Genome Biology最新文献

{"title":"A LINE-1 insertion upstream of FOXP2 promotes neuronal differentiation during primate evolution.","authors":"Jinhao Liu, Zihang Yin, Yonglin Peng, Shuang Cui, Bo Shi, Xinrui Jiang, Ziyi Yang, Sijie Gu, Yude Lin, Lingfeng Xu, Zhen Xu, Xuankai Wang, Tienan Chen, Wei Zhang, Shaojiao Wang, Zhiwei Xiao, Zhibo Huang, Rujiang Zhou, Zhengju Yao, Xiaodong Zhao, Ya Guo, Shuhua Xu, Weidong Li, Xizhi Guo","doi":"10.1186/s13059-026-04098-8","DOIUrl":"https://doi.org/10.1186/s13059-026-04098-8","url":null,"abstract":"<p><strong>Background: </strong>The evolutionary trajectory of FOXP2, a key regulator of language acquisition and higher-order cognitive function, remains incompletely understood. Retrotransposons such as long interspersed element-1 (LINE-1) are recognized as important contributors to genome regulation, influencing embryonic and neuronal developmental programs. However, the potential contribution of LINE-1 to shaping the cis-regulatory landscape of FOXP2 remains unclear.</p><p><strong>Results: </strong>Through comparative evolutionary genomic analyses, we identify a locus positioned 342 kb upstream of the FOXP2 transcription start site that has experienced multiple, successive LINE-1 retrotransposition events during primate evolution. This process produces a 10.6 kb composite LINE-1 element present in both human and chimpanzee genomes, which we designate FOXP2-342L1. This element exhibits moderate signals of recent positive selection in modern human populations and resides near a topologically associating domain boundary within the FOXP2 regulatory landscape. Integrative 3D genome mapping in human and marmoset neural stem cell models demonstrates that FOXP2-342L1 mediates long range chromatin interactions across the FOXP2 cis regulatory domain. Notably, FOXP2-342L1 functions as an evolutionary \"hub\" that accumulates successive LINE-1 insertions during primate evolution, thereby regulating FOXP2 activity and promoting cortical neuron differentiation.</p><p><strong>Conclusion: </strong>Our findings uncover a LINE-1 driven reconfiguration of 3D chromatin architecture within the FOXP2 regulatory landscape and reveal a primate specific model of neuronal development shaped by retrotransposon activity during primate evolution.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":" ","pages":""},"PeriodicalIF":12.3,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822496","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":"Jointly-hic: joint decomposition of contact frequency maps captures salient features of genome architecture across tissues and development.","authors":"Thomas Reimonn, Vedat O Yilmaz, Hoang Tran, Garrett Ng, Derek Liu, Nezar Abdennur","doi":"10.1186/s13059-026-04067-1","DOIUrl":"https://doi.org/10.1186/s13059-026-04067-1","url":null,"abstract":"<p><p>Chromosome conformation capture methods, such as Hi-C, have been used to profile chromosome organization from a wide variety of biosamples and conditions; however, existing methods for analyzing such datasets have disadvantages for large-scale integrative studies of long-range interactions. To address this shortcoming, we introduce an analytical framework, jointly-hic, that computes harmonized projections across arbitrarily many contact frequency matrices, suitable for integrative studies of compartmentalization and long-range interactions. Our approach produces robust and directly comparable first and higher-order principal component scores that collectively capture biologically meaningful information beyond traditional A/B compartment scores.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":" ","pages":""},"PeriodicalIF":12.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822442","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":"EvoRMD: integrating biological context and evolutionary RNA language models for interpretable prediction of RNA modifications.","authors":"Bo Wang, Hao Zhang, Taoyong Cui, Xiaoyu Wang, Jiangning Song, Hao Xu","doi":"10.1186/s13059-026-04097-9","DOIUrl":"https://doi.org/10.1186/s13059-026-04097-9","url":null,"abstract":"<p><p>RNA modifications regulate post transcriptional gene expression, yet most computational methods model each modification independently and overlook competition among modification types at a single site. We present EvoRMD, a biologically contextualized and interpretable framework for RNA modification prediction. EvoRMD combines RNA language model embeddings with structured metadata, including species, organ, cell type, and subcellular localization, and uses attention to identify informative sequence positions. A shared multiclass classifier produces context conditioned predictions across 11 modification types. EvoRMD achieves strong performance and provides interpretable insights through attention patterns and motif analyses, supporting biologically grounded prioritization of candidate RNA modifications.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":" ","pages":""},"PeriodicalIF":12.