GigaScience最新文献

{"title":"TransHLA: a Hybrid Transformer model for HLA-presented epitope detection.","authors":"Tianchi Lu, Xueying Wang, Wan Nie, Miaozhe Huo, Shuaicheng Li","doi":"10.1093/gigascience/giaf008","DOIUrl":"10.1093/gigascience/giaf008","url":null,"abstract":"<p><strong>Background: </strong>Precise prediction of epitope presentation on human leukocyte antigen (HLA) molecules is crucial for advancing vaccine development and immunotherapy. Conventional HLA-peptide binding affinity prediction tools often focus on specific alleles and lack a universal approach for comprehensive HLA site analysis. This limitation hinders efficient filtering of invalid peptide segments.</p><p><strong>Results: </strong>We introduce TransHLA, a pioneering tool designed for epitope prediction across all HLA alleles, integrating Transformer and Residue CNN architectures. TransHLA utilizes the ESM2 large language model for sequence and structure embeddings, achieving high predictive accuracy. For HLA class I, it reaches an accuracy of 84.72% and an area under the curve (AUC) of 91.95% on IEDB test data. For HLA class II, it achieves 79.94% accuracy and an AUC of 88.14%. Our case studies using datasets like CEDAR and VDJdb demonstrate that TransHLA surpasses existing models in specificity and sensitivity for identifying immunogenic epitopes and neoepitopes.</p><p><strong>Conclusions: </strong>TransHLA significantly enhances vaccine design and immunotherapy by efficiently identifying broadly reactive peptides. Our resources, including data and code, are publicly accessible at https://github.com/SkywalkerLuke/TransHLA.</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/PMC11878767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556462","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":"Cerebellocerebral connectivity predicts body mass index: a new open-source Python-based framework for connectome-based predictive modeling.","authors":"Tobias Bachmann, Karsten Mueller, Simon N A Kusnezow, Matthias L Schroeter, Paolo Piaggi, Christopher M Weise","doi":"10.1093/gigascience/giaf010","DOIUrl":"10.1093/gigascience/giaf010","url":null,"abstract":"<p><strong>Background: </strong>The cerebellum is one of the major central nervous structures consistently altered in obesity. Its role in higher cognitive function, parts of which are affected by obesity, is mediated through projections to and from the cerebral cortex. We therefore investigated the relationship between body mass index (BMI) and cerebellocerebral connectivity.</p><p><strong>Methods: </strong>We utilized the Human Connectome Project's Young Adults dataset, including functional magnetic resonance imaging (fMRI) and behavioral data, to perform connectome-based predictive modeling (CPM) restricted to cerebellocerebral connectivity of resting-state fMRI and task-based fMRI. We developed a Python-based open-source framework to perform CPM, a data-driven technique with built-in cross-validation to establish brain-behavior relationships. Significance was assessed with permutation analysis.</p><p><strong>Results: </strong>We found that (i) cerebellocerebral connectivity predicted BMI, (ii) task-general cerebellocerebral connectivity predicted BMI more reliably than resting-state fMRI and individual task-based fMRI separately, (iii) predictive networks derived this way overlapped with established functional brain networks (namely, frontoparietal networks, the somatomotor network, the salience network, and the default mode network), and (iv) we found there was an inverse overlap between networks predictive of BMI and networks predictive of cognitive measures adversely affected by overweight/obesity.</p><p><strong>Conclusions: </strong>Our results suggest obesity-specific alterations in cerebellocerebral connectivity, specifically with regard to task execution. With brain areas and brain networks relevant to task performance implicated, these alterations seem to reflect a neurobiological substrate for task performance adversely affected by obesity.</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/PMC11899596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614577","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":"Defining the limits of plant chemical space: challenges and estimations.","authors":"Chloe Engler Hart, Yojana Gadiya, Tobias Kind, Christoph A Krettler, Matthew Gaetz, Biswapriya B Misra, David Healey, August Allen, Viswa Colluru, Daniel Domingo-Fernández","doi":"10.1093/gigascience/giaf033","DOIUrl":"10.1093/gigascience/giaf033","url":null,"abstract":"<p><p>The plant kingdom, encompassing nearly 400,000 known species, produces an immense diversity of metabolites, including primary compounds essential for survival and secondary metabolites specialized for ecological interactions. These metabolites constitute a vast and complex phytochemical space with significant potential applications in medicine, agriculture, and biotechnology. However, much of this chemical diversity remains unexplored, as only a fraction of plant species has been studied comprehensively. In this work, we estimate the size of the plant chemical space by leveraging large-scale metabolomics and literature datasets. We begin by examining the known chemical space, which, while containing at most several hundred thousand unique compounds, remains sparsely covered. Using data from over 1,000 plant species, we apply various mass spectrometry-based approaches-a formula prediction model, a de novo prediction model, a combination of library search and de novo prediction, and MS2 clustering-to estimate the number of unique structures. Our methods suggest that the number of unique compounds in the metabolomics dataset alone may already surpass existing estimates of plant chemical diversity. Finally, we project these findings across the entire plant kingdom, estimating that the total plant chemical space likely spans millions, if not more, with most still unexplored.