Genomics, proteomics & bioinformatics最新文献_第4页

Nphos: Database and Predictor of Protein N-phosphorylation. Nphos：蛋白质 N-磷酸化数据库和预测器。

Genomics, proteomics & bioinformatics Pub Date : 2024-09-13 DOI: 10.1093/gpbjnl/qzae032

Ming-Xiao Zhao, Ruo-Fan Ding, Qiang Chen, Junhua Meng, Fulai Li, Songsen Fu, Biling Huang, Yan Liu, Zhi-Liang Ji, Yufen Zhao

{"title":"Nphos: Database and Predictor of Protein N-phosphorylation.","authors":"Ming-Xiao Zhao, Ruo-Fan Ding, Qiang Chen, Junhua Meng, Fulai Li, Songsen Fu, Biling Huang, Yan Liu, Zhi-Liang Ji, Yufen Zhao","doi":"10.1093/gpbjnl/qzae032","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae032","url":null,"abstract":"Protein N-phosphorylation is widely present in nature and participates in various biological processes. However, current knowledge on N-phosphorylation is extremely limited compared to that on O-phosphorylation. In this study, we collected 11,710 experimentally verified N-phosphosites of 7344 proteins from 39 species and subsequently constructed the database Nphos to share up-to-date information on protein N-phosphorylation. Upon these substantial data, we characterized the sequential and structural features of protein N-phosphorylation. Moreover, after comparing hundreds of learning models, we chose and optimized gradient boosting decision tree (GBDT) models to predict three types of human N-phosphorylation, achieving mean area under the receiver operating characteristic curve (AUC) values of 90.56%, 91.24%, and 92.01% for pHis, pLys, and pArg, respectively. Meanwhile, we discovered 488,825 distinct N-phosphosites in the human proteome. The models were also deployed in Nphos for interactive N-phosphosite prediction. In summary, this work provides new insights and points for both flexible and focused investigations of N-phosphorylation. It will also facilitate a deeper and more systematic understanding of protein N-phosphorylation modification by providing a data and technical foundation. Nphos is freely available at http://www.bio-add.org/Nphos/ and http://ppodd.org.cn/Nphos/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142396284","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

CBioProfiler: A Web and Standalone Pipeline for Cancer Biomarker and Subtype Characterization. CBioProfiler：用于癌症生物标记物和亚型特征描述的网络和独立管道。

Genomics, proteomics & bioinformatics Pub Date : 2024-09-13 DOI: 10.1093/gpbjnl/qzae045

Xiaoping Liu, Zisong Wang, Hongjie Shi, Sheng Li, Xinghuan Wang

{"title":"CBioProfiler: A Web and Standalone Pipeline for Cancer Biomarker and Subtype Characterization.","authors":"Xiaoping Liu, Zisong Wang, Hongjie Shi, Sheng Li, Xinghuan Wang","doi":"10.1093/gpbjnl/qzae045","DOIUrl":"10.1093/gpbjnl/qzae045","url":null,"abstract":"Cancer is a leading cause of death worldwide, and the identification of biomarkers and subtypes that can predict the long-term survival of cancer patients is essential for their risk stratification, treatment, and prognosis. However, there are currently no standardized tools for exploring cancer biomarkers or subtypes. In this study, we introduced Cancer Biomarker and subtype Profiler (CBioProfiler), a web server and standalone application that includes two pipelines for analyzing cancer biomarkers and subtypes. The cancer biomarker pipeline consists of five modules for identifying and annotating cancer survival-related biomarkers using multiple survival-related machine learning algorithms. The cancer subtype pipeline includes three modules for data preprocessing, subtype identification using multiple unsupervised machine learning methods, and subtype evaluation and validation. CBioProfiler also includes CuratedCancerPrognosisData, a novel R package that integrates reviewed and curated gene expression and clinical data from 268 studies. These studies cover 43 common blood and solid tumors and draw upon 47,686 clinical samples. The web server is available at https://www.cbioprofiler.com/ and https://cbioprofiler.znhospital.cn/CBioProfiler/, and the standalone app and source code can be found at https://github.com/liuxiaoping2020/CBioProfiler.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning for AI Breeding in Plants. 植物人工智能育种的机器学习。

Genomics, proteomics & bioinformatics Pub Date : 2024-09-13 DOI: 10.1093/gpbjnl/qzae051

Qian Cheng, Xiangfeng Wang

引用次数: 0

GenBase: A Nucleotide Sequence Database. GenBase：核苷酸序列数据库。

Genomics, proteomics & bioinformatics Pub Date : 2024-09-13 DOI: 10.1093/gpbjnl/qzae047

Congfan Bu, Xinchang Zheng, Xuetong Zhao, Tianyi Xu, Xue Bai, Yaokai Jia, Meili Chen, Lili Hao, Jingfa Xiao, Zhang Zhang, Wenming Zhao, Bixia Tang, Yiming Bao

