GigaScience最新文献

{"title":"Exploring the cellular and molecular basis of murine cardiac development through spatiotemporal transcriptome sequencing.","authors":"Jingmin Kang, Qingsong Li, Jie Liu, Lin Du, Peng Liu, Fuyan Liu, Yue Wang, Xunan Shen, Xujiao Luo, Ninghe Wang, Renhua Wu, Lei Song, Jizheng Wang, Xin Liu","doi":"10.1093/gigascience/giaf012","DOIUrl":"10.1093/gigascience/giaf012","url":null,"abstract":"<p><strong>Background: </strong>Spatial transcriptomics is a powerful tool that integrates molecular data with spatial information, thereby facilitating a deeper comprehension of tissue morphology and cellular interactions. In our study, we utilized cutting-edge spatial transcriptome sequencing technology to explore the development of the mouse heart and construct a comprehensive spatiotemporal cell atlas of early murine cardiac development.</p><p><strong>Results: </strong>Through the analysis of this atlas, we elucidated the spatial organization of cardiac cellular lineages and their interactions during the developmental process. Notably, we observed dynamic changes in gene expression within fibroblasts and cardiomyocytes. Moreover, we identified critical genes, such as Igf2, H19, and Tcap, as well as transcription factors Tcf12 and Plagl1, which may be associated with the loss of myocardial regeneration ability during early heart development. In addition, we successfully identified marker genes, like Adamts8 and Bmp10, that can distinguish between the left and right atria.</p><p><strong>Conclusion: </strong>Our study provides novel insights into murine cardiac development and offers a valuable resource for future investigations in the field of heart research, highlighting the significance of spatial transcriptomics in understanding the complex processes of organ development.</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/PMC11831923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440617","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":"xRead: a coverage-guided approach for scalable construction of read overlapping graph.","authors":"Tangchao Kong, Yadong Wang, Bo Liu","doi":"10.1093/gigascience/giaf007","DOIUrl":"10.1093/gigascience/giaf007","url":null,"abstract":"<p><strong>Background: </strong>The development of long-read sequencing is promising for the high-quality and comprehensive de novo assembly for various species around the world. However, it is still challenging for assemblers to handle thousands of genomes, tens of gigabase-level assembly sizes, and terabase-level datasets efficiently, which is a bottleneck to large-scale de novo sequencing studies. A major cause is the read overlapping graph construction that state-of-the-art tools usually have to cost terabyte-level RAM space and tens of days for large genomes. Such lower performance and scalability are not suited to handle the numerous samples being sequenced.</p><p><strong>Findings: </strong>Herein, we propose xRead, a novel iterative overlapping graph construction approach that achieves high performance, scalability, and yield simultaneously. Under the guidance of its coverage-based model, xRead converts read-overlapping to heuristic read-mapping and incremental graph construction tasks with highly controllable RAM space and faster speed. It enables the processing of very large datasets (such as the 1.28 Tb Ambystoma mexicanum dataset) with less than 64 GB RAM and obviously lower time costs. Moreover, benchmarks suggest that it can produce highly accurate and well-connected overlapping graphs, which are also supportive of various kinds of downstream assembly strategies.</p><p><strong>Conclusions: </strong>xRead is able to break through the major bottleneck to graph construction and lays a new foundation for de novo assembly. This tool is suited to handle a large number of datasets from large genomes and may play important roles in many de novo 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/PMC11831799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440619","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":"Enhancing inbreeding estimation and global conservation insights through chromosome-level assemblies of the Chinese and Malayan pangolin.","authors":"Tianming Lan, Yinping Tian, Minhui Shi, Boyang Liu, Yu Lin, Yanling Xia, Yue Ma, Sunil Kumar Sahu, Qing Wang, Jun Li, Jin Chen, Fanghui Hou, Chuanling Yin, Kai Wang, Yuan Fu, Tengcheng Que, Wenjian Liu, Huan Liu, Haimeng Li, Yan Hua","doi":"10.1093/gigascience/giaf003","DOIUrl":"10.1093/gigascience/giaf003","url":null,"abstract":"<p><p>A high-quality reference genome coupled with resequencing data is a promising strategy to address issues in conservation genomics. This has greatly enhanced the development of conservation plans for endangered species. Pangolins are fascinating animals with a variety of unique features. Unfortunately, they are the most trafficked wild animal in the world. In this study, we assembled a chromosome-scale genome with HiFi long reads and Hi-C short reads for the Chinese and Malayan pangolin and provided two new representative reference genomes for the pangolin species. We found a great improvement in the evaluation of genetic diversity and inbreeding based on these high-quality genomes and obtained different results for the detection of genome-wide extinction risks compared with genomes assembled using short reads. Moderate inbreeding and genetic diversity were reverified in these two pangolin species, except for one Malayan pangolin population with high inbreeding and low genetic diversity. Moreover, we identified a much higher inbreeding level (FROH = 0.54) in the Chinese pangolin individual from Taiwan Province compared with that from Mainland China, but more than 99.6% runs of homozygosity (ROH) fragments were restricted to less than 1 Mb, indicating that the high FROH in Taiwan Chinese pangolins may have accumulated from historical inbreeding events. Furthermore, our study is the first to detect relatively mild genetic purging in pangolin populations. These two high-quality reference genomes will provide valuable genetic resources for future studies and contribute to the protection and conservation of pangolins.