Genome Biology最新文献

{"title":"A new phased assembly of the Antarctic spiny plunderfish provides novel insights into the evolution of the notothenioid radiation.","authors":"Jacopo Martelossi, Ksenia Krasheninnikova, Amy Denton, Jonathan M D Wood, Thomas C Mathers, Richard Durbin, Nova Fong, David L Bentley, Melody S Clark, Iliana Bista","doi":"10.1186/s13059-026-04293-7","DOIUrl":"10.1186/s13059-026-04293-7","url":null,"abstract":"<p><strong>Background: </strong>Notothenioids are a well characterised species flock endemic to the Antarctic and an important model group for studying genome adaptation to extreme cold. A new reference assembly and clade-wide comparative genomic analysis allow for investigation of cryonotothenioid evolution and the appearance of novel functionalities linked to cold adaptation.</p><p><strong>Results: </strong>A new phased assembly of a model notothenioid, Harpagifer antarcticus, demonstrated low levels of haplotypic variability across the genome. Nevertheless, numerous insertions from multiple LINE-L2 clades were found, suggesting ongoing transposition with potential contribution to speciation. Contrary to expectations the afgp locus was highly similar between haplotypes, except for large length allelic variants of afgp genes. Analysis suggests a model for the afgp locus expansion in H. antarcticus through segmental tandem duplications involving two pairs of afgp genes at time. Syntenic reconstruction of genomes from across the clade demonstrates conserved macrosyntenic relationships and group specific chromosomal fusions of notothenioids. Quantification of genome gain and transposition rates during cryonotothenioid diversification showed a first ancestral slow genome expansion concurrent with historic temperature drops. This was followed by lineage-specific massive peaks of genomic gain and transposition activity. Finally, we identified a set of genes that underwent ancestral diversifying selection and acquired novel conserved non-coding elements during the cryonotothenioid emergence. These were related to antioxidants and proteostasis, which may have facilitated the notothenioid Antarctic radiation.</p><p><strong>Conclusions: </strong>Diversifying selection and genomic gain linked to transposon activity are primary contributors to lineage-specific evolutionary dynamics through the clade which facilitated adaptation to life in the cold.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13670615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197252","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}

{"title":"Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.","authors":"Yuhan Sun, Olivia J Cheng, Anjun Ma, Samia Shabnaz, Xuelian Huang, Jing Zhao, Aik Choon Tan, Qin Ma","doi":"10.1186/s13059-026-04284-8","DOIUrl":"10.1186/s13059-026-04284-8","url":null,"abstract":"<p><p>Multi-omics technologies, coupled with AI technologies, have the potential to enable the systematic investigation of complex cancer microbiome biology by uncovering informative patterns and associations across complementary datasets. Here, we review existing and emerging cancer microbiome data, discuss the development, interpretation, and validation of AI models as key considerations for their integration and analysis, and provide practical suggestions for improving the reliability and biological relevance of AI-driven discoveries. We further highlight the opportunities and challenges of translating these discoveries into clinical practice, emphasizing strategies to bridge the gap between research-oriented models and clinical implementations.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13584529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148984025","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}

{"title":"Genolator enables protein function interpretation using a multimodal large language model fusing genomic and structural interpretation with natural language interaction.","authors":"Martin Danner, Tanhim Islam, Matthias Begemann, Florian Kraft, Miriam Elbracht, Ingo Kurth, Jeremias Krause","doi":"10.1186/s13059-026-04274-w","DOIUrl":"10.1186/s13059-026-04274-w","url":null,"abstract":"<p><strong>Background: </strong>Decoding the genetic code to unveil its genome functionality is a monumental task which would greatly advance the understanding of disease mechanisms and development of targeted treatments. Although large language models (LLMs) have transformed natural language processing across diverse domains, translating the complex language of DNA into human-readable form remains challenging due to genomic data complexity and unexplored regions of the human genome. Current language models either are capable of processing natural language or the genomic code. Models fusing both aspects are largely lacking.</p><p><strong>Results: </strong>Here we present Genolator, a multimodal large language model that integrates embeddings from DNA sequences, amino acid sequences, and protein structures with natural language queries. Fine-tuned on over 365,000 question-answer pairs generated using abstracted Gene-Ontology (GO) terms, Genolator effectively answers queries regarding protein subcellular localization, molecular function, and biological processes. Evaluation demonstrates high accuracy in confirming or denying protein function associations, outperforming baseline models such as openly available allrounder LLMs like GPT 4.1 as well as smaller domain-specific models integrating knowledge from a protein structure transformer. Explorations of Genolator's hidden states unveil a biologically and linguistically plausible organization of its learned representations. Analysis of the attention heads of the underlying language model and an ablation study provide evidence for a benefit of the multi-modal approach.</p><p><strong>Conclusion: </strong>Genolator enhances accessibility to genomic information by enabling natural language interaction with protein data, facilitating biological discovery, and clinical research. It represents a step towards bridging genomic code and human language through the integration of a multimodal LLM.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13579901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148983078","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}