{"title":"GenBase: A Nucleotide Sequence Database.","authors":"Congfan Bu, Xinchang Zheng, Xuetong Zhao, Tianyi Xu, Xue Bai, Yaokai Jia, Meili Chen, Lili Hao, Jingfa Xiao, Zhang Zhang, Wenming Zhao, Bixia Tang, Yiming Bao","doi":"10.1093/gpbjnl/qzae047","DOIUrl":"10.1093/gpbjnl/qzae047","url":null,"abstract":"The rapid advancement of sequencing technologies poses challenges in managing the large volume and exponential growth of sequence data efficiently and on time. To address this issue, we present GenBase (https://ngdc.cncb.ac.cn/genbase), an open-access data repository that follows the International Nucleotide Sequence Database Collaboration (INSDC) data standards and structures, for efficient nucleotide sequence archiving, searching, and sharing. As a core resource within the National Genomics Data Center (NGDC) of the China National Center for Bioinformation (CNCB; https://ngdc.cncb.ac.cn), GenBase offers bilingual submission pipeline and services, as well as local submission assistance in China. GenBase also provides a unique Excel format for metadata description and feature annotation of nucleotide sequences, along with a real-time data validation system to streamline sequence submissions. As of April 23, 2024, GenBase received 68,251 nucleotide sequences and 689,574 annotated protein sequences across 414 species from 2319 submissions. Out of these, 63,614 (93%) nucleotide sequences and 620,640 (90%) annotated protein sequences have been released and are publicly accessible through GenBase's web search system, File Transfer Protocol (FTP), and Application Programming Interface (API). Additionally, in collaboration with INSDC, GenBase has constructed an effective data exchange mechanism with GenBank and started sharing released nucleotide sequences. Furthermore, GenBase integrates all sequences from GenBank with daily updates, demonstrating its commitment to actively contributing to global sequence data management and sharing.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11434157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-cell RNA-seq Reveals that Methamphetamine Inhibits Liver Immunity with Involvement of Dopamine Receptor D1. 单细胞 RNA 截图揭示甲基苯丙胺抑制肝脏免疫与多巴胺受体 D1 的关系

Genomics, proteomics & bioinformatics Pub Date : 2024-08-28 DOI: 10.1093/gpbjnl/qzae060

Jin-Ting Zhou, Yungang Xu, Xiao-Huan Liu, Cheng Cheng, Jing-Na Fan, Xiaoming Li, Jun Yu, Shengbin Li

{"title":"Single-cell RNA-seq Reveals that Methamphetamine Inhibits Liver Immunity with Involvement of Dopamine Receptor D1.","authors":"Jin-Ting Zhou, Yungang Xu, Xiao-Huan Liu, Cheng Cheng, Jing-Na Fan, Xiaoming Li, Jun Yu, Shengbin Li","doi":"10.1093/gpbjnl/qzae060","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae060","url":null,"abstract":"Methamphetamine (METH) is a highly addictive psychostimulant that causes physical and psychological damage and immune system disorder, especially in the liver that contains a significant number of immune cells. Dopamine, a key neurotransmitter in METH addiction and immune regulation, plays a crucial role in this process. Here, we developed a chronic METH administration model and conducted single-cell RNA sequencing (scRNA-seq) to investigate the effect of METH on liver immune cells and involvement of dopamine receptor D1 (DRD1). Our findings reveal that chronic exposure to METH induces immune cell identity shifts from Ifitm3+Macrophage (Mac) and Ccl5+Mac to Cd14+Mac, and from Fyn+CD4+T effector (Teff), CD8+T, and natural killer T cells (NKT) to Fos+CD4+T and Rora+ group 2 innate lymphoid cells (ILC2), along with suppression of multiple functional immune pathways. DRD1 is implicated in regulating certain pathways and identity shifts among the hepatic immune cells. Our results provide valuable insights into development of targeted therapies to mitigate METH-induced immune impairment.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086421","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

Variation and Interaction of Distinct Subgenomes Contribute to Growth Diversity in Intergeneric Hybrid Fish. 不同亚基因组的变异和相互作用促成了杂交鱼类的生长多样性。

Genomics, proteomics & bioinformatics Pub Date : 2024-07-23 DOI: 10.1093/gpbjnl/qzae055

Li Ren, Mengxue Luo, Jialin Cui, Xin Gao, Hong Zhang, Ping Wu, Zehong Wei, Yakui Tai, Mengdan Li, Kaikun Luo, Shaojun Liu

