GigaScience最新文献

{"title":"Multiscale Transcriptomic Network Models of Parkinson's Disease at the Single Cell Level.","authors":"Linh Chu, Gefei Yu, Xianxiao Zhou, Minghui Wang, Zhenyu Yue, Bin Zhang","doi":"10.1093/gigascience/giag096","DOIUrl":"https://doi.org/10.1093/gigascience/giag096","url":null,"abstract":"<p><p>Multiple single nuclei RNA-sequencing (snRNA-seq) studies of the vulnerable brain regions of Parkinson's Disease (PD) have revealed alterations in brain cell populations and cell type specific transcriptomes. However, a systematic analysis of cell-type-resolved gene regulatory architecture in PD is lacking. Here, we develop an integrative, meta-cell based multiscale network analysis (MCMNA) of snRNA-seq data from the substantia nigra to systematically uncover molecular mechanisms and identify potential therapeutic targets for PD. MCMNA overcomes the inherent sparsity of single cell data by leveraging metacell-based aggregation, enabling construction of robust gene regulatory networks. Gene co-expression network analysis identifies cell-type specific gene modules associated with PD. Integration of meta-cell based differential gene expression and a Bayesian causal network systematically reveals putative driver genes and their hierarchies. Our multiscale network models provide a framework for prioritizing candidate molecular regulators and pathways for further investigation of disease mechanisms and therapeutic targets.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108084","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":"A Proposed Unified, Scalable Platform for Integrative Research on Venomous Species.","authors":"Shaadi Mehr, Todd Castoe, Marymegan Daly, Florence Jungo, Kim N Kirchhoff, Ivan Koludarov, Stephen P Mackessy, Jason Macrander, Praveena Naidu, Maria Vittoria Modica, Elda E Sanchez, Giulia Zancolli, Mandë Holford","doi":"10.1093/gigascience/giaf153","DOIUrl":"https://doi.org/10.1093/gigascience/giaf153","url":null,"abstract":"<p><p>Venomous animal research is hampered by fragmented, specialized, and non-interoperable databases (isolated genomic, proteomic, and ecological data). Despite the immense promise of venomous organisms to yield novel bioactive compounds for pharmacological and evolutionary applications, the informatics landscape for such taxa has remained patchy, lacking macro-scale integration across species. We present VenomsBase, an integrated, modular resource that synthesizes multi-omics data, ecological metadata, and functional annotations for venom-bearing organisms. Following the FAIR guidelines, VenomsBase combines an ontology-driven architecture with big-data cloud workflows for sequence integration, motif clustering, 3D display, and linking ecological metadata. Standardized tools and training modules facilitate worldwide access to resources for both researchers in developed countries and in resource-limited areas. Its plug-and-play design allows for integration of additional analytical modules and extension to other species. One can also examine evolutionary trends and connect venom chemistry to ecological niches. VenomsBase would (i) accelerate the pace of venom discovery, whether for therapeutic purposes or evolutionary significance, by providing validated, cross-referenced data sets and community-driven curation, and (ii) foster an open, just, and innovation-ready venom research ecosystem.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146092995","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":"Charting human organogenesis across the Carnegie stages from a whole-embryo perspective.","authors":"Jiexue Pan, Zhongliang Lin, Gaochen Zhang, Hefeng Huang, Guolian Ding","doi":"10.1093/gigascience/giag063","DOIUrl":"10.1093/gigascience/giag063","url":null,"abstract":"<p><p>The Carnegie stages represent a critical window in human development, during which organ primordia emerge, tissue identities diversify, and many congenital disorders are thought to originate. However, this period has remained difficult to study at the whole-embryo scale. Recent advances in spatial and single-cell genomics are beginning to close this gap. This article discusses the significance of the spatiotemporal transcriptomic atlas of post-gastrulation human embryos spanning Carnegie stages 12-23, which provides one of the most comprehensive dynamic molecular maps of this developmental window to date. Beyond its value as a reference resource, this atlas offers biological insights into early cardiac patterning, brain regionalization, the timing of inhibitory and excitatory neurogenesis, tissue-specific susceptibility to prenatal infection, and spatially resolved allelic imbalance. Equally importantly, it illustrates a broader shift in the field from fragmented organ-specific datasets toward integrated developmental frameworks that can connect embryology, human genetics, and disease mechanisms. Future progress will depend not only on generating more data but also on harmonizing multimodal datasets across sources, stages, and platforms, an effort that will increasingly rely on advances in computational biology and artificial intelligence.