</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/PMC11825179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143412817","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-fidelity wheat plant reconstruction using 3D Gaussian splatting and neural radiance fields.","authors":"Lewis A G Stuart, Darren M Wells, Jonathan A Atkinson, Simon Castle-Green, Jack Walker, Michael P Pound","doi":"10.1093/gigascience/giaf022","DOIUrl":"10.1093/gigascience/giaf022","url":null,"abstract":"<p><strong>Background: </strong>The reconstruction of 3-dimensional (3D) plant models can offer advantages over traditional 2-dimensional approaches by more accurately capturing the complex structure and characteristics of different crops. Conventional 3D reconstruction techniques often produce sparse or noisy representations of plants using software or are expensive to capture in hardware. Recently, view synthesis models have been developed that can generate detailed 3D scenes, and even 3D models, from only RGB images and camera poses. These models offer unparalleled accuracy but are currently data hungry, requiring large numbers of views with very accurate camera calibration.</p><p><strong>Results: </strong>In this study, we present a view synthesis dataset comprising 20 individual wheat plants captured across 6 different time frames over a 15-week growth period. We develop a camera capture system using 2 robotic arms combined with a turntable, controlled by a re-deployable and flexible image capture framework. We trained each plant instance using two recent view synthesis models: 3D Gaussian splatting (3DGS) and neural radiance fields (NeRF). Our results show that both 3DGS and NeRF produce high-fidelity reconstructed images of a plant subject from views not captured in the initial training sets. We also show that these approaches can be used to generate accurate 3D representations of these plants as point clouds, with 0.74-mm and 1.43-mm average accuracy compared with a handheld scanner for 3DGS and NeRF, respectively.</p><p><strong>Conclusion: </strong>We believe that these new methods will be transformative in the field of 3D plant phenotyping, plant reconstruction, and active vision. To further this cause, we release all robot configuration and control software, alongside our extensive multiview dataset. We also release all scripts necessary to train both 3DGS and NeRF, all trained models data, and final 3D point cloud representations. Our dataset can be accessed via https://plantimages.nottingham.ac.uk/ or https://https://doi.org/10.5524/102661. Our software can be accessed via https://github.com/Lewis-Stuart-11/3D-Plant-View-Synthesis.</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/PMC11945317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729555","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":"EnrichDO: a global weighted model for Disease Ontology enrichment analysis.","authors":"Haixiu Yang, Hongyu Fu, Meiyi Zhang, Yangyang Liu, Yongqun Oliver He, Chao Wang, Liang Cheng","doi":"10.1093/gigascience/giaf021","DOIUrl":"10.1093/gigascience/giaf021","url":null,"abstract":"<p><strong>Background: </strong>Disease Ontology (DO) has been widely studied in biomedical research and clinical practice to describe the roles of genes. DO enrichment analysis is an effective means to discover associations between genes and diseases. Compared to hundreds of Gene Ontology (GO)-based enrichment analysis methods, however, DO-based methods are relatively scarce, and most current DO-based approaches are term-for-term and thus are unable to solve over-enrichment problems caused by the \"true-path\" rule.</p><p><strong>Results: </strong>Here, we describe a novel double-weighted model, EnrichDO, which leverages the latest annotations of the human genome with DO terms and integrates DO graph topology on a global scale. Compared to classic enrichment methods (mainly for GO) and existing DO-based enrichment tools, EnrichDO performs better in both GO and DO enrichment analysis cases. It can accurately identify more specific terms, without ignoring the truly associated parent terms, as shown in the Alzheimer's disease (AD) case (AD ranked first). Moreover, both a simulated test and a data perturbation test validate the accuracy and robustness of EnrichDO. Finally, EnrichDO is applied to other types of datasets to expand its application, including gene expression profile datasets, a host gene set of microorganisms, and hallmark gene sets. Based on the findings reported here, EnrichDO shows significant improvement via all experimental results.</p><p><strong>Conclusions: </strong>EnrichDO provides an effective DO enrichment analysis model for gaining insight into the significance of a particular gene set in the context of disease. To increase the usability of EnrichDO, we have developed an R-based software package, which is freely available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/EnrichDO.html) or at https://github.com/liangcheng-hrbmu/EnrichDO.