Genome research最新文献

{"title":"De novo structural variants in autism spectrum disorder disrupt distal regulatory interactions of neuronal genes.","authors":"Ketrin Gjoni, Xingjie Ren, Amanda Everitt, Yin Shen, Katherine S Pollard","doi":"10.1101/gr.280394.124","DOIUrl":"10.1101/gr.280394.124","url":null,"abstract":"<p><p>Three-dimensional genome organization plays a critical role in gene regulation, and disruptions can lead to developmental disorders by altering the contact between genes and their distal regulatory elements. Structural variants (SVs) can disturb local genome organization, such as the merging of topologically associating domains upon boundary deletion. Testing large numbers of SVs experimentally for their effects on chromatin structure and gene expression is time and cost prohibitive. To address this, we propose a computational approach to predict SV impacts on genome folding, which can help prioritize causal hypotheses for functional testing. We develop a weighted scoring method that measures chromatin contact changes specifically affecting regions of interest, such as regulatory elements or promoters, and implement it in the SuPreMo-Akita software. With this tool, we rank hundreds of de novo SVs (dnSVs) from autism spectrum disorder (ASD) individuals and their unaffected siblings based on predicted disruptions to nearby neuronal regulatory interactions. This reveals that putative <i>cis</i>-regulatory element interactions (CREints) are more disrupted by dnSVs from ASD probands versus unaffected siblings. We prioritize candidate variants that disrupt ASD CREints and validate our top-ranked locus using isogenic excitatory neurons with and without the dnSV, confirming accurate predictions of disrupted chromatin contacts. This study suggests that disrupted genome folding is a potential genetic mechanism in a subset of ASD cases and provides a general strategy for prioritizing variants predicted to disrupt regulatory interactions across tissues.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"36 9","pages":"1825-1835"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148873896","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":"Fast and memory efficient partial order alignment with minipoa.","authors":"Haodong Liu, Pinglu Zhang, Yanming Wei, Qinzhong Tian, Yixiao Zhai, Quan Zou, Mengting Niu","doi":"10.1101/gr.282046.126","DOIUrl":"10.1101/gr.282046.126","url":null,"abstract":"<p><p>Partial order alignment (POA) has emerged as a fundamental component in long-read error correction, assembly and pangenomics. However, conventional POA algorithms are limited by high time and memory requirements, making them inefficient for large-scale data sets. Here, we present minipoa, a fast and memory-efficient POA tool that incorporates seed-chain-align heuristics, adaptive or static banding strategies, and single-instruction multiple-data optimizations. Minipoa achieves up to a fivefold speedup over abPOA, reduces memory usage by up to 16-fold, and improves correction accuracy, while maintaining strong performance on both Pacific Biosciences and Oxford Nanopore Technologies simulated data sets, and can be readily integrated into existing long-read error correction and assembly workflows. In multiple sequence alignment data sets, minipoa demonstrates highly competitive computational efficiency and alignment accuracy, achieving total column scores up to 2.5-fold higher compared with MAFFT in low-similarity scenarios. Moreover, minipoa enables multiple sequence alignment of megabase-long genomes and million-sequence data sets, demonstrated by 342 <i>Mycobacterium tuberculosis</i> sequences and 1 million SARS-CoV-2 sequences, respectively. Collectively, minipoa is well positioned to become a cornerstone in the era of large-scale pangenomics.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1849-1862"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148584107","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":"Convergence and conflict among telomere-specialized transposons across 60 million years of Drosophilid evolution.","authors":"Jae Hak Son, Matthew A Lawlor, Mahek Virani, Weihuan Cao, Mia T Levine, Christopher E Ellison","doi":"10.1101/gr.281112.125","DOIUrl":"10.1101/gr.281112.125","url":null,"abstract":"<p><p>The <i>Drosophila</i> telomere is one of the best-studied examples of active transposable elements (TEs) benefiting, rather than harming, the host genome. All <i>Drosophila</i> species lack telomerase, and most species instead have telomeres composed of head-to-tail arrays of specialized retrotransposons. These TEs ostensibly act as mutualists by elongating chromosome ends, but evidence from species closely related to <i>Drosophila melanogaster</i> suggests that telomeric transposons may also antagonize their host genome. Importantly, the limited number of <i>Drosophila</i> species characterized thus far has precluded our ability to delineate idiosyncrasies from universal evolutionary forces and genetic mechanisms that shape the history of these TEs. Here, we have surveyed long-read genome assemblies of more than 100 species of <i>Drosophila</i>, identifying a total of 396 telomeric TE families. Our findings