi":"10.1186/s13059-025-03744-x","DOIUrl":"https://doi.org/10.1186/s13059-025-03744-x","url":null,"abstract":"Protein structure prediction with neural networks is a powerful new method for linking protein sequence, structure, and function, but structures have generally been predicted for only a single isoform of each gene, neglecting splice variants. To investigate the structural implications of alternative splicing, we use AlphaFold2 to predict the structures of more than 11,000 human isoforms. We employ multiple metrics to identify splicing-induced structural alterations, including template matching score, secondary structure composition, surface charge distribution, radius of gyration, accessibility of post-translational modification sites, and structure-based function prediction. We identify examples of how alternative splicing induces clear changes in each of these properties. Structural similarity between isoforms largely correlates with degree of sequence identity, but we identify a subset of isoforms with low structural similarity despite high sequence similarity. Exon skipping and alternative last exons tend to increase the surface charge and radius of gyration. Splicing also buries or exposes numerous post-translational modification sites, most notably among the isoforms of BAX. Functional prediction identifies numerous functional differences between isoforms of the same gene, with loss of function compared to the reference predominating. Finally, we use single-cell RNA-seq data from the Tabula Sapiens to determine the cell types in which each structure is expressed. Our work represents an important resource for studying the structure and function of splice isoforms across the cell types of the human body.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"17 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071923","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}