GigaScience最新文献

{"title":"Integrative single-cell transcriptomics and proteomics reveal an immunometabolic framework for MSC-exosome-mediated remodeling of expanded NK cells.","authors":"Yunyun Fu, Yi Liu, Mingwen Xu, Gaojun Liu, Jianzhi Sun, Fanyu Bu, Wenqing Xie, Jiayi Zhao, Jun Luo, Qiang Guo, Yinghua Huang, Fengping Xu, Siqi Liu, Longqi Liu, Ying Fu, Xuan Dong","doi":"10.1093/gigascience/giag049","DOIUrl":"10.1093/gigascience/giag049","url":null,"abstract":"<p><strong>Background: </strong>Natural killer (NK) cells play a central role in anti-tumor immunity and immunosurveillance of senescence, yet their clinical performance is frequently limited by functional exhaustion during ex vivo expansion. Mesenchymal stem cell-derived exosomes (MSC-Exos) are increasingly recognized as immunomodulators, but their broader effects on NK cell fitness and functional states remain incompletely characterized.</p><p><strong>Results: </strong>Here, we assessed MSC-Exos-mediated regulation of human NK cells using a standardized ex vivo priming platform integrated with single-cell transcriptomics and proteomic profiling. MSC-Exos significantly improved NK cell viability in a dose- and time-dependent manner while preserving a CD56⁺CD3- NK cell-enriched phenotype. MSC-Exos-treated NK cells showed enhanced cytotoxicity against K562 tumor cells and senescent fibroblasts. This phenotype was accompanied by increased expression of the activating receptors NKG2D and CD16, reduced LAG3 expression, and enhanced granzyme B expression and degranulation. Consistent with improved NK cell fitness, MSC-Exos treatment was also associated with upregulated expression of genes involved in NRF2-linked redox programs and improved mitochondrial readouts in NK cells. Single-cell analyses of MSC-Exos-treated NK cells revealed enhanced immune-effector programs and reduced inflammatory stress, while trajectory inference indicated that MSC-Exos may bias the NK cell state distribution toward more cytotoxic effector-like states. Proteomic profiling of MSC-Exos identified enrichment of FcγR-associated signaling components, supporting the hypothesis that exosomal composition may be related to the FcγR/CD16-associated transcriptional and phenotypic features observed in MSC-Exos-treated NK cells.</p><p><strong>Conclusions: </strong>Our data indicate that MSC-Exos improve NK cell viability and functional fitness during ex vivo expansion and bias NK cells toward a more effector-cytotoxic state. Together, these findings provide an immunometabolic framework for MSC-Exos-assisted NK cell manufacturing while underscoring the need for further causal validation.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13273429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147729001","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":"ClusterGraph: a new tool for visualisation and compression of multidimensional data.","authors":"Paweł Dłotko, Davide Gurnari, Mathis Hallier, Anna Jurek-Loughrey","doi":"10.1093/gigascience/giag070","DOIUrl":"10.1093/gigascience/giag070","url":null,"abstract":"<p><strong>Background: </strong>Understanding the organization of high-dimensional data is of primary interest for many branches of applied sciences. It is typically achieved by applying dimensionality reduction techniques, which, while preserving local features, often miss the global structure of the dataset. Clustering techniques are another class of methods operating in the ambient space, grouping together similar points. However, unlike dimensionality reduction techniques, they do not provide information about the organization of the data.</p><p><strong>Results: </strong>Leveraging ideas from Topological Data Analysis, we introduce ClusterGraph, an additional layer on the output of any clustering algorithm that represents clusters as vertices and inter-cluster relationships as weighted edges. This structure captures the large-scale organisation of a dataset without forcing it into a two-dimensional Euclidean embedding. The method comes with a built-in quality criterion, metric distortion, which quantifies how faithfully the graph reflects the intrinsic geometry of the data and provides a principled basis for pruning, model selection, and parameter tuning. ClusterGraph, possibly with an appropriate structure-preserving simplification, can be visualized and used in synergy with state-of-the-art exploratory data analysis techniques.</p><p><strong>Conclusion: </strong>ClusterGraph is complementary to methods such as UMAP, t-SNE, and PHATE: by encoding inter-cluster geometry directly in graph form, it serves not only as a visualisation tool but also as a diagnostic layer that can validate, refine, or question conclusions drawn from low-dimensional embeddings.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13354933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148250997","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 large dataset of brain imaging linked to health systems data: curation and access to a whole system national cohort from NHS Scotland.","authors":"Michael P J Camilleri, Dorian Gouzou, Salim Al-Wasity, Muthu R K Mookiah, María Valdes Hernandez, Bea Alex, Sotirios A Tsaftaris, Andrew Brooks, Ruairidh MacLeod, Honghan Wu, Brenda Bauer, Claire Grover, Parminder Reel, Susan Krueger, Richard Tobin, J Douglas Steele, Grant Mair, Joanna Wardlaw, Alexander Doney, Emanuele Trucco, William Whiteley","doi":"10.1093/gigascience/giag072","DOIUrl":"10.1093/gigascience/giag072","url":null,"abstract":"<p><strong>Background: </strong>We present the design and implementation of a data curation framework to generate a large-scale clinical brain imaging dataset suitable for artificial intelligence (AI) enabled image analysis.