Genomics, proteomics & bioinformatics最新文献_第2页

Hypoxia Response Is Associated with Reduced HPV Activity and Tumor Microenvironment Remodeling in Cervical Cancer. 缺氧反应与宫颈癌中HPV活性降低和肿瘤微环境重塑相关

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-09-15 DOI: 10.1093/gpbjnl/qzag094

Pengfei Ren, Liang Wen, Zhe Zhang, Meng Wu, Wen Yang, Yanping Zhao, Zongxu Zhang, Mingxia Ye, Jialu Zhou, Rui Zhang, Zexian Zeng, Yuanguang Meng

{"title":"Hypoxia Response Is Associated with Reduced HPV Activity and Tumor Microenvironment Remodeling in Cervical Cancer.","authors":"Pengfei Ren, Liang Wen, Zhe Zhang, Meng Wu, Wen Yang, Yanping Zhao, Zongxu Zhang, Mingxia Ye, Jialu Zhou, Rui Zhang, Zexian Zeng, Yuanguang Meng","doi":"10.1093/gpbjnl/qzag094","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag094","url":null,"abstract":"Human papillomavirus (HPV) significantly influences cervical cancer progression and treatment, yet its interactions with the tumor microenvironment remain incompletely understood. We performed single-cell and spatial transcriptomic sequencing on cervical cancer samples to explore these interactions. By aligning sequencing reads to a merged HPV16-human reference genome, we characterized HPV16 heterogeneity and its association with host states at the single-cell and single-gene levels. E5 transcriptional activity was negatively associated with the host interferon response, indicating a role in immune evasion. A hypoxic environment was correlated with the downregulation of E5 activity and elevated MHC-I expression, which may contribute to stronger interactions between hypoxic cancer cells and cytotoxic CD8⁺ T cells. Additionally, HPV16 integration in host cells was associated with increased fatty acid metabolism. These findings suggest that combining anti-angiogenic drugs and fatty acid metabolism inhibitors has the potential to improve cervical cancer treatment.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148972337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive Evaluation and Explainable Interpretation of Peptide-HLA Binding Prediction Tools. 肽- hla结合预测工具的综合评价和解释。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-09-07 DOI: 10.1093/gpbjnl/qzag096

Yuting Wang, Guojia Wu, Xiaochuan Liu, Yang Yang

{"title":"Comprehensive Evaluation and Explainable Interpretation of Peptide-HLA Binding Prediction Tools.","authors":"Yuting Wang, Guojia Wu, Xiaochuan Liu, Yang Yang","doi":"10.1093/gpbjnl/qzag096","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag096","url":null,"abstract":"Accurate prediction of peptide binding to human leukocyte antigen class I (HLA-I) molecules is critical for advancing immunological research, particularly in vaccine design and immunotherapy. However, limitations in model performance, interpretability, and dataset quality impede the widespread adoption of existing predictive tools. Here, we present a comprehensive evaluation of 17 HLA-I peptide binding prediction models, utilizing a meticulously curated dataset comprising over 290,000 peptides spanning 44 HLA-I alleles. We assessed model accuracy, robustness, and interpretability, employing explainability techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to elucidate underlying prediction mechanisms. Our results reveal substantial performance disparities, with self-attention-based models, including STMHCpan and BigMHC, exhibiting superior accuracy. Notably, the capsule network model CapsNet-MHC_AN demonstrated robust performance. Models trained on eluted ligand datasets outperformed those relying on binding affinity data, underscoring the critical role of high-quality training data. Ensemble and multi-algorithm approaches further improved prediction reliability. These findings highlight the need for ongoing innovation in model architecture, integration of diverse and high-quality datasets, and incorporation of structural predictors to develop more accurate, interpretable, and clinically applicable HLA-I peptide binding prediction tools.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ori-Finder-Arch: An Updated Web Server for the Annotation and Visualization of Archaeal Replication Origins. Ori-Finder-Arch：用于古细菌复制起源注释和可视化的更新Web服务器。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-09-07 DOI: 10.1093/gpbjnl/qzag095

