GigaScience最新文献

{"title":"NanoMnT: an STR analysis tool for Oxford Nanopore sequencing data driven by a comprehensive analysis of error profile in STR regions.","authors":"Gyumin Park, Hyunsu An, Han Luo, Jihwan Park","doi":"10.1093/gigascience/giaf013","DOIUrl":"10.1093/gigascience/giaf013","url":null,"abstract":"<p><p>Oxford Nanopore Technology (ONT) sequencing is a third-generation sequencing technology that enables cost-effective long-read sequencing, with broad applications in biological research. However, its high sequencing error rate in low-complexity regions hampers its applications in short tandem repeat (STR)-related research. To address this, we generated a comprehensive STR error profile of ONT by analyzing publicly available Nanopore sequencing datasets. We show that the sequencing error rate is influenced not only by STR length but also by the repeat unit and the flanking sequences of STR regions. Interestingly, certain flanking sequences were associated with higher sequencing accuracy, suggesting that certain STR loci are more suitable for Nanopore sequencing compared to other loci. While base quality scores of substitution errors within the STR regions were lower than those of correctly sequenced bases, such patterns were not observed for indel errors. Furthermore, choosing the most recent basecaller version and using the super accuracy model significantly improved STR sequencing accuracy. Finally, we present NanoMnT, a lightweight Python tool that corrects STR sequencing errors in sequencing data and estimates STR allele sizes. NanoMnT leverages the characteristics of ONT when estimating STR allele size and exhibits superior results for 1-bp- and 2-bp repeat STR compared to existing tools. By integrating our findings, we improved STR allele estimation accuracy for Ax10 repeats from 55% to 78% and up to 85% when excluding loci with unfavorable flanking sequences. Using NanoMnT, we present the utility of our findings by identifying microsatellite instability status in cancer sequencing data. NanoMnT is publicly available at https://github.com/18parkky/NanoMnT.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143648038","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":"Genomes reveal pervasive distant hybridization in nature among cyprinid fishes.","authors":"Li Ren, Xiaolong Tu, Mengxue Luo, Qizhi Liu, Jialin Cui, Xin Gao, Hong Zhang, Yakui Tai, Yiyan Zeng, Mengdan Li, Chang Wu, Wuhui Li, Jing Wang, Dongdong Wu, Shaojun Liu","doi":"10.1093/gigascience/giae117","DOIUrl":"10.1093/gigascience/giae117","url":null,"abstract":"<p><strong>Background: </strong>Genomic data have unveiled a fascinating aspect of the evolutionary past, showing that the mingling of different species through hybridization has left its mark on the histories of numerous life forms. However, the relationship between hybridization events and the origins of cyprinid fishes remains unclear.</p><p><strong>Results: </strong>In this study, we generated de novo assembled genomes of 8 cyprinid fishes and conducted phylogenetic analyses on 24 species. Widespread allele sharing across species boundaries was observed within 7 subfamilies of cyprinid fishes. Based on a systematic analysis of multiple tissues, we found that the testis exhibited a conserved pattern of divergence between the herbivorous Megalobrama amblycephala and the carnivorous Culter alburnus, suggesting a potential link to incomplete reproductive isolation. Significant differences in the expression of 4 genes (dpp2, ctrl, psb7, and ppce) in the liver and intestine, accompanied by variations in enzyme activities, indicated swift divergence in digestive enzyme secretion. Moreover, we identified introgressed genes linked to organ development in sympatric fishes with analogous feeding habits within the Cultrinae and Leuciscinae subfamilies.</p><p><strong>Conclusions: </strong>Our findings highlight the significant role played by incomplete reproductive isolation and frequent gene flow events, particularly those associated with the development of digestive organs, in driving speciation among cyprinid fishes in diverse freshwater ecosystems.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11779505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143065175","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":"Interspecific hybridization in Brassica species leads to changes in agronomic traits through the regulation of gene expression by chromatin accessibility and DNA methylation.","authors":"Chengtao Quan, Qin Zhang, Xiaoni Zhang, Kexin Chai, Guoting Cheng, Chaozhi Ma, Cheng Dai","doi":"10.1093/gigascience/giaf029","DOIUrl":"https://doi.org/10.1093/gigascience/giaf029","url":null,"abstract":"<p><p>Interspecific hybridization is a common method in plant breeding to combine traits from different species, resulting in allopolyploidization and significant genetic and epigenetic changes. However, our understanding of genome-wide chromatin and gene expression dynamics during allopolyploidization remains limited. This study generated two Brassica allotriploid hybrids via interspecific hybridization. We observed that accessible chromatin regions (ACRs) and DNA methylation interact to regulates gene expression after interspecific hybridization, ultimately influencing the agronomic traits of the hybrids. In total, 234,649 ACRs were