</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/PMC11970369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784433","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":"Mutation impact on mRNA versus protein expression across human cancers.","authors":"Yuqi Liu, Abdulkadir Elmas, Kuan-Lin Huang","doi":"10.1093/gigascience/giae113","DOIUrl":"10.1093/gigascience/giae113","url":null,"abstract":"<p><strong>Background: </strong>Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression-in addition to gene expression-has rarely been systematically investigated. This is significant as mRNA and protein levels frequently show only moderate correlation, driven by factors such as translation efficiency and protein degradation. Proteogenomic datasets from large tumor cohorts provide an opportunity to systematically analyze the effects of somatic mutations on mRNA and protein abundance and identify mutations with distinct impacts on these molecular levels.</p><p><strong>Results: </strong>We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across 6 cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including CDH1 and MSH3 truncations, as well as other mutations from likely \"long-tail\" driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins are more likely to be experimentally confirmed as functional.</p><p><strong>Conclusion: </strong>This study reveals that somatic mutations can exhibit distinct impacts on mRNA and protein levels, underscoring the necessity of integrating proteogenomic data to comprehensively identify functionally significant cancer mutations. These insights provide a framework for prioritizing mutations for further functional validation and therapeutic targeting.</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/PMC11702362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142947474","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":"Micromix: web infrastructure for visualizing and remixing microbial 'omics data.","authors":"Regan J Hayward, Titus Ebbecke, Hanna Fricke, Vo Quang Nguyen, Lars Barquist","doi":"10.1093/gigascience/giae120","DOIUrl":"10.1093/gigascience/giae120","url":null,"abstract":"<p><p>Micromix is a flexible web platform for sharing and integrating microbial omics data, including RNA sequencing and transposon-insertion sequencing. Currently, the lack of solutions for making data web-accessible results in omics data being fragmented across supplementary spreadsheets or languishing as raw read data in public repositories. Micromix solves this problem and can be easily deployed on a standard web server or using cloud services. It is organism-agnostic, accommodates data and annotations from various sources, and allows filtering based on KEGG pathways, Gene Ontology terms, and curated gene sets. Visualizations are provided through a plug-in system that integrates existing visualization services and allows rapid development of new services, with available plug-ins currently supporting interactive heatmap and clustering functions. Users can upload their own data in a variety of formats to perform integrative analyses in the context of existing datasets. To support collaborative research, Micromix allows sharing of interactive sessions that maintain defined filtering and/or visualization options. We demonstrate the utility of Micromix with case studies focusing on the SPI-2 pathogenicity island in Salmonella enterica and polysaccharide utilization loci in Bacteroides thetaiotaomicron, showcasing the platform's capabilities for integrating, filtering, and visualizing diverse functional genomic datasets. Micromix is available at http://micromix.systems.</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/PMC11788673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143079386","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-quality phenotypic and genotypic dataset of barley genebank core collection to unlock untapped genetic diversity.","authors":"Zhihui Yuan, Maximilian Rembe, Martin Mascher, Nils Stein, Axel Himmelbach, Murukarthick Jayakodi, Andreas Börner, Klaus Oldach, Ahmed Jahoor, Jens Due Jensen, Julia Rudloff, Viktoria-Elisabeth Dohrendorf, Luisa Pauline Kuhfus, Emmanuelle Dyrszka, Matthieu Conte, Frederik Hinz, Salim Trouchaud, Jochen C Reif, Samira El Hanafi","doi":"10.1093/gigascience/giae121","DOIUrl":"10.1093/gigascience/giae121","url":null,"abstract":"<p><strong>Background: </strong>Genebanks around the globe serve as valuable repositories of genetic diversity, offering not only access to a broad spectrum of plant material but also critical resources for enhancing crop resilience, advancing scientific research, and supporting global food security. To this end, traditional genebanks are evolving into biodigital resource centers where the integration of phenotypic and genotypic data for accessions can drive more informed decision-making, optimize resource allocation, and unlock new opportunities for plant breeding and research. However, the curation and availability of interoperable phenotypic and genotypic data for genebank accessions is still in its infancy and represents an obstacle to rapid scientific discoveries in this field. Therefore, effectively promoting FAIR (i.e., findable, accessible, interoperable, and reusable) access to these data is vital for maximizing the potential of genebanks and driving progress in agricultural innovation.