</p><p><strong>Findings: </strong>The dataset is accessible through the Brain Health Data (BHD) initiative, which includes ~417,341 magnetic resonance imaging (MRI) and 846,077 computerized tomography head studies, linked electronic health records, and associated free-text imaging reports from clinical practice between 2010 and 2018 in Scotland, exceeding 185 TB in size. The data curation framework was developed during the SCottish AI in Neuroimaging to Predict Dementia and Neurodegenerative Disease (SCANDAN) study, which used a subset of 41,966 MRI series from the BHD for dementia prediction.We describe the processing of the BHD metadata and our multilabel classification output. We discuss the strengths of the BHD, including clinical relevance thanks to its unprecedented scale, population-wide representativeness of a national free-at-the-point-of-delivery healthcare, long-term follow-up to neurodegenerative disease, and real-world variability. We describe the challenges and lessons learnt in developing a framework to curate data, including the time needed to obtain permissions, the need for easily accessible, secure, responsive and affordable computational environments, the variability of clinical data, and the challenge of extracting linked clinical data and images at scale.</p><p><strong>Conclusion: </strong>This resource will be crucial for clinical research, fostering the development of personalized medicine approaches, and fast-tracking the implementation of AI models in clinical workflows. We encourage the use of the BHD data through a streamlined application to the Public Benefit and Privacy Panel for Health and Care via the electronic Data Research and Innovation Service of Public Health Scotland (eDRIS).</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13347094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148210837","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":"Extending protein language models to a viral genomic scale using biologically induced sparse attention.","authors":"Thibaut Dejean, Barbra D Ferrell, William Harrigan, Zachary D Schreiber, Rajan Sawhney, K Eric Wommack, Shawn W Polson, Mahdi Belcaid","doi":"10.1093/gigascience/giag081","DOIUrl":"10.1093/gigascience/giag081","url":null,"abstract":"<p><strong>Background: </strong>The transformer architecture in deep learning has revolutionized protein sequence analysis. Recent advancements in protein language models have paved the way for significant progress across various domains, including protein function and structure prediction, multiple sequence alignments, and mutation effect prediction. A protein language model is commonly trained on individual proteins, ignoring the interdependencies between sequences within a genome. However, biological understanding reveals that protein-protein interactions span entire genomic regions, underscoring the limitations of focusing solely on individual proteins.</p><p><strong>Findings: </strong>To address these limitations, we propose a novel approach that extends the context size of transformer models across the entire viral genome. By training on large genomic fragments, our method captures putative long-range dependencies consistent with inter-protein relationships and encodes protein sequences with integrated information from distant proteins within the same genome, offering benefits across downstream tasks. Viruses, with their densely packed genomes, minimal intergenic regions, and protein annotation challenges, are ideal candidates for genome-wide learning. We introduce a long-context protein language model, trained on entire viral genomes, leveraging a biologically informed sparse attention mechanism in which inter-protein links are inferred computationally and used as sparsity priors. Our semi-supervised approach supports long sequences of up to 61,000 amino acids (aa).</p><p><strong>Conclusion: </strong>Our evaluations show improved prediction of masked aa and improved downstream discrimination relative to single-protein models and long-context baselines, with additional validation that our inferred links correlate with independently curated interaction resources.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13495794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148548598","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":"Correction to: 'GigaDB: a redesigned repository for data publishing and management'.","authors":"","doi":"10.1093/gigascience/giag085","DOIUrl":"https://doi.org/10.1093/gigascience/giag085","url":null,"abstract":"","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"15 ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148791645","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":"Concordance of genetic determinants of acute coronary artery disease between European and Chinese populations.","authors":"Ahmed Edris, Kuang Lin, Sofia Massa, Iain Turnbull, Iona Y Millwood, Paul Sherliker, Liming Li, Jun Lv, Canqing Yu, Dianjianyi Sun, Pei Pei, Daniel Avery, Fang Liu, Anuj Goel, Hugh Watkins, Zhengming Chen, Robin G Walters, Derrick A Bennett, Robert Clarke","doi":"10.1093/gigascience/giag084","DOIUrl":"10.1093/gigascience/giag084","url":null,"abstract":"<p><strong>Aims: </strong>This study examined the concordance of associations of single nucleotide polymorphisms (SNPs) for acute coronary artery disease (CAD) discovered in Europeans with acute CAD, chronic ischaemic heart disease (IHD) or stroke in Chinese adults.