</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/PMC11945307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729552","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":"The molecular basis of octocoral calcification revealed by genome and skeletal proteome analyses.","authors":"Yanshuo Liang, Kuidong Xu, Junyuan Li, Jingyuan Shi, Jiehong Wei, Xiaoyu Zheng, Wanying He, Xin Zhang","doi":"10.1093/gigascience/giaf031","DOIUrl":"10.1093/gigascience/giaf031","url":null,"abstract":"<p><p>The ability of octocorals and stony corals to deposit calcium carbonate (CaCO3) has contributed to their ecological success. Whereas stony corals possess a homogeneous aragonite skeleton, octocorals have developed distinct skeletal structures composed of different CaCO3 polymorphs and a skeletal organic matrix. Nevertheless, the molecular basis of skeletal structure formation in octocorals remains inadequately understood. Here, we sequenced the genomes and skeletal proteomes of two calcite-forming octocorals, namely Paragorgia papillata and Chrysogorgia sp. The assembled genomes sizes were 618.13 Mb and 781.04 Mb for P. papillata and Chrysogorgia sp., respectively, with contig N50s of 2.67 Mb and 2.61 Mb. Comparative genomic analyses identified 162 and 285 significantly expanded gene families in the genomes of P. papillata and Chrysogorgia sp., respectively, which are primarily associated with biomineralization and immune response. Furthermore, comparative analyses of skeletal proteomes demonstrated that corals with different CaCO3 polymorphs share a fundamental toolkit comprising cadherin, von Willebrand factor type A, and carbonic anhydrase domains for calcified skeleton deposition. In contrast, collagen is abundant in the calcite-forming octocoral skeletons but occurs rarely in aragonitic stony corals. Additionally, certain collagens have developed domains related to matrix adhesion and immunity, which may confer novel genetic functions in octocoral calcification. These findings enhance our understanding of the diverse forms of coral biomineralization processes and offer preliminary insights into the formation and evolution of the octocoral skeleton.</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/PMC11959691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752101","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":"DeepAnnotation: A novel interpretable deep learning-based genomic selection model that integrates comprehensive functional annotations.","authors":"Wenlong Ma, Weigang Zheng, Shenghua Qin, Chao Wang, Bowen Lei, Yuwen Liu","doi":"10.1093/gigascience/giaf083","DOIUrl":"https://doi.org/10.1093/gigascience/giaf083","url":null,"abstract":"<p><strong>Background: </strong>Genomic selection, which leverages genomic information to predict the breeding value of individuals, has dramatically accelerated the improvement of economically important traits. The growing availability of multiomics data in agricultural species offers an unprecedented opportunity to enrich this process with prior biological knowledge. However, fully harnessing these rich data sources for accurate phenotype prediction in genomic selection remains in its early stages.</p><p><strong>Results: </strong>In this study, we present DeepAnnotation, a novel interpretable genomic selection model designed for phenotype prediction by integrating comprehensive multiomics functional annotations using deep learning. To capture the complex information flow from genotype to phenotype, DeepAnnotation aligns multiomics biological annotations with sequential network layers in a deep learning architecture, mirroring the natural regulatory cascade from genotype to intermediate molecular phenotypes-such as cis-regulatory elements, genes, and gene modules-and ultimately to phenotypes of economic traits. Comparing against 7 classical models (rrBLUP, LightGBM, KAML, BLUP, BayesR, MBLUP, and BayesRC), DeepAnnotation demonstrated significantly superior prediction accuracy (Pearson correlation coefficient increased by 6.4% to 120.0%) and computational efficiency for 3 pork production traits (lean meat percentage, loin muscle depth, and back fat thickness) using a dataset of 1,700 training Duroc boars and 240 independent validation individuals, each genotyped for 11,633,164 single-nucleotide polymorphisms (SNPs), particularly in identifying top-performing individuals. Furthermore, the interpretability embedded within our framework enables the identification of potential causal SNPs and the exploration of their mediated molecular mechanisms underlying trait variation.</p><p><strong>Conclusions: </strong>DeepAnnotation is an open-source, interpretable deep learning approach for phenotype prediction, leveraging comprehensive multiomics functional annotations. Freely accessible via GitHub and Docker, it provides a valuable tool for researchers and practitioners in genomic selection.