Genomics, proteomics & bioinformatics最新文献_第8页

SCancerRNA: Expression at the Single-cell Level and Interaction Resource of Non-coding RNA Biomarkers for Cancers. SCancerRNA：癌症非编码 RNA 生物标志物的单细胞水平表达和交互资源。

Genomics, proteomics & bioinformatics Pub Date : 2024-09-13 DOI: 10.1093/gpbjnl/qzae023

Hongzhe Guo, Liyuan Zhang, Xinran Cui, Liang Cheng, Tianyi Zhao, Yadong Wang

{"title":"SCancerRNA: Expression at the Single-cell Level and Interaction Resource of Non-coding RNA Biomarkers for Cancers.","authors":"Hongzhe Guo, Liyuan Zhang, Xinran Cui, Liang Cheng, Tianyi Zhao, Yadong Wang","doi":"10.1093/gpbjnl/qzae023","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae023","url":null,"abstract":"Non-coding RNAs (ncRNAs) participate in multiple biological processes associated with cancers as tumor suppressors or oncogenic drivers. Due to their high stability in plasma, urine, and many other fluids, ncRNAs have the potential to serve as key biomarkers for early diagnosis and screening of cancers. During cancer progression, tumor heterogeneity plays a crucial role, and it is particularly important to understand the gene expression patterns of individual cells. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering gene expression in different cell types for human cancers has become feasible by profiling transcriptomes at the cellular level. However, a well-organized and comprehensive online resource that provides access to the expression of genes corresponding to ncRNA biomarkers in different cell types at the single-cell level is not available yet. Therefore, we developed the SCancerRNA database to summarize experimentally supported data on long ncRNA, microRNA, PIWI-interacting RNA, small nucleolar RNA, and circular RNA biomarkers, as well as data on their differential expression at the cellular level. Furthermore, we collected biological functions and clinical applications of biomarkers to facilitate the application of ncRNA biomarkers to cancer diagnosis, as well as the monitoring of progression and targeted therapies. SCancerRNA also allows users to explore interaction networks of different types of ncRNAs, and build computational models in the future. SCancerRNA is freely accessible at http://www.scancerrna.com/BioMarker.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142335113","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

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":"22 3","pages":""},"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":" ","pages":""},"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

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":" ","pages":""},"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

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

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":"22 2","pages":""},"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

BSAlign: A Library for Nucleotide Sequence Alignment. BSAlign：核苷酸序列比对库。

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae025

Haojing Shao, Jue Ruan

{"title":"BSAlign: A Library for Nucleotide Sequence Alignment.","authors":"Haojing Shao, Jue Ruan","doi":"10.1093/gpbjnl/qzae025","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae025","url":null,"abstract":"Increasing the accuracy of the nucleotide sequence alignment is an essential issue in genomics research. Although classic dynamic programming (DP) algorithms (e.g., Smith-Waterman and Needleman-Wunsch) guarantee to produce the optimal result, their time complexity hinders the application of large-scale sequence alignment. Many optimization efforts that aim to accelerate the alignment process generally come from three perspectives: redesigning data structures [e.g., diagonal or striped Single Instruction Multiple Data (SIMD) implementations], increasing the number of parallelisms in SIMD operations (e.g., difference recurrence relation), or reducing search space (e.g., banded DP). However, no methods combine all these three aspects to build an ultra-fast algorithm. In this study, we developed a Banded Striped Aligner (BSAlign) library that delivers accurate alignment results at an ultra-fast speed by knitting a series of novel methods together to take advantage of all of the aforementioned three perspectives with highlights such as active F-loop in striped vectorization and striped move in banded DP. We applied our new acceleration design on both regular and edit distance pairwise alignment. BSAlign achieved 2-fold speed-up than other SIMD-based implementations for regular pairwise alignment, and 1.5-fold to 4-fold speed-up in edit distance-based implementations for long reads. BSAlign is implemented in C programing language and is available at https://github.com/ruanjue/bsalign.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":"22 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142116457","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