identified in the parental lines and hybrids; the hybridization process induces changes in the distribution and abundance of their accessible chromatin regions, particularly in gene regions and their proximity. Genes associated with proximal ACRs were more highly expressed than those associated with distal and genic ACRs. More than half of novel ACRs drove transgressive gene expression in the hybrids, and the transgressive upregulated genes showed significant enrichment in metal ion binding, especially magnesium ion, calcium ion, and potassium ion binding. We also identified Bna.bZIP11 in the single-parent activation ACR, which binds to BnaA06.UF3GT to promote anthocyanin accumulation in F1 hybrids. DNA methylation plays a role in repressing gene expression, and unmethylated ACRs are more transcriptionally active. Additionally, the A-subgenome ACRs were associated with genome dosage rather than DNA methylation. The interplay among DNA methylation, transposable elements, and sRNA contributes to the dynamic landscape of ACRs during interspecific hybridization, resulting in distinct gene expression patterns on the genome.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979240","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":"Lifting the curse from high-dimensional data: automated projection pursuit clustering for a variety of biological data modalities.","authors":"Claire Simpson, Evgeniy Tabatsky, Zainab Rahil, Devon J Eddins, Sasha Tkachev, Florian Georgescauld, Derek Papalegis, Martin Culka, Tyler Levy, Ivan Gregoretti, Connor Meehan, Chiara Schiller, Kresimir Bestak, Denis Schapiro, Andrei Chernyshev, Guenther Walther, Eliver E B Ghosn, Darya Orlova","doi":"10.1093/gigascience/giaf052","DOIUrl":"10.1093/gigascience/giaf052","url":null,"abstract":"<p><p>Unsupervised clustering is a powerful machine-learning technique widely used to analyze high-dimensional biological data. It plays a crucial role in uncovering patterns, structures, and inherent relationships within complex datasets without relying on predefined labels. In the context of biology, high-dimensional data may include transcriptomics, proteomics, and a variety of single-cell omics data. Most existing clustering algorithms operate directly in the high-dimensional space, and their performance may be negatively affected by the phenomenon known as the curse of dimensionality. Here, we show an alternative clustering approach that alleviates the curse by sequentially projecting high-dimensional data into a low-dimensional representation. We validated the effectiveness of our approach, named automated projection pursuit (APP), across various biological data modalities, including flow and mass cytometry data, scRNA-seq, multiplex imaging data, and T-cell receptor repertoire data. APP efficiently recapitulated experimentally validated cell-type definitions and revealed new biologically meaningful patterns.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173575","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":"A Case for estradiol: younger brains in women with earlier menarche and later menopause.","authors":"Eileen Luders, Inger Sundström Poromaa, Claudia Barth, Christian Gaser","doi":"10.1093/gigascience/giaf060","DOIUrl":"10.1093/gigascience/giaf060","url":null,"abstract":"<p><p>The transition to menopause is marked by a gradual decrease of estradiol. Concurrently, the risk of dementia in women increases around menopause, suggesting that estradiol (or the lack thereof) plays a role in the development of dementia and other age-related neuropathologies. Here, we set out to investigate whether there is a link between brain aging and estradiol-associated events, such as menarche and menopause. For this purpose, we applied a well-validated machine learning approach to analyze both cross-sectional and longitudinal data from a sample of 1,006 postmenopausal women who underwent structural magnetic resonance imaging twice, approximately 2 years apart. We observed less brain aging in women with an earlier menarche, a later menopause, and a longer reproductive span (i.e., the time interval between menarche and menopause). These effects were evident both cross-sectionally and longitudinally, supporting the notion that estradiol has neuroprotective properties and contributes to brain preservation. However, further research is required because the observed effects were small, estradiol was not directly measured, and other factors may modulate female brain health. Future studies might benefit from incorporating actual estradiol (and other hormone) measures, as well as considering genetic predispositions and lifestyle factors alongside indicators of brain aging to deepen our understanding of estradiol's role in maintaining brain health. Additionally, including more diverse study populations (e.g., varying in ethnicity, socioeconomic status, and health status) in follow-up research would enhance the generalizability and applicability of these findings.