GigaScience最新文献

{"title":"KG-Microbe: Building modular and scalable knowledge graphs for microbiome and microbial sciences.","authors":"Brook E Santangelo, Harshad Hegde, J Harry Caufield, Justin Reese, Tomas Kliegr, Lawrence E Hunter, Catherine A Lozupone, Christopher J Mungall, Marcin P Joachimiak","doi":"10.1093/gigascience/giag077","DOIUrl":"10.1093/gigascience/giag077","url":null,"abstract":"<p><strong>Background: </strong>The integration of many disparate forms of data is essential for understanding the microbial world and its interaction with the environment and human health. Doing so is particularly challenging in the context of microbe-host and microbe-microbe interactions that contribute to health or environmental outcomes. There are thousands of relevant microbial species, and millions of interactions among those microbes and with their environment or host. Integrated information (e.g., about host and microbial physiology, genetics, and metabolism) facilitates deeper understanding of complex mechanisms and helps interpret correlative results.</p><p><strong>Results: </strong>The KG-Microbe construction framework is a novel approach to harmonizing bacterial and archaeal data in the form of a findable, accessible, interoperable, reusable and AI-ready knowledge graph (KG). Starting from a core KG with organismal traits, environments, and growth preferences and the integration of established ontologies, the framework generates a hierarchy of related KGs targeting specific use cases, including the human microbiome in the context of disease, or environmental microbiomes. The framework supports customizable taxa subsets representing communities or clades of interest. Evaluations of the KG-Microbe KGs through a series of competency questions demonstrate the accuracy and effectiveness of the data harmonization, and the utility of the resulting KGs in studies of inflammatory bowel disease and Parkinson's disease. Finally, the predictive and environmental capabilities of the KGs are demonstrated by predicting growth preferences using graph features.</p><p><strong>Conclusions: </strong>The KG-Microbe framework unifies microbial contexts in a single resource to support integrative analyses across biomedical, host, and environmental domains. KG-Microbe is a flexible, modular enabling technology for humans and machine learning methods to uncover candidate mechanistic explanations of microbial associations.</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/PMC13536490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148420594","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":"ViralBindPredict: empowering viral protein-ligand binding sites through deep learning and protein sequence-derived insights.","authors":"A M B Amorim, C Marques-Pereira, T Almeida, N Rosário-Ferreira, H S Pinto, C Vaz, A Francisco, I S Moreira","doi":"10.1093/gigascience/giag010","DOIUrl":"10.1093/gigascience/giag010","url":null,"abstract":"<p><strong>Background: </strong>The development of a single therapeutic compound can exceed 1.8 billion USD and take more than a decade, underscoring the urgent need to accelerate drug discovery. Computational methods have become indispensable; however, traditional approaches, such as docking simulations, face limitations because they depend on protein and ligand structures that may be unavailable, incomplete, or of low accuracy. Even recent breakthroughs, such as AlphaFold, do not consistently provide models precise enough to identify ligand-binding sites or drug-target interactions.</p><p><strong>Results: </strong>We present ViralBindPredict, a deep learning framework that predicts viral protein-ligand binding sites directly from sequence. We also introduce the first curated large-scale benchmark of viral protein-ligand interactions, comprising >10,000 viral chains and ≈13,000 interactions processed using a 4.5 Å heavy-atom contact threshold. ViralBindPredict combines Mordred ligand descriptors with contextual protein embeddings from ESM2 or ProtTrans, enabling structure-free learning of binding preferences. Leakage-controlled data splits were applied to prevent overlap across protein sequence clusters and ligand scaffolds (Cluster90%, NoRed90%→Cluster90%, Cluster40%, NoRed90%→Cluster40%). Across most regimes, multilayer perceptrons, especially with ESM-2 embeddings, outperformed LightGBM baselines, maintaining strong precision-recall for unseen ligands but showing larger drops for unseen proteins, indicating that the protein context dominates generalization.