GigaScience最新文献_第6页

SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets. 空间组学数据集中使用贝叶斯融合方法的空间共表达分析。

IF 5 2区生物学

GigaScience Pub Date : 2026-01-21 DOI: 10.1093/gigascience/giag006

Souvik Seal, Brian Neelon

{"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":"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.","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}

引用次数: 0

Integrated multi-omics profiling identifies genetic loci of African swine fever resistance in pigs. 综合多组学分析确定了猪对非洲猪瘟抗性的遗传位点。

IF 5 2区生物学

GigaScience Pub Date : 2026-01-21 DOI: 10.1093/gigascience/giag066

Xiaowei Ye, Qinqin Xie, Caiyun Cao, Shuang Liu, Wenbo Sun, Zhe Zhang, Qishan Wang, Yuchun Pan, Zhen Wang

{"title":"Integrated multi-omics profiling identifies genetic loci of African swine fever resistance in pigs.","authors":"Xiaowei Ye, Qinqin Xie, Caiyun Cao, Shuang Liu, Wenbo Sun, Zhe Zhang, Qishan Wang, Yuchun Pan, Zhen Wang","doi":"10.1093/gigascience/giag066","DOIUrl":"10.1093/gigascience/giag066","url":null,"abstract":"Background: African swine fever (ASF) remains a persistent threat to global pig production, with no licensed vaccines or effective treatments available. Observations of surviving individuals within low-virulence infected herds suggest that host genetic resistance plays a crucial role.Results: Here, we present a multi-dimensional integrative analysis to uncover host genomic variants associated with ASF resistance. Combining genome-wide association studies (GWAS), genetic differentiation, and functional genomic approaches, including TWAS, SMR, colocalization, and Bayesian network GWAS, we prioritized 135 high-priority candidate resistance genes from an initial gene set of 1,102 candidates. These prioritized genes are enriched in immune-related pathways, such as chemokine signaling and IL-15-mediated activation. Heritability enrichment and transcriptomic analyses further revealed tissue- and cell-type-specific expression patterns, particularly in peripheral immune organs and pulmonary alveolar macrophages. Dynamic infection-responsive genes, including CXCL10, CXCL11, and IL15, exhibited robust antiviral signatures, which highlighted Mac_CD163 as key cellular mediators in the immune response to ASF. Moreover, multiple genes (such as SOS1, FCGR2B, FCGR3) converged on the PI3K-AKT and Fcγ receptor signaling axes pathways, underscoring their functional importance. Finally, we developed a polygenic resistance score using 40 prioritized independent SNPs, which effectively discriminates phenotypic outcomes and showed a positive correlation with health traits such as platelet distribution width.Conclusions: These findings provided a genomic foundation for the precision breeding of ASF-resistant pigs and inform host-targeted disease control strategies.","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/PMC13289747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148101263","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}

引用次数: 0

SCSEQ: A web tool for analyzing single-cell RNA-seq data. SCSEQ：用于分析单细胞RNA-seq数据的网络工具。

