Genome research最新文献

{"title":"Meso-scale spatial analysis of papilloma formation and clonal expansion in cancerized skin","authors":"Veronica F Busa,Mikaela Behm,Pablo Baeza-Centurion,Jusung Lee,Marie-Luise Koch,Meike Schopp,Daniela Sohn,Stefania Del Prete,Michaela Frye,Angela Goncalves,Duncan Odom","doi":"10.1101/gr.281596.125","DOIUrl":"https://doi.org/10.1101/gr.281596.125","url":null,"abstract":"Somatic driver mutations pervade phenotypically normal epithelia, yet only a minority of clones progress to tumors. To understand how tissue context modulates clonal selection, we apply the classical two-stage DMBA/TPA model of carcinogenesis to mouse back skin. We collect coordinate-registered, meso-scale grids spanning multiple centimeters with 1-mm punches and profile genotype and transcriptome from the same positions (WES, n = 473; bulk RNA-seq, n = 690), complemented by matched single-cell RNA-seq. This paired, spatially resolved design enables direct coupling of clonal selection metrics to local transcriptional programs. All sequenced papillomas ( n = 6) harbor Hras Q61L mutation events, but expected frequency versus observed papillomas indicate that Hras Q61L is necessary but insufficient to drive tumorigenesis within the observation period. To quantify how field-level signaling constrains driver potency, we compute pathway activities and introduce a Laplacian-based \"ruggedness\" score to capture meso-scale heterogeneity. We find that field-level signaling is rewired before visible tumorigenesis: TNF-associated epithelial necroptosis rises broadly across promoted skin, while TGFB activity becomes spatially \"rugged\"with papillomas at local TGFB signaling minima. Selection mapping reveals a context shift: in untreated skin, positive selection corresponds with cell cycle deregulation; in the cancerized field, it correlates with impaired differentiation. Moreover, the coupling between WNT signaling and selection evident in untreated tissue is abrogated for DMBA-initiated mutations. These data show that programs such as differentiation regulation, necroptosis, and TGFB topology reshape the selective landscape, constraining the effective potency of driver mutations and explaining why clones differ in their capacity to expand and progress to tumors.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"9 1","pages":"gr.281596.125"},"PeriodicalIF":7.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast and accurate construction of multiple sequence alignments from protein language embeddings with ARIES","authors":"Minh Hoang,Isabel Armour-Garb,Mona Singh","doi":"10.1101/gr.282254.126","DOIUrl":"https://doi.org/10.1101/gr.282254.126","url":null,"abstract":"Multiple sequence alignment (MSA) is a foundational task in computational biology, underpinning protein structure prediction, evolutionary analysis, and domain annotation. Traditional MSA algorithms rely on pairwise amino acid substitution matrices derived from conserved protein families. While effective for aligning closely related sequences, these scoring schemes struggle in the low-identity \"twilight zone\". Here, we present a new approach for constructing MSAs leveraging amino acid embeddings generated by protein language models (PLMs), which capture rich evolutionary and contextual information from massive and diverse sequence datasets. We introduce a windowed reciprocal-weighted embedding similarity metric that is surprisingly effective in identifying corresponding amino acids across sequences. Building on this metric, we develop ARIES (Alignment via Reciprocal Embedding Similarity), an algorithm that constructs a PLM-generated template embedding and aligns each sequence to this template via dynamic time warping in order to build a global MSA. Across diverse benchmark datasets, ARIES achieves higher accuracies than existing state-of-the-art approaches, especially in low-identity regimes where traditional methods degrade, while scaling almost linearly with the number of sequences to be aligned. Together, these results provide the first large-scale demonstration of the power of PLMs for accurate and scalable MSA construction across protein families of varying sizes and levels of similarity, highlighting the potential of PLMs to transform comparative sequence analysis.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"37 1","pages":"gr.282254.126"},"PeriodicalIF":7.