</p><p><strong>Conclusions: </strong>ViralBindPredict introduces the first leakage-controlled benchmark for viral protein-ligand interactions and demonstrates accurate ligand-binding residue prediction directly from protein sequence. Together, these advances establish ViralBindPredict as a robust and extensible workflow for sequence-based antiviral discovery, supporting rapid target prioritization, compound repurposing, and de novo drug design, even in the absence of structural 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/PMC13014472/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040769","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":"scDenorm: a denormalization tool for integrating single-cell transcriptomics data.","authors":"Yin Huang, Anna Vathrakokoili Pournara, Ying Ao, Ziliang Huang, Hui Zhang, Yongjian Zhang, Sheng Liu, Alvis Brazma, Irene Papatheodorou, Xinlu Yang, Ming Shi, Zhichao Miao","doi":"10.1093/gigascience/giag032","DOIUrl":"10.1093/gigascience/giag032","url":null,"abstract":"<p><p>Integrating single-cell omics data at an atlas scale enhances our understanding of cell types and disease mechanisms. However, the integration of data processed by different normalization methods can lead to biases, such as unexpected batch effects and gene expression distortion, leading to misinterpretations in downstream analysis. To address these challenges, we present scDenorm, an algorithm that reverts delta-method normalized single-cell omics data to raw counts, preserving the integrity of the original measurements and ensuring consistent data processing during integration. We evaluated scDenorm's performance on large-scale datasets and benchmarked its impact on data integration and downstream analysis across 3 datasets.</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/PMC13142155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580984","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":"FPNuNet: a frequency-aware prompt-guided network for nuclear segmentation and classification in immunohistochemistry images.","authors":"Lulu Qin, Zhigang Pei, Xudong He, Jiarui Zhou, Xianhong Xu, Zexuan Zhu","doi":"10.1093/gigascience/giag091","DOIUrl":"10.1093/gigascience/giag091","url":null,"abstract":"<p><strong>Background: </strong>Accurate nuclear segmentation and classification (NuSC) in immunohistochemistry (IHC)-stained whole-slide images is essential for reliable biomarker quantification in computational pathology. Although existing NuSC methods perform well on Hematoxylin and Eosin images, they frequently underperform on IHC-stained images owing to stain heterogeneity, low nuclear contrast, and scarce biomarker-specific annotations. Prior efforts based on stain normalization or cross-domain knowledge transfer have yielded only marginal improvements under these conditions. To address these limitations, we introduce FPNuNet, a frequency-aware prompt-guided network tailored for NuSC of IHC-stained images.</p><p><strong>Results: </strong>FPNuNet extracts multi-source features through 4 parallel encoders: a frozen SAM-based structural encoder and a frozen UNI-based semantic encoder-both adapted via lightweight discrete cosine transform-based prompt generators-together with a wavelet feature encoder and a multi-scale context encoder that capture complementary spatial and frequency-domain descriptors from the raw input. A discrete cosine transform-enabled frequency-aware fusion neck integrates all 4 feature streams with spectral enhancement, and 3 collaborative decoder branches jointly predict binary masks, horizontal-vertical vectors, and nuclear types. FPNuNet is evaluated on CD47-IHCNuSC, a dataset comprising 86 CD47-stained esophageal cancer patches with 18,483 manually annotated nuclei spanning 7 clinically meaningful subtypes. On CD47-IHCNuSC, FPNuNet achieves the best instance-segmentation and aggregate subtype-classification performance among all evaluated methods under challenging IHC staining conditions.</p><p><strong>Conclusions: </strong>These results demonstrate the effectiveness of frequency-aware prompt-guided feature learning for nuclear segmentation and classification in IHC-stained images and suggest that FPNuNet provides a promising framework for robust biomarker-oriented computational pathology analysis.</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/PMC13618258/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148905824","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":"Charting immune variation through genetics and single-cell genomics.","authors":"Joseph E Powell","doi":"10.1093/gigascience/giaf161","DOIUrl":"10.1093/gigascience/giaf161","url":null,"abstract":"<p><p>Large-scale single-cell genomics projects have revolutionized our understanding of human immune variation. Yet most studies to date have been Eurocentric, limited in cell-type resolution, or restricted to a single data modality. The newly published Chinese Immune Multi-Omics Atlas helps address these gaps by profiling 428 healthy Chinese adults using a multiomics single-cell approach that combines single-cell RNA sequencing and single-cell chromatin accessibility sequencing across over 10 million immune cells. This integrated strategy enabled the identification of 73 distinct immune cell subsets and the construction of cell-type-specific gene regulatory networks linking noncoding enhancers to target genes. The atlas delineated hundreds of enhancer modules (eRegulons), highlighting both established and novel regulators of immune cell identity. By aligning transcriptomic and epigenomic maps, Yin et al. show how expanding both the ancestral diversity and data modalities of immune cell genomics can reveal new biology and provide a valuable addition to global reference cell atlases.