Genomics, proteomics & bioinformatics最新文献_第10页

Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer. 小细胞肺癌预后和治疗方案的蛋白质组学分层

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae033

Zitian Huo, Yaqi Duan, Dongdong Zhan, Xizhen Xu, Nairen Zheng, Jing Cai, Ruifang Sun, Jianping Wang, Fang Cheng, Zhan Gao, Caixia Xu, Wanlin Liu, Yuting Dong, Sailong Ma, Qian Zhang, Yiyun Zheng, Liping Lou, Dong Kuang, Qian Chu, Jun Qin, Guoping Wang, Yi Wang

{"title":"Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer.","authors":"Zitian Huo, Yaqi Duan, Dongdong Zhan, Xizhen Xu, Nairen Zheng, Jing Cai, Ruifang Sun, Jianping Wang, Fang Cheng, Zhan Gao, Caixia Xu, Wanlin Liu, Yuting Dong, Sailong Ma, Qian Zhang, Yiyun Zheng, Liping Lou, Dong Kuang, Qian Chu, Jun Qin, Guoping Wang, Yi Wang","doi":"10.1093/gpbjnl/qzae033","DOIUrl":"10.1093/gpbjnl/qzae033","url":null,"abstract":"Small cell lung cancer (SCLC) is a highly malignant and heterogeneous cancer with limited therapeutic options and prognosis prediction models. Here, we analyzed formalin-fixed, paraffin-embedded (FFPE) samples of surgical resections by proteomic profiling, and stratified SCLC into three proteomic subtypes (S-I, S-II, and S-III) with distinct clinical outcomes and chemotherapy responses. The proteomic subtyping was an independent prognostic factor and performed better than current tumor-node-metastasis or Veterans Administration Lung Study Group staging methods. The subtyping results could be further validated using FFPE biopsy samples from an independent cohort, extending the analysis to both surgical and biopsy samples. The signatures of the S-II subtype in particular suggested potential benefits from immunotherapy. Differentially overexpressed proteins in S-III, the worst prognostic subtype, allowed us to nominate potential therapeutic targets, indicating that patient selection may bring new hope for previously failed clinical trials. Finally, analysis of an independent cohort of SCLC patients who had received immunotherapy validated the prediction that the S-II patients had better progression-free survival and overall survival after first-line immunotherapy. Collectively, our study provides the rationale for future clinical investigations to validate the current findings for more accurate prognosis prediction and precise treatments.","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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499987","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

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data. 单细胞 ATAC-seq 数据基因组评分基准算法。

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae014

Xi Wang, Qiwei Lian, Haoyu Dong, Shuo Xu, Yaru Su, Xiaohui Wu

{"title":"Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.","authors":"Xi Wang, Qiwei Lian, Haoyu Dong, Shuo Xu, Yaru Su, Xiaohui Wu","doi":"10.1093/gpbjnl/qzae014","DOIUrl":"10.1093/gpbjnl/qzae014","url":null,"abstract":"Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.","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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763492","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

Reprogramming of RNA m6A Modification Is Required for Acute Myeloid Leukemia Development. 急性髓性白血病的发展需要对 RNA m6A 修饰进行重编程。

Genomics, proteomics & bioinformatics Pub Date : 2024-06-24 DOI: 10.1093/gpbjnl/qzae049

Weidong Liu, Yuhua Wang, Shuxin Yao, Guoqiang Han, Jin Hu, Rong Yin, Fuling Zhou, Ying Cheng, Haojian Zhang