IF 5 2区生物学

GigaScience Pub Date : 2026-01-21 DOI: 10.1093/gigascience/giag029

Shiyu Du, Pengcheng Sun, Li Shen, Jian He

{"title":"SCSEQ: A web tool for analyzing single-cell RNA-seq data.","authors":"Shiyu Du, Pengcheng Sun, Li Shen, Jian He","doi":"10.1093/gigascience/giag029","DOIUrl":"10.1093/gigascience/giag029","url":null,"abstract":"Background: Single-cell RNA sequencing has emerged as a powerful approach to reveal cellular heterogeneity within biological systems. With the continuous advancement of high-throughput sequencing technologies, studies are generating vast amounts of complex data, posing a significant challenge for researchers in effective data processing and analysis.Results: To address this issue, we developed SCSEQ, an interactive web-based bioinformatics analysis platform. This platform enables even users without programming expertise to conveniently process and analyze sequencing data. SCSEQ provides a comprehensive workflow encompassing data preprocessing, normalization, clustering, dimension reduction, differential expression analysis, cell type identification, and downstream analyses. The downstream analysis tasks include gene enrichment analysis, transcription factor analysis, cell-cell communication analysis, copy number variation detection, trajectory inference, and pan-cancer analysis. SCSEQ facilitates information transfer between different workflows, accepts various input formats, and generates graphical and tabular outputs. As a user-friendly platform, we enhance user experience through detailed parameter settings and dynamic interactions. This enables users to precisely regulate research processes and customize result figures. Additionally, we provide comprehensive user manuals to assist with parameter configuration and workflow execution.Conclusions: SCSEQ provides an intuitive and convenient solution for single-cell transcriptome sequencing data analysis. Our platform has successfully completed full-process analyses on real-world data with reliable results, demonstrating its applicability in practical scenarios. The platform is available at https://scseq.com.cn/.","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/PMC13142160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147836726","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}

引用次数: 0

nf-core/proteinfamilies: a scalable pipeline for the generation of protein families. nf-core/proteinfamilies：一个可扩展的蛋白质家族生成管道。

IF 5 2区生物学

GigaScience Pub Date : 2026-01-21 DOI: 10.1093/gigascience/giag009

Evangelos Karatzas, Martin Beracochea, Fotis A Baltoumas, Eleni Aplakidou, Lorna Richardson, James A Fellows Yates, Daniel Lundin, Aydin Buluç, Nikos C Kyrpides, Ilias Georgakopoulos-Soares, Georgios A Pavlopoulos, Robert D Finn

{"title":"nf-core/proteinfamilies: a scalable pipeline for the generation of protein families.","authors":"Evangelos Karatzas, Martin Beracochea, Fotis A Baltoumas, Eleni Aplakidou, Lorna Richardson, James A Fellows Yates, Daniel Lundin, Aydin Buluç, Nikos C Kyrpides, Ilias Georgakopoulos-Soares, Georgios A Pavlopoulos, Robert D Finn","doi":"10.1093/gigascience/giag009","DOIUrl":"10.1093/gigascience/giag009","url":null,"abstract":"The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity, and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterized sequences with the few experimentally characterized ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates nascent protein families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage scales approximately linearly with input size, and the biological benchmarks showed that the generated protein families closely resemble manually curated families in widely used databases.","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/PMC12950615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009872","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}

引用次数: 0

Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen health care settings. 用于在看不见的医疗环境中快速预测抗菌素耐药性的通用机器学习模型。

IF 5 2区生物学

GigaScience Pub Date : 2026-01-21 DOI: 10.1093/gigascience/giaf156

Diane Duroux, Paul P Meyer, Giovanni Visonà, Niko Beerenwinkel

{"title":"Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen health care settings.","authors":"Diane Duroux, Paul P Meyer, Giovanni Visonà, Niko Beerenwinkel","doi":"10.1093/gigascience/giaf156","DOIUrl":"10.1093/gigascience/giaf156","url":null,"abstract":"Background: The deployment of machine learning in clinical settings is often hindered by the limited generalizability of the models. Models that perform well during development tend to underperform in new environments, limiting their clinical utility. This issue affects models designed for the rapid identification of antimicrobial resistance, which is essential to guide treatment decisions. Traditional susceptibility tests can take up to 3 days, whereas integrating matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry with machine learning has the potential to reduce this to 1 day. However, model performance declines drastically in hospitals or time frames outside the training data.Results: To improve robustness, we develop advanced feature representations using masked autoencoders (MAEs) for MALDI-TOF spectra and chemical language models and SELF-referencing embedded strings (SELFIES) for antimicrobials. Cross-validated on data from 4 medical institutions, our models demonstrate improved performance and stability. The MAE and SELFIES encodings increase the area under the precision-recall curve by 4% when evaluated on unseen time periods, while the MAE and Molformer language model encodings improve it by 10% when applied across different hospitals.Conclusions: These results underscore the value of combining deep learning with chemical and spectral information to build generalizable, high-impact clinical artificial intelligence.","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/PMC12908719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145997116","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}

