GigaScience最新文献

{"title":"Emerging AI Approaches for Cancer Spatial Omics.","authors":"Javad Noorbakhsh, Ali Foroughi Pour, Jeffrey Chuang","doi":"10.1093/gigascience/giaf128","DOIUrl":"https://doi.org/10.1093/gigascience/giaf128","url":null,"abstract":"<p><p>Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity which demands not only improved data integration, but also new conceptual frameworks. We discuss emerging paradigms - in particular data-driven spatial AI, constraint-based spatial AI, and mechanistic spatial modeling - as well as the importance of integrating AI with hypothesis-driven strategies and model systems to realize the value of cancer spatial information.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SeedGerm-VIG: an open and comprehensive pipeline to quantify seed vigour in wheat and other cereal crops using deep learning powered dynamic phenotypic analysis.","authors":"Jie Dai, Zhenjie Wen, Mujahid Ali, Jinlong Huang, Shuchen Liu, Jianhua Zhao, Felipe Pinheiro, Changcai Yang, Bin Wang, Lingzhen Ye, Xueying Guan, Ji Zhou","doi":"10.1093/gigascience/giaf129","DOIUrl":"https://doi.org/10.1093/gigascience/giaf129","url":null,"abstract":"<p><p>As one of the most important cereal crops, wheat (Triticum aestivum L.) production and grain quality are essential to many nations in the world. Early developmental phases such as seed germination and seedling establishment are key to wheat's growth and development as they impact directly on crop's early performance and yield potential. Hence, it is critical to develop varieties with favourable early growth characteristics under various growing conditions. Here, we present SeedGerm-VIG, an automated and comprehensive pipeline developed for assessing seed vigour in wheat and other cereal crops. Building on the SeedGerm system, we integrated multiple deep learning models (i.e. YOLOv8x-Germ and optimised U-Net) and computer vision algorithms into the automated seed-level analysis pipeline to identify key germination phases and measure seed-, root-, and seedling-level phenotypic traits. Then, by using time series directed graph, not only did we track root tips to measure root emergence during the germination procedure (seed-lot R2 = 84.1%), but we also established a new approach to examine speed and uniformity of germination. These resulted in the establishment of a vigour scoring matrix, through which 21 commercial genotypes' (i.e. 494 randomly sampled seeds, with over 29,500 seed-level images) vigour scores were summarised and evaluated at key phases such as protrusion, radicle emergence, and chloroplast biogenesis, which largely matched with manual assessment based on the International Seed Testing Association (ISTA) guidelines. Finally, we also demonstrated that the SeedGerm-VIG pipeline could be used to assess seed vigour for other cereal crops such as rice (n = 120 seeds) and barley (n = 240 seeds), reliably. In conclusion, we believe that our work demonstrates a valuable step forward to enable the broader plant and crop research community to examine seed vigour and vigour-related features in an automated manner, facilitating effective and reproducible plant selection and relevant seed science research for crop improvement.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An analysis of performance bottlenecks in MRI preprocessing.","authors":"Mathieu Dugré, Yohan Chatelain, Tristan Glatard","doi":"10.1093/gigascience/giae098","DOIUrl":"10.1093/gigascience/giae098","url":null,"abstract":"<p><p>Magnetic resonance imaging (MRI) preprocessing is a critical step for neuroimaging analysis. However, the computational cost of MRI preprocessing pipelines is a major bottleneck for large cohort studies and some clinical applications. While high-performance computing and, more recently, deep learning have been adopted to accelerate the computations, these techniques require costly hardware and are not accessible to all researchers. Therefore, it is important to understand the performance bottlenecks of MRI preprocessing pipelines to improve their performance. Using the Intel VTune profiler, we characterized the bottlenecks of several commonly used MRI preprocessing pipelines from the Advanced Normalization Tools (ANTs), FMRIB Software Library, and FreeSurfer toolboxes. We found few functions contributed to most of the CPU time and that linear interpolation was the largest contributor. Data access was also a substantial bottleneck. We identified a bug in the Insight Segmentation and Registration Toolkit library that impacts the performance of the ANTs pipeline in single precision and a potential issue with the OpenMP scaling in FreeSurfer recon-all. Our results provide a reference for future efforts to optimize MRI preprocessing pipelines.