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fetal signatures in the 3D genome of iPSC-derived neurons and their implications for disease modeling.","authors":"Diana R Zagirova, Anna D Kononkova, Kirill V Morozov, Maria N Molodova, Nikita S Vaulin, Anastasiia V Dudkovskaia, Polina I Dozorova, Olga I Efimova, Anna V Tvorogova, Kirill A Ulianov, Philipp E Khaitovich, Sergey V Razin, Maria A Lagarkova, Sergey V Ulianov, Ekaterina E Khrameeva","doi":"10.1101/gr.281488.125","DOIUrl":"10.1101/gr.281488.125","url":null,"abstract":"<p><p>Induced pluripotent stem cells (iPSCs) have revolutionized neuroscience, providing an approach to generate patient-specific neurons for modeling of neurological diseases. However, it remains unclear how closely iPSC-derived neurons replicate the chromatin architecture of authentic brain neurons. Here, we uniformly processed newly generated Hi-C data from iPSC-derived neurons and neurons isolated from the human postmortem brain, together with previously published data sets comprising 228 human and 89 mouse Hi-C and snm3C-seq samples from different cell subtypes. These data were merged into 96 high-coverage contact maps used to examine chromatin features ranging from chromatin compartments and topologically associating domains (TADs) to chromatin loops, Polycomb-mediated contacts, and frequently interacting regions (FIREs). We find that iPSC-derived neurons largely retain the chromatin state of undifferentiated cells and resemble fetal rather than mature neurons. iPSC-derived neurons exhibit unusually strong compartmentalization, an enrichment of developmental genes at TAD borders, and a marked reduction of long-range repressive Polycomb-mediated contacts that typically silence early fetal programs. Although immature, iPSC-derived neurons offer advantages for modeling interactions between disease-associated SNPs and target genes, as many psychiatric disorders have neurodevelopmental origins. Integrating iPSC-derived and postmortem neuronal data sets therefore provides complementary insights into the chromatin landscape underlying disease-associated interactions. Our study offers a valuable Hi-C resource for the community and provides a detailed comparison of chromatin architecture throughout neuronal maturation, underscoring its importance for validating neuronal models and providing a robust framework for future studies.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1977-1996"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148669296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT.","authors":"Ziwei Chen, Bingwei Zhang, Linrui Tang, Fuzhou Gong, Lin Wan, Liang Ma","doi":"10.1101/gr.281460.125","DOIUrl":"10.1101/gr.281460.125","url":null,"abstract":"<p><p>The advent of single-cell lineage-tracing technologies has enabled the simultaneous profiling of gene expression and lineage barcodes. However, accurate, high-resolution reconstruction of cell lineage trees remains challenging because most existing approaches treat these modalities separately and therefore fail to fully exploit their complementary information. Here we present BiLinT, a Bayesian framework that jointly models multimodal single-cell lineage-tracing data for lineage tree reconstruction. BiLinT integrates barcode evolution (a continuous-time Markov chain) with gene expression dynamics (an Ornstein-Uhlenbeck process) within a unified probabilistic model. Across synthetic and real data sets, BiLinT provides accurate lineage-tree reconstruction and reveals differentiation-associated clonal structure and developmental fate biases.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"2053-2067"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148792095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Massively parallel characterization of adolescent idiopathic scoliosis risk variants.","authors":"Darius Ramkhalawan, Justin Koesterich, Fahim Rejanur Tasin, Carlos Cuna, Anat Kreimer, Nadja Makki","doi":"10.1101/gr.281888.126","DOIUrl":"10.1101/gr.281888.126","url":null,"abstract":"<p><p>Adolescent idiopathic scoliosis (AIS) is a common pediatric musculoskeletal disorder characterized by lateral spinal curvature, often leading to chronic pain and deformity. Although a significant genetic component to AIS is recognized, the functional impact of most associated genetic variants, particularly those in noncoding regions, remains largely unknown. Using massively parallel reporter assays, we characterize 1664 variant positions in linkage disequilibrium with 26 AIS lead variants identified by genome-wide association studies (GWASs) in chondrocytes, a major cell type implicated in AIS pathogenesis. Using a library of 7173 candidate regulatory sequences, we compare the 1664 reference alleles against 4708 alternate alleles in two human chondrocyte cell lines (TC28a2 and SW1353). Our analysis identifies 92 variants that exhibit significant differential regulatory activity between their reference and alternate alleles, 79 of which are predicted to disrupt transcription factor binding sites, often correlating with their observed regulatory effect. Notably, we validate rs9496392, a single-nucleotide variant near the <i>ADGRG6</i> locus, which shows consistent differential regulatory activity in both cell lines. <i>ADGRG6</i> is a key regulator of cartilage homeostasis, and its cartilage-specific knockout in mice results in a scoliosis-like phenotype. The AIS risk allele of rs9496392 (T) is predicted to strongly disrupt several TFBSs, including SP1. This study provides a foundational catalog of functional AIS-associated regulatory variants active in chondrocytes, offering crucial insights into the perturbed gene regulatory networks in AIS. These findings lay the groundwork for identifying biomarkers and potential therapeutic targets for this complex childhood disease.