Genome Biology最新文献

{"title":"Genomic prediction with kinship-based multiple kernel learning produces hypothesis on the underlying inheritance mechanisms of phenotypic traits","authors":"Daniele Raimondi, Nora Verplaetse, Antoine Passemiers, Deborah Sarah Jans, Isabelle Cleynen, Yves Moreau","doi":"10.1186/s13059-025-03544-3","DOIUrl":"https://doi.org/10.1186/s13059-025-03544-3","url":null,"abstract":"Genomic prediction encompasses the techniques used in agricultural technology to predict the genetic merit of individuals towards valuable phenotypic traits. It is related to Genome Interpretation in humans, which models the individual risk of developing disease traits. Genomic prediction is dominated by linear mixed models, such as the Genomic Best Linear Unbiased Prediction (GBLUP), which computes kinship matrices from SNP array data, while Genome Interpretation applications to clinical genetics rely mainly on Polygenic Risk Scores. In this article, we exploit the positive semidefinite characteristics of the kinship matrices that are conventionally used in GBLUP to propose a novel Genomic Multiple Kernel Learning method (GMKL), in which the multiple kinship matrices corresponding to Additive, Dominant, and Epistatic Inheritance Mechanisms are used as kernels in support vector machines, and we apply it to both worlds. We benchmark GMKL on simulated cattle phenotypes, showing that it outperforms the classical GBLUP predictors for genomic prediction. Moreover, we show that GMKL ranks the kinship kernels representing different inheritance mechanisms according to their compatibility with the observed data, allowing it to produce hypotheses on the normally unknown inheritance mechanisms generating the target phenotypes. We then apply GMKL to the prediction of two inflammatory bowel disease cohorts with more than 6500 samples in total, consistently obtaining results suggesting that epistasis might have a relevant, although underestimated role in inflammatory bowel disease (IBD). We show that GMKL performs similarly to GBLUP, but it can formulate biological hypotheses about inheritance mechanisms, such as suggesting that epistasis influences IBD.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"13 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic optimization and prediction of cre recombinase for precise genome editing in mice","authors":"Valerie Erhardt, Elli Hartig, Kristian Lorenzo, Hannah R. Megathlin, Basile Tarchini, Vishnu Hosur","doi":"10.1186/s13059-025-03560-3","DOIUrl":"https://doi.org/10.1186/s13059-025-03560-3","url":null,"abstract":"The Cre-Lox system is a powerful tool in mouse genetics, enabling precise spatiotemporal control of gene expression and conditional knockout models. Since its development, it has transformed genome editing by facilitating targeted deletions, translocations, inversions, and complex modifications—double-floxed inverse orientation. Its utility extends beyond mice to rats, pigs, and zebrafish. However, challenges such as high costs, lengthy timelines, and unpredictable recombination remain, highlighting the need for ongoing improvements to enhance efficiency, reliability, and applicability across genetic models. In this study, we perform a systematic analysis of Cre-mediated recombination in mice, creating 11 new strains with conditional alleles at the Rosa26 locus, using the C57BL/6J background. Factors influencing recombination efficiency include inter-loxP distance, mutant loxP sites, zygosity, chromosomal location, and breeder age. Our results demonstrate that the choice of Cre-driver strain plays a significant role in recombination efficiency. Optimal recombination is achieved when loxP sites are spaced by less than 4 kb and mutant loxP sites by 3 kb. Complete recombination fails with wildtype loxP sites spaced ≥ 15 kb or mutant lox71/66 sites spaced ≥ 7 kb. The best recombination efficiency is observed in breeders aged 8–20 weeks and when using heterozygous floxed alleles. The Cre-Lox system remains indispensable for genetic engineering, offering flexibility beyond standalone applications by integrating with CRISPR-based methods to expand its utility. Despite challenges, our findings provide a framework for optimizing Cre-mediated recombination. By refining Cre-Lox strategies, this knowledge enhances experimental precision, improves reproducibility, and ultimately reduces the time and cost of genome modification.