GigaScience最新文献

{"title":"CamK-DB: A k-mer MinHash fingerprint database for reference-free genotyping of Camellia accessions.","authors":"Feiquan Wang, Noor-Ul Áin, Fang Wang, Shengcheng Zhang, Weilong Kong, Yutao Shi, Hua Feng, Bo Zhang, Xingtan Zhang","doi":"10.1093/gigascience/giag088","DOIUrl":"10.1093/gigascience/giag088","url":null,"abstract":"<p><p>Tea (Camellia sinensis L.), a major global economic crop in Asia, poses challenges for genetic identification because its highly heterozygous, repetitive genome reduces the efficacy of conventional single-nucleotide polymorphism (SNP) and microsatellite markers, and interspecific hybridization further complicates the situation. To address these issues, CamK-DB was developed as a reference-free Camellia fingerprinting database built on MIKE MinHash sketches. We curated 418 candidate resequencing datasets, and built a database using standardized 5× genome-coverage fingerprints. Each accession is stored as a MIKE. jac fingerprint generated with k = 21 and recommended sketch/pre_cnt = 2000. CamK-DB provides a command-line interface for data management and a custom C++ query engine that computes top-10 matches using Jaccard similarity, complemented by a QT-based graphical interface for interactive analysis. This resource offers a robust and scalable framework for precise and routine germplasm identification, genomic phylogenetic inference, and strategic breeding program design. CamK-DB (database and code) is publicly available at https://github.com/sc-zhang/CamK-DB. CamK-DB binaries are provided for Windows 10/11 and Linux (x86_64, glibc ≥ 2.27).</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/PMC13548848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148791698","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":"Within-host diversity and phased variant analysis reveal structures and recombination of Helicobacter pylori subpopulations in stomach.","authors":"Xiaomin Zhang, Hongjie Liu, Siyue Xu, Shuang Zhang, Tingting Yang, Zhongyi Lei, Weili Xu, Xiaochen Bo, Chenghai Yang, Ming Ni","doi":"10.1093/gigascience/giag046","DOIUrl":"10.1093/gigascience/giag046","url":null,"abstract":"<p><strong>Background: </strong>Helicobacter pylori (H. pylori) has a highly plastic genome and can generate substantial within-host diversity during chronic gastric colonization. However, the delineation of its within-host subpopulations, particularly regarding the emergence and spread of antibiotic resistance-conferring mutations, remains poorly understood.</p><p><strong>Results: </strong>In this study, we enrolled 25 chronic gastritis patients from southern China, collecting multiple isolates from distinct gastric regions. Among them, 14 patients exhibited heterogeneity in antibiotic susceptibility across isolates (heteroresistant), while the remaining 11 showed consistent profiles (homoresistant). Using ultra-deep short- and long-read sequencing, we showed that co-existing H. pylori subpopulations were prevalent in these patients, particularly within the same anatomical niche. Two patients presented mixed infections involving different strains as subpopulations, while others exhibited microevolution from a common ancestor. We reconstructed the subpopulation structures and found that isolates from heteroresistant patients had greater within-host diversity compared to those from homoresistant patients. Notably, subpopulations in the antrum demonstrated higher diversity than those in the gastric corpus and incisura angularis. Through a custom-developed phasing bioinformatics workflow, we resolved subpopulation-level genomic regions and directly observed extensive homologous recombination among them. Importantly, we traced the distribution of levofloxacin- and clarithromycin-associated resistance mutations across subpopulations, which was mainly mediated by recombination.</p><p><strong>Conclusions: </strong>To our knowledge, this study provides the first detailed depiction of H. pylori subpopulation distribution within the human stomach, illustrating how recombination drives within-host diversification and contributes to the spread of antibiotic resistance mutations.</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/PMC13188224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147698556","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":"LinkML: an open data modeling framework.","authors":"Sierra A T Moxon, Harold Solbrig, Nomi L Harris, Patrick Kalita, Mark A Miller, Sujay Patil, Kevin Schaper, Chris Bizon, J Harry Caufield, Silvano Cirujano Cuesta, Corey Cox, Frank Dekervel, Damion M Dooley, William D Duncan, Tim Fliss, Sarah Gehrke, Adam S L Graefe, Harshad Hegde, A J Ireland, Julius O B Jacobsen, Madan Krishnamurthy, Carlo Kroll, David Linke, Ryan Ly, Nicolas Matentzoglu, James A Overton, Jonny L Saunders, Deepak R Unni, Gaurav Vaidya, Wouter-Michiel A M Vierdag, Oliver Ruebel, Christopher G Chute, Matthew H Brush, Melissa A Haendel, Christopher J Mungall","doi":"10.1093/gigascience/giaf152","DOIUrl":"10.1093/gigascience/giaf152","url":null,"abstract":"<p><strong>Background: </strong>Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, nonstandardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult.</p><p><strong>Findings: </strong>LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics.</p><p><strong>Conclusions: </strong>LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at their origin. LinkML documentation and code are available at https://linkml.io/.