</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/PMC13231842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148041908","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 evaluation of computational methods for reconstruction of human viral DNA genomes.","authors":"Maria J P Sousa, Mari Toppinen, Lari Pyöriä, Klaus Hedman, Antti Sajantila, Maria F Perdomo, Diogo Pratas","doi":"10.1093/gigascience/giaf159","DOIUrl":"10.1093/gigascience/giaf159","url":null,"abstract":"<p><strong>Background: </strong>The increasing availability of viral sequencing data has led to the emergence of many optimized viral genome reconstruction tools. Given that the number of new tools is steadily increasing, it is complex to identify functional and optimized tools that offer an equilibrium between accuracy and computational resources, as well as the features that each tool provides.</p><p><strong>Results: </strong>In this article, we surveyed open-source computational tools (including pipelines) used for human viral genome reconstruction, identifying specific characteristics, features, similarities, and dissimilarities between these tools. For quantitative comparison, we created an open-source reconstruction benchmark based on viral data. The benchmark was executed using both synthetic and real datasets. With the former, we evaluated the effects on the reconstruction process of using different human DNA viruses with simulated mutation rates, contamination and mitochondrial DNA inclusion, and various coverage depths. Each reconstruction program was also evaluated using real datasets, demonstrating their performance in real-life scenarios. The evaluation measures include the identity, a normalized compression semi-distance, and the normalized relative compression between the genomes before and after reconstruction, as well as metrics regarding the length of the genomes reconstructed, computational time, and resources spent by each tool.</p><p><strong>Conclusions: </strong>We provide a fully reproducible benchmark capable of evaluating currently available reconstruction programs. The benchmark is open-source and freely available at https://github.com/viromelab/HVRS. Additionally, based on the knowledge obtained from the systematic review and the benchmark, we provide some program recommendations for different reconstruction scenarios.</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/PMC12927430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145843567","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":"Comparative analysis of eccDNA and circRNA tools shows increased accuracy of tool combination.","authors":"Aitor Zabala, Alex M Ascensión, Iñigo Prada-Luengo, David Otaegui","doi":"10.1093/gigascience/giag017","DOIUrl":"10.1093/gigascience/giag017","url":null,"abstract":"<p><strong>Introduction: </strong>Circular nucleic acids such as extrachromosomal circular DNA (eccDNA) and circular RNA (circRNA) are increasingly recognized for their biological relevance and potential as biomarkers in disease contexts. Despite their growing importance, their detection remains challenging due to tool-specific biases, limited validation frameworks, and high variability in performance across datasets.</p><p><strong>Methods: </strong>We benchmarked 10 circle detection tools across diverse conditions using both simulated and biological datasets. Our evaluation included classical performance metrics and a novel internal measure of read distribution symmetry ($Delta$CJ) to assess circle prediction confidence. We explored the impact of sequencing protocols, filtering strategies, and combined tool consensus.</p><p><strong>Results: </strong>We found that detection accuracy was highly influenced by sequencing depth, alignment algorithm, and experimental enrichment protocols. $Delta$CJ proved effective in flagging potential false positive circles, showing improved accuracy of Intersect (circles detected by all tools) and Rosette (circles detected by $ge$2 tools) combinations.</p><p><strong>Discussion: </strong>This study offers a broad evaluation of circular detection tools, suggesting that the combination of $ge$3 tools is necessary for a correct prediction. These insights will inform future experimental design and data analysis pipelines in both experimental and clinical settings.