</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/PMC11899568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614576","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":"Similar, but not the same: multiomics comparison of human valve interstitial cells and osteoblast osteogenic differentiation expanded with an estimation of data-dependent and data-independent PASEF proteomics.","authors":"Arseniy Lobov, Polina Kuchur, Nadezhda Boyarskaya, Daria Perepletchikova, Ivan Taraskin, Andrei Ivashkin, Daria Kostina, Irina Khvorova, Vladimir Uspensky, Egor Repkin, Evgeny Denisov, Tatiana Gerashchenko, Rashid Tikhilov, Svetlana Bozhkova, Vitaly Karelkin, Chunli Wang, Kang Xu, Anna Malashicheva","doi":"10.1093/gigascience/giae110","DOIUrl":"10.1093/gigascience/giae110","url":null,"abstract":"<p><p>Osteogenic differentiation is crucial in normal bone formation and pathological calcification, such as calcific aortic valve disease (CAVD). Understanding the proteomic and transcriptomic landscapes underlying this differentiation can unveil potential therapeutic targets for CAVD. In this study, we employed RNA sequencing transcriptomics and proteomics on a timsTOF Pro platform to explore the multiomics profiles of valve interstitial cells (VICs) and osteoblasts during osteogenic differentiation. For proteomics, we utilized 3 data acquisition/analysis techniques: data-dependent acquisition (DDA)-parallel accumulation serial fragmentation (PASEF) and data-independent acquisition (DIA)-PASEF with a classic library-based (DIA) and machine learning-based library-free search (DIA-ML). Using RNA sequencing data as a biological reference, we compared these 3 analytical techniques in the context of actual biological experiments. We use this comprehensive dataset to reveal distinct proteomic and transcriptomic profiles between VICs and osteoblasts, highlighting specific biological processes in their osteogenic differentiation pathways. The study identified potential therapeutic targets specific for VICs osteogenic differentiation in CAVD, including the MAOA and ERK1/2 pathway. From a technical perspective, we found that DIA-based methods demonstrate even higher superiority against DDA for more sophisticated human primary cell cultures than it was shown before on HeLa samples. While the classic library-based DIA approach has proved to be a gold standard for shotgun proteomics research, the DIA-ML offers significant advantages with a relatively minor compromise in data reliability, making it the method of choice for routine proteomics.</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/PMC11724719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055932","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":"How to select predictive models for decision-making or causal inference.","authors":"Matthieu Doutreligne, Gaël Varoquaux","doi":"10.1093/gigascience/giaf016","DOIUrl":"10.1093/gigascience/giaf016","url":null,"abstract":"<p><strong>Background: </strong>We investigate which procedure selects the most trustworthy predictive model to explain the effect of an intervention and support decision-making.</p><p><strong>Methods: </strong>We study a large variety of model selection procedures in practical settings: finite samples settings and without a theoretical assumption of well-specified models. Beyond standard cross-validation or internal validation procedures, we also study elaborate causal risks. These build proxies of the causal error using \"nuisance\" reweighting to compute it on the observed data. We evaluate whether empirically estimated nuisances, which are necessarily noisy, add noise to model selection and compare different metrics for causal model selection in an extensive empirical study based on a simulation and 3 health care datasets based on real covariates.</p><p><strong>Results: </strong>Among all metrics, the mean squared error, classically used to evaluate predictive modes, is worse. Reweighting it with a propensity score does not bring much improvement in most cases. On average, the $Rtext{-risk}$, which uses as nuisances a model of mean outcome and propensity scores, leads to the best performances. Nuisance corrections are best estimated with flexible estimators such as a super learner.</p><p><strong>Conclusions: </strong>When predictive models are used to explain the effect of an intervention, they must be evaluated with different procedures than standard predictive settings, using the $Rtext{-risk}$ from causal inference.</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/PMC11927402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673822","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 haplotype-resolved T2T genome for Bauhinia × blakeana sheds light on the genetic basis of flower heterosis.","authors":"Weixue Mu, Joshua Casey Darian, Wing-Kin Sung, Xing Guo, Tuo Yang, Mandy Wai Man Tang, Ziqiang Chen, Steve Kwan Hok Tong, Irene Wing Shan Chik, Robert L Davidson, Scott C Edmunds, Tong Wei, Stephen Kwok-Wing Tsui","doi":"10.1093/gigascience/giaf044","DOIUrl":"https://doi.org/10.1093/gigascience/giaf044","url":null,"abstract":"<p><strong>Background: </strong>The Hong Kong orchid tree Bauhinia × blakeana Dunn has long been proposed to be a sterile interspecific hybrid exhibiting flower heterosis when compared to its likely parental species, Bauhinia purpurea L. and Bauhinia variegata L. Here, we report comparative genomic and transcriptomic analyses of the 3 Bauhinia species.