GigaScience最新文献_第5页

Streamlining remote nanopore data access with slow5curl. 利用 slow5curl 简化远程纳米孔数据访问。

IF 3.5 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae016

Bonson Wong, James M Ferguson, Jessica Y Do, Hasindu Gamaarachchi, Ira W Deveson

{"title":"Streamlining remote nanopore data access with slow5curl.","authors":"Bonson Wong, James M Ferguson, Jessica Y Do, Hasindu Gamaarachchi, Ira W Deveson","doi":"10.1093/gigascience/giae016","DOIUrl":"10.1093/gigascience/giae016","url":null,"abstract":"Background: As adoption of nanopore sequencing technology continues to advance, the need to maintain large volumes of raw current signal data for reanalysis with updated algorithms is a growing challenge. Here we introduce slow5curl, a software package designed to streamline nanopore data sharing, accessibility, and reanalysis.Results: Slow5curl allows a user to fetch a specified read or group of reads from a raw nanopore dataset stored on a remote server, such as a public data repository, without downloading the entire file. Slow5curl uses an index to quickly fetch specific reads from a large dataset in SLOW5/BLOW5 format and highly parallelized data access requests to maximize download speeds. Using all public nanopore data from the Human Pangenome Reference Consortium (>22 TB), we demonstrate how slow5curl can be used to quickly fetch and reanalyze raw signal reads corresponding to a set of target genes from each individual in large cohort dataset (n = 91), minimizing the time, egress costs, and local storage requirements for their reanalysis.Conclusions: We provide slow5curl as a free, open-source package that will reduce frictions in data sharing for the nanopore community: https://github.com/BonsonW/slow5curl.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11010652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140848401","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

RNAVirHost: a machine learning-based method for predicting hosts of RNA viruses through viral genomes. RNAVirHost：一种通过病毒基因组预测 RNA 病毒宿主的基于机器学习的方法。

IF 3.5 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae059

Guowei Chen, Jingzhe Jiang, Yanni Sun

{"title":"RNAVirHost: a machine learning-based method for predicting hosts of RNA viruses through viral genomes.","authors":"Guowei Chen, Jingzhe Jiang, Yanni Sun","doi":"10.1093/gigascience/giae059","DOIUrl":"10.1093/gigascience/giae059","url":null,"abstract":"Background: The high-throughput sequencing technologies have revolutionized the identification of novel RNA viruses. Given that viruses are infectious agents, identifying hosts of these new viruses carries significant implications for public health and provides valuable insights into the dynamics of the microbiome. However, determining the hosts of these newly discovered viruses is not always straightforward, especially in the case of viruses detected in environmental samples. Even for host-associated samples, it is not always correct to assign the sample origin as the host of the identified viruses. The process of assigning hosts to RNA viruses remains challenging due to their high mutation rates and vast diversity.Results: In this study, we introduce RNAVirHost, a machine learning-based tool that predicts the hosts of RNA viruses solely based on viral genomes. RNAVirHost is a hierarchical classification framework that predicts hosts at different taxonomic levels. We demonstrate the superior accuracy of RNAVirHost in predicting hosts of RNA viruses through comprehensive comparisons with various state-of-the-art techniques. When applying to viruses from novel genera, RNAVirHost achieved the highest accuracy of 84.3%, outperforming the alignment-based strategy by 12.1%.Conclusions: The application of machine learning models has proven beneficial in predicting hosts of RNA viruses. By integrating genomic traits and sequence homologies, RNAVirHost provides a cost-effective and efficient strategy for host prediction. We believe that RNAVirHost can greatly assist in RNA virus analyses and contribute to pandemic surveillance.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11340644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142035646","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

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma. 深度学习将局部数字病理表型与胶质母细胞瘤的转录亚型和患者预后联系起来。

IF 3.5 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae057

Thomas Roetzer-Pejrimovsky, Karl-Heinz Nenning, Barbara Kiesel, Johanna Klughammer, Martin Rajchl, Bernhard Baumann, Georg Langs, Adelheid Woehrer

{"title":"Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.","authors":"Thomas Roetzer-Pejrimovsky, Karl-Heinz Nenning, Barbara Kiesel, Johanna Klughammer, Martin Rajchl, Bernhard Baumann, Georg Langs, Adelheid Woehrer","doi":"10.1093/gigascience/giae057","DOIUrl":"10.1093/gigascience/giae057","url":null,"abstract":"Background: Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome.Results: We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017).Conclusions: We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11345537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055362","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

Celebrating 30 years of access to NASA Space Life Sciences data. 庆祝 NASA 太空生命科学数据开放 30 周年。

IF 9.2 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae066

Lauren M Sanders,Danielle K Lopez,Alan E Wood,Ryan T Scott,Samrawit G Gebre,Amanda M Saravia-Butler,Sylvain V Costes