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"33 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782639","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":"Integrative omics analysis reveals the genetic basis of fatty acid composition in Brassica napus seeds","authors":"Yuting Zhang, Yunhao Liu, Zhanxiang Zong, Liang Guo, Wenhao Shen, Hu Zhao","doi":"10.1186/s13059-025-03558-x","DOIUrl":"https://doi.org/10.1186/s13059-025-03558-x","url":null,"abstract":"The fatty acid content represents a crucial quality trait in Brassica napus or rapeseed. Improvements in fatty acid composition markedly enhance the quality of rapeseed oil. Here, we perform a genome-wide association study (GWAS) to identify quantitative trait locus (QTLs) associated with fatty acid content. We identify a total of seven stable QTLs and find two loci, qFA.A08 and qFA.A09.1, subjected to strong selection pressure. By transcriptome-wide association analysis (TWAS), we characterize 3295 genes that are significantly correlated with the composition of at least one fatty acid. To elucidate the genetic underpinnings governing fatty acid composition, we then employ a combination of GWAS, TWAS, and dynamic transcriptomic analysis during seed development, along with the POCKET algorithm. We predict six candidate genes that are associated with fatty acid composition. Experimental validation reveals that four genes (BnaA09.PYRD, BnaA08.PSK1, BnaA08.SWI3, and BnaC02.LTP15) positively modulate oleic acid content while negatively impact erucic acid content. Comparative analysis of transcriptome profiles suggests that BnaA09.PYRD may influence fatty acid composition by regulating energy metabolism during seed development. This study establishes a genetic framework for a better understanding of plant oil biosynthesis in addition to providing theoretical foundation and valuable genetic resources for enhancing fatty acid composition in rapeseed breeding.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"73 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766906","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":"Chromosome-level genome assembly and single-cell analysis unveil molecular mechanisms of arm regeneration in the ophiuroid Ophiura sarsii vadicola","authors":"Qin-Zeng Xu, Yi-Xuan Li, Wen-Ge Shi, Yue Dong, Zhong Li, Jack Chi-Ho Ip, Matthew P. Galaska, Chen Han, Qian Zhang, Yu-Yao Sun, Lin-Lin Zhao, Kai-Ming Sun, Zong-Ling Wang, Jian-Wen Qiu, Xue-Lei Zhang","doi":"10.1186/s13059-025-03542-5","DOIUrl":"https://doi.org/10.1186/s13059-025-03542-5","url":null,"abstract":"Ophiuroids, belonging to Ophiuroidea in Echinodermata, possess remarkable regenerative capacities in their arms, relying on cellular recruitment and de-differentiation. However, limited high-quality genomic resources have hindered the investigation of the underlying molecular mechanisms of ophiuroid regeneration. Here, we report a chromosome-level genome of Ophiura sarsii vadicola, 259.28 Mbp in length with a scaffold N50 length of 66.91 Mbp. We then perform bulk and single-cell RNA sequencing analysis to investigate gene expression and cellular dynamics during arm regeneration. We identify five distinct cellular clusters involved in the arm regeneration and infer the dynamic transformations from sensory stimulation to injury response, wound healing, and tissue regeneration. We find that progenitor cells derived from connective tissue cells differentiate into muscle, cartilage, endothelial, and epithelial cells. Pseudotime analysis indicates that muscle differentiation occurs early in the regeneration process. Our genomic resource and single-cell atlas shed light on the mechanisms of organ regeneration in ophiuroids.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"31 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736571","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":"Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms","authors":"Brandon M. Wenz, Yuan He, Nae-Chyun Chen, Joseph K. Pickrell, Jeremiah H. Li, Max F. Dudek, Taibo Li, Rebecca Keener, Benjamin F. Voight, Christopher D. Brown, Alexis Battle","doi":"10.1186/s13059-025-03538-1","DOIUrl":"https://doi.org/10.1186/s13059-025-03538-1","url":null,"abstract":"Understanding the genetic causes underlying variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from distinct projects and laboratories. We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1454 unique donor individuals across 653 studies from public databases, we catalog 24,159 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility across human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes. Our work provides a proof of principle for caQTL calling from previously ungenotyped samples and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"69 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736575","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":"SOAPy: a Python package to dissect spatial architecture, dynamics, and communication","authors":"Heqi Wang, Jiarong Li, Siyu Jing, Ping Lin, Yiling Qiu, Xi Yan, Jiao Yuan, ZhiXuan Tang, Yu Li, Haibing Zhang, Yujie Chen, Zhen Wang, Hong Li","doi":"10.1186/s13059-025-03550-5","DOIUrl":"https://doi.org/10.1186/s13059-025-03550-5","url":null,"abstract":"Advances in spatial omics enable deeper insights into tissue microenvironments while posing computational challenges. Therefore, we developed SOAPy, a comprehensive tool for analyzing spatial omics data, which offers methods for spatial domain identification, spatial expression tendency, spatiotemporal expression pattern, cellular co-localization, multi-cellular niches, cell–cell communication, and so on. SOAPy can be applied to diverse spatial omics technologies and multiple areas in physiological and pathological contexts, such as tumor biology and developmental biology. Its versatility and robust performance make it a universal platform for spatial omics analysis, providing diverse insights into the dynamics and architecture of tissue microenvironments.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"35 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733957","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":"Publisher Correction: The haplotype-resolved genome assembly of an ancient citrus variety provides insights into the domestication history and fruit trait formation of loose-skin mandarins","authors":"Minqiang Yin, Xiaochan Song, Chao He, Xiyuan Li, Mengyuan Li, Jiangbo Li, Hao Wu, Chuanwu Chen, Li Zhang, Zhenmei Cai, Liqing Lu, Yanhui Xu, Xin Wang, Hualin Yi, Juxun Wu","doi":"10.1186/s13059-025-03551-4","DOIUrl":"https://doi.org/10.1186/s13059-025-03551-4","url":null,"abstract":"<p><b>Correction</b><b>: </b><b>Genome Biol 26, 61 (2025)</b></p><p><b>https://doi.org/10.1186/s13059-025–03535-4</b></p><br/><p>Following publication of the original article [1], the authors identified a typesetting error in the PDF version of the article. Figure 2b was erroneously missing content.</p><p>The incorrect Fig. 2 is as follows:</p><figure><picture><source srcset=\"//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs13059-025-03551-4/MediaObjects/13059_2025_3551_Figa_HTML.png?as=webp\" type=\"image/webp\"/><img alt=\"figure a\" aria-describedby=\"Figa\" height=\"538\" loading=\"lazy\" src=\"//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs13059-025-03551-4/MediaObjects/13059_2025_3551_Figa_HTML.png\" width=\"685\"/></picture></figure><p>The correct Fig. 2 is as follows:</p><figure><picture><source srcset=\"//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs13059-025-03551-4/MediaObjects/13059_2025_3551_Figb_HTML.png?as=webp\" type=\"image/webp\"/><img alt=\"figure b\" aria-describedby=\"Figb\" height=\"539\" loading=\"lazy\" src=\"//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs13059-025-03551-4/MediaObjects/13059_2025_3551_Figb_HTML.png\" width=\"685\"/></picture></figure><p>The original article [1] has been corrected.</p><ol data-track-component=\"outbound reference\" data-track-context=\"references section\"><li data-counter=\"1.\"><p>Yin M, Song X, He C, et al. The haplotype-resolved genome assembly of an ancient citrus variety provides insights into the domestication history and fruit trait formation of loose-skin mandarins. Genome Biol. 2025;26:61. https://doi.org/10.1186/s13059-025-03535-4.</p><p>Article CAS PubMed PubMed Central Google Scholar </p></li></ol><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><span>Author notes</span><ol><li><p>Minqiang Yin and Xiaochan Song are co-first authors.</p></li></ol><h3>Authors and Affiliations</h3><ol><li><p>National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Huazhong Agricultural University, Wuhan, 430070, China</p><p>Minqiang Yin, Xiaochan Song, Chao He, Xiyuan Li, Mengyuan Li, Li Zhang, Zhenmei Cai, Liqing Lu, Yanhui Xu, Xin Wang, Hualin Yi & Juxun Wu</p></li><li><p>Fuzhou Agricultural and Rural Industry Development Service Center, Fuzhou, 344100, China</p><p>Jiangbo Li</p></li><li><p>Fuzhou Institute of Agricultural Sciences, Fuzhou, 344100, China</p><p>Hao Wu</p></li><li><p>Guangxi Key Laboratory of Germplasm Innovation and Utilization of Specialty Commercial Crops in North Guangxi, Guangxi Academy of Specialty Crops, Guilin, 541004, China</p><p>Chuanwu Chen</p></li></ol><span>Authors</span><ol><li><span>Minqiang Yin</span>View author publications<p><span>You can also search for this author in</span><span>PubMed<span> </span>Google Scholar","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"12377 