show that these telomere-specialized elements evolve dynamically and also undergo striking convergent evolution: The complete loss of telomeric TEs has occurred repeatedly across the genus, whereas individual telomeric TE lineages have repeatedly lost one of their two protein-coding genes. These elements have also repeatedly undergone horizontal transfer between distantly related <i>Drosophila</i> lineages and have repeatedly captured host gene fragments that promote their selfish suppression of host TE-silencing systems. Furthermore, telomere specialization itself appears to have evolved convergently, as some nontelomeric families have gained the ability to target their insertions to telomeres. These results provide unprecedented resolution into the evolution of these unusual TEs and highlight several novel mechanisms by which they evolve in conflict both with each other and their host genome despite the essential telomere function they provide.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1800-1817"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534243/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148367391","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":"High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV.","authors":"Herui Liao, Arolyn Conwill, Ian Light-Maka, Martin Fenk, Alyssa H Mitchell, Evan B Qu, Paul Torrillo, Jacob S Baker, Lilly R Bartsch, Felix M Key, Tami D Lieberman","doi":"10.1101/gr.281341.125","DOIUrl":"10.1101/gr.281341.125","url":null,"abstract":"<p><p>Accurate detection of mutations within bacterial species is critical for fundamental studies of microbial evolution, reconstruction of transmission events, and identification of antimicrobial resistance mutations. Although many tools have been developed to identify single-nucleotide variants (SNVs) from whole-genome sequencing, they often suffer from high false-positive rates owing to the complexity of bacterial genomes and the need for different filtering cutoffs across sample types and sequencing depths. As data sets increase in size, the manual filtering required for high accuracy presents a significant obstacle. Here, we present AccuSNV, a novel deep learning-based tool for high-precision and automated bacterial SNV calling. Unlike traditional methods that process one sample at a time, AccuSNV leverages a convolutional neural network (CNN) that integrates alignment information across multiple samples, enhancing precision through learned across-sample patterns. We evaluate AccuSNV against seven popular SNV-calling tools using simulated data from six bacterial species with varied sequencing depths, numbers of isolates, mutations, and divergence levels. To further validate its real-world utility, we test AccuSNV on multiple curated bacterial data sets containing reported SNVs. In both simulated and real-world scenarios, AccuSNV consistently achieves the best performance. Moreover, AccuSNV provides comprehensive user-friendly downstream analysis modules and outputs, including mutation annotation information, phylogenetic inference, <i>d</i> _N/<i>d</i> _S calculations, and optional manual filtering. Together with the automated deep learning-based calling, these features make AccuSNV broadly accessible to users with different levels of computational expertise.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1889-1901"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148396414","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":"Identification of genomic features that uniquely impact estrogen receptor alpha binding and its effects on gene expression in endometrial cancer.","authors":"Alexandra Richey, Noel Kitchen, Craig M Rush, Jeffery M Vahrenkamp, Hosiana Abewe, Kyle S Osbourne, Alex J Bott, Brian K Dalley, Jared Rutter, Jason Gertz","doi":"10.1101/gr.281792.125","DOIUrl":"10.1101/gr.281792.125","url":null,"abstract":"<p><p>Estrogen receptor 1 (ESR1, also known as estrogen receptor alpha or ER) is an established oncogenic transcription factor in breast and endometrial cancer; however, more is known about the mechanisms controlling ER behavior in breast cancer, and therapies targeting ER have been much more successful in breast cancer. To address this disparity, we characterize the genomic features that control ER in endometrial cancer and determine to what extent these factors differ from those in breast cancer. We focus on the locations of estrogen response elements (EREs), ER's preferred DNA-binding motif, throughout the human genome. To identify factors that predict ER genomic binding and effects on target gene expression, we apply machine learning to genomic data for each ERE in Ishikawa cells (ER-positive endometrial cancer) and T-47D cells (ER-positive breast cancer). Many of these factors, such as chromatin accessibility and histone modifications, are predictive of ER activity in both cell lines. However, the transcription factors that predict ER activity are cell type specific, including FOXA1 and GATA3 in T-47D cells and ETV4 and SOX17 in Ishikawa cells. In addition, the features that predict ER binding and effects on gene expression differ, with transcription at EREs in the absence of estrogen being predictive of ER regulatory activity. A CRISPR knockout screen in Ishikawa cells, as well as follow-up experiments, confirms the discovery that SOX17 controls ER activity in endometrial cancer cells. These results identify important genomic features of ER binding and regulatory activity and how these features differ between endometrial cancer and breast cancer cells.