</p><p><strong>Findings: </strong>Here we provide whole genome sequencing data of 812 barley (Hordeum vulgare L.) plant genetic resources and 298 European elite materials released between 1949 and 2021, as well as the phenotypic data for 4 disease resistance traits and 3 agronomic traits. The robustness of the investigated traits and the interoperability of genomic and phenotypic data were assessed in the current publication, aiming to make this panel publicly available as a resource for future genetic research in barley.</p><p><strong>Conclusions: </strong>The data showed broad phenotypic variability and high association mapping potential, offering a key resource for identifying genebank donors with untapped genes to advance barley breeding while safeguarding genetic diversity.</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/PMC11811526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143390809","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":"VCF2Dis: an ultra-fast and efficient tool to calculate pairwise genetic distance and construct population phylogeny from VCF files.","authors":"Lian Xu, Weiming He, Shuaishuai Tai, Xiaoli Huang, Mumu Qin, Xun Liao, Yi Jing, Jian Yang, Xiaodong Fang, Jianhua Shi, Nana Jin","doi":"10.1093/gigascience/giaf032","DOIUrl":"10.1093/gigascience/giaf032","url":null,"abstract":"<p><strong>Background: </strong>Genetic distance metrics are crucial for understanding the evolutionary relationships and population structure of organisms. Progress in next-generation sequencing technology has given rise of genotyping data of thousands of individuals. The standard Variant Call Format (VCF) is widely used to store genomic variation information, but calculating genetic distance and constructing population phylogeny directly from large VCF files can be challenging. Moreover, the existing tools that implement such functions remain limited and have low performance in processing large-scale genotype data, especially in the area of memory efficiency.</p><p><strong>Findings: </strong>To address these challenges, we introduce VCF2Dis, an ultra-fast and efficient tool that calculates pairwise genetic distance directly from large VCF files and then constructs distance-based population phylogeny using the ape package. Benchmarking results demonstrate the tool's efficiency, with rapid processing times, minimal memory usage (e.g., 0.37 GB for the complete analysis of 2,504 samples with 81.2 million variants), and high accuracy, even when handling datasets with millions of variants from thousands of individuals. Its straightforward command-line interface, compatibility with downstream phylogenetic analysis tools (e.g., MEGA, Phylip, and FastTree), and support for multithreading make it a valuable tool for researchers studying population relationships. These advantages meaning VCF2Dis has already been widely utilized in many published genomic studies.</p><p><strong>Conclusion: </strong>We present VCF2Dis, a straightforward and efficient tool for calculating genetic distance and constructing population phylogeny directly from large-scale genotype data. VCF2Dis has been widely applied, facilitating the exploration of population relationship in extensive genome sequencing 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/PMC11970368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784444","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":"Galaxy as a gateway to bioinformatics: Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for scRNA-seq.","authors":"Camila L Goclowski, Julia Jakiela, Tyler Collins, Saskia Hiltemann, Morgan Howells, Marisa Loach, Jonathan Manning, Pablo Moreno, Alex Ostrovsky, Helena Rasche, Mehmet Tekman, Graeme Tyson, Pavankumar Videm, Wendi Bacon","doi":"10.1093/gigascience/giae107","DOIUrl":"10.1093/gigascience/giae107","url":null,"abstract":"<p><strong>Background: </strong>Bioinformatics is fundamental to biomedical sciences, but its mastery presents a steep learning curve for bench biologists and clinicians. Learning to code while analyzing data is difficult. The curve may be flattened by separating these two aspects and providing intermediate steps for budding bioinformaticians. Single-cell analysis is in great demand from biologists and biomedical scientists, as evidenced by the proliferation of training events, materials, and collaborative global efforts like the Human Cell Atlas. However, iterative analyses lacking reinstantiation, coupled with unstandardized pipelines, have made effective single-cell training a moving target.