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723336","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":"Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset","authors":"Mei R. M. Du, Changqing Wang, Charity W. Law, Daniela Amann-Zalcenstein, Casey J. A. Anttila, Ling Ling, Peter F. Hickey, Callum J. Sargeant, Yunshun Chen, Lisa J. Ioannidis, Pradeep Rajasekhar, Raymond K. H. Yip, Kelly L. Rogers, Diana S. Hansen, Rory Bowden, Matthew E. Ritchie","doi":"10.1186/s13059-025-03543-4","DOIUrl":"https://doi.org/10.1186/s13059-025-03543-4","url":null,"abstract":"Spatial transcriptomics allows gene expression to be measured within complex tissue contexts. Among the array of spatial capture technologies available is 10x Genomics’ Visium platform, a popular method which enables transcriptome-wide profiling of tissue sections. Visium offers a range of sample handling and library construction methods which introduces a need for benchmarking to compare data quality and assess how well the technology can recover expected tissue features and biological signatures. Here we present SpatialBenchVisium, a unique reference dataset generated from spleen tissue of mice responding to malaria infection spanning several tissue preparation protocols (both fresh frozen and FFPE, with either manual or CytAssist tissue placement). We note better quality control metrics in reference samples prepared using probe-based capture methods, particularly those processed with CytAssist, validating the improvement in data quality produced with the platform. Our analysis of replicate samples extends to explore spatially variable gene detection, the outcomes of clustering and cell deconvolution using matched single-cell RNA-sequencing data and publicly available reference data to identify cell types and tissue regions expected in the spleen. Multi-sample differential expression analysis recovered known gene signatures related to biological sex or gene knockout.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"17 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723337","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":"MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models","authors":"Devlin C. Moyer, Justin Reimertz, Daniel Segrè, Juan I. Fuxman Bass","doi":"10.1186/s13059-025-03533-6","DOIUrl":"https://doi.org/10.1186/s13059-025-03533-6","url":null,"abstract":"Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. Erroneous or missing reactions, scattered throughout densely interconnected networks, are a limiting factor in these applications. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a suite of algorithms that helps to identify and visualize errors at the level of connected pathways, rather than individual reactions. We show how MACAW highlights inaccuracies of varying severity in manually curated and automatically generated GSMMs for humans, yeast, and bacteria and helps to identify systematic issues to be addressed in future model construction efforts.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"23 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723335","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":"DEMINERS enables clinical metagenomics and comparative transcriptomic analysis by increasing throughput and accuracy of nanopore direct RNA sequencing","authors":"Junwei Song, Li-an Lin, Chao Tang, Chuan Chen, Qingxin Yang, Dan Zhang, Yuancun Zhao, Han-cheng Wei, Kepan Linghu, Zijie Xu, Tingfeng Chen, Zhifeng He, Defu Liu, Yu Zhong, Weizhen Zhu, Wanqin Zeng, Li Chen, Guiqin Song, Mutian Chen, Juan Jiang, Juan Zhou, Jing Wang, Bojiang Chen, Binwu Ying, Yuan Wang, Jia Geng, Jing-wen Lin, Lu Chen","doi":"10.1186/s13059-025-03536-3","DOIUrl":"https://doi.org/10.1186/s13059-025-03536-3","url":null,"abstract":"Nanopore direct RNA sequencing (DRS) is a powerful tool for RNA biology but suffers from low basecalling accuracy, low throughput, and high input requirements. We present DEMINERS, a novel DRS toolkit combining an RNA multiplexing workflow, a Random Forest-based barcode classifier, and an optimized convolutional neural network basecaller with species-specific training. DEMINERS enables accurate demultiplexing of up to 24 samples, reducing RNA input and runtime. Applications include clinical metagenomics, cancer transcriptomics, and parallel transcriptomic comparisons, uncovering microbial diversity in COVID-19 and m6A’s role in malaria and glioma. DEMINERS offers a robust, high-throughput solution for precise transcript and RNA modification analysis.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"49 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723338","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}