{"title":"Population genomics, demography, and circum-Baltic connectivity of Early Medieval southwestern Finland.","authors":"Ronan James O'Sullivan, Ulla Nordfors, Nelli-Johanna Saari, Kerttu Majander, Jussi-Pekka Taavitsainen, Kati Salo, Adam B Rohrlach, Elina Salmela, Thiseas C Lamnidis, Luca Traverso, Johannes Krause, Sanni Peltola, Päivi Onkamo","doi":"10.1186/s13059-026-04277-7","DOIUrl":"10.1186/s13059-026-04277-7","url":null,"abstract":"<p><strong>Background: </strong>Knowledge of Early Medieval Finland (1050-1250 CE) relies primarily on archaeological evidence, as contemporary sources are scarce. The available evidence indicates two distinct cultural-economic zones: coastal and inland. Using newly generated genomic data from 34 ancient individuals alongside modern Finnish genomes, we characterise Late Iron Age and Early Medieval ancestry in southwestern Finland, reconstruct demographic patterns, and place individuals within a circum-Baltic relatedness network.</p><p><strong>Results: </strong>Early Medieval ancestry in inland southwestern Finland was very similar to that of present-day inhabitants. Ancient coastal and inland individuals were genetically indistinguishable, whereas modern coastal populations showed substantially more Scandinavian ancestry, and less Baltic ancestry compared to their ancient counterparts. IBD (identity-by-descent) analyses also indicate a major genetic shift in the coastal zone since the Early Medieval Period. Effective population size increased throughout the study period and was ~ 13,000 by 1250 CE. IBD links between Scandinavia and Early Medieval Finland align with known archaeological connections. Furthermore, we identify IBD links between individuals from Early Medieval Finland and victims of the Kronan warship sinking.</p><p><strong>Conclusions: </strong>We demonstrate nearly a millenium of population continuity in the inland zone of southwestern Finland, contrasted by a large, contemporaneous genetic shift in the coastal zone. This ancestry shift corresponds with documented medieval emigration from Sweden to Finland. The regional population rapidly expanded during this time period, likely due to new agricultural practices and favourable climatic conditions. Our circum-Baltic IBD network indicates that southwestern Finland was firmly embedded into the wider, pre-modern Baltic world.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148971867","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}

{"title":"COSIGT: population-scalable genotyping of complex loci from low-coverage sequencing data using pangenome graphs.","authors":"Davide Bolognini, Andrea Guarracino, Chiara Paleni, Thomas S Dudley, Licia Iacoviello, Alessandro Raveane, Peter H Sudmant, Erik Garrison, Nicole Soranzo","doi":"10.1186/s13059-026-04242-4","DOIUrl":"10.1186/s13059-026-04242-4","url":null,"abstract":"<p><p>Pangenome graphs capture extensive structural diversity, but resolving complex loci from shallow sequencing remains challenging, particularly when samples are of low quality such as in ancient DNA. We introduce COSIGT (COsine SImilarity-based GenoTyper), which assigns diploid genotypes by matching read-depth distributions to haplotype paths via cosine similarity. Because this metric evaluates relative coverage profiles rather than absolute read counts, COSIGT substantially outperforms existing likelihood-based tools at low coverage (1-2X). We demonstrate scalability to thousands of modern and ancient genomes, enabling robust, population-scale analyses of complex variation directly from low-coverage datasets.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913498","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}