</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/PMC12392413/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144950553","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 novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks.","authors":"Mark Schuiveling, Hong Liu, Daniel Eek, Gerben E Breimer, Karijn P M Suijkerbuijk, Willeke A M Blokx, Mitko Veta","doi":"10.1093/gigascience/giaf011","DOIUrl":"10.1093/gigascience/giaf011","url":null,"abstract":"<p><strong>Background: </strong>Melanoma is an aggressive form of skin cancer in which tumor-infiltrating lymphocytes (TILs) are a biomarker for recurrence and treatment response. Manual TIL assessment is prone to interobserver variability, and current deep learning models are not publicly accessible or have low performance. Deep learning models, however, have the potential of consistent spatial evaluation of TILs and other immune cell subsets with the potential of improved prognostic and predictive value. To make the development of these models possible, we created the Panoptic Segmentation of nUclei and tissue in advanced MelanomA (PUMA) dataset and assessed the performance of several state-of-the-art deep learning models. In addition, we show how to improve model performance further by using heuristic postprocessing in which nuclei classes are updated based on their tissue localization.</p><p><strong>Results: </strong>The PUMA dataset includes 155 primary and 155 metastatic melanoma hematoxylin and eosin-stained regions of interest with nuclei and tissue annotations from a single melanoma referral institution. The Hover-NeXt model, trained on the PUMA dataset, demonstrated the best performance for lymphocyte detection, approaching human interobserver agreement. In addition, heuristic postprocessing of deep learning models improved the detection of noncommon classes, such as epithelial nuclei.</p><p><strong>Conclusion: </strong>The PUMA dataset is the first melanoma-specific dataset that can be used to develop melanoma-specific nuclei and tissue segmentation models. These models can, in turn, be used for prognostic and predictive biomarker development. Incorporating tissue and nuclei segmentation is a step toward improved deep learning nuclei segmentation performance. To support the development of these models, this dataset is used in the PUMA challenge.</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/PMC11837757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457766","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":"CellBinDB: a large-scale multimodal annotated dataset for cell segmentation with benchmarking of universal models.","authors":"Can Shi, Jinghong Fan, Zhonghan Deng, Huanlin Liu, Qiang Kang, Yumei Li, Jing Guo, Jingwen Wang, Jinjiang Gong, Sha Liao, Ao Chen, Ying Zhang, Mei Li","doi":"10.1093/gigascience/giaf069","DOIUrl":"10.1093/gigascience/giaf069","url":null,"abstract":"<p><p>In recent years, cell segmentation techniques have played a critical role in the analysis of biological images, especially for quantitative studies. Deep learning-based cell segmentation models have demonstrated remarkable performance in segmenting cell and nucleus boundaries, but they are typically tailored to specific modalities or require manual tuning of hyperparameters, limiting their generalizability to unseen data. Comprehensive datasets that support both the training of universal models and the evaluation of various segmentation techniques are essential for overcoming these limitations and promoting the development of more versatile cell segmentation solutions. Here, we present CellBinDB, a large-scale multimodal annotated dataset established for these purposes. CellBinDB contains more than 1,000 annotated images, each labeled to identify the boundaries of cells or nuclei, including 4',6-diamidino-2-phenylindole, single-stranded DNA, hematoxylin and eosin, and multiplex immunofluorescence staining, covering over 30 normal and diseased tissue types from human and mouse samples. Based on CellBinDB, we benchmarked 8 state-of-the-art and widely used cell segmentation technologies/methods, and our further analysis reveals that complex cell shapes reduce segmentation accuracy while higher image gradients improve boundary detection, offering insights for refining segmentation strategies across diverse imaging scenarios.</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/PMC12206155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474806","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":"The telomere-to-telomere gap-free reference genome and taxonomic reassessment of Siniperca roulei.","authors":"Min Jiang, Chenxi Zhao, Fengjiao Ma, Denghua Yin, Chenhe Wang, Jianbo Jian, Kai Liu","doi":"10.1093/gigascience/giaf068","DOIUrl":"10.1093/gigascience/giaf068","url":null,"abstract":"<p><p>Siniperca roulei is primarily distributed in the eastern waters of China, with its population being both scarce and vulnerable. Research on this species remains limited, with few studies conducted on its biology and genetics, which hampers efforts to conserve its germplasm resources. To support breeding and conservation efforts, we generated a gap-free genome assembly using a combination of DNBSeq short reads, PacBio HiFi long reads, Nanopore ultra-long reads, and Hi-C data. The nearly telomere-to-telomere (T2T) genome of S. roulei spans 717.34 Mb, with a contig N50 of 30.25 Mb, and each chromosome is represented by a single contig. A total of 26,596 genes were predicted, with 87.97% functionally annotated. These high-precision genomic data provide valuable insights into the germplasm resources of S. roulei, offering crucial information for clarifying the taxonomic status and evolutionary history of sinipercids. These findings are significant for the conservation and sustainable use of its germplasm resources.</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/PMC12263216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144642308","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}