</p><p><strong>Methods: </strong>In a nested case-control study of acute CAD (4,748 cases/66,227 controls) in China Kadoorie Biobank (CKB), we compared associations of 224 SNPs for acute CAD discovered in Europeans in CARDIOGRAMplusC4D (CC4D) with acute CAD in Chinese. We compared associations of a genetic score (GS-CAD) for 224 SNPs for acute CAD with chronic IHD (n = 9,000), heart failure (n = 1,229), stroke (n = 10,208), CVD risk factors (n = 70,364), and plasma proteomics (n = 3,904) in CKB.</p><p><strong>Results: </strong>The strength of associations of 224 CAD SNPs discovered in Europeans was moderately correlated with those for acute CAD in Chinese (r = 0.53), but significant associations were only replicated (P < 0.05) for 25 SNPs. The strength of associations with acute CAD for a 1 SD higher GS for acute CAD in Chinese was 3-fold greater than for chronic IHD or heart failure (1.22 [1.18-1.26] vs. 1.07 [1.05-1.10] vs. 1.06 [1.00-1.12], respectively) and 7-fold greater than for stroke (1.03; 1.01-1.05). Higher levels of GS-CAD were strongly associated with low-density lipoprotein cholesterol, moderately with systolic blood pressure and weakly with body mass index, but not with proteomics.</p><p><strong>Conclusions: </strong>The findings demonstrate a moderate concordance of genetic determinants for acute CAD between Europeans and Chinese that were specific for CAD and only weakly associated with chronic IHD, heart failure, or ischaemic stroke.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13543311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148706269","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":"Country specific hybridization of honey bees from lineage M.","authors":"Julita Machlowska, Dariusz Gerula, Andrzej Oleksa, Henk Kok, Grace P McCormack, Alexandra Valentine, Lars Kirkerud, Per Kryger, Laima Blažytė-Čereškienė, Martin Hasselmann, Teweldemedhn Gebretinsae Hailu, Benjamin Rutschmann, Adam Tofilski","doi":"10.1093/gigascience/giag093","DOIUrl":"10.1093/gigascience/giag093","url":null,"abstract":"<p><strong>Background: </strong>Honey bees are essential pollinators supporting agricultural production and wild plant diversity. In evolutionary lineage M, some populations are threatened by genetic erosion caused by the widespread introduction of commercially bred queens. To assess this risk, wing images from existing and new datasets were used to assign them to 4 evolutionary lineages (A, C, M, and O). The new dataset consisted of 29,043 wing images representing 1,342 colony samples from 10 countries.</p><p><strong>Results: </strong>Overall, 63.7% of colonies belonged to lineage M, whereas 27.5% were classified as A, 7.9% as C, and 0.8% as O. Lineage M remains prevalent in unprotected populations in Portugal, Spain, and Ireland, as well as in protected populations elsewhere. In contrast, a pronounced decline was observed in unprotected populations in northeastern Poland.</p><p><strong>Conclusions: </strong>These findings reveal strong regional differences in the persistence of lineage M and underscore the need for coordinated conservation efforts throughout Europe. The data provided in this study should allow for more accurate discrimination between native and introduced phenotypes.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148967781","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 chromosome-level, haplotype-resolved genome assembly for the barn owl, Tyto alba.","authors":"Hugo Corval, Anne-Lyse Ducrest, Marianne Bachmann Salvy, Allison Burns, Alexandros Topaloudis, Céline Simon, Elisa Cora, Daniel Cavaleri, Bettina Almasi, Alexandre Roulin, Christian Iseli, Nicolas Guex, Tristan Cumer, Jérôme Goudet","doi":"10.1093/gigascience/giag092","DOIUrl":"10.1093/gigascience/giag092","url":null,"abstract":"<p><strong>Background: </strong>Recent advances in long-read sequencing have enabled near telomere-to-telomere (T2T) assemblies across diverse taxa. However, avian genomes remain challenging due to numerous microchromosomes, small, typically <20Mb, DNA molecules that are gene-, GC-, and repeat-rich. As a consequence, microchromosomes are often missing from genome assemblies.</p><p><strong>Results: </strong>Here, we present a chromosome-level, haplotype-resolved genome assembly for the Western barn owl (Tyto alba). Using a trio-binning strategy with Illumina parental reads combined with PacBio HiFi and Oxford Nanopore Technologies data, we generated 2 phased contig sets. These were scaffolded into 40 linkage groups using a linkage map. Comparative analyses identified unplaced HiFi scaffolds corresponding to microchromosomes, which we integrated into 6 additional microchromosomes using long reads information. The 2 assemblies present 46 chromosomes, matching the karyotype of the species. They exhibit strong synteny between parental haplotypes, except for a ∼38 Mb complex region on chromosome 7 containing nested inversions.