Genomics, Proteomics & Bioinformatics最新文献_第2页

MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search MARS 和 RNAcmap3：所有可能 RNA 序列的主数据库，与 RNAcmap 集成用于 RNA 同源搜索

Genomics, Proteomics & Bioinformatics Pub Date : 2024-03-01 DOI: 10.1093/gpbjnl/qzae018

Ke'ai Chen, Thomas Litfin, Jaswinder Singh, Jian Zhan, Yaoqi Zhou

{"title":"MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search","authors":"Ke'ai Chen, Thomas Litfin, Jaswinder Singh, Jian Zhan, Yaoqi Zhou","doi":"10.1093/gpbjnl/qzae018","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae018","url":null,"abstract":"\u0000 Recent success of AlphaFold2 in protein structure prediction relied heavily on co-evolutionary information derived from homologous protein sequences found in the huge, integrated database of protein sequences (Big Fantastic Database). In contrast, the existing nucleotide databases were not consolidated to facilitate wider and deeper homology search. Here, we built a comprehensive database by including the non-coding RNA (ncRNA) sequences from RNAcentral, the transcriptome assembly and metagenome assembly from metagenomics RAST (MG-RAST), the genomic sequences from Genome Warehouse (GWH), and the genomic sequences from MGnify, in addition to nucleotide database (nt) and its subsets in National Center of Biotechnology Information (NCBI). The resulting Master database of All possible RNA sequences (MARS) is 20-fold larger than NCBI’s nt database or 60-fold larger than RNAcentral. The new dataset along with a new split–search strategy allows a substantial improvement in homology search over existing state-of-the-art techniques. It also yields more accurate and more sensitive multiple sequence alignments (MSAs) than manually curated MSAs from Rfam for the majority of structured RNAs mapped to Rfam. The results indicate that MARS coupled with the fully automatic homology search tool RNAcmap will be useful for improved structural and functional inference of ncRNAs and RNA language models based on MSAs. MARS is accessible at https://ngdc.cncb.ac.cn/omix/release/OMIX003037 and RNAcmap3 is accessible at http://zhouyq-lab.szbl.ac.cn/download/.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"114 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090549","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

BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies BLUPmrMLM：全基因组关联研究中的快速 mrMLM 算法

Genomics, Proteomics & Bioinformatics Pub Date : 2024-02-29 DOI: 10.1093/gpbjnl/qzae020

Hong-Fu Li, Jing-Tian Wang, Qiong Zhao, Yuan-Ming Zhang

{"title":"BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies","authors":"Hong-Fu Li, Jing-Tian Wang, Qiong Zhao, Yuan-Ming Zhang","doi":"10.1093/gpbjnl/qzae020","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae020","url":null,"abstract":"\u0000 Multilocus genome-wide association study has become the state-of-the-art tool for dissecting the genetic architecture of complex and multiomic traits. However, most existing multilocus methods require relatively long computational time when analyzing large datasets. To address this issue, in this study, we propose a fast mrMLM method, namely, best linear unbiased prediction multilocus random-SNP-effect mixed linear model (BLUPmrMLM). First, genome-wide single-marker scanning in mrMLM is replaced by vectorized Wald tests based on the best linear unbiased prediction (BLUP) values of marker effects and their variances in BLUPmrMLM. Then, adaptive best subset selection is used to identify potentially associated markers on each chromosome to reduce computational time when estimating marker effects via empirical bayes. Finally, shared memory and parallel computing schemes were used to reduce the computation time. In simulation studies, BLUPmrMLM outperformed GEMMA, EMMAX, mrMLM, FarmCPU, and the control method of BLUPmrMLM in terms of computational time, power, accuracy for estimating quantitative trait nucleotide positions and effects, false positive rate, false discovery rate, false negative rate, and F1 score. According to the reanalysis of two large rice datasets, compared with the above methods, BLUPmrMLM significantly reduced the computation time and identified more previously reported genes. This study provides an excellent multilocus model method for the analysis of large-scale and multiomic datasets. The software mrMLM v5.1 is available at BioCode (https://ngdc.cncb.ac.cn/biocode/tools/BT007388) or GitHub (https://github.com/YuanmingZhang65/mrMLM).","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"24 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140414093","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

Substrate and Functional Diversity of Protein Lysine Post-translational Modifications 蛋白质赖氨酸翻译后修饰的底物和功能多样性

Genomics, Proteomics & Bioinformatics Pub Date : 2024-02-28 DOI: 10.1093/gpbjnl/qzae019