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1966-1976"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13629691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148792099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments.","authors":"Budha Chatterjee, Katrina Gorga, Carly Blair, Yuko Ohta, Michelle Radov, Elizabeth M Hill, Christopher T Boughter, Martin Meier-Schellersheim, Nevil J Singh","doi":"10.1101/gr.281624.125","DOIUrl":"10.1101/gr.281624.125","url":null,"abstract":"<p><p>Minimizing experimental noise is integral to robust data generation in single-cell omics. The current standard for avoiding batch effects during sample processing is barcode- or hashtag-assisted combining of different experimental treatments into one pool, allowing all samples to be subject to the technical protocols uniformly. The final data points for each treatment group are then computationally separated based on the original hashtag labels. Clearly, whereas hashtagging all groups and pooling them in a single well is expected to minimize batch effects, the procedure can also lead to a loss of cells that cannot be confidently decoded during the computational demultiplexing step. Here, we examine four alternate experimental designs, namely, compound, reference, chain, and confounded, that could be used instead of a single-pool approach and quantify the batch effects as well as cell loss in each case. We find a linear relationship-the percentage of cells lost is double the number of hashtags used in the experiment. We use these analyses to identify experimental designs that can successfully mitigate batch effects while minimizing multiplexing, hence the cell loss, in each well. Although a reference design offers the best overall performance, this study can help individual investigators choose particular approaches that are best suited for their biological questions.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"2027-2036"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13629688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing intra- and intertumor heterogeneity in ovarian high-grade serous carcinoma subtypes using single-cell and spatial transcriptomics.","authors":"Weishan Li, Laurie Grieshober, Jason Gertz, Adriana Ivich, Jennifer A Doherty, Casey S Greene, Stephanie C Hicks","doi":"10.1101/gr.281433.125","DOIUrl":"10.1101/gr.281433.125","url":null,"abstract":"<p><p>Ovarian high-grade serous carcinoma (HGSC) is an aggressive ovarian cancer with a heterogeneous tumor microenvironment (TME). Advances in single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics have enabled the study of complex TME. This study explores connections between molecular subtypes described from bulk transcriptomes and spatial domains characterized by distinct gene expression in HGSC and their variability between patients. We quantify both intra- and intertumor heterogeneity across 2D space and identify differing spatial patterns of gene expression pertaining to immune pathways and vasculature development. Functional characterization of tumor spaces reveals potentially shared cell states across molecular subtypes, whereas correlation analysis underscores subtype-specific spatial anticolocalization between spots exhibiting antigen-presenting functions and B cell-mediated immunity. Lastly, we perform spatially aware cell-cell communication analysis on the spatial samples and identify a molecular subtype specific difference in total signaling activity and heterogeneity in midkine signaling between the differentiated subtypes. Our results suggest that generating multiple tissue slices per patient might be necessary to enable comprehensive characterization of HGSC spatial transcriptomes.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"2011-2026"},"PeriodicalIF":6.