</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/PMC12821369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145855259","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":"METRIN-KG: A knowledge graph integrating plant metabolites, traits, and biotic interactions.","authors":"Disha Tandon, Tarcisio Mendes De Farias, Pierre-Marie Allard, Emmanuel Defossez","doi":"10.1093/gigascience/giag051","DOIUrl":"10.1093/gigascience/giag051","url":null,"abstract":"<p><strong>Background: </strong>In recent years, biodiversity data management has emerged as a critical pillar in global conservation efforts. Today, the ability to efficiently collect, structure, and analyze biodiversity data is central to breakthroughs in conservation, drug development, disease monitoring, ecological forecasting, and agri-tech innovation. However, due to the vastness and heterogeneity of biodiversity data, it is often confined to databases for specific research areas in isolated formats and disconnected from other relevant resources. Crucial components of such data in the kingdom Plantae comprise metabolomes, with the vast array of compounds produced by plants; traits, which are measurable characteristics of plants that influence their growth, survival, and reproduction and affect ecosystem processes; and biotic interactions, including relationships of plants with other living organisms, affecting the ecosystem functions.</p><p><strong>Results: </strong>In this work, we present METRIN-KG (MEtabolomes, TRaits, and INteractions-Knowledge Graph), a powerful data resource simplifying the integration of diverse and heterogeneous data resources such as plant metabolomes, traits, and biotic interactions.</p><p><strong>Conclusions: </strong>The proposed knowledge graph provides an interface to interactively search for data relating to plant metabolomes, traits, and interactions. This, in turn, will facilitate the development of research questions in life sciences. In this context, we provide representative case studies on how to frame queries that can be used to search for relevant data in the knowledge graph.</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/PMC13296791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147769038","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-definition likelihood inference of genetic colocalization reveals protein biomarkers for human complex diseases.","authors":"Yuying Li, Ranran Zhai, Zhijian Yang, Ting Li, Yudi Pawitan, Xia Shen","doi":"10.1093/gigascience/giaf155","DOIUrl":"10.1093/gigascience/giaf155","url":null,"abstract":"<p><strong>Background: </strong>Genetic colocalization analysis is essential for understanding the shared genetic basis between phenotypic traits. Such an analysis is particularly useful for identifying plasma proteins with potential as therapeutic targets or clinical biomarkers. Improvements to existing tools are needed for more accurate inference of potentially causal biomarkers.</p><p><strong>Findings: </strong>We develop high-definition likelihood for colocalization inference (HDL-C), a method for genetic colocalization analysis. Based on simulations and observed rediscovery rates in real data analyses, we demonstrate that the HDL-C approach outperforms state-of-the-art methods, COLOC, SuSiE, and SharePro, in detecting genetic colocalization, thus enabling a more complete understanding of genetic connections at specific loci. Analyses of the top 50 protein-disease pairs identified by HDL-C in the male and female cohorts of the UK Biobank uncovered 40 previously validated drug-protein-disease combinations with approved drugs matching the phenotypes and 62 combinations with potential drug re-purposing opportunities. Additionally, we identified 63 novel protein-disease pairs that suggest promising candidates for future therapeutic interventions.</p><p><strong>Conclusion: </strong>This research establishes a robust framework for detecting genetic colocalization signals, enabling the prioritization of disease-relevant protein targets and informing therapeutic development strategies.</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/PMC12916012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146029313","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":"Dynamont: A comprehensive cross-species comparison of ONT segmentation tools.","authors":"Jannes Spangenberg, Christian Höner Zu Siederdissen, Winfried Goettsch, Lennart Köhler, Liz Maria Luke, Kai Papenfort, Manja Marz","doi":"10.1093/gigascience/giag005","DOIUrl":"10.1093/gigascience/giag005","url":null,"abstract":"<p><strong>Background: </strong>Oxford Nanopore Technologies (ONT) sequencing enables direct, long-read sequencing of DNA and RNA, preserving nucleotide modifications. During basecalling, deep neural networks translate raw nanopore signals into nucleotide sequences, internally segmenting the signal to align it with the corresponding bases. This is a challenging task due to uneven motor protein rotation, signal variability, low-quality reads, and the presence of nucleotide modifications. However, the signal to nucleotide assignment is critical for novel downstream signal analysis. Existing tools, such as Tombo Resquiggle, f5c Eventalign, f5c Resquiggle, and Uncalled4, operate after basecalling and rely on event-based segmentation and mapping approaches that often fail to align low-quality or modified reads and lack confidence estimates for segmentation accuracy.