{"title":"Reprogramming of RNA m6A Modification Is Required for Acute Myeloid Leukemia Development.","authors":"Weidong Liu, Yuhua Wang, Shuxin Yao, Guoqiang Han, Jin Hu, Rong Yin, Fuling Zhou, Ying Cheng, Haojian Zhang","doi":"10.1093/gpbjnl/qzae049","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae049","url":null,"abstract":"Hematopoietic homeostasis is maintained by hematopoietic stem cells (HSCs), and it is tightly controlled at multiple levels to sustain the self-renewal capacity and differentiation potential of HSCs. Dysregulation of self-renewal and differentiation of HSCs leads to the development of hematologic diseases, including acute myeloid leukemia (AML). Thus, understanding the underlying mechanisms of HSC maintenance and the development of hematologic malignancies is one of the fundamental scientific endeavors in stem cell biology. N 6-methyladenosine (m6A) is a common modification in mammalian messenger RNAs (mRNAs) and plays important roles in various biological processes. In this study, we performed a comparative analysis of the dynamics of the RNA m6A methylome of hematopoietic stem and progenitor cells (HSPCs) and leukemia-initiating cells (LICs) in AML. We found that RNA m6A modification regulates the transformation of long-term HSCs into short-term HSCs and determines the lineage commitment of HSCs. Interestingly, m6A modification leads to reprogramming that promotes cellular transformation during AML development, and LIC-specific m6A targets are recognized by different m6A readers. Moreover, the very long chain fatty acid transporter ATP-binding cassette subfamily D member 2 (ABCD2) is a key factor that promotes AML development, and deletion of ABCD2 damages clonogenic ability, inhibits proliferation, and promotes apoptosis of human leukemia cells. This study provides a comprehensive understanding of the role of m6A in regulating cell state transition in normal hematopoiesis and leukemogenesis, and identifies ABCD2 as a key factor in AML development.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447874","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

Acknowledgments to Reviewers 2023. 鸣谢审稿人 2023.

Genomics, proteomics & bioinformatics Pub Date : 2024-05-09 DOI: 10.1093/gpbjnl/qzae038

引用次数: 0

RNase P: Beyond Precursor tRNA Processing. RNase P：超越前体 tRNA 处理。

Genomics, proteomics & bioinformatics Pub Date : 2024-05-09 DOI: 10.1093/gpbjnl/qzae016

Peipei Wang, Juntao Lin, Xiangyang Zheng, Xingzhi Xu

引用次数: 0

KoNA: Korean Nucleotide Archive as A New Data Repository for Nucleotide Sequence Data. KoNA：作为核苷酸序列数据新数据储存库的韩国核苷酸档案。

Genomics, proteomics & bioinformatics Pub Date : 2024-05-09 DOI: 10.1093/gpbjnl/qzae017

Gunhwan Ko, Jae Ho Lee, Young Mi Sim, Wangho Song, Byung-Ha Yoon, Iksu Byeon, Bang Hyuck Lee, Sang-Ok Kim, Jinhyuk Choi, Insoo Jang, Hyerin Kim, Jin Ok Yang, Kiwon Jang, Sora Kim, Jong-Hwan Kim, Jongbum Jeon, Jaeeun Jung, Seungwoo Hwang, Ji-Hwan Park, Pan-Gyu Kim, Seon-Young Kim, Byungwook Lee

{"title":"KoNA: Korean Nucleotide Archive as A New Data Repository for Nucleotide Sequence Data.","authors":"Gunhwan Ko, Jae Ho Lee, Young Mi Sim, Wangho Song, Byung-Ha Yoon, Iksu Byeon, Bang Hyuck Lee, Sang-Ok Kim, Jinhyuk Choi, Insoo Jang, Hyerin Kim, Jin Ok Yang, Kiwon Jang, Sora Kim, Jong-Hwan Kim, Jongbum Jeon, Jaeeun Jung, Seungwoo Hwang, Ji-Hwan Park, Pan-Gyu Kim, Seon-Young Kim, Byungwook Lee","doi":"10.1093/gpbjnl/qzae017","DOIUrl":"10.1093/gpbjnl/qzae017","url":null,"abstract":"During the last decade, the generation and accumulation of petabase-scale high-throughput sequencing data have resulted in great challenges, including access to human data, as well as transfer, storage, and sharing of enormous amounts of data. To promote data-driven biological research, the Korean government announced that all biological data generated from government-funded research projects should be deposited at the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. Here, we introduce the Korean Nucleotide Archive (KoNA), a repository of nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next-generation sequencing data from national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure was adopted, which is similar to that of the International Nucleotide Sequence Database Collaboration. The standard operating procedure includes quality control processes for submitted data and metadata using an automated pipeline, followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed using a cloud computing service called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances the data experience, including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea but also provides datasets to researchers globally and contributes to advances in genomics. The KoNA is available at https://www.kobic.re.kr/kona/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307671","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: dbDEMC 3.0: Functional Exploration of Differentially Expressed miRNAs in Cancers of Human and Model Organisms. 更正为：dbDEMC 3.0：人类和模式生物癌症中差异表达 miRNA 的功能探索。