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13629689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148792097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Viral haplotype reconstruction from long reads with virCHap","authors":"Yun Gao,Bingqiang Liu,Guojun Li,Ting Yu","doi":"10.1101/gr.281975.126","DOIUrl":"https://doi.org/10.1101/gr.281975.126","url":null,"abstract":"Resolving genomes at the haplotype level for viral populations is crucial for understanding the prevalence of viral diseases and for the development of effective therapeutic treatments. However, viral haplotype reconstruction still presents challenges, such as an unknown number of strains, high inter-strain similarity, repetitive regions, and difficulties in abundance estimation. Here, we develop virCHap, a new reference-based haplotype phasing algorithm for viruses, which applies graph partitioning followed by iteratively quantifiable cluster merging on long-read sequencing data. Benchmarking on simulated and real datasets demonstrates that virCHap outperforms current tools in terms of recall, accurate abundance estimates and read clustering accuracy. On the simulated large-genome VZV experiment, virCHap has a 96% recall, 13.6% higher than the second-best method, and has the most accurate abundance estimates. On a real 5-strain PVY dataset, virCHap has a precision exceeding 94.4%, a recall of over 97%, and a read clustering accuracy of 93.4%, outperforming the second-best method by 33%. On a real 6-strain SARS-CoV-2 dataset, virCHap achieves &gt;96.7% accuracy, and the most accurate abundance estimates within the spike gene.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"17 1","pages":"gr.281975.126"},"PeriodicalIF":7.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149020799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilevel ensemble for genome-wide prediction of cell-specific G-quadruplexes utilizing transformed sequences and enhanced chromatin accessibility with G4Beacon2","authors":"Tiantong Tao,Rongxin Zhang,Huiling Shu,Yuqing Ma,Zhuofan Zhang,Jing Tu,Xiao Sun","doi":"10.1101/gr.280606.125","DOIUrl":"https://doi.org/10.1101/gr.280606.125","url":null,"abstract":"G-quadruplexes (G4) are prevalent non-B DNA structures playing crucial biological roles in cells. Although experimental technologies for G4 identification in vitro and in vivo are advancing, computational prediction methods are increasingly preferred for their efficiency, convenience, and cost effectiveness. However, existing tools primarily perform in vitro G4 predictions that lack cell-specific information and are often non-genome-wide, with high-performance genome-wide in vivo cell-specific G4 prediction models still lacking. Here, we present G4Beacon2, a genome-wide cell-specific G4 prediction model based on multilevel ensemble learning. G4Beacon2 leverages DNABERT2 to capture semantic information from DNA sequences, normalizes chromatin accessibility data using Z -score, and establishes a three-level ensemble framework to achieve accurate in vivo G4 predictions across the genome. Across the evaluated intra-cell-line and cross-cell-line tests, G4Beacon2 shows consistently favorable performance. Exploratory analyses further indicate that mouse-derived training information may contribute to human prediction under the currently available data sets. Moreover, the fusion model, integrating high-quality multisource data, improves the stability and performance of human cell-specific G4 prediction. In summary, G4Beacon2 presents a novel solution for human genome-wide in vivo G4 prediction by integrating multisource data, offering a user-friendly and advanced prediction tool for researchers.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"2 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149020800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep genomic models of allele-specific measurements.","authors":"Xinming Tu, Alexander Sasse, Kaitavjeet Chowdhary, Anna Spiro, Liang Yang, Maria Chikina, Christophe Benoist, Sara Mostafavi","doi":"10.1101/gr.282189.126","DOIUrl":"https://doi.org/10.1101/gr.282189.126","url":null,"abstract":"<p><p>Allele-specific quantification of sequencing data allows for a systematic investigation of how DNA sequence variations influence <i>cis</i> gene regulation. Current methods for analyzing allele-specific measurements for causal analysis rely on statistical associations between genetic variation across individuals and allelic imbalance. Instead, we propose DeepAllele, a novel deep learning sequence-to-function model using paired allele-specific input, designed to learn sequence features that predict subtle changes in gene regulation between alleles. Our approach is suited for datasets with unambiguous phasing, such as F1 hybrids and other controlled genetic crosses, or long-read sequencing technologies used in Fiber-seq, in which reads can be assigned to complete allele sequences. We apply our framework to allele-specific measurements in immune cells from F1 hybrid mice, and show that the model's additionally learned <i>cis</i>-regulatory grammar aligns with known biological mechanisms across a significantly larger number of genomic regions compared to baseline models. In summary, our work presents a computational framework to leverage genetic variation to uncover functionally-relevant regulatory motifs, enhancing discovery in genomics.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149016347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}