</p><p><strong>Findings: </strong>We generated chromosome-level assemblies for the parental species and applied a trio-binning approach to construct a haplotype-resolved telomere-to-telomere (T2T) genome for B. blakeana. Comparative chloroplast genome analysis confirmed B. purpurea as the maternal parent. Transcriptome profiling of flower tissues highlighted a closer resemblance of B. blakeana to its maternal parent. Differential gene expression analyses revealed distinct expression patterns among the 3 species, particularly in biosynthetic and metabolic processes. To investigate the genetic basis of flower heterosis observed in B. blakeana, we focused on gene expression patterns within pigment biosynthesis-related pathways. High-parent dominance and overdominance expression patterns were observed, particularly in genes associated with carotenoid biosynthesis. Additionally, allele-specific expression analysis revealed a balanced contribution of maternal and paternal alleles in shaping the gene expression patterns in B. blakeana.</p><p><strong>Conclusions: </strong>Our study offers valuable insights into the genome architecture of hybrid B. blakeana, establishing a comprehensive genomic and transcriptomic resource for future functional genetics research within the Bauhinia genus. It also serves as a model for exploring the characteristics of hybrid species using T2T haplotype-resolved genomes, providing a novel approach to understanding genetic interactions and evolutionary mechanisms in complex genomes with high heterozygosity.</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/PMC12012898/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143964846","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":"Overture: an open-source genomics data platform.","authors":"Mitchell Shiell, Rosi Bajari, Dusan Andric, Jon Eubank, Brandon F Chan, Anders J Richardsson, Azher Ali, Bashar Allabadi, Yelizar Alturmessov, Jared Baker, Ann Catton, Kim Cullion, Daniel DeMaria, Patrick Dos Santos, Henrich Feher, Francois Gerthoffert, Minh Ha, Robin A Haw, Atul Kachru, Alexandru Lepsa, Alexis Li, Rakesh N Mistry, Hardeep K Nahal-Bose, Aleksandra Pejovic, Samantha Rich, Leonardo Rivera, Ciarán Schütte, Edmund Su, Robert Tisma, Jaser Uddin, Chang Wang, Alex N Wilmer, Linda Xiang, Junjun Zhang, Lincoln D Stein, Vincent Ferretti, Mélanie Courtot, Christina K Yung","doi":"10.1093/gigascience/giaf038","DOIUrl":"https://doi.org/10.1093/gigascience/giaf038","url":null,"abstract":"<p><strong>Background: </strong>Next-generation sequencing has created many new technological challenges in organizing and distributing genomics datasets, which now can routinely reach petabyte scales. Coupled with data-hungry artificial intelligence and machine learning applications, findable, accessible, interoperable, and reusable genomics datasets have never been more valuable. While major archives like the Genomics Data Commons, Sequence Reads Archive, and European Genome-Phenome Archive have improved researchers' ability to share and reuse data, and general-purpose repositories such as Zenodo and Figshare provide valuable platforms for research data publication, the diversity of genomics research precludes any one-size-fits-all approach. In many cases, bespoke solutions are required, and despite funding agencies and journals increasingly mandating reusable data practices, researchers still lack the technical support needed to meet the multifaceted challenges of data reuse.</p><p><strong>Findings: </strong>Overture bridges this gap by providing open-source software for building and deploying customizable genomics data platforms. Its architecture consists of modular microservices, each of which is generalized with narrow responsibilities that together combine to create complete data management systems. These systems enable researchers to organize, share, and explore their genomics data at any scale. Through Overture, researchers can connect their data to both humans and machines, fostering reproducibility and enabling new insights through controlled data sharing and reuse.</p><p><strong>Conclusions: </strong>By making these tools freely available, we can accelerate the development of reliable genomic data management across the research community quickly, flexibly, and at multiple scales. Overture is an open-source project licensed under AGPLv3.0 with all source code publicly available from https://github.com/overture-stack and documentation on development, deployment, and usage available from www.overture.bio.</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/PMC12020472/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143996787","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":"Telomere-to-telomere genome assembly of Electrophorus electricus provides insights into the evolution of electric eels.","authors":"Zan Qi, Qun Liu, Haorong Li, Yaolei Zhang, Ziwei Yu, Wenkai Luo, Kun Wang, Yuxin Zhang, Shoupeng Pan, Chao Wang, Hui Jiang, Qiang Qiu, Wen Wang, Guangyi Fan, Yongxin Li","doi":"10.1093/gigascience/giaf024","DOIUrl":"10.1093/gigascience/giaf024","url":null,"abstract":"<p><strong>Background: </strong>Electric eels evolved remarkable electric organs that enable them to instantaneously discharge hundreds of volts for predation, defense, and communication. However, the absence of a high-quality reference genome has extremely constrained the studies of electric eels in various aspects.