Zhisong You, Zetong Zhang, Hao Luo, Feng Gao

{"title":"Ori-Finder-Arch: An Updated Web Server for the Annotation and Visualization of Archaeal Replication Origins.","authors":"Zhisong You, Zetong Zhang, Hao Luo, Feng Gao","doi":"10.1093/gpbjnl/qzag095","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag095","url":null,"abstract":"Archaea are promising chassis organisms in biotechnology, and the accurate annotation of their chromosomal replication origins (oriCs) is the key to unlocking their full potential. However, the existing Ori-Finder 2 web server suffers from low accuracy, slow speed, and limited scalability. In this study, we present Ori-Finder-Arch, an updated web server for high-performance oriC prediction in archaea. This pipeline integrates HMMER-based replication initiation protein (RIP) annotation, refined consensus motif recognition, and GC profile-based DNA unwinding element (DUE) detection. On a benchmark set of experimentally validated oriCs, Ori-Finder-Arch achieved a recall of 95.6% and a precision of 86.0%, substantially outperforming Ori-Finder 2 (62.2% and 63.6%, respectively), while running 4.75 times faster and supporting diverse assembly levels. When applied to the available archaeal assemblies, it successfully annotated 17,472 oriCs. Meanwhile, the web server provides interactive visualizations at different levels. In conclusion, Ori-Finder-Arch offers an efficient, accurate, and user-friendly platform for advanced studies of archaeal DNA replication initiation and synthetic biology applications, and is freely available at https://tubic.org/Ori-Finder-Arch/ and https://tubic.tju.edu.cn/Ori-Finder-Arch/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-layer Cis-molQTLs Reveal Regulatory Architecture and Enhance Heritability Explanation for Complex Traits in Cattle. 多层顺式- molqtl揭示了牛复杂性状的调控结构，增强了遗传力解释。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-09-02 DOI: 10.1093/gpbjnl/qzag093

Shiyuan Qiu, Lili Du, Boyu Zhang, Jiahao Wang, Keanning Li, Li Qian, Zhenwei Du, Hao Xiao, Jinbu Wang, Meng Mao, Anyi Tang, Shengyun Jin, Bo Zhu, Yan Chen, Zezhao Wang, Xue Gao, Lingyang Xu, Lingzhao Fang, Junya Li, Lupei Zhang, Huijiang Gao, Caihong Zheng

{"title":"Multi-layer Cis-molQTLs Reveal Regulatory Architecture and Enhance Heritability Explanation for Complex Traits in Cattle.","authors":"Shiyuan Qiu, Lili Du, Boyu Zhang, Jiahao Wang, Keanning Li, Li Qian, Zhenwei Du, Hao Xiao, Jinbu Wang, Meng Mao, Anyi Tang, Shengyun Jin, Bo Zhu, Yan Chen, Zezhao Wang, Xue Gao, Lingyang Xu, Lingzhao Fang, Junya Li, Lupei Zhang, Huijiang Gao, Caihong Zheng","doi":"10.1093/gpbjnl/qzag093","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag093","url":null,"abstract":"Genome-wide association study (GWAS) analyses have identified numerous loci associated with economic traits in cattle. Many of these loci reside in noncoding regions, and the regulatory mechanisms through which they influence complex traits remain poorly understood. Here, we integrated 657 RNA-seq libraries from 275 Huaxi cattle across three tissues (longissimus dorsi muscle, liver, and subcutaneous backfat) with ∼ 10 million imputed SNP genotypes to systematically map cis-molecular quantitative trait loci (cis-molQTLs) across four transcriptomic regulatory layers: gene expression (eQTLs), splicing (sQTLs), alternative polyadenylation (aQTLs), and RNA editing (edQTLs). These cis-molQTL classes display distinct genomic distributions and functional enrichments, yet operate in a coordinated manner within complex trait regulatory networks and are significantly enriched near GWAS- and QTLdb-reported loci for growth, carcass, and meat quality traits. Using 1788 genotyped and phenotyped Huaxi cattle, a GREML framework showed that these multi-layer cis-molQTL SNPs collectively explain 61.9% of total SNP-based heritability across 19 complex traits. Incorporating cis-molQTL annotations into genomic prediction models, including MultiBLUP, BayesRC, and molGBLUP, improved prediction accuracy for most traits relative to the baseline GBLUP model (mean increase of 0.05), highlighting the value of multi-layer regulatory variation for functionally informed genomic prediction and precision breeding.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Functional Characterization of RNA Profiles in Processing Bodies of Embryonic Stem Cells and Mesodermal Cells. 胚胎干细胞和中胚层细胞加工体RNA谱的功能表征。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-31 DOI: 10.1093/gpbjnl/qzag092

Jin Jiang, Qizhe Shao, Sisi Xie, Xiaoying Xiao, Wenjing Zhang, Zhenfu Li, Ruisi Guo, Chenyang Huang, Mengyuan He, Mingxuan Pan, Kexin Lin, Zhengyi Li, Yuxin Chen, Yuxin Wu, Jianzhao Liu, Wanlu Liu, Min Jin, Di Chen