{"title":"Variation and Interaction of Distinct Subgenomes Contribute to Growth Diversity in Intergeneric Hybrid Fish.","authors":"Li Ren, Mengxue Luo, Jialin Cui, Xin Gao, Hong Zhang, Ping Wu, Zehong Wei, Yakui Tai, Mengdan Li, Kaikun Luo, Shaojun Liu","doi":"10.1093/gpbjnl/qzae055","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae055","url":null,"abstract":"Intergeneric hybridization greatly reshapes regulatory interactions among allelic and non-allelic genes. However, their effects on growth diversity remain poorly understood in animals. In this study, we conducted whole-genome sequencing and RNA sequencing (RNA-seq) analyses in diverse hybrid varieties resulting from the intergeneric hybridization of goldfish (Carassius auratus red var.) and common carp (Cyprinus carpio). These hybrid individuals were characterized by distinct mitochondrial genomes and copy number variations. Through a weighted gene correlation network analysis, we identified 3693 genes as candidate growth-regulated genes. Among them, the expression of 3672 genes in subgenome R (originating from goldfish) displayed negative correlations with growth rate, whereas 20 genes in subgenome C (originating from common carp) exhibited positive correlations. Notably, we observed intriguing patterns in the expression of slc2a12 in subgenome C, showing opposite correlations with body weight that changed with water temperatures, suggesting differential interactions between feeding activity and weight gain in response to seasonal changes for hybrid animals. In 40.31% of alleles, we observed dominant trans-regulatory effects in the regulatory interaction between distinct alleles from subgenomes R and C. Integrating analyses of allelic-specific expression and DNA methylation data revealed that the influence of DNA methylation on both subgenomes shapes the relative contribution of allelic expression to the growth rate. These findings provide novel insights into the interaction of distinct subgenomes that underlie heterosis in growth traits and contribute to a better understanding of multiple allele traits in animals.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141750100","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

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics. 利用单细胞组学推进植物研究的机遇与挑战。

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae026

Mohammad Saidur Rhaman, Muhammad Ali, Wenxiu Ye, Bosheng Li

引用次数: 0

Genome-wide Studies Reveal Genetic Risk Factors for Hepatic Fat Content. 全基因组研究揭示肝脏脂肪含量的遗传风险因素

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae031

Yanni Li, Eline H van den Berg, Alexander Kurilshikov, Dasha V Zhernakova, Ranko Gacesa, Shixian Hu, Esteban A Lopera-Maya, Alexandra Zhernakova, Vincent E de Meijer, Serena Sanna, Robin P F Dullaart, Hans Blokzijl, Eleonora A M Festen, Jingyuan Fu, Rinse K Weersma

{"title":"Genome-wide Studies Reveal Genetic Risk Factors for Hepatic Fat Content.","authors":"Yanni Li, Eline H van den Berg, Alexander Kurilshikov, Dasha V Zhernakova, Ranko Gacesa, Shixian Hu, Esteban A Lopera-Maya, Alexandra Zhernakova, Vincent E de Meijer, Serena Sanna, Robin P F Dullaart, Hans Blokzijl, Eleonora A M Festen, Jingyuan Fu, Rinse K Weersma","doi":"10.1093/gpbjnl/qzae031","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae031","url":null,"abstract":"Genetic susceptibility to metabolic associated fatty liver disease (MAFLD) is complex and poorly characterized. Accurate characterization of the genetic background of hepatic fat content would provide insights into disease etiology and causality of risk factors. We performed genome-wide association study (GWAS) on two noninvasive definitions of hepatic fat content: magnetic resonance imaging proton density fat fraction (MRI-PDFF) in 16,050 participants and fatty liver index (FLI) in 388,701 participants from the United Kingdom (UK) Biobank (UKBB). Heritability, genetic overlap, and similarity between hepatic fat content phenotypes were analyzed, and replicated in 10,398 participants from the University Medical Center Groningen (UMCG) Genetics Lifelines Initiative (UGLI). Meta-analysis of GWASs of MRI-PDFF in UKBB revealed five statistically significant loci, including two novel genomic loci harboring CREB3L1 (rs72910057-T, P = 5.40E-09) and GCM1 (rs1491489378-T, P = 3.16E-09), respectively, as well as three previously reported loci: PNPLA3, TM6SF2, and APOE. GWAS of FLI in UKBB identified 196 genome-wide significant loci, of which 49 were replicated in UGLI, with top signals in ZPR1 (P = 3.35E-13) and FTO (P = 2.11E-09). Statistically significant genetic correlation (rg) between MRI-PDFF (UKBB) and FLI (UGLI) GWAS results was found (rg = 0.5276, P = 1.45E-03). Novel MRI-PDFF genetic signals (CREB3L1 and GCM1) were replicated in the FLI GWAS. We identified two novel genes for MRI-PDFF and 49 replicable loci for FLI. Despite a difference in hepatic fat content assessment between MRI-PDFF and FLI, a substantial similar genetic architecture was found. FLI is identified as an easy and reliable approach to study hepatic fat content at the population level.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984187","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

Correction to: m6A Profile Dynamics Indicates Regulation of Oyster Development by m6A-RNA Epitranscriptomes. 更正：m6A-RNA 表转录组对牡蛎发育的调控显示了 m6A 配置文件的动态变化。

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae021

引用次数: 0

Correction to: Single-cell RNA Sequencing Reveals Sexually Dimorphic Transcriptome and Type 2 Diabetes Genes in Mouse Islet β Cells. 更正：单细胞 RNA 测序揭示了小鼠胰岛 β 细胞中的性别二态转录组和 2 型糖尿病基因。

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae022

引用次数: 0