引用次数: 0

NanoporeDB: a structural resource of multimeric protein nanopores for single-molecule sensing. 纳米oredb：用于单分子传感的多聚体蛋白质纳米孔结构资源。

IF 5 2区生物学

GigaScience Pub Date : 2026-01-21 DOI: 10.1093/gigascience/giag076

Yuqian Liu, Zidong Su, Wenzhen Yang, Denghui Li, Jiawen Zhang, Yuning Zhang, Tao Zeng, Yong Zhang, Yuxiang Li, Guangyi Fan, Kailong Ma, Shanshan Liu, Xun Xu, Yuliang Dong, Zongan Wang

{"title":"NanoporeDB: a structural resource of multimeric protein nanopores for single-molecule sensing.","authors":"Yuqian Liu, Zidong Su, Wenzhen Yang, Denghui Li, Jiawen Zhang, Yuning Zhang, Tao Zeng, Yong Zhang, Yuxiang Li, Guangyi Fan, Kailong Ma, Shanshan Liu, Xun Xu, Yuliang Dong, Zongan Wang","doi":"10.1093/gigascience/giag076","DOIUrl":"10.1093/gigascience/giag076","url":null,"abstract":"Background: Protein nanopores are essential molecular gateways in biology and have inspired transformative technologies in biosensing and single-molecule sequencing. However, the discovery and engineering of novel nanopore scaffolds remains limited due to the scarcity of experimentally resolved pore structures.Results: Here, we present NanoporeDB, an open-access structural resource comprising about 7,000 high-confidence multimeric models across 4 representative pore types. Using a structure- and sequence-guided mining strategy, we identified candidate nanopores from large protein datasets, including the AlphaFold Protein Structure Database, UniRef90, and MGnify90, and generated high-confidence multimeric models using AlphaFold-Multimer and AlphaFold3. Collectively, these models represent a >170-fold expansion of the structurally annotated nanopore repertoire. Each model is further annotated with predicted membrane embedding, pore geometry, and constriction profiles, enabling structure-informed functional inference. NanoporeDB features an interactive web interface with 3D visualization and quantitative metrics.Conclusions: NanoporeDB provides the first comprehensive structural resource of multimeric protein nanopores with explicit membrane and pore annotations. This resource provides a structural gateway for advancing nanopore-based molecular sensing, precision diagnostics, and synthetic biology. NanoporeDB is publicly available at https://db.genomics.cn/nanopore.","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/PMC13397529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148328600","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}

引用次数: 0

Expression-driven genetic dependency reveals targets for precision oncology. 表达驱动的基因依赖性揭示了精确肿瘤学的靶标。

IF 5 2区生物学

GigaScience Pub Date : 2026-01-21 DOI: 10.1093/gigascience/giag011

Abdulkadir Elmas, Hillary M Layden, Jacob D Ellis, Luke N Bartlett, Xian Zhao, Reika Kawabata-Iwakawa, Zishan Wang, Hideru Obinata, Scott W Hiebert, Kuan-Lin Huang