引用次数: 0

Mechanisms of hepatic steatosis in chickens: integrated analysis of the host genome, molecular phenomics and gut microbiome. 鸡肝脏脂肪变性的机制：宿主基因组、分子表型组学和肠道微生物组的综合分析。

IF 3.5 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae023

Congjiao Sun, Fangren Lan, Qianqian Zhou, Xiaoli Guo, Jiaming Jin, Chaoliang Wen, Yanxin Guo, Zhuocheng Hou, Jiangxia Zheng, Guiqin Wu, Guangqi Li, Yiyuan Yan, Junying Li, Qiugang Ma, Ning Yang

{"title":"Mechanisms of hepatic steatosis in chickens: integrated analysis of the host genome, molecular phenomics and gut microbiome.","authors":"Congjiao Sun, Fangren Lan, Qianqian Zhou, Xiaoli Guo, Jiaming Jin, Chaoliang Wen, Yanxin Guo, Zhuocheng Hou, Jiangxia Zheng, Guiqin Wu, Guangqi Li, Yiyuan Yan, Junying Li, Qiugang Ma, Ning Yang","doi":"10.1093/gigascience/giae023","DOIUrl":"10.1093/gigascience/giae023","url":null,"abstract":"Hepatic steatosis is the initial manifestation of abnormal liver functions and often leads to liver diseases such as nonalcoholic fatty liver disease in humans and fatty liver syndrome in animals. In this study, we conducted a comprehensive analysis of a large chicken population consisting of 705 adult hens by combining host genome resequencing; liver transcriptome, proteome, and metabolome analysis; and microbial 16S ribosomal RNA gene sequencing of each gut segment. The results showed the heritability (h2 = 0.25) and duodenal microbiability (m2 = 0.26) of hepatic steatosis were relatively high, indicating a large effect of host genetics and duodenal microbiota on chicken hepatic steatosis. Individuals with hepatic steatosis had low microbiota diversity and a decreased genetic potential to process triglyceride output from hepatocytes, fatty acid β-oxidation activity, and resistance to fatty acid peroxidation. Furthermore, we revealed a molecular network linking host genomic variants (GGA6: 5.59-5.69 Mb), hepatic gene/protein expression (PEMT, phosphatidyl-ethanolamine N-methyltransferase), metabolite abundances (folate, S-adenosylmethionine, homocysteine, phosphatidyl-ethanolamine, and phosphatidylcholine), and duodenal microbes (genus Lactobacillus) to hepatic steatosis, which could provide new insights into the regulatory mechanism of fatty liver development.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11152177/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261606","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

Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder. 利用连体图自动编码器从空间解析转录组学中解密空间域

IF 3.5 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae003

Lei Cao, Chao Yang, Luni Hu, Wenjian Jiang, Yating Ren, Tianyi Xia, Mengyang Xu, Yishuai Ji, Mei Li, Xun Xu, Yuxiang Li, Yong Zhang, Shuangsang Fang

{"title":"Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder.","authors":"Lei Cao, Chao Yang, Luni Hu, Wenjian Jiang, Yating Ren, Tianyi Xia, Mengyang Xu, Yishuai Ji, Mei Li, Xun Xu, Yuxiang Li, Yong Zhang, Shuangsang Fang","doi":"10.1093/gigascience/giae003","DOIUrl":"10.1093/gigascience/giae003","url":null,"abstract":"Background: Cell clustering is a pivotal aspect of spatial transcriptomics (ST) data analysis as it forms the foundation for subsequent data mining. Recent advances in spatial domain identification have leveraged graph neural network (GNN) approaches in conjunction with spatial transcriptomics data. However, such GNN-based methods suffer from representation collapse, wherein all spatial spots are projected onto a singular representation. Consequently, the discriminative capability of individual representation feature is limited, leading to suboptimal clustering performance.Results: To address this issue, we proposed SGAE, a novel framework for spatial domain identification, incorporating the power of the Siamese graph autoencoder. SGAE mitigates the information correlation at both sample and feature levels, thus improving the representation discrimination. We adapted this framework to ST analysis by constructing a graph based on both gene expression and spatial information. SGAE outperformed alternative methods by its effectiveness in capturing spatial patterns and generating high-quality clusters, as evaluated by the Adjusted Rand Index, Normalized Mutual Information, and Fowlkes-Mallows Index. Moreover, the clustering results derived from SGAE can be further utilized in the identification of 3-dimensional (3D) Drosophila embryonic structure with enhanced accuracy.Conclusions: Benchmarking results from various ST datasets generated by diverse platforms demonstrate compelling evidence for the effectiveness of SGAE against other ST clustering methods. Specifically, SGAE exhibits potential for extension and application on multislice 3D reconstruction and tissue structure investigation. The source code and a collection of spatial clustering results can be accessed at https://github.com/STOmics/SGAE/.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10939418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139905504","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