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822512","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":"Ubiquitous impact of natural selection on nucleotide diversity in 178 species of primates.","authors":"Bjarke Meyer Pedersen, Juraj Bergman, Vasili Pankratov, Mikkel Heide Schierup","doi":"10.1186/s13059-026-04093-z","DOIUrl":"https://doi.org/10.1186/s13059-026-04093-z","url":null,"abstract":"<p><strong>Background: </strong>Genetic diversity shows great variation across the genome. To investigate the major evolutionary forces causing this variation in primates, we analyse genomic diversity in 178 species in coordinates of the human genome reference to enable direct comparisons.</p><p><strong>Results: </strong>Across species, we find that genetic diversity decreases with declining recombination rates, with an average difference of 40% between high and low recombination regions (ranging from 0.76% to 79%). The magnitude of this effect scales with the estimated effective population size (Ne), being more pronounced in species with larger Ne. Using forward-in-time simulations, we show that these patterns are consistent with the expectations under both background selection and genetic hitchhiking and cannot arise under neutrality. We observe a skew in the site frequency spectrum toward rare variants in low-recombination regions in all species where this could be assessed, which our simulations show is consistent with both forms of linked selection.</p><p><strong>Conclusions: </strong>Understanding the importance of linked selection across the genome and between species has important implications for how genetic variation is shaped and the assumptions we use to infer population structure, demography, and selection. Our results show that linked selection is pervasive across primate genomes and leaves very few regions to evolve neutrally, even in regions of high recombination.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":" ","pages":""},"PeriodicalIF":12.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822461","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":"The SMARCA4 subunit of the SWI/SNF complex prevents genome instability at G quadruplexes.","authors":"Alison Harrod, Hugang Feng, Nagham Ghaddar, Federica Schiavoni, Karen A Lane, Lillian Wu, Pedro Zuazua-Villar, Zuzanna Kozik, Felix M Dobbs, Patrick van Eijk, Gene Ching Chiek Koh, Navita Somaiah, Jyoti Choudhary, Serena Nik-Zainal, Simon H Reed, Jessica A Downs","doi":"10.1186/s13059-026-04080-4","DOIUrl":"https://doi.org/10.1186/s13059-026-04080-4","url":null,"abstract":"<p><strong>Background: </strong>G-quadruplex (G4) structures are secondary structures that can form in guanine-rich single stranded DNA sequences. These play important roles in biological processes such as regulation of gene expression but can also pose challenges to DNA replication and lead to genome instability. The SMARCA4 (BRG1) subunit of the SWI/SNF chromatin remodelling complexes has been identified as a G4 binding protein, and evidence suggests that this interaction can promote SWI/SNF-dependent gene expression. SMARCA4 is frequently misregulated in cancer, where genome instability is common, but whether there is an impact of SMARCA4 on G4 stability was not known.</p><p><strong>Results: </strong>Here, we show that SMARCA4 prevents genome instability at G4s. Mapping unrepaired DNA breaks reveals that these preferentially co-localise with G4 forming structures in SMARCA4-deficient cells. Moreover, using whole genome sequencing approaches, we find that misrepair events in SMARCA4-deficient cells are more likely to map to G4 forming sequences. Consistent with this, SMARCA4-deficient cells show sensitivity to the G4 ligand pyridostatin and defective pyridostatin-induced DNA damage responses. Notably, analysis of cancer patient data shows that SMARCA4-deficient samples have an increased proportion of G4-associated mutations when compared with SMARCA4-proficient samples.</p><p><strong>Conclusions: </strong>These findings suggest that SMARCA4 plays a crucial role in maintaining stability at G4 motifs. This insight provides valuable information about the functional significance of G4 structures and their interaction with SMARCA4, particularly in the context of cancer.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":" ","pages":""},"PeriodicalIF":12.3,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724177","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 recurrent sequencing artifact on Illumina sequencers with two-color fluorescent dye chemistry and its impact on somatic variant detection.","authors":"Beverly J Fu, Vinayak V Viswanadham, Dominika Maziec, Hu Jin, Peter J Park","doi":"10.1186/s13059-026-04081-3","DOIUrl":"10.1186/s13059-026-04081-3","url":null,"abstract":"<p><strong>Background: </strong>The sequencing-by-synthesis technology by Illumina, Inc. enables efficient and scalable readouts of mutations from genomic data. To enhance sequencing speed and efficiency, Illumina has shifted from the four-color base calling chemistry of the HiSeq series to a two-color fluorescent dye chemistry in the NovaSeq series. Benchmarking sequencing artifacts due to biases in the newer chemistry is important to evaluate the quality of identified mutations.