</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/PMC12993438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145742108","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":"MPLID (Membrane Protein-Lipid Interaction Database): A Large-Scale Experimental Resource of Residue-Level Protein-Lipid Contacts.","authors":"Folorunsho Bright Omage, Goran Neshich","doi":"10.1093/gigascience/giag080","DOIUrl":"10.1093/gigascience/giag080","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Membrane proteins constitute approximately 20-30% of all proteomes and represent over 60% of current drug targets. Although protein-lipid interactions play important structural and regulatory roles in membrane-associated proteins, most existing structural resources focus on identifying whether a residue lies within a membrane region, typically inferred from computational hydrophobicity-based positioning algorithms. This approach does not directly address a distinct biological question: which residues at the protein surface make direct physical contact with lipid molecules? Answering this question from experimental data is critical for understanding lipid-mediated allostery, designing lipid-mimetic therapeutics, and training accurate machine learning models for lipid-binding-site prediction. We present MPLID (Membrane Protein-Lipid Interaction Database), a curated residue-level dataset comprising 4,704 membrane proteins representing 813 sequence clusters at 30% identity, 8,055,325 residues, and 80,439 structurally observed lipid-contact annotations (1.00% observed positive rate). Labels are derived exclusively from experimentally resolved lipid molecules in experimentally determined Protein Data Bank structures using a 4.0 Å all-atom heavy-atom distance cutoff. Because many native lipid interactions are lost or remain unresolved during purification and structure determination, this observed rate represents a lower bound, and the non-contact class inevitably contains false negatives. The dataset uses a curated list of 117 candidate lipid identifiers across ten functional categories, including 90 PDB-derived ligand codes audited against the RCSB Chemical Component Dictionary and 27 CHARMM-style lipid identifiers encountered in cryo-EM depositions. These identifiers span phospholipids, cardiolipin, sphingolipids, sterols, fatty acids, glycerolipids, detergent mimetics, lipid A components, and CHARMM simulation nomenclature. To minimize data leakage, proteins are clustered at 30% sequence identity using MMseqs2, yielding 813 clusters partitioned into training (2,578), validation (1,051), and test (1,075) splits. Amino acid composition analysis reveals biologically consistent enrichment at lipid-contact sites: tryptophan (1.88×), arginine (1.44×), glycine (1.36×), lysine (1.33×), and phenylalanine (1.23×) are enriched, whereas proline (0.51×), isoleucine (0.57×), and aspartate (0.59×) are depleted. MPLID addresses a distinct biological question from existing resources such as OPM, MemBlob, and BioDolphin/PLIP by identifying residues that directly contact experimentally resolved lipid molecules rather than residues positioned within computationally defined membrane boundaries. With 4,704 proteins and more than 8 million annotated residues, MPLID provides the scale needed for training deep-learning models for lipid-contact prediction, with applications in structure-guided drug design and membrane-protein engineering. The dataset adheres to FAIR pri","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/PMC13435803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148601738","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":"Programmatic access to ICTV virus taxonomy through a public ontology API.","authors":"Philippe Lieutaud, James McLaughlin, R Curtis Hendrickson, Romain David, Helen Parkinson, Elliot J Lefkowitz, Donald M Dempsey, Bruno Coutard","doi":"10.1093/gigascience/giag089","DOIUrl":"10.1093/gigascience/giag089","url":null,"abstract":"<p><strong>Background: </strong>The International Committee on Taxonomy of Viruses (ICTV) is responsible for developing and maintaining a universal virus taxonomy. As the reference framework for organizing the viral world, it is essential for virology and related fields. Despite its widespread use in research and public health, programmatic access to ICTV taxonomy has remained limited, posing challenges for integration, versioning, and interoperability across databases and bioinformatics resources requiring up-to-date virus taxonomy.</p><p><strong>Findings: </strong>To address this, we developed a public and sustainable solution leveraging ontology-based APIs. All available ICTV Master Species List (MSL) releases, from MSL1 to MSL41, were transformed into a unified, semantically structured ontology comprising more than 195,000 current and historical entities and deployed through the Ontology Lookup Service. The ontology is automatically rebuilt and republished whenever a new MSL release becomes available. Complementary ICTV-NCBI mappings and helper libraries support integration into downstream systems.</p><p><strong>Conclusions: </strong>Together, these resources enable, for the first time, public programmatic retrieval of current and historical ICTV taxon names, taxonomic relationships, metadata, and persistent identifiers through stable endpoints, including resolution of former taxonomic terms to their current accepted taxon or taxa and retrieval of taxon histories across releases. More broadly, this work illustrates a general strategy for transforming structured biological datasets into semantically enriched graph resources exposed through scalable public APIs. These developments enhance interoperability, reduce manual curation, and support Findable, Accessible, Interoperable, and Reusable (FAIR)-aligned taxonomic data management in virology and pandemic preparedness.