{"title":"Functional Characterization of RNA Profiles in Processing Bodies of Embryonic Stem Cells and Mesodermal Cells.","authors":"Jin Jiang, Qizhe Shao, Sisi Xie, Xiaoying Xiao, Wenjing Zhang, Zhenfu Li, Ruisi Guo, Chenyang Huang, Mengyuan He, Mingxuan Pan, Kexin Lin, Zhengyi Li, Yuxin Chen, Yuxin Wu, Jianzhao Liu, Wanlu Liu, Min Jin, Di Chen","doi":"10.1093/gpbjnl/qzag092","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag092","url":null,"abstract":"Processing bodies (P-bodies) are membraneless organelles that exist in eukaryotic cells, contributing to the post-transcriptional regulation of gene expression. Abnormalities in P-bodies lead to diseases and developmental disorders. Recent studies have demonstrated the involvement of P-bodies in stem cell fate regulation by associating the depletion of P-bodies with cell fate change. However, how the dynamics of RNA components in different cell types are related to corresponding cell fates remains poorly understood. Here, we systematically characterized the RNA profiles of P-bodies from HEK293T cells, human embryonic stem cells (hESCs), and hESC-derived mesodermal cells. By functional analysis of the P-body-enriched and P-body-depleted RNAs across different developmental stages, we discovered the cell type-specific enrichment of P-body RNAs. We also captured the non-coding RNAs, including long intergenic non-coding RNAs and transposable elements in P-bodies in a cell type-specific manner. Furthermore, we verified the functions of P-bodies in regulating the differentiation of hESCs toward mesodermal cell fate by over-expressing LSM14A, one of the key P-body protein components, and knocking down SPTAN1, one of the P-body-enriched mRNAs. In summary, we characterized the mRNAs and non-coding RNAs in P-bodies from HEK293T, hESCs and hESC-derived mesodermal cells, deciphering the potential developmental roles of P-bodies in the maintenance and differentiation of hESCs.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapping Allele-specific RBP Binding by a Machine Learning Coupled RNA Editing Strategy in Human Embryonic Stem Cells. 通过机器学习耦合RNA编辑策略在人类胚胎干细胞中定位等位基因特异性RBP结合。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-31 DOI: 10.1093/gpbjnl/qzag091

Jin Zhang, Yueqi Wang, Jilai Xie, Shuo Cai, Huanchang Tu, Lingling Tong, Wenjing Zhang, Yashi Gu, Shenghua Dong, Huilin Huang, Xushen Xiong, Min Jin, Di Chen

{"title":"Mapping Allele-specific RBP Binding by a Machine Learning Coupled RNA Editing Strategy in Human Embryonic Stem Cells.","authors":"Jin Zhang, Yueqi Wang, Jilai Xie, Shuo Cai, Huanchang Tu, Lingling Tong, Wenjing Zhang, Yashi Gu, Shenghua Dong, Huilin Huang, Xushen Xiong, Min Jin, Di Chen","doi":"10.1093/gpbjnl/qzag091","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag091","url":null,"abstract":"RNA-binding proteins (RBPs) play critical roles in regulating the maintenance and differentiation of embryonic stem cells. A critical step in understanding the functions of RBPs is the identification of their bound RNA transcripts. To achieve this, we developed an inducible strategy termed RNA-Editing-Based-RNA-seq (REB-seq) coupled with machine learning to capture the potential RNA targets bound by the RBPs-of-interest. By fusing RBPs to the catalytic domain of ADAR or APOBEC1, which mediates the A-to-I (A-to-G) or C-to-U (C-to-T) editing, respectively, REB-seq enables transcriptome-wide identification of RBP-bound RNAs without high-quality antibodies. Using REB-seq, we characterized the mRNAs bound by the N6-methyladenosine (m6A) readers IGF2BP1, IGF2BP2, and IGF2BP3 in human embryonic stem cells. Coupling REB-seq analysis with machine learning further improves the accuracy of target RNA characterization. Furthermore, REB-seq identifies associated single nucleotide polymorphisms that may affect the RBP binding and imply disease pathogenesis. Collectively, REB-seq coupled with machine learning provides a robust and accessible approach for profiling allele-specific RNA transcripts bound by RBPs-of-interest.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-cell Atlas Reveals Myelopoietic Bias and Immunosuppressive Reprogramming in Metastatic Bone Marrow Across Cancers. 单细胞图谱揭示骨髓在癌症转移性骨髓中的造血偏倚和免疫抑制重编程。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-31 DOI: 10.1093/gpbjnl/qzag066