</p><p><strong>Results: </strong>We re-analyze a series of whole-genome sequencing experiments in which the same samples were sequenced on the NovaSeq 6000 (two-color) and HiSeq X10 (four-color) platforms by independent groups. In several samples, we observe a higher frequency of T-to-G and A-to-C putative substitutions (\"T > G\") at the read level for NovaSeq 6000 versus HiSeq X10. As the per-base error rate is still low, the artifactual substitutions have a negligible effect in identifying germline or high variant allele frequency (VAF) somatic mutations. However, such errors can confound the detection of low-VAF somatic variants in high-depth sequencing samples, particularly in studies of mosaic mutations in normal tissues, where variants have low read support and are called without a matched normal. The artifactual T > G variant calls disproportionately occur at NT[TG] trinucleotides, and we leverage this observation to bioinformatically reduce the T > G excess in somatic mutation callsets.</p><p><strong>Conclusions: </strong>We identified a recurrent artifact specific to the Illumina two-color chemistry platform on the NovaSeq 6000 with the potential to contaminate low-VAF somatic mutation calls. Thus, an unexpected enrichment of T > G mutations in mosaicism studies warrants caution.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":" ","pages":""},"PeriodicalIF":12.3,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718653","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 sequence knowledge-guided deep learning method for single-cell multi-omics translation.","authors":"Mengyuan Zhao, Jiawei Li, Yanlin Jiang, Jiahui Yan, Xinyue Tang, Cheng Liang, Jijun Tang, Fei Guo","doi":"10.1186/s13059-026-04070-6","DOIUrl":"https://doi.org/10.1186/s13059-026-04070-6","url":null,"abstract":"<p><strong>Background: </strong>Analysis of proteins is key to understanding biological processes, disease pathogenesis, and advancing therapeutic development. However, proteome profiling remains significantly limited when compared to the exponential growth of single-cell RNA sequencing data, owing to technical challenges and prohibitive costs associated with large-scale protein detection. Recent advancements in multi-omics technologies have established essential connections between transcriptome and proteome layers, facilitating innovative computational approaches for predicting protein abundance based on transcriptome data.</p><p><strong>Results: </strong>Here, we present scProTrans, an interpretable deep learning framework that synergizes sequence knowledge and multi-omics integration to achieve cross-omics translation in single-cell resolution. Our framework deciphers gene-protein associations through three innovative components: Firstly, a hierarchical attention mechanism that aligns gene/protein sequences with cellular contexts using CITE-seq training data; secondly a bidirectional encoder architecture implementing sequence-to-embedding-to-profile learning for modality translation; finally cell-specific associations capturing dynamic gene-protein interplay across heterogeneous cell populations. Extensive evaluations across 17 multi-omics datasets demonstrate that scProTrans surpasses state-of-the-art methods in single-cell protein abundance translation and enhances downstream analyses, including cell clustering, subtype identification, and biomarker discovery. scProTrans improves protein prediction accuracy and preserves low-abundance protein signals, two significant aspects of single-cell protein abundance translation. Additionally, scProTrans is extended to tri-omics scenarios (ATAC-RNA-protein) via modular encoder refactoring, achieving cross-modal prediction concordance comparable to experimental replication.</p><p><strong>Conclusions: </strong>This work advances multi-omics integration by establishing a sequence-aware paradigm for cross-modal translation, overcoming key limitations in proteome data acquisition. This modular architecture and its zero-shot capability make it a versatile platform for emerging multi-modal single-cell technologies.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":" ","pages":""},"PeriodicalIF":12.3,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677292","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":"Proteogenomic decoding of chemotherapy resistance in patients with triple-negative breast cancer.","authors":"Dong Ki Lee, Min Hwan Kim, Yumi Hwang, Seul-Gi Kim, Won-Ji Ryu, Geon-Uk Kim, Hyun Myoung Yun, Shinyoung Park, Jeong Dong Lee, Hyun Ju Han, Gun Min Kim, Kyung-Hee Kim, Jong Bae Park, Min Jung Kim, Ja Seung Koo, Jee Ye Kim, Hyung Seok Park, Seung Il Kim, Heon Yung Gee, Seho Park, Joohyuk Sohn","doi":"10.1186/s13059-026-04053-7","DOIUrl":"10.1186/s13059-026-04053-7","url":null,"abstract":"<p><strong>Background: </strong>The clinical utility of integrated proteogenomic biomarkers for predicting chemotherapy response in triple-negative breast cancer remains underexplored. We prospectively analyzed paired baseline and post-treatment tumor samples from 50 patients with stage II-III TNBC treated with anthracycline- and taxane-based neoadjuvant chemotherapy, integrating whole-exome sequencing, RNA sequencing, global proteomics, and phosphoproteomics.