</p><p><strong>Results: </strong>Using high-depth, multiplatform sequencing data, we successfully assembled the first telomere-to-telomere high-quality reference genome of Electrophorus electricus, which has a genome size of 833.43 Mb and comprises 26 chromosomes. Multiple evaluations, including N50 statistics (30.38 Mb), BUSCO scores (97.30%), and mapping ratio of short-insert sequencing data (99.91%), demonstrate the high contiguity and completeness of the electric eel genome assembly we obtained. Genome annotation predicted 396.63 Mb repetitive sequences and 20,992 protein-coding genes. Furthermore, evolutionary analyses indicate that Gymnotiformes, which the electric eel belongs to, has a closer relationship with Characiformes than Siluriformes and diverged from Characiformes 95.00 million years ago. Pairwise sequentially Markovian coalescent analysis found a sharply decreased trend of the population size of E. electricus over the past few hundred thousand years. Furthermore, many regulatory factors related to neurotransmitters and classical signaling pathways during embryonic development were significantly expanded, potentially contributing to the generation of high-voltage electricity.</p><p><strong>Conclusions: </strong>This study not only provided the first high-quality telomere-to-telomere reference genome of E. electricus but also greatly enhanced our understanding of electric eels.</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/PMC11959694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752095","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":"New implementation of data standards for AI in oncology: Experience from the EuCanImage project.","authors":"Teresa García-Lezana, Maciej Bobowicz, Santiago Frid, Michael Rutherford, Mikel Recuero, Katrine Riklund, Aldar Cabrelles, Marlena Rygusik, Lauren Fromont, Roberto Francischello, Emanuele Neri, Salvador Capella, Arcadi Navarro, Fred Prior, Jonathan Bona, Pilar Nicolas, Martijn P A Starmans, Karim Lekadir, Jordi Rambla","doi":"10.1093/gigascience/giae101","DOIUrl":"10.1093/gigascience/giae101","url":null,"abstract":"<p><strong>Background: </strong>An unprecedented amount of personal health data, with the potential to revolutionize precision medicine, is generated at health care institutions worldwide. The exploitation of such data using artificial intelligence (AI) relies on the ability to combine heterogeneous, multicentric, multimodal, and multiparametric data, as well as thoughtful representation of knowledge and data availability. Despite these possibilities, significant methodological challenges and ethicolegal constraints still impede the real-world implementation of data models.</p><p><strong>Technical details: </strong>The EuCanImage is an international consortium aimed at developing AI algorithms for precision medicine in oncology and enabling secondary use of the data based on necessary ethical approvals. The use of well-defined clinical data standards to allow interoperability was a central element within the initiative. The consortium is focused on 3 different cancer types and addresses 7 unmet clinical needs. We have conceived and implemented an innovative process to capture clinical data from hospitals, transform it into the newly developed EuCanImage data models, and then store the standardized data in permanent repositories. This new workflow combines recognized software (REDCap for data capture), data standards (FHIR for data structuring), and an existing repository (EGA for permanent data storage and sharing), with newly developed custom tools for data transformation and quality control purposes (ETL pipeline, QC scripts) to complement the gaps.</p><p><strong>Conclusion: </strong>This article synthesizes our experience and procedures for health care data interoperability, standardization, and reproducibility.</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/PMC12071370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010593","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":"Best-practice guidance for Earth BioGenome Project sample collection and processing: progress and challenges in biodiverse reference genome creation.","authors":"Mara K N Lawniczak, Kevin M Kocot, Jonas J Astrin, Mark Blaxter, Cibele G Sotero-Caio, Katharine B Barker, Anna K Childers, Jonathan Coddington, Paul Davis, Kerstin Howe, Warren E Johnson, Duane D McKenna, Jeremy G Wideman, Olga Vinnere Pettersson, Verena Ras, Bernardo F Santos","doi":"10.1093/gigascience/giaf041","DOIUrl":"10.1093/gigascience/giaf041","url":null,"abstract":"<p><p>The Earth BioGenome Project has the extremely ambitious goal of generating, at scale, high-quality reference genomes across the entire Tree of Life. Currently in its first phase, the project is targeting family-level representatives and is progressing rapidly. Here we outline recommended standards and considerations in sample acquisition and processing for those involved in biodiverse reference genome creation. These standards and recommendations will evolve with advances in related processes. Additionally, we discuss the challenges raised by the ambitions for later phases of the project, highlighting topics related to sample collection and processing that require further 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/PMC12121479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173608","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}