</p><p><strong>Conclusions: </strong>This high-quality reference provides a haplotype-resolved and chromosome-level genome for Strigiformes, enabling fine-scale studies of structural variation and avian genome evolution.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148911382","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":"An integrative multiomics random forest framework for robust biomarker discovery.","authors":"Wei Zhang, Hanchen Huang, Lily Wang, Brian D Lehmann, X Steven Chen","doi":"10.1093/gigascience/giaf148","DOIUrl":"10.1093/gigascience/giaf148","url":null,"abstract":"<p><strong>Background: </strong>High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating multiple omics layers measured on the same samples can reveal cross-layer molecular hubs that single-layer analyses miss. However, many existing integrative methods rely on linear assumptions or univariate feature importance, limiting their ability to capture nonlinear and interaction-driven dependencies across data modalities.</p><p><strong>Results: </strong>We present an unsupervised, multivariate random forest (MRF) framework with an inverse minimal depth (IMD) importance to prioritize shared biomarkers across omics. In each forest, one layer serves as a multivariate response and the other as predictors; IMD summarizes how early a predictor (or response maximal splitting response variable) appears across trees, yielding interpretable, cross-layer feature rankings. We provide two IMD-based selection strategies and introduce an optional IMD power transform to enhance sensitivity to interaction signals. In extensive simulations spanning linear, nonlinear, and interaction regimes, our method matches sparse partial least squares/canonical correlation analysis under linear settings and outperforms them as nonlinearity increases, while adapted univariate ensemble learners (random forest, gradient boosting machine, XGBoost) underperform in the multivariate, unsupervised context. Applied to breast invasive carcinoma and colon adenocarcinoma in The Cancer Genome Atlas (TCGA), MRF-IMD identifies genes, CpGs, and microRNAs enriched for cancer-relevant pathways and yields more robust survival stratification than linear integrators with matched model sizes. In a TCGA pan-cancer analysis, MRF-IMD features achieve a higher Adjusted Rand Index than alternatives and recover coherent tumor-type clusters; in the Alzheimer's Disease Neuroimaging Initiative (ADNI), the integrative signature improves dementia progression stratification over a published methylation risk score.</p><p><strong>Conclusions: </strong>MRF-IMD provides a scalable and interpretable framework for multiomics integration that reliably identifies cross-layer biomarkers when nonlinear and interaction-driven dependencies are present. This approach advances robust biomarker discovery beyond the limits of linear integrative methods.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12821379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145707663","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":"What needs to be standardized for reliable, reproducible, and robust tractography?","authors":"Jon Haitz Legarreta, Simona Schiavi, Wei Tang, Garrett Banks, Matthew Cieslak, Kurt Schilling, Alberto De Luca, Jacques-Donald Tournier, John Kruper, Francois Rheault, Stamatios N Sotiropoulos, Franco Pestilli, Jelle Veraart, Joseph Yuan-Mou Yang, Maxime Descoteaux, Sarah Heilbronner, Ariel Rokem","doi":"10.1093/gigascience/giag034","DOIUrl":"10.1093/gigascience/giag034","url":null,"abstract":"<p><p>Tractography is a key component of efforts to map brain connectivity. As a rapidly evolving field of neuroscience, current tractography methods are diverse, often varying across research laboratories and different software pipelines. Therefore, it suffers from a lack of standardization, leading to inconsistencies in results, which can limit reproducibility and affect the robustness needed for research and clinical applications of these methods. Variability in data acquisition procedures, inconsistencies in spatial referencing schemes and implementations, and anatomical heterogeneity-at the individual level, across the lifespan, and across species-hinder comparative analyses. Additionally, the lack of consensus on best practices complicates the development of robust automated quality control pipelines and limits the clinical translation of tractography-based procedures. Establishing standardized protocols for acquisition, preprocessing, and tractography reconstruction is critical toward enabling reliable tract-specific analyses, facilitating cross-study harmonization, and supporting replicable large-scale population studies. The present article provides an overview of the current challenges in tractography standardization and identifies the key aspects that require standardization for reliable, reproducible, and robust tractography.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13137869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511296","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}