</p><p><strong>Results: </strong>Here, we present a large-scale comparative study in which 5 segmentation tools, including our novel tool Dynamont, are applied to 16 ONT-sequenced datasets spanning different kingdoms of life. Overall, we segmented 160,000 reads and evaluated the tools' performance on a combination of 12 signal and downstream assembly metrics. Our study is accompanied by a comprehensive and extensible supplement that summarizes all datasets, execution instructions, and evaluation results. We score the segmentation results using an aggregated metric score, created from all our analyzed metrics.</p><p><strong>Conclusions: </strong>No tool delivered the best results for all datasets. We recommend a careful choice and normalization of evaluation metrics to select the best segmentation tool as a critical step in the process of ONT signal segmentation. Across nearly all RNA datasets, Dynamont outperforms other segmentation tools in terms of aggregated metric scores. For DNA datasets, however, the performance is more variable, with mixed results observed across tools.</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/PMC13014473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145997919","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":"Single-nucleus multiple-organ chromatin accessibility landscape in the adult rat.","authors":"Ronghai Li, Shanshan Duan, Qiuting Deng, Wen Ma, Chang Liu, Peng Gao, Li Lu, Yue Yuan","doi":"10.1093/gigascience/giag013","DOIUrl":"10.1093/gigascience/giag013","url":null,"abstract":"<p><strong>Background: </strong>Chromatin accessibility landscape is the basis of cell-specific gene expression and reflects the regulatory logic underlying cellular identity and function. However, a systematic multi-organ chromatin accessibility atlas in the rat (Rattus norvegicus), an important biomedical model organism, has been lacking.</p><p><strong>Results: </strong>We generated a multi-organ single-nucleus chromatin accessibility landscape of Rattus norvegicus using snATAC-seq. For this single-cell atlas, we constructed 25 libraries via snATAC-seq from 9 organs in the adult rat, with a total of over 110,000 cells. Cell classification integrating gene activity scores with known marker genes identified 77 cell types, which were strongly correlated with those in published mouse single-cell transcriptome atlases. We further investigated the enrichment of cell-type- and organ-specific transcription factors, shared and organ-specific features of endothelial and stromal cells, as well as cross-organ macrophage regulatory states, and the conservation and specificity of gene regulatory programs across species.</p><p><strong>Conclusion: </strong>This single-nucleus chromatin accessibility landscape provides a valuable foundation for dissecting tissue-specific regulatory mechanisms in the rat and facilitates cross-organ and cross-species cell type annotation and functional inference, supporting broader applications of rat model in systems biology and biomedical research.</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/PMC12954174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112898","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":"Promises and pitfalls of long-read sequencing for resolving microbial complexity.","authors":"Xing Rao, Yuzheng Gu, Gabriella, Jiayi Ma, Haoyu Wang, Yuanqiang Zou","doi":"10.1093/gigascience/giag087","DOIUrl":"10.1093/gigascience/giag087","url":null,"abstract":"<p><p>Long-read sequencing (LRS) has driven a transition in microbial genomics, overcoming the assembly fragmentation inherent to short-read sequencing. This review elucidates the impact of LRS across isolate genomics, metagenomics, and multi-omics domains. By spanning extensive repetitive regions, LRS facilitates the reconstruction of circular chromosomes and precisely resolves mobile genetic elements (MGEs). In metagenomics, LRS enables strain-level resolution, the recovery of circular metagenome-assembled genomes, and the precise localization of MGEs within host replicons. Furthermore, the single-molecule, amplification-free properties of LRS provide enhanced resolution of native epigenetic modifications and full-length transcriptomes. Despite these advancements, widespread implementation remains constrained by multidimensional challenges, including stringent high-molecular-weight DNA requirements, depth deficits, and computational overhead. Nevertheless, LRS is increasingly becoming the method of choice for isolate genomics and metagenomics. As detection technologies and algorithms progress, LRS will further improve our ability to decipher the structural and functional diversity of microbial ecosystems.</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/PMC13543314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148791701","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}