Chromosome-level genome assemblies of two littorinid marine snails indicate genetic basis of intertidal adaptation and ancient karyotype evolved from bilaterian ancestors. 两种海蜗牛的染色体级基因组组装表明了潮间带适应性的遗传基础以及从两栖类祖先演化而来的古老核型。

IF 3.5 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae072

Yan-Shu Wang, Meng-Yu Li, Yu-Long Li, Yu-Qiang Li, Dong-Xiu Xue, Jin-Xian Liu

{"title":"Chromosome-level genome assemblies of two littorinid marine snails indicate genetic basis of intertidal adaptation and ancient karyotype evolved from bilaterian ancestors.","authors":"Yan-Shu Wang, Meng-Yu Li, Yu-Long Li, Yu-Qiang Li, Dong-Xiu Xue, Jin-Xian Liu","doi":"10.1093/gigascience/giae072","DOIUrl":"10.1093/gigascience/giae072","url":null,"abstract":"Living in the intertidal environment, littorinid snails are excellent models for understanding genetic mechanisms underlying adaptation to harsh fluctuating environments. Furthermore, the karyotypes of littorinid snails, with the same chromosome number as the presumed bilaterian ancestor, make them valuable for investigating karyotype evolution from the bilaterian ancestor to mollusks. Here, we generated high-quality, chromosome-scale genome assemblies for 2 littorinid marine snails, Littorina brevicula (927.94 Mb) and Littoraria sinensis (882.51 Mb), with contig N50 of 3.43 Mb and 2.31 Mb, respectively. Comparative genomic analyses identified 92 expanded gene families and 85 positively selected genes as potential candidates possibly associated with intertidal adaptation in the littorinid lineage, which were functionally enriched in stimulus responses, innate immunity, and apoptosis process regulation and might be involved in cellular homeostasis maintenance in stressful intertidal environments. Genome macrosynteny analyses indicated that 4 fissions and 4 fusions led to the evolution from the 17 presumed bilaterian ancestral chromosomes to the 17 littorinid chromosomes, implying that the littorinid snails have a highly conserved karyotype with the bilaterian ancestor. Based on the most parsimonious reconstruction of the common ancestral karyotype of scallops and littorinid snails, 3 chromosomal fissions and 1 chromosomal fusion from the bilaterian ancient linkage groups were shared by the bivalve scallop and gastropoda littorinid snails, indicating that the chromosome-scale ancient gene linkages were generally preserved in the mollusk genomes for over 500 million years. The highly conserved karyotype makes the littorinid snail genomes valuable resources for understanding early bilaterian evolution and biology.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142344885","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

V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation. V-pipe 3.0：用于样本内病毒遗传多样性估计的可持续管道。

IF 3.5 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae065

Lara Fuhrmann, Kim Philipp Jablonski, Ivan Topolsky, Aashil A Batavia, Nico Borgsmüller, Pelin Icer Baykal, Matteo Carrara, Chaoran Chen, Arthur Dondi, Monica Dragan, David Dreifuss, Anika John, Benjamin Langer, Michal Okoniewski, Louis du Plessis, Uwe Schmitt, Franziska Singer, Tanja Stadler, Niko Beerenwinkel

引用次数: 0

Correction to: A graph clustering algorithm for detection and genotyping of structural variants from long reads. Correction to：从长读数中检测结构变异并进行基因分型的图聚类算法。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae077

引用次数: 0

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma. 全蛋白质组关联研究和功能验证确定了胰腺导管腺癌的新型蛋白质标记物。

IF 3.5 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae012

Jingjing Zhu, Ke Wu, Shuai Liu, Alexandra Masca, Hua Zhong, Tai Yang, Dalia H Ghoneim, Praveen Surendran, Tanxin Liu, Qizhi Yao, Tao Liu, Sarah Fahle, Adam Butterworth, Md Ashad Alam, Jaydutt V Vadgama, Youping Deng, Hong-Wen Deng, Chong Wu, Yong Wu, Lang Wu

{"title":"Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.","authors":"Jingjing Zhu, Ke Wu, Shuai Liu, Alexandra Masca, Hua Zhong, Tai Yang, Dalia H Ghoneim, Praveen Surendran, Tanxin Liu, Qizhi Yao, Tao Liu, Sarah Fahle, Adam Butterworth, Md Ashad Alam, Jaydutt V Vadgama, Youping Deng, Hong-Wen Deng, Chong Wu, Yong Wu, Lang Wu","doi":"10.1093/gigascience/giae012","DOIUrl":"10.1093/gigascience/giae012","url":null,"abstract":"Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.Significance: PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11010651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140856176","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