</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/PMC13154841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147283386","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":"Patterns of aDNA damage through time and environments-lessons from herbarium specimens.","authors":"Stefano Porrelli, Alice Fornasiero, Hong Phuong Le, Wenzhe Yin, María E Navarrete Rodríguez, Nahed Mohammed, Axel Himmelbach, Andrew C Clarke, Nils Stein, Paul J Kersey, Rod A Wing, Rafal M Gutaker","doi":"10.1093/gigascience/giag026","DOIUrl":"10.1093/gigascience/giag026","url":null,"abstract":"<p><strong>Background: </strong>Herbarium collections are a vast but underutilized resource for ancient DNA research, containing over 400 million specimens with detailed metadata and spanning centuries of global biodiversity. Understanding patterns of DNA preservation in natural collections is crucial for optimizing ancient DNA studies and informing future curation practices. We analysed genomic data for 573 herbarium specimens from 6 plant species from the genera Hordeum and Oryza collected from the Americas and Eurasia over 220 years. Using standardized laboratory protocols and shotgun sequencing, we quantified DNA degradation and elucidated factors that accelerate it.</p><p><strong>Results: </strong>We find significant age-dependent DNA fragmentation rates, indicating temporal degradation processes not detected in prehistoric samples. In our analysis, DNA decay rates in herbarium specimens were almost 8 times faster than in moa bones, reflecting fundamental differences in tissue composition and preservation environments. Environmental conditions at the time of specimen collection emerged as the major determinants of post-mortem damage rates, with the interaction term between temperature and genus being the dominant driver of cytosine deamination. We find no effect of sample storage on DNA damage and degradation.</p><p><strong>Conclusions: </strong>These findings provide insights into how climatic origin, preservation environment, taxonomic identity, and age influence DNA preservation while highlighting opportunities for improving institutional preservation practices. Due to standardized preservation conditions, museum collections can provide better insights into DNA damage and degradation over time than archaeological and paleontological samples.</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/PMC13108254/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354631","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":"Beyond volume and toward coherence: a research parasite's perspective.","authors":"Gina Turco","doi":"10.1093/gigascience/giag001","DOIUrl":"10.1093/gigascience/giag001","url":null,"abstract":"<p><p>The Pacific Symposium on Biocomputing recognized my work with the 2024 Junior Research Parasite Award, an honor established to highlight the scientific value of reanalyzing, integrating, and reinterpreting existing datasets. The award invites recipients to reflect on the role of research parasites within the broader ecosystem of computational biology and data reuse. For me, this perspective is rooted in years of working across diverse -omics datasets, where I've seen firsthand how the structure, resolution, and context of a dataset shape the biological insight it can support. Rather than focusing on data volume alone, meaningful discovery often emerges from understanding what each dataset can-and cannot-reveal. Here, I outline how different modes of secondary analysis, from integrating complementary datasets to deeply mining a single omics layer.</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/PMC12927410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009879","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 interpretable Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis.","authors":"Zexuan Wang, Qipeng Zhan, Shu Yang, Zhuoping Zhou, Mengyuan Kan, Tianhuan Zhai, Li Shen","doi":"10.1093/gigascience/giag012","DOIUrl":"10.1093/gigascience/giag012","url":null,"abstract":"<p><strong>Background: </strong>Recent advancements in single-cell omics technologies have enabled detailed characterization of cellular processes. However, coassay sequencing technologies remain limited, resulting in unpaired single-cell omics datasets with differing feature dimensions.</p><p><strong>Finding: </strong>We present GROTIA (Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis), a computational method to align multi-omics datasets without requiring any prior correspondence information. GROTIA achieves global alignment through optimal transport while preserving local relationships via graph regularization. Additionally, our approach provides interpretability by deriving domain-specific feature importance from partial derivatives, highlighting key biological markers. Moreover, the transport plan between modalities can be leveraged for post-integration clustering, enabling a data-driven approach to discover novel cell subpopulations.