{"title":"Determinants of mutation susceptibility along the genome are largely invariant across human tissues.","authors":"Corinna Lewis Schmalohr, Yashna Paul, Nina Bundschuh, Andreas Beyer","doi":"10.1186/s13059-026-04245-1","DOIUrl":"10.1186/s13059-026-04245-1","url":null,"abstract":"<p><strong>Background: </strong>The propensity for accumulating somatic mutations varies along the genome, which critically influences somatic mosaicism, tumor evolution and the potential role of somatic mutations in the context of age-associated diseases. Genomic factors contributing to the variability of mutation rates have been established, including, for example, distance from the replication origin, chromatin structure and sequence context. However, their relative importance for explaining variable mutation rates along the genome as well as variable mutation rates between tissues remains elusive.</p><p><strong>Results: </strong>Here, we present a modelling strategy that integrates 146 genomic features at different scales to predict susceptibilities for point mutations in 25 human tissues along the genome. These models faithfully predict mutation rates in coding and non-coding parts of the human genome in cancer and healthy tissues, including even unseen tissue types that were not used during the model training. Our work revealed that the dependency of mutation rates on chromatin structure and other genomic features is remarkably invariant across tissues, pointing to fundamental, conserved processes underlying mutagenic processes. Local variability in mutation rates on the scale of a few base pairs is almost exclusively driven by the sequence context, whereas large-scale variability is dominated by chromatin features, gene expression and GC content.</p><p><strong>Conclusions: </strong>Our modelling strategy quantifies the relative contribution of genomic factors to mutation susceptibility, predicting mutational biases at any genomic resolution across human tissues, and provides a basis for better understanding tumor evolution and age-related diseases.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13501610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814595","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}

{"title":"Interpretable distillation reveals that deep learning splicing models suffer from pervasive confounders and blind spots.","authors":"Simon Liu, Wenjing Zhang, Oded Regev","doi":"10.1186/s13059-026-04124-9","DOIUrl":"https://doi.org/10.1186/s13059-026-04124-9","url":null,"abstract":"<p><strong>Background: </strong>Predicting RNA splicing from genomic sequence is a crucial task for understanding gene regulation and interpreting genetic variation. Recent deep learning advancements have led to splicing prediction algorithms that achieve state-of-the-art performance compared to earlier models. However, owing to the limited interpretability of deep learning models, the predictive mechanisms of current splicing models remain poorly understood.</p><p><strong>Results: </strong>Here we develop a framework to explain model prediction logic using interpretable distillation. Applying our framework, we find that RNA splicing prediction models suffer from pervasive confounders and blind spots, leading to poor performance on non-reference sequences. We find that splicing models recognize exons through surprisingly simple additive combinations of sequence motifs, including known splicing regulatory elements. Critically, our analysis also reveals that splicing models exploit genomic confounders unrelated to splicing and fail to adequately capture the effects of RNA structure, leading to systematic prediction errors.</p><p><strong>Conclusions: </strong>Our findings illuminate fundamental limitations of training models on genomic sequences and suggest ways to overcome them.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13495316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799844","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}

{"title":"Benchmarking cell-type deconvolution in cross-platform transcriptomic data.","authors":"Aakanksha Singh, Pinar Cakmak, Jennifer H Lun, Jadranka Macas, Karl H Plate, Yvonne Reiss, Jonathan Schupp, Katharina Imkeller","doi":"10.1186/s13059-026-04222-8","DOIUrl":"10.1186/s13059-026-04222-8","url":null,"abstract":"<p><strong>Background: </strong>Transcriptomic data from diverse measurement technologies are widely used to study tissue heterogeneity. Cell-type deconvolution, which resolves mixed transcriptomic signals into cellular components, is a key analytical approach. However, achieving accurate deconvolution across platforms remains challenging due to platform-specific experimental and technological biases.</p><p><strong>Results: </strong>We systematically benchmarked deconvolution performance using real-world cross-platform datasets and simulated data modeling distinct technological features. SpatialDecon and cell2location demonstrated the most reliable and consistent performance across both simulated and experimental settings across a broad range of technological biases.</p><p><strong>Conclusions: </strong>Our results highlight how the different deconvolution tools are affected by data properties that depend on technological differences between transcriptomic platforms. Moreover, we provide practical guidelines for selecting computational methods dependent on experimental design for robust deconvolution of cross-platform transcriptomic data.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13495209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799924","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}