</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/PMC13615544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891365","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 molecularly anchored spatial transcriptomic framework for precise CA1-subiculum parcellation and region-resolved analysis in Alzheimer's disease.","authors":"Yuyang Liu, Youzhe He, Yanrong Wei, Pan Wang, Chunyu Huang, Quyuan Tao, Langjian Zhu, Xun Xu, Longqi Liu, Shiping Liu, Lei Han, Jing Zhang, Lifang Wang","doi":"10.1093/gigascience/giag094","DOIUrl":"10.1093/gigascience/giag094","url":null,"abstract":"<p><strong>Background: </strong>The precise molecular delineation of the interface between the Subiculum (Sub) and cornu ammonis 1 (CA1) is a challenge in hippocampal research, as conventional cytoarchitectural boundaries are often ambiguous and limit reproducible regional annotation. Here, we developed a molecularly anchored spatial transcriptomic framework to define CA1-Sub regional identities using high-definition spatial transcriptomics (Stereo-seq) and single-nucleus RNA sequencing (snRNA-seq) references.</p><p><strong>Findings: </strong>Using a human hippocampal Stereo-seq dataset from 12 donors, we established a data-driven parcellation framework that defines reproducible molecular features distinguishing CA1 and Sub while capturing the transition between these regions. FN1 was identified as a Sub-enriched marker in a subset of EX_Sub and, together with ETV1 and additional regional markers, enabled molecular assignment of CA1 and Sub identities across datasets. The Sub association of FN1 and ETV1 was further supported by human 10X Genomics spatial transcriptomics, mouse in situ hybridization data, and a mouse spatial transcriptomic dataset. Applying this framework to Alzheimer's disease tissues revealed region-specific transcriptional alterations across CA1 and Sub, including enrichment of mitochondrial energy metabolism-related transcripts in the Sub, suggesting exploratory transcriptional associations of altered metabolic function.</p><p><strong>Conclusions: </strong>This study provides a molecularly anchored framework for human CA1-Sub parcellation that complements conventional annotation. By defining regional molecular states while preserving the biological continuum across CA1-Sub interface, this approach enables more consistent regional analysis of human hippocampus tissue across donors, datasets, and disease conditions.</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/PMC13674357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148988903","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":"Giant chromosomes of a tiny plant-the complete telomere-to-telomere genome assembly of the simple thalloid liverwort Apopellia endiviifolia (Jungermanniopsida, Marchantiophyta).","authors":"Joanna Szablińska-Piernik, Paweł Sulima, Jakub Sawicki","doi":"10.1093/gigascience/giaf145","DOIUrl":"10.1093/gigascience/giaf145","url":null,"abstract":"<p><strong>Background: </strong>The liverwort Apopellia endiviifolia, a dioicous, simple thalloid species, is notable for its cryptic diversity, habitat adaptability, and genomic innovation, and it represents a clade that is sister to all other Jungermanniopsida. These features make A. endiviifolia an essential model for exploring speciation mechanisms and the evolution of genome structures within liverworts.</p><p><strong>Findings: </strong>We present the genome assembly of a haploid A. endiviifolia isolate with a total size of 2,914,960,273 bp and an N50 of 468,157,909 bp, demonstrating high completeness (99.2% BUSCO) and a high consensus quality (quality value 47.6). The assembly consisted of 9 chromosomes, which included 18 telomeres and 9 centromeres (ranging from 1.9 to 5 Mbp in length). RNA sequencing-based annotation identified 34,615 genes, predominantly protein coding. The transposable elements comprised 12.16% long terminal repeat elements and 57 Helitrons. Among the retroelements, the Copia and Gypsy superfamilies comprised 8.94% and 2.95% of the genome, respectively. The Ty3/Gypsy superfamily was significantly enriched in centromeric regions. The average GC content ranged from 38.8% to 39.6%, with gene density varying between 5.52 and 9.78 genes per 500 kbp. Synteny analysis of related liverwort species has revealed complex chromosomal relationships, indicating extensive genome rearrangements among species.</p><p><strong>Conclusions: </strong>This study provides the first high-quality reference genome assembly of the haploid liverwort A. endiviifolia. Assembly and annotation offer valuable resources for investigating liverwort evolution, centromere biology, and genome expansion in simple thalloid liverworts.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145632216","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":"Leveraging machine learning and network biology approaches to predict brain gene expression from blood transcriptomes.","authors":"Cigdem Sevim Bayrak, Qi Zeng, Marjan Ilkov, Scott J Russo, Minghui Wang, Bin Zhang","doi":"10.1093/gigascience/giag058","DOIUrl":"10.1093/gigascience/giag058","url":null,"abstract":"<p><strong>Background: </strong>Blood-based biomarkers offer a promising non-invasive strategy for detecting disease-related changes and monitoring tissue and organ health, including brain function. While recent studies have leveraged blood transcriptomic data to predict gene expression in the brain, existing models generally suffer from poor accuracy, limiting their translational utility.