{"title":"Expression-driven genetic dependency reveals targets for precision oncology.","authors":"Abdulkadir Elmas, Hillary M Layden, Jacob D Ellis, Luke N Bartlett, Xian Zhao, Reika Kawabata-Iwakawa, Zishan Wang, Hideru Obinata, Scott W Hiebert, Kuan-Lin Huang","doi":"10.1093/gigascience/giag011","DOIUrl":"10.1093/gigascience/giag011","url":null,"abstract":"Background: Cancer cells are heterogeneous, each harboring distinct molecular aberrations and being dependent on different genes for their survival and proliferation. While targeted therapies based on driver DNA mutations have shown success, many tumors lack druggable mutations, limiting treatment options. We hypothesize that new precision oncology targets may be identified through \"expression-driven dependency,\" where cancer cells with high expression of specific genes are more vulnerable to the knockout of those same genes.Results: We developed BEACON, a Bayesian approach to identify expression-driven dependency targets by analyzing global transcriptomic and proteomic profiles alongside genetic dependency data from cancer cell lines across 17 tissue lineages. BEACON successfully identified known druggable genes, including BCL2, ERBB2, EGFR, ESR1, and MYC, while revealing novel targets confirmed by both mRNA- and protein-expression-driven dependency. The identified genes showed a 3.8-fold enrichment for approved drug targets and a 7- to 10-fold enrichment for druggable oncology targets. Experimental validation demonstrated that depletion of GRHL2, TP63, and PAX5 effectively reduced tumor cell growth and survival in their dependent cells.Conclusions: Our approach provides a systematic method to identify precision oncology targets based on expression-driven dependency patterns. By integrating multi-omics data with genetic dependency screens, we have created a comprehensive catalog of potential therapeutic targets that may expand treatment options for cancer patients lacking druggable mutations. This resource offers new opportunities for precision oncology target discovery beyond mutation-based approaches.","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/PMC12970598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085410","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}

引用次数: 0

Community-curated Galaxy interfaces with the Galaxy Labs Engine. 社区策划的银河接口与银河实验室引擎。

IF 5 2区生物学

GigaScience Pub Date : 2026-01-21 DOI: 10.1093/gigascience/giag041

Cameron J Hyde, Anna Syme, Bérénice Batut, Paul F Zierep, Winnie Mok, Wendi A Bacon, Gareth R Price

{"title":"Community-curated Galaxy interfaces with the Galaxy Labs Engine.","authors":"Cameron J Hyde, Anna Syme, Bérénice Batut, Paul F Zierep, Winnie Mok, Wendi A Bacon, Gareth R Price","doi":"10.1093/gigascience/giag041","DOIUrl":"10.1093/gigascience/giag041","url":null,"abstract":"The Galaxy platform is a globally distributed environment for data-intensive research, providing thousands of analysis tools across major public servers. However, this decentralized ecosystem presents usability challenges for both users and administrators, particularly in surfacing relevant tools and workflows for specific communities. To improve discoverability and support global collaboration, the Galaxy project has employed community-driven \"Galaxy Flavours\"-subdomains with curated content for defined research domains. While conceptually valuable, Flavours suffer from critical limitations: They are statically deployed, difficult to replicate across servers, and often provide inconsistent and unintuitive user interfaces. To address these challenges, we developed the Galaxy Labs Engine (GLE), a service that enables the creation of Galaxy Labs. This new paradigm enables globally synchronized, domain-specific entry points built from structured, reusable web content. GLE separates content from deployment, allowing communities to define a shared canonical representation of their domain, while enabling individual Galaxy servers to locally customize presentation. Labs are designed to guide users through curated tools, workflows, and training resources, and are aimed at researchers who are new to the analytical methods or technologies specific to the domain. The GLE provides a consistent, customizable, and community-driven interface layer for the Galaxy ecosystem. By fostering FAIR principles, labs offer a scalable improvement to Flavours and enhance Galaxy's ability to support diverse research communities. GLE is open-source and currently deployed at https://labs.usegalaxy.org.au, with multiple Labs already supporting active user groups. This work strengthens Galaxy's role as a collaborative platform for reproducible, user-centred science.","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/PMC13148399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147662389","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}

引用次数: 0

Breaking the cold chain: solutions for room temperature preservation of mosquitoes leading to high quality reference genomes. 打破冷链：室温保存蚊子的解决方案，从而获得高质量的参考基因组。