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127148","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":"A near telomere-to-telomere genome assembly of the Jinhua pig: enabling more accurate genetic research.","authors":"Caiyun Cao, Jian Miao, Qinqin Xie, Jiabao Sun, Hong Cheng, Zhenyang Zhang, Fen Wu, Shuang Liu, Xiaowei Ye, Huanfa Gong, Zhe Zhang, Qishan Wang, Yuchun Pan, Zhen Wang","doi":"10.1093/gigascience/giaf048","DOIUrl":"10.1093/gigascience/giaf048","url":null,"abstract":"<p><strong>Background: </strong>Pigs are crucial sources of meat and protein, valuable animal models, and potential donors for xenotransplantation. However, the existing reference genome for pigs is incomplete, with thousands of segments and centromeres and telomeres missing, which limits our understanding of the important traits in these genomic regions.</p><p><strong>Findings: </strong>We present a near-complete genome assembly for the Jinhua pig (JH-T2T) and provide a set of diploid Jinhua reference genomes, constructed using PacBio HiFi, ONT long reads, and Hi-C reads. This assembly includes all 18 autosomes and the X and Y sex chromosomes, with only 6 gaps. It features annotations of 46.90% repetitive sequences, 33 telomeres, 17 centromeres, and 23,924 high-confident genes. Compared to the Sscrofa11.1, JH-T2T closes nearly all gaps, extends sequences by 177 Mb, predicts more intact telomeres and centromeres, and gains 799 more genes and loses 114 genes. Moreover, it enhances the mapping rate for both Western and Chinese local pigs, outperforming Sscrofa11.1 as a reference genome. Additionally, this comprehensive genome assembly will facilitate large-scale variant detection.</p><p><strong>Conclusions: </strong>This study produced a near-gapless assembly of the pig genome and provides a set of haploid Jinhua reference genomes. Our findings represent a significant advance in pig genomics, providing a robust resource that enhances genetic research, breeding programs, and biomedical applications.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12080228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077470","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":"CODARFE: Unlocking the prediction of continuous environmental variables based on microbiome.","authors":"Murilo Caminotto Barbosa, João Fernando Marques da Silva, Leonardo Cardoso Alves, Robert D Finn, Alexandre Rossi Paschoal","doi":"10.1093/gigascience/giaf055","DOIUrl":"10.1093/gigascience/giaf055","url":null,"abstract":"<p><strong>Background: </strong>Despite the surge in microbiome data acquisition, there is a limited availability of tools capable of effectively analyzing it and identifying correlations between taxonomic compositions and continuous environmental factors. Furthermore, existing tools also do not predict the environmental factors in new samples, underscoring the pressing need for innovative solutions to enhance our understanding of microbiome dynamics and fulfill the prediction gap. Here we introduce CODARFE, a novel tool for sparse compositional microbiome predictor selection and prediction of continuous environmental factors.</p><p><strong>Results: </strong>We tested CODARFE against 4 state-of-the-art tools in 2 experiments. First, CODARFE outperformed predictor selection in 21 of 24 databases in terms of correlation. Second, among all the tools, CODARFE achieved the highest number of previously identified bacteria linked to environmental factors for human data-that is, at least 7% more. We also tested CODARFE in a cross-study, using the same biome but under different external effects, using a model trained on 1 dataset to predict environmental factors on another dataset, achieving 11% of mean absolute percentage error. Finally, CODARFE is available in 5 formats, including a Windows version with a graphical interface, to installable source code for Linux servers and an embedded Jupyter notebook available at MGnify.</p><p><strong>Conclusions: </strong>Our findings underscore the robustness and broad applicability of CODARFE across diverse fields, even under varying experimental conditions. Additionally, the ability to predict outcomes in new samples allows for the generation of new insights in previously unexplored contexts, providing researchers with a versatile tool.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12365963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474816","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":"SynProtX: a large-scale proteomics-based deep learning model for predicting synergistic anticancer drug combinations.","authors":"Bundit Boonyarit, Matin Kositchutima, Tisorn Na Phattalung, Nattawin Yamprasert, Chanitra Thuwajit, Thanyada Rungrotmongkol, Sarana Nutanong","doi":"10.1093/gigascience/giaf080","DOIUrl":"10.1093/gigascience/giaf080","url":null,"abstract":"<p><strong>Motivation: </strong>Drug combination therapy plays a pivotal role in addressing the molecular heterogeneity of cancer, improving treatment efficacy, minimizing resistance, and reducing toxicity. Deep learning approaches have significantly advanced drug combination discovery by addressing the limitations of conventional laboratory experiments, which are time-consuming and costly. While most existing models rely on the molecular structure of drugs and gene expression data, incorporating protein-level expression provides a more accurate representation of cellular behavior and drug responses. In this study, we introduce SynProtX, an enhanced deep learning model that explicitly integrates large-scale proteomics with deep neural networks (DNNs) and the molecular structure of drugs with graph neural networks (GNNs).