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1771-1784"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148470175","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":"Knowledge-driven interpretable neural networks provide mechanistic insight.","authors":"Yan Ke, Tianwei Yu","doi":"10.1101/gr.281686.125","DOIUrl":"10.1101/gr.281686.125","url":null,"abstract":"<p><p>Analyzing omics data in the context of pathway knowledge is critical for understanding the molecular mechanisms underlying pathological changes. However, current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting the understanding of pathway behavior to a relatively shallow level. To address this issue, we present a knowledge-driven machine learning framework that embeds features into pathway graphs and models reactions analytically, producing interpretable feature hierarchies and subnetworks in which functional associations are estimated to model biological interactions. The approach is agnostic to feature selection, enabling the use of full omics data sets without discarding weak signals. Applications to breast cancer microRNA-gene regulation data and COVID-19 metabolomic data highlight immune and metabolic pathways relevant to disease progression. This framework bridges predictive modeling with mechanistic interpretation and offers a foundation for integrative pathway analysis.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1836-1848"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148584084","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":"Methylation variability and LINE-1 transcriptional activation in multiple myeloma.","authors":"Qianhui Wan, Amy Leung, Mahek Vinod Bhandari, Hiroyuki Kato, Joo Y Song, Dustin E Schones","doi":"10.1101/gr.281420.125","DOIUrl":"10.1101/gr.281420.125","url":null,"abstract":"<p><p>Multiple myeloma (MM) is a type of hematological cancer that arises from uncontrolled proliferation of plasma cells. In addition to frequent genetic mutations, malignant plasma cells are characterized by alterations to the epigenome. Myeloma cells display a genome-wide loss of DNA methylation and a corresponding increase in \"active\" chromatin modifications. The epigenetic remodeling that occurs in cancer genomes is associated with loss of silencing at transposable elements, which can impact genome regulation. Through paired epigenome and transcriptome profiling of patient-derived MM samples, we have found that loss of DNA methylation in MM genomes results in the formation of partially methylated domains that is variable across patients. This loss of DNA methylation coincides with the expression of hundreds of transcripts driven by LINE-1 (L1) retrotransposons that are epigenetically silenced in normal cells. MM samples can be stratified based on L1 transcriptional activity with distinct gene expression signatures. The high L1 samples are characterized by a more proliferative, less differentiated state as well as inhibition of interferon and genome defense pathways. Several L1 promoters generate chimeric transcripts with adjacent oncogenes. We further find that KRAB-containing zinc finger proteins (KZFPs) that are responsible for the epigenetic silencing of L1s have abnormally low abundance in MM samples with high L1 transcriptional activity. These results indicate that cell proliferation in MM is associated with a loss of KZFP expression and transcriptional activation of L1 elements.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1742-1753"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148470151","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":"Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow.","authors":"Jonathan D Rosen, Arjun Devadas Vasanthakumari, Kilian Salomon, Nikola de Lange, Pyaree Mohan Dash, Pia Keukeleire, Ali Hassan, Alejandro Barrera, Beniamin Krupkin, Grace Oualline, Martin Kircher, Michael I Love, Max Schubach","doi":"10.1101/gr.281462.125","DOIUrl":"10.1101/gr.281462.125","url":null,"abstract":"<p><p>As researchers and clinicians seek to identify human genomic alterations relevant to traits and disorders, identifying and aggregating evidence providing mechanistic support for associations between alterations and phenotypes remains challenging. In particular, the study of noncoding genomic variation remains a major challenge because of the lack of accurate functional annotation for activity in a given context and across alleles. Experimental evidence is critical for prioritizing and interpreting functional effects of genetic alterations. Massively parallel reporter assays (MPRAs) have emerged as a powerful high-throughput approach, enabling quantification of regulatory element activity and allelic effects, as well as systematic dissection of gene regulatory logic and variant effects across different contexts. However, the diversity of MPRA