Zhilong Shen, Shaoxuan Wang, Hao Jiang, Pengru Wang, Shangbin Zhou, Bo Li, Hao Yuan, Liangwei Ni, Yongxin Mao, Gan Xu, Bowen Lai, Hao Zhang, Zhipeng Zhu, Xin Zhang, Congling Xu, Jianru Xiao, Wei Xu

{"title":"Single-cell Atlas Reveals Myelopoietic Bias and Immunosuppressive Reprogramming in Metastatic Bone Marrow Across Cancers.","authors":"Zhilong Shen, Shaoxuan Wang, Hao Jiang, Pengru Wang, Shangbin Zhou, Bo Li, Hao Yuan, Liangwei Ni, Yongxin Mao, Gan Xu, Bowen Lai, Hao Zhang, Zhipeng Zhu, Xin Zhang, Congling Xu, Jianru Xiao, Wei Xu","doi":"10.1093/gpbjnl/qzag066","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag066","url":null,"abstract":"Bone metastasis can remodel the bone marrow microenvironment, yet how metastatic tumors reshape hematopoiesis and immunity across marrow regions remains poorly defined. Here, we profile a cross-cancer single-cell atlas with 126,986 cells by integrating bone marrow samples (tumor, involved, and distal sites) from liver, prostate, and kidney cancer bone metastases, as well as benign controls, resolving 7 major lineages and 66 subpopulations. We identified coordinated immune suppression and niche remodeling that innate cells rose in peri-tumoral marrow but dropped in tumor sites, while adaptive T/B cells progressively depleted and stromal/epithelial compartments expanded. We further revealed a shift in myelopoiesis toward immunosuppressive monocyte/macrophage states with GMP-level lineage bias, alongside impaired erythropoiesis and B lymphopoiesis driven by myeloid-like reprogramming of precursors. Tumor sites were enriched with exhausted/stressed T cells, linked to inhibitory progenitor-T cell interactions (e.g., LGALS9-HAVCR2 and CLEC2-KLRB1). Non-hematopoietic support signals (CXCL12-CXCR4, ICAM1-SPN) were attenuated, and tumor programs included a marrow-specific metaprogram with IGFBP3/NAMPT signaling predicted to further destabilize the HSC niche. Overall, our study offers an integrated framework for decoding the metastatic marrow ecosystem by jointly targeting hematopoietic distortion, stromal collapse, and immune dysfunction.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HOSCA: Human Ocular Single-Cell Atlas for Decoding Ocular Biology and Disease Complexity. 人类眼部单细胞图谱用于解码眼部生物学和疾病复杂性。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-25 DOI: 10.1093/gpbjnl/qzag090

Jingzhe Huang, Ke Li, Hongbo Zhang, Jiazhu Chen, Meng Zhou, Jie Sun

{"title":"HOSCA: Human Ocular Single-Cell Atlas for Decoding Ocular Biology and Disease Complexity.","authors":"Jingzhe Huang, Ke Li, Hongbo Zhang, Jiazhu Chen, Meng Zhou, Jie Sun","doi":"10.1093/gpbjnl/qzag090","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag090","url":null,"abstract":"The human eye is a highly specialized organ composed of different tissue types, each with a unique cellular microenvironment critical to visual function. Recent advances in single-cell RNA sequencing (scRNA-seq) have provided unprecedented opportunities to explore the cellular heterogeneity of ocular tissues and diseases. However, the rapid accumulation of scRNA-seq data in ophthalmology poses challenges in integrating, aligning, and effectively reusing different datasets. Here, we present the Human Ocular Single-Cell Atlas (HOSCA), an atlas-scale curated database and analysis platform that integrates and harmonizes more than 3.88 million single-cell transcriptomic profiles from 704 human samples and 60 high-quality scRNA-seq datasets, covering 16 anatomical regions of the eye and 15 associated diseases. HOSCA provides user-friendly, interactive tools for conducting cross-tissue, cross-disease, and demographic comparative analyses using a unified bioinformatics pipeline for data standardization, cross-platform batch correction, and accurate cell type annotation. As the largest ocular single-cell resource to date, HOSCA bridges the gap between atlas-scale data and precision ophthalmology, providing a powerful platform for advancing our understanding of ocular biology and disease. HOSCA is publicly available at http://www.bio-data.cn/HOSCA/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genomic Insights into Divergent Evolutionary Strategies and Vector Capacities of Ticks. 蜱的不同进化策略和媒介能力的基因组见解。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-24 DOI: 10.1093/gpbjnl/qzag089