</p><p><strong>Findings: </strong>We present an integrative prediction system (IPS) that combines machine learning with network biology to predict region-specific brain gene expression from blood transcriptomic data. Our framework integrates global blood transcriptomic signals, co-expression network features, and inter-tissue gene-gene interaction data linking blood genes to their target genes in the brain. Applied to the Genotype-Tissue Expression cohort, IPS substantially outperforms existing approaches in both the number and accuracy of brain genes that can be reliably predicted from blood. Notably, immune-related blood genes emerged as key contributors to model performance, underscoring the systematic interplay between peripheral immune signaling and central nervous system.</p><p><strong>Conclusions: </strong>These findings highlight the potential of blood-based transcriptomic models as scalable, non-invasive tools for studying brain function and developing diagnostic and prognostic biomarkers for neurological and psychiatric disorders.</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/PMC13201078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147963220","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":"HVRLocator: a computationally efficient tool for identifying hypervariable regions in large 16S rRNA datasets.","authors":"Clara Arboleda-Baena, Felipe Borim Corrêa, João Pedro Saraiva, Santiago Castillo-Rivadeneira, Jonas Coelho Kasmanas, Antonis Chatzinotas, Stephanie D Jurburg","doi":"10.1093/gigascience/giag040","DOIUrl":"10.1093/gigascience/giag040","url":null,"abstract":"<p><strong>Background: </strong>Metabarcoding of the 16S rRNA gene is widely used to assess microbial diversity due to its cost-effectiveness and efficiency. However, publicly available 16S rRNA metabarcoding datasets often lack standardized metadata, particularly information on the sequenced hypervariable regions or primers used, which are critical to their accurate reuse. To address this, we present HVRLocator, a computational tool that (1) identifies the start and end positions of 16S rRNA amplicons, (2) determines their corresponding hypervariable regions, and (3) detects the presence of primer sequences. This tool was validated on four datasets comprising 41,513 samples generated with different primers and sequencing platforms.</p><p><strong>Results: </strong>HVRLocator can process archived 16S rRNA sequences from NCBI SRA at an average rate of 6.5 samples per minute. Validation showed it reliably detects amplicon start and end positions across datasets sequenced with different primers and platforms, achieving 100% accuracy within single-platform studies and correctly revealing length heterogeneity across platforms. It also flagged misannotated metadata and problematic sequences, underscoring its value as a sequence data curation tool. Finally, HVRLocator can select comparable sequences to build large 16S rRNA amplicon databases spanning the same hypervariable region, facilitating cross-study comparisons.</p><p><strong>Conclusion: </strong>HVRLocator overcomes unreliable metadata by accurately identifying 16S rRNA amplicon start and end positions, determining hypervariable regions, and detecting primer sequences, enabling accurate curation and large-scale processing of 16S rRNA data for reliable and reproducible microbial studies, syntheses, and meta-analyses.</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/PMC13188219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147662409","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":"SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.","authors":"Souvik Seal, Brian Neelon","doi":"10.1093/gigascience/giag006","DOIUrl":"10.1093/gigascience/giag006","url":null,"abstract":"<p><p>Advances in spatial omics enable measurement of genes (spatial transcriptomics) and peptides, lipids, or N-glycans (mass spectrometry imaging) across thousands of locations within a tissue. While detecting spatially variable molecules is a well-studied problem, robust methods for identifying spatially varying co-expression between molecule pairs remain limited. We introduce SpaceBF, a Bayesian fused modeling framework that estimates co-expression at both local (location-specific) and global (tissue-wide) levels. SpaceBF enforces spatial smoothness via a fused horseshoe prior on the edges of a predefined spatial adjacency graph, allowing large, edge-specific differences to escape shrinkage while preserving overall structure. In extensive simulations, SpaceBF achieves higher specificity and power than commonly used methods that leverage geospatial metrics, including bivariate Moran's I and Lee's L. We also benchmark the proposed prior against standard alternatives, such as intrinsic conditional autoregressive and Matérn priors. Applied to spatial transcriptomics and proteomics datasets, SpaceBF reveals cancer-relevant molecular interactions and patterns of cell-cell communication (e.g., ligand-receptor signaling), demonstrating its utility for principled, uncertainty-aware co-expression analysis of spatial omics data.</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/PMC12954175/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009819","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}