IF 5 2区生物学

GigaScience Pub Date : 2026-01-21 DOI: 10.1093/gigascience/giag061

Fiona Teltscher, Petra Korlević, Edel Sheerin, Alex Makunin, Mara K N Lawniczak

{"title":"Breaking the cold chain: solutions for room temperature preservation of mosquitoes leading to high quality reference genomes.","authors":"Fiona Teltscher, Petra Korlević, Edel Sheerin, Alex Makunin, Mara K N Lawniczak","doi":"10.1093/gigascience/giag061","DOIUrl":"10.1093/gigascience/giag061","url":null,"abstract":"Background: The Earth BioGenome Project (EBP) is a global endeavour to produce reference genomes for all described eukaryotic species. The majority of described species are arthropods, which tend to be small and require taxonomic expertise to identify at species level. Therefore, the ability to collect and preserve specimens in a suitable way for long read and Hi-C data generation using very simple approaches with minimal infrastructure will be important in scaling up reference genome generation. Field collections requiring dry ice or dry shippers can be logistically challenging to arrange, are notoriously expensive, and DNA degrades rapidly if ultra-cold temperature is not maintained, which is devastating given how expensive and time consuming fieldwork can be.Findings: Using Anopheles mosquitoes as an insect representative, we evaluate how well different preservation liquids protect high molecular weight DNA, RNA, and nuclei for Hi-C at room temperature when mosquitoes are held intact versus slightly squished. We find that squished samples stored in 100% ethanol and Allprotect held at room temperature for 1 week result in excellent preservation of both high molecular weight DNA and nuclei for Hi-C. Other tested buffers, including RNAlater, EDTA at several pHs, and dimethyl sulfoxide EDTA salt solution (DESS), performed satisfactorily for long read data generation and RNA retrieval, but less ideally for Hi-C, which may have bigger negative impacts when aiming to generate data for organisms with larger genomes.Conclusions: We present multiple viable options for room temperature collection and/or shipment for arthropod samples, with direct suggestions for shipment and storage of mosquitoes and similarly sized specimens. Further exploration across a broader range of species will hopefully enable cheaper and more widely available reference genome generation globally.","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/PMC13221836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147987278","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}

引用次数: 0

MADRe: Strain-level metagenomic classification through assembly-driven database reduction. 通过装配驱动的数据库简化的菌株水平宏基因组分类。

IF 5 2区生物学

GigaScience Pub Date : 2026-01-21 DOI: 10.1093/gigascience/giag030

Josipa Lipovac, Mile Šikić, Riccardo Vicedomini, Krešimir Križanović

{"title":"MADRe: Strain-level metagenomic classification through assembly-driven database reduction.","authors":"Josipa Lipovac, Mile Šikić, Riccardo Vicedomini, Krešimir Križanović","doi":"10.1093/gigascience/giag030","DOIUrl":"10.1093/gigascience/giag030","url":null,"abstract":"Strain-level metagenomic classification is essential for understanding microbial diversity and functional potential, yet remains challenging, particularly when sample composition is unknown and reference databases are large and redundant. Here, we present MADRe, a modular and scalable pipeline for long-read strain-level metagenomic classification based on Metagenome Assembly-Driven Database Reduction. Beyond system-level integration, MADRe introduces statistical strategies that leverage assembly-derived genomic context to guide database reduction and probabilistic read reassignment. Specifically, it combines long-read metagenome assembly, contig-to-reference reassignment using an expectation-maximization framework for reference reduction, and probabilistic read mapping reassignment on a reduced database to achieve sensitive and precise strain-level classification. We extensively evaluated MADRe on simulated datasets, mock communities, and a real anaerobic digester sludge metagenome. Across diverse similarity and coverage conditions, MADRe consistently improves precision by reducing false-positive strain detections. MADRe's design allows users to apply either the database reduction or read classification step individually. Using only the read classification step shows results on par with other tested tools. MADRe is open source and publicly available at https://github.com/lbcb-sci/MADRe.","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/PMC13211987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147503595","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}

引用次数: 0