</p><p><strong>Results: </strong>Non-negative matrix factorization clustering identifies five proteogenomic subtypes. The immune-enriched subtype demonstrates the highest pathologic complete response rate (55.6%), whereas no pathologic complete response was observed in the xenobiotic metabolism or epithelial-mesenchymal transition subtypes. Immune-related pathways are enriched in tumors with pathologic complete response, while epithelial-mesenchymal transition pathways are enriched in non-pathologic complete response tumors. The estrogen response pathway is selectively enriched in non-pathologic complete response tumors at the proteomic level and inversely correlated with immune activation. Post-translational modification and in vitro analyses suggest estrogen-linked GRK2 activation contributes to chemotherapy resistance. ITGB8 copy number loss is associated with higher pathologic complete response rates and immune activation, while non-pathologic complete response tumors of the immunomodulatory subtype show increased expression of AKR1C2 and ABCA13. Comparison of baseline and post-treatment tumors reveals AURKB pathway activation in residual disease, with Aurora B kinase inhibition synergizing with paclitaxel. A predictive model incorporating these biomarkers outperforms RNA-based models in predicting response.</p><p><strong>Conclusion: </strong>Integrative proteogenomic profiling enables robust prediction of chemotherapy resistance in triple-negative breast cancer and identifies actionable biomarkers providing a framework for advancing personalized therapeutic strategies.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13077823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677644","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":"DNA damaging properties of G-quadruplex ligand QN-302 are potentiated by the DNA repair inhibitor Olaparib and mitigated by the molecular helicase PhpC.","authors":"Garance Psalmon, Angélique Pipier, Manon Barbotte, Robert H E Hudson, Stephen Neidle, David Monchaud","doi":"10.1186/s13059-026-04035-9","DOIUrl":"https://doi.org/10.1186/s13059-026-04035-9","url":null,"abstract":"<p><strong>Background: </strong>QN-302 is a tetra-substituted naphthalene diimide (NDI) designed to bind G-quadruplex (G4) DNA and is in a phase 1 clinical trial for pancreatic ductal adenocarcinoma (PDAC) and other solid tumors. The mechanistic basis of its anticancer activity remains to be fully understood.</p><p><strong>Results: </strong>Using in vitro fluorescence quenching and FRET-melting assays together with cell-based in situ click imaging and γH2AX immunodetection, we show that QN-302 engages cellular G4s and triggers G4-associated DNA damage in human cancer cells. In HeLa cells, short exposures increase G4 foci and double strand break (DSB) markers, whereas pre-incubation with the G4-disruptor PhpC reduces both, supporting a G4-dependent mechanism. In PDAC (MIA PaCa-2) cells, QN-302's antiproliferative activity synergizes with the PARP1 inhibitor Olaparib. Combination treatment produces supra-additive increases in γH2AX foci and yields Bliss synergy across multiple dose pairs, consistent with chemically induced synthetic lethality.</p><p><strong>Conclusions: </strong>QN-302 induces G4-mediated DNA damage that underpins potent antiproliferative effects. Inhibition of DNA repair with Olaparib augments this activity, whereas pharmacological G4 destabilization with PhpC attenuates it. These findings support a defined mechanism of action for QN-302 and provide a rationale for clinical combination strategies in PDAC and potentially other cancer.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":" ","pages":""},"PeriodicalIF":12.3,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147663460","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":"Experimental and computational methods for allelic imbalance analysis from single-nucleus RNA-seq data.","authors":"Sean K Simmons, Xian Adiconis, Nathan Haywood, Jacob Parker, Zechuan Lin, Zhixiang Liao, Idil Tuncali, Asa Shin, Karthik Jagadeesh, Kirk Gosik, Michael Gatzen, Jonathan T Smith, Daniel N El Kodsi, Yuliya Kuras, Clare Baecher-Allan, Geidy E Serrano, Thomas G Beach, Kiran Garimella, Aziz M Al'Khafaji, Orit Rozenblatt-Rosen, Aviv Regev, Xianjun Dong, Clemens R Scherzer, Joshua Z Levin","doi":"10.1186/s13059-026-04062-6","DOIUrl":"10.1186/s13059-026-04062-6","url":null,"abstract":"<p><p>Combining allele-specific expression (ASE) analysis with single-cell RNA-seq can elucidate how genomic variation affects RNA expression at the single-cell level. We explore how experimental and computational choices impact the power of ASE-based methods and develop a suite of single-cell ASE computational tools. With single-nucleus RNA-Seq, we extract more ASE information from reads in intronic than exonic regions. We show how read length can increase power and that hybrid selection improves power to detect ASE in targeted genes. We apply our methods to a Parkinson's disease cohort and show that ASE analysis has more power than eQTL analysis.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":" ","pages":""},"PeriodicalIF":12.3,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147663457","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}