Wenqiang Shi, Jia-Jing Zheng, Yi-Fei Wang, Hua Wei, Yi Sun, Li-Feng Du, Di Tian, Xiao-Ming Cui, Na Jia, Ming-Zhu Zhang, Xiao-Yu Shi, Jin Luo, Weifei Yang, Ning Wang, Bai-Hui Wang, Wan-Ying Gao, Zhijun Yu, Tian-Hong Wang, Di Zhao, Ming-Xiang Wang, Zhongsi Wang, Zhi-Hong Liu, Jia-Fu Jiang, Guangyuan Liu, Wu-Chun Cao

{"title":"Genomic Insights into Divergent Evolutionary Strategies and Vector Capacities of Ticks.","authors":"Wenqiang Shi, Jia-Jing Zheng, Yi-Fei Wang, Hua Wei, Yi Sun, Li-Feng Du, Di Tian, Xiao-Ming Cui, Na Jia, Ming-Zhu Zhang, Xiao-Yu Shi, Jin Luo, Weifei Yang, Ning Wang, Bai-Hui Wang, Wan-Ying Gao, Zhijun Yu, Tian-Hong Wang, Di Zhao, Ming-Xiang Wang, Zhongsi Wang, Zhi-Hong Liu, Jia-Fu Jiang, Guangyuan Liu, Wu-Chun Cao","doi":"10.1093/gpbjnl/qzag089","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag089","url":null,"abstract":"Ticks, which are globally distributed, transmit a wide range of human and animal pathogens. Soft and hard ticks are two major tick families exhibiting distinct vector capacities and blood-feeding abilities. However, understanding the genetic basis of these differences is hindered by the scarcity of high-quality tick genomes, particularly for soft ticks. Here, we present high-quality genomes of two soft and two hard tick species of both evolutionary and medical importance, and compare the genomic characteristics of the two tick families. Compared with soft ticks, hard ticks exhibited faster genome evolution, characterized by larger genome sizes, higher rates of gene gain and loss, and a greater number of positively selected genes. Both tick families exhibited frequent positive selection across metabolism and organismal systems, sharing a core set of adaptive genes but undergoing refinement at different sites. Gene families underpinning various vector capacities, including those involved in cuticle structure, heme biosynthesis, egg waxing, and immune pathways, were identified. Additionally, we assembled the genomes of two novel Rickettsia endosymbionts from soft ticks, revealing high genomic divergence and lineage-specific adaptations to their hosts. These genomic resources provide fundamental insights into the genetic basis of tick biology and vector capacity, highlighting the divergent evolutionary strategies of blood-feeding arthropods shaped by their ecological niches.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bio-OS: An Integrated Cloud-native Environment for Data-Intensive Biomedical Research. Bio-OS：用于数据密集型生物医学研究的集成云原生环境。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-20 DOI: 10.1093/gpbjnl/qzag085

Jilong Liu, Liguang Yang, Qingyu Xiao, Yanqing Wang, Zhen Wang, Rui Zhang, Zhaoqiang Li, Jianwen Zhou, Hengshen Liu, Lu Yao, Kaixin Yang, Ziru Chen, Xiang Wan, Tsunghui Chang, Ye Li, Tao Huang, Wenming Zhao, Xin Zhang, Yixue Li

{"title":"Bio-OS: An Integrated Cloud-native Environment for Data-Intensive Biomedical Research.","authors":"Jilong Liu, Liguang Yang, Qingyu Xiao, Yanqing Wang, Zhen Wang, Rui Zhang, Zhaoqiang Li, Jianwen Zhou, Hengshen Liu, Lu Yao, Kaixin Yang, Ziru Chen, Xiang Wan, Tsunghui Chang, Ye Li, Tao Huang, Wenming Zhao, Xin Zhang, Yixue Li","doi":"10.1093/gpbjnl/qzag085","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag085","url":null,"abstract":"The increasing volume and complexity of biomedical data, especially driven by high-throughput biotechnologies, have highlighted the need for scalable, interoperable, and reproducible data analysis frameworks. While cloud computing has revolutionized data processing and collaboration in life sciences, challenges remain in building cloud platforms tailored to biomedical research and ensuring platform interoperability. To address these limitations, we propose Bio-OS, an open-source, cloud-native framework designed to support the rapid development of interoperable biomedical cloud platforms. Bio-OS aligns with the Global Alliance for Genomics and Health (GA4GH) cloud workstream standards-including Workflow Execution Service (WES), Task Execution Service (TES), Data Repository Service (DRS), and Tool Registry Service (TRS) - while introducing innovations such as interactive execution models, workspace-centric data management, and AI-driven enhancements. These features facilitate adherence to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, promoting reproducibility and collaboration in biomedical research. Deployments across multiple research institutions demonstrated Bio-OS's ability to build biomedical cloud platforms and validated its efficacy in supporting data-intensive applications in life sciences.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0