Bingbing Hao, Kaifeng Chen, Linhui Zhai, Muyin Liu, Bin Liu, Minjia Tan

{"title":"Substrate and Functional Diversity of Protein Lysine Post-translational Modifications","authors":"Bingbing Hao, Kaifeng Chen, Linhui Zhai, Muyin Liu, Bin Liu, Minjia Tan","doi":"10.1093/gpbjnl/qzae019","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae019","url":null,"abstract":"\u0000 Lysine post-translational modifications (PTMs) are widespread and versatile protein PTMs that are involved in diverse biological processes by regulating the fundamental functions of histone and non-histone proteins. Dysregulation of lysine PTMs is implicated in many diseases, and targeting lysine PTM regulatory factors, including writers, erasers, and readers, has become an effective strategy for disease therapy. The continuing development of mass spectrometry technologies coupled with antibody-based affinity enrichment technologies greatly promotes the discovery and decoding of PTMs. The global characterization of lysine PTMs is crucial for deciphering the regulatory networks, molecular functions, and mechanisms of action of lysine PTMs. In this review, we focus on lysine PTMs, and provide a summary of the corresponding regulatory enzymes of lysine PTMs and the proteomics advances in lysine PTMs by mass spectrometry technologies. We also discuss the types and biological functions of lysine PTM crosstalk on histone and non-histone proteins and current druggable targets of lysine PTM regulatory factors for disease therapy.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"18 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140418684","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

HCCDB v2.0: Decompose expression variations by single-cell RNA-seq and spatial transcriptomics in HCC HCCDB v2.0：通过单细胞 RNA-seq 和空间转录组学分解 HCC 中的表达变化

Genomics, Proteomics & Bioinformatics Pub Date : 2024-02-03 DOI: 10.1093/gpbjnl/qzae011

Ziming Jiang, Yanhong Wu, Yuxin Miao, Kaige Deng, Fan Yang, Shuhuan Xu, Yupeng Wang, Renke You, Lei Zhang, Yuhan Fan, Wenbo Guo, Qiuyu Lian, Lei Chen, Xuegong Zhang, Yongchang Zheng, Jin Gu

{"title":"HCCDB v2.0: Decompose expression variations by single-cell RNA-seq and spatial transcriptomics in HCC","authors":"Ziming Jiang, Yanhong Wu, Yuxin Miao, Kaige Deng, Fan Yang, Shuhuan Xu, Yupeng Wang, Renke You, Lei Zhang, Yuhan Fan, Wenbo Guo, Qiuyu Lian, Lei Chen, Xuegong Zhang, Yongchang Zheng, Jin Gu","doi":"10.1093/gpbjnl/qzae011","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae011","url":null,"abstract":"\u0000 Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). Integrated 15 transcriptomic datasets of HCC clinical samples, the first version of HCCDB (HCC database) was released in 2018. Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets, it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness. With four years having passed, the database now needs integration of recently published datasets. Furthermore, the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture. Here, we present HCCDB v2.0, an updated version that combines bulk, single-cell, and spatial transcriptomic data of HCC clinical samples. It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples, thereby enhancing the reliability of transcriptomic meta-analysis. A total of 182,832 cells and 69,352 spatial spots were added to the single-cell and spatial transcriptomics sections, respectively. A novel single-cell level and 2-dimension (sc-2D) metric was proposed as well to summarize cell type-specific and dysregulated gene expression patterns. Results are all graphically visualized in our online portal, allowing users to easily retrieve data through a user-friendly interface and navigate between different views. With extensive clinical phenotypes and transcriptomic data in the database, we show two applications for identifying prognosis-associated cells and tumor microenvironment. HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868077","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

HCCDB v2.0: Decompose expression variations by single-cell RNA-seq and spatial transcriptomics in HCC HCCDB v2.0：通过单细胞 RNA-seq 和空间转录组学分解 HCC 中的表达变化

Genomics, Proteomics & Bioinformatics Pub Date : 2024-02-03 DOI: 10.1093/gpbjnl/qzae011

Ziming Jiang, Yanhong Wu, Yuxin Miao, Kaige Deng, Fan Yang, Shuhuan Xu, Yupeng Wang, Renke You, Lei Zhang, Yuhan Fan, Wenbo Guo, Qiuyu Lian, Lei Chen, Xuegong Zhang, Yongchang Zheng, Jin Gu

{"title":"HCCDB v2.0: Decompose expression variations by single-cell RNA-seq and spatial transcriptomics in HCC","authors":"Ziming Jiang, Yanhong Wu, Yuxin Miao, Kaige Deng, Fan Yang, Shuhuan Xu, Yupeng Wang, Renke You, Lei Zhang, Yuhan Fan, Wenbo Guo, Qiuyu Lian, Lei Chen, Xuegong Zhang, Yongchang Zheng, Jin Gu","doi":"10.1093/gpbjnl/qzae011","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae011","url":null,"abstract":"\u0000 Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). Integrated 15 transcriptomic datasets of HCC clinical samples, the first version of HCCDB (HCC database) was released in 2018. Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets, it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness. With four years having passed, the database now needs integration of recently published datasets. Furthermore, the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture. Here, we present HCCDB v2.0, an updated version that combines bulk, single-cell, and spatial transcriptomic data of HCC