</p><p><strong>Results: </strong>The SynProtX-GATFP model, which combines molecular graphs and fingerprints through a graph attention network architecture, demonstrated superior predictive performance for the FRIEDMAN study dataset. We further evaluated its cell line-specific performance, which achieved accuracy across diverse tissue and study datasets. By incorporating protein expression data, the model consistently enhanced predictive performance over gene expression-only models, reflecting the functional state of cancer cells. The generalizability of SynProtX was rigorously validated using cold-start prediction, including leave-drug-combination-out, leave-drug-out, and leave-cell-line-out validation strategies, highlighting its robust performance and potential for clinical applicability. Additionally, SynProtX identified key cancer-associated proteins and molecular substructures, offering novel insights into the biological mechanisms underlying drug synergy. These findings highlight the potential of integrating large-scale proteomics and multiomics data to advance anticancer drug design and combination therapy strategies for personalized medicine. Availability and implementation:  https://github.com/manbaritone/SynProtX.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12343095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834815","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":"A holistic genome dataset of bacteria and archaea of mangrove sediments.","authors":"Shijun Pan, Huan Du, Ruiqi Zheng, Cuijing Zhang, Jie Pan, Xilan Yang, Cheng Wang, Xiaolan Lin, Jinhui Li, Wan Liu, Haokui Zhou, Xiaoli Yu, Shuming Mo, Guoqing Zhang, Guoping Zhao, Zhili He, Yun Tian, Chengjian Jiang, Wu Qu, Yang Liu, Meng Li","doi":"10.1093/gigascience/giaf081","DOIUrl":"10.1093/gigascience/giaf081","url":null,"abstract":"<p><strong>Background: </strong>Mangroves are one of the most productive marine ecosystems with high ecosystem service value. The sediment microbial communities contribute to pivotal ecological functions in mangrove ecosystems. However, the study of mangrove sediment microbiomes is limited.</p><p><strong>Findings: </strong>Here, we applied metagenome sequencing analysis of microbial communities in mangrove sediments across Southeast China from 2014 to 2020. This genome dataset includes 966 metagenome-assembled genomes with ≥50% completeness and ≤10% contamination generated from 6 groups of samples. Phylogenomic analysis and taxonomy classification show that mangrove sediments are inhabited by microbial communities with high species diversity. Thermoplasmatota, Thermoproteota, and Asgardarchaeota in archaea, as well as Proteobacteria, Desulfobacterota, Chloroflexota, Acidobacteriota, and Gemmatimonadota in bacteria, dominate the mangrove sediments across Southeast China. Functional analyses suggest that the microbial communities may contribute to carbon, nitrogen, and sulfur cycling in mangrove sediments.</p><p><strong>Conclusions: </strong>These combined microbial genomes provide an important complement of global mangrove genome datasets and may serve as a foundational resource for enhancing our understanding of the composition and functions of mangrove sediment microbiomes.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12343073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834874","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":"Group-specific discriminant analysis enhances detection of sex differences in brain functional network lateralization.","authors":"Shuo Zhou, Junhao Luo, Yaya Jiang, Haolin Wang, Haiping Lu, Gaolang Gong","doi":"10.1093/gigascience/giaf082","DOIUrl":"https://doi.org/10.1093/gigascience/giaf082","url":null,"abstract":"<p><strong>Background: </strong>Lateralization is the asymmetry in function and cognition between the brain hemispheres, with notable sex differences. Conventional neuroscience studies on lateralization use univariate statistical comparisons between male and female groups, with limited and ineffective validation for group specificity. This article proposes to model sex differences in brain functional network lateralization as a dual-classification problem: first-order classification of left versus right hemispheres and second-order classification of male versus female models. To capture sex-specific patterns, we developed an interpretable group-specific discriminant analysis (GSDA) for first-order classification, followed by logistic regression for second-order classification.</p><p><strong>Findings: </strong>Evaluations on 2 large-scale neuroimaging datasets show GSDA's effectiveness in learning sex-specific patterns, significantly improving model group specificity over baseline methods. Major sex differences were identified in the strength of lateralization and interaction patterns within and between lobes.</p><p><strong>Conclusions: </strong>The GSDA-based analysis challenges the conventional approach to investigating group-specific lateralization and indicates that previous findings on sex-specific lateralization will need revisits and revalidation. This method is generic and can be adapted for other group-specific analyses, such as treatment-specific or disease-specific studies.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12398281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144950490","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}