{"title":"DeMixNB: deconvolution of sparse-count RNA sequencing data for tumor cells using embedded negative binomial distributions.","authors":"Matthew D Montierth, Hao Yan, Liyang Xie, Kinga Nemeth, Xiaoxi Pan, Ruonan Li, Caner Ercan, Peng Yang, Ansam Sinjab, Tieling Zhou, Fuduan Peng, Manisha Singh, Linghua Wang, Scott Kopetz, Humam Kadara, Yinyin Yuan, George A Calin, Wenyi Wang","doi":"10.1186/s13059-026-04234-4","DOIUrl":"https://doi.org/10.1186/s13059-026-04234-4","url":null,"abstract":"<p><p>Estimating tumor-specific transcript proportions from mixed bulk samples has potential to inform novel biology. However, estimation accuracy using existing methods in sparse-count data such as microRNA-seq and spatial transcriptomics has yet to be established. We generate a mixed small RNA benchmark dataset to demonstrate analytical challenges. To resolve them, we develop DeMixNB, a semi-reference-based deconvolution model assuming a sum of negative binomial distributions. Applications to miRNA-seq from 885 patients with breast cancer and 4,709 spatial spots from lung cancer generates clinical and mechanistic insights into tumor cell plasticity. This supports the important utility of DeMixNB to investigate cancer RNomes.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149372026","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":"Pangenome analyses reveal divergence and extensive introgression in two global corn pests.","authors":"Yan Peng, Yubo Zhang, Kaikai Mao, Zhuting Zhang, Lei Zhang, Jingyun Zhu, Xinye Liu, Chongjun Zhao, Wenmin Fan, Alexander Yesaya, Hongru Wang, David G Heckel, Erik B Dopman, Wei Zhang, Yutao Xiao","doi":"10.1186/s13059-026-04216-6","DOIUrl":"10.1186/s13059-026-04216-6","url":null,"abstract":"<p><strong>Background: </strong>Introgression between closely related species can profoundly influence evolutionary trajectories, yet how genomic divergence interacts with introgression, especially in invasive hybridization, remains insufficiently understood. Structural variants (SVs), particularly chromosomal inversions, are key contributors to divergence and may modulate patterns of gene flow. The Asian corn borer (ACB) and European corn borer (ECB), two globally important agricultural pests, provide an excellent system to investigate these processes.</p><p><strong>Results: </strong>We construct a graph-based pangenome from 23 high-quality genome assemblies to characterize genome-wide SVs and introgression between ACB and ECB. We identify over 216,000 SVs, most of which are associated with transposable elements and contribute to interspecific divergence. Population genomic analyses reveal widespread yet asymmetric introgression, predominantly from ACB into ECB populations in China. Introgressed regions are more prevalent in autosomes than in sex chromosomes and are enriched for genes involved in adaptive pathways. In contrast, large inversion regions on the Z chromosome exhibit strong genetic differentiation and reduced introgression, suggesting a role in maintaining species barriers. Notably, a highly divergent region containing the circadian clock gene period (per) shows signatures of selection. Functional validation using CRISPR/Cas9 demonstrates that per significantly influences diapause regulation, linking genomic divergence to adaptive phenotypic variation.</p><p><strong>Conclusions: </strong>Our results demonstrate that introgression and structural variation jointly shape genomic divergence in these species. While introgression facilitates the spread of adaptive variation, chromosomal inversions restrict gene flow and maintain species integrity. This study provides new insights into the evolutionary mechanisms underlying divergence, adaptation, and invasion in major agricultural pests.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13733210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149335758","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}