designs, lack of standardized formats, and many potential processing parameters hamper data integration, reproducibility, and meta-analyses across studies. To address these challenges, the Impact of Genomic Variation on Function (IGVF) Consortium established an MPRA focus group to develop community standards, including harmonized file formats, and robust analysis pipelines for a wide range of library types and experimental designs. Here, we present these formats and comprehensive computational tools, MPRAlib and MPRAsnakeflow, for uniform processing from raw sequencing reads to counts, processing, and visualization. Using diverse MPRA data sets, we investigated technical variability sources including barcode sequence bias, outlier barcodes, and delivery method (episomal vs. lentiviral). Our results establish best practices for MPRA data generation and analysis, facilitating robust, reproducible research and large-scale integration. The presented tools and standards are publicly available, providing a foundation for future collaborative efforts in regulatory genomics.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1940-1951"},"PeriodicalIF":6.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148712126","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":"Quartet-based species tree methods enable fast and consistent tree of blobs reconstruction under the network multispecies coalescent.","authors":"Junyan Dai, Yunheng Han, Erin K Molloy","doi":"10.1101/gr.282203.126","DOIUrl":"https://doi.org/10.1101/gr.282203.126","url":null,"abstract":"<p><p>Hybridization between species is an important force in evolution, commonly modeled by the network multispecies coalescent. Reconstructing evolutionary histories under this model is computationally challenging, even for level-1 networks where hybridization events are isolated. Divide-and-conquer is a promising path forward, but current methods with statistical guarantees rely on an estimated tree of blobs (TOB) for the network, which compresses each nontree-like part into a single vertex. TOB reconstruction is itself challenging, with the only available method TINNiK having time complexity <i>O</i>(<i>n</i> ⁵ + <i>n</i> ⁴ <i>k</i>) for <i>k</i> genes and <i>n</i> species. Here, we present a new framework for scalable TOB reconstruction with statistical guarantees. Our approach operates by (1) seeking a refinement of the TOB and then (2) contracting edges in it. For step (1), we show that any optimal solution to Weighted Quartet Consensus is a TOB refinement almost surely, as the number of genes goes to infinity, motivating the use of methods, such as ASTRAL or TREE-QMC. For step (2), we show that applying the same hypothesis tests as TINNiK to just <i>O</i>(<i>n</i>) four-taxon subsets around each edge is sufficient for statistically consistent TOB reconstruction when the underlying network is level-1. Leveraging TREE-QMC for the first step gives our method time complexity <i>O</i>(<i>n</i> ³ <i>k</i>) and its name: TOB-QMC. On simulated data, TOB-QMC typically matches or exceeds TINNiK in accuracy while being more scalable. TOB-QMC also enables fast exploration of nontreelike evolution, as demonstrated through reanalysis of three phylogenomic data sets. Lastly, our study clarifies the theoretical utility of quartet-based species tree methods in the context of hybridization, which is critical given the recent result that ASTRAL can be misleading.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850486","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":"Achieving spatial multiomics diagonal integration from unaligned serial sections with DIME.","authors":"Pengyu Sun, Tian Mou, Xinlei Huang, Xubin Zheng","doi":"10.1101/gr.282244.126","DOIUrl":"https://doi.org/10.1101/gr.282244.126","url":null,"abstract":"<p><p>Learning integrated representations from spatial multiomics data is a fundamental challenge, particularly in the context of diagonal integration, where data are collected from serial tissue sections across distinct omics modalities. Existing methods typically rely on the assumption of feature intersection to construct a common metric space, a prerequisite that is absent in this setting. To address this, we propose the Diagonal Integration model for spatial Multiomics Embedding (DIME), a deep learning framework that couples a graph contrastive learning objective with cross-modal correspondence. This global correspondence is established by a hybrid alignment strategy: it first anchors high-confidence regions using Coherent Point Drift with Linear Assignment, and then extends matching to the entire tissue manifold via an Optimal Transport formulation encoding relative geodesic distances. Designed to balance inter-modal guidance with intra-modal structure preservation, DIME enables robust fusion and denoising. Experiments on simulated and real human tissue datasets demonstrate DIME's superior robustness and versatility, where its learned representations achieve outstanding clustering accuracy and unlock the identification of biologically meaningful spatial domains.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148827509","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}