Genomics, proteomics & bioinformatics Pub Date : 2024-05-09 DOI: 10.1093/gpbjnl/qzae037

引用次数: 0

EryDB: A Transcriptomic Profile Database for Erythropoiesis and Erythroid-related Diseases. EryDB：红细胞生成和红细胞相关疾病的转录组特征数据库。

Genomics, proteomics & bioinformatics Pub Date : 2024-04-02 DOI: 10.1093/gpbjnl/qzae029

Guangmin Zheng, Song Wu, Zhaojun Zhang, Zijuan Xin, Lijuan Zhang, Siqi Zhao, Jing Wu, Yanxia Liu, Meng Li, Xiuyan Ruan, Nan Qiao, Yiming Bao, Hongzhu Qu, Xiangdong Fang

{"title":"EryDB: A Transcriptomic Profile Database for Erythropoiesis and Erythroid-related Diseases.","authors":"Guangmin Zheng, Song Wu, Zhaojun Zhang, Zijuan Xin, Lijuan Zhang, Siqi Zhao, Jing Wu, Yanxia Liu, Meng Li, Xiuyan Ruan, Nan Qiao, Yiming Bao, Hongzhu Qu, Xiangdong Fang","doi":"10.1093/gpbjnl/qzae029","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae029","url":null,"abstract":"Erythropoiesis is a finely regulated and complex process that involves multiple transformations from hematopoietic stem cells to mature red blood cells at hematopoietic sites from the embryonic to the adult stages. Investigations into its molecular mechanisms have generated a wealth of expression data, including bulk and single-cell RNA sequencing data. A comprehensively integrated and well-curated erythropoiesis-specific database will greatly facilitate the mining of gene expression data and enable large-scale research of erythropoiesis and erythroid-related diseases. Here, we present EryDB, an open-access and comprehensive database dedicated to the collection, integration, analysis, and visualization of transcriptomic data for erythropoiesis and erythroid-related diseases. Currently, the database includes expertly curated quality-assured data of 3803 samples and 1,187,119 single cells derived from 107 public studies of three species (Homo sapiens, Mus musculus, and Danio rerio), nine tissue types, and five diseases. EryDB provides users with the ability to not only browse the molecular features of erythropoiesis between tissues and species, but also perform computational analyses of single-cell and bulk RNA sequencing data, thus serving as a convenient platform for customized queries and analyses. EryDB v1.0 is freely accessible at https://ngdc.cncb.ac.cn/EryDB/home.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484013","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

mvPPT: A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants mvPPT:一种高效灵敏的错义变异致病性预测工具

Genomics, proteomics & bioinformatics Pub Date : 2022-01-06 DOI: 10.1101/2022.01.05.475156

S. Tong, Ke Fan, Zai-wei Zhou, Lin-Yun Liu, Shu-Qing Zhang, Yinghui Fu, Guangchao Wang, Ying Zhu, Yong-Chun Yu

引用次数: 1

deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues deCS:人体组织中单细胞RNA测序数据的系统细胞类型注释工具

Genomics, proteomics & bioinformatics Pub Date : 2021-09-22 DOI: 10.1101/2021.09.19.460993

Guangsheng Pei, F. Yan, L. Simon, Yulin Dai, P. Jia, Zhongming Zhao

{"title":"deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues","authors":"Guangsheng Pei, F. Yan, L. Simon, Yulin Dai, P. Jia, Zhongming Zhao","doi":"10.1101/2021.09.19.460993","DOIUrl":"https://doi.org/10.1101/2021.09.19.460993","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell-type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and less accurate. The increasing number of scRNA-seq data sets, as well as numerous published genetic studies, motivated us to build a comprehensive human cell type reference atlas. Here, we present deCS (decoding Cell type-Specificity), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth and feature selection strategies. Our results demonstrated that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deeper insights into the cellular mechanisms of disease pathogenesis. All documents, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":"64 1","pages":"370 - 384"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86778324","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: 12