</p><p><strong>Conclusions: </strong>We demonstrate GROTIA's superior performance on four simulated and four real-world datasets, surpassing state-of-the-art unsupervised alignment methods and confirming the biological significance of the top features identified in each domain.</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/PMC12970605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146141754","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":"Cancer genome standards for long-read sequencing using cancer cell line mixtures.","authors":"Jia Zhang, Ho Yi Wong, Lingchen Liu, Lambros T Koufariotis, Scott Wood, Nadine Fitzpatrick, Jenny Quiatchon, Paul Collins, John V Pearson, Nicola Waddell","doi":"10.1093/gigascience/giag037","DOIUrl":"10.1093/gigascience/giag037","url":null,"abstract":"<p><strong>Background: </strong>Long-read sequencing (LRS) improves genome alignment and facilitates resolving variants in genomic regions of low complexity, making it a promising approach for cancer variant detection and biomarker discovery.</p><p><strong>Results: </strong>Here, we evaluate the performance of LRS using Oxford Nanopore sequencing to detect somatic variants across different tumour purities and sequencing depths by comparing to somatic variants from short-read sequencing. We generated experimental mixtures of cancer cell lines and matched normal cell lines to simulate 10 tumour purities (ranging from 0 to 100% tumour content). This resulted in 22 samples, which were sequenced using whole-genome LRS to a targeted depth of 60x, and somatic variants were identified. We down-sampled data to explore the optimal read depth for somatic variant detection. Our results show long-read variant calling tools achieve recall rates comparable to short-read gold standards. While tumour sequencing depth of 30x-60x is generally sufficient for detecting common variants, particularly structural variants, sequencing the matched normal sample at adequate depth is crucial for accuracy. Notably, we found variants unique to LRS that may represent real and previously undetected events.</p><p><strong>Conclusion: </strong>This study highlights key factors for optimising cancer genome sequencing with LRS and provides a comprehensive dataset of cell line mixtures for future research and tool development.</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/PMC13137868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615751","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":"Synchrotron radiation micro-computed tomography of the small-spotted catshark embryonic development (Chondrichthyes: Scyliorhinus canicula).","authors":"Elio Escamilla-Vega, Ann-Katrin Koch, Louk W G Seton, Andrea P Murillo-Rincón, Stella Kyomen, Jörg U Hammel, Timo Moritz, Markéta Kaucká","doi":"10.1093/gigascience/giag054","DOIUrl":"10.1093/gigascience/giag054","url":null,"abstract":"<p><strong>Background: </strong>Sharks occupy a key position on vertebrate phylogeny, making them essential for understanding the early origins of jawed vertebrates (gnathostomes) and the functional adaptation of vertebrate traits like jaws or complex sensory systems. As such, sharks are important model organisms in evolutionary developmental biology (evo-devo), but sparse data and limited availability of samples hinder their inclusion in contemporary evo-devo research. The knowledge of their distinctive morphology will not only shed light on the anatomical architecture and physiology of basal living gnathostomes but also reveal the evolutionary divergence of developmental processes that establish the foundational vertebrate blueprint.</p><p><strong>Findings: </strong>We performed synchrotron radiation micro-computed tomography (SRµCT) scanning of the small-spotted catshark (Scyliorhinus canicula) embryonic development, spanning from gastrulation (Stage 12) to late organogenesis (Stage 31), enhanced by tissue contrasting with phosphotungstic acid. We obtained 36 whole-embryo scans that encompass the formation of key embryonic structures, such as sensory organs, fins, muscles, and skeletal elements. The achieved resolution allows for segmentation of all tissue types and both internal and external structures.</p><p><strong>Conclusions: </strong>We present a comprehensive dataset of 4D high-resolution SRµCT of the small-spotted catshark embryonic development. The dataset spans consecutive embryonic stages, allowing the reconstruction and morphometric analyses of tissues, organs, and structures, along with the tracking of their development. The deposited data are publicly available and provide a valuable resource for comparative research, additionally allowing the identification of conserved and derived developmental processes and features and understanding the evolution of vertebrates.</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/PMC13240850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147836687","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}