</p><p><strong>Findings: </strong>To address these challenges, we present a Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for single-cell RNA sequencing (scRNA-seq) analysis, which offers parallel analytical methods using a graphical interface (buttons) or code. With clear, interoperable materials, MIGHTS facilitates smooth transitions between environments. Bridging the biologist-programmer gap, MIGHTS emphasizes interdisciplinary communication for effective learning at all levels. Real-world data analysis in MIGHTS promotes critical thinking and best practices, while FAIR data principles ensure validation of results. MIGHTS is freely available, hosted on the Galaxy Training Network, and leverages Galaxy interfaces for analyses in both settings. Given the ongoing popularity of Python-based (Scanpy) and R-based (Seurat & Monocle) scRNA-seq analyses, MIGHTS enables analyses using both.</p><p><strong>Conclusions: </strong>MIGHTS consists of 11 tutorials, including recordings, slide decks, and interactive visualizations, and a demonstrated track record of sustainability via regular updates and community collaborations. Parallel pathways in MIGHTS enable concurrent training of scientists at any programming level, addressing the heterogeneous needs of novice bioinformaticians.</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/PMC11707610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142947515","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":"Dimensionality reduction for visualizing spatially resolved profiling data using SpaSNE.","authors":"Yuansheng Zhou, Chen Tang, Xue Xiao, Xiaowei Zhan, Tao Wang, Guanghua Xiao, Lin Xu","doi":"10.1093/gigascience/giaf002","DOIUrl":"10.1093/gigascience/giaf002","url":null,"abstract":"<p><strong>Background: </strong>Spatially resolved profiling technologies to quantify transcriptomes, epigenomes, and proteomes have been emerging as groundbreaking methods for comprehensive molecular characterizations. Dimensionality reduction and visualization is an essential step to analyze and interpret spatially resolved profiling data. However, state-of-the-art dimensionality reduction methods for single-cell sequencing data, such as the t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), were not tailored for spatially resolved profiling data.</p><p><strong>Results: </strong>Here we developed a spatially resolved t-SNE (SpaSNE) method to integrate both spatial and molecular information. We applied it to a variety of public spatially resolved profiling datasets that were generated from 3 experimental platforms and consisted of cells from different diseases, tissues, and cell types. To compare the performances of SpaSNE, t-SNE, and UMAP, we applied them to 4 spatially resolved profiling datasets obtained from 3 distinct experimental platforms (Visium, STARmap, and MERFISH) on both diseased and normal tissues. Comparisons between SpaSNE and these state-of-the-art approaches reveal that SpaSNE achieves more accurate and meaningful visualization that better elucidates the underlying spatial and molecular data structures.</p><p><strong>Conclusions: </strong>This work demonstrates the broad application of SpaSNE for reliable and robust interpretation of cell types based on both molecular and spatial information, which can set the foundation for many subsequent analysis steps, such as differential gene expression and trajectory or pseudotime analysis on the spatially resolved profiling data.</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/PMC11831803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440606","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":"Multiomics analysis provides insights into musk secretion in muskrat and musk deer.","authors":"Tao Wang, Maosen Yang, Xin Shi, Shilin Tian, Yan Li, Wenqian Xie, Zhengting Zou, Dong Leng, Ming Zhang, Chengli Zheng, Chungang Feng, Bo Zeng, Xiaolan Fan, Huimin Qiu, Jing Li, Guijun Zhao, Zhengrong Yuan, Diyan Li, Hang Jie","doi":"10.1093/gigascience/giaf006","DOIUrl":"10.1093/gigascience/giaf006","url":null,"abstract":"<p><strong>Background: </strong>Musk, secreted by the musk gland of adult male musk-secreting mammals, holds significant pharmaceutical and cosmetic potential. However, understanding the molecular mechanisms of musk secretion remains limited, largely due to the lack of comprehensive multiomics analyses and available platforms for relevant species, such as muskrat (Ondatra zibethicus Linnaeus) and Chinese forest musk deer (Moschus berezovskii Flerov).</p><p><strong>Results: </strong>We generated chromosome-level genome assemblies for the 2 species of muskrat (Ondatra zibethicus Linnaeus) and musk deer (Moschus berezovskii Flerov), along with 168 transcriptomes from various muskrat tissues. Comparative analysis with 11 other vertebrate genomes revealed genes and amino acid sites with signs of adaptive convergent evolution, primarily linked to lipid metabolism, cell cycle regulation, protein binding, and immunity. Single-cell RNA sequencing in muskrat musk glands identified increased acinar/glandular epithelial cells during secretion, highlighting the role of lipometabolism in gland development and evolution. Additionally, we developed MuskDB (http://muskdb.cn/home/), a freely accessible multiomics database platform for musk-secreting mammals.</p><p><strong>Conclusions: </strong>The study concludes that the evolution of musk secretion in muskrats and musk deer is likely driven by lipid metabolism and cell specialization. This underscores the complexity of the musk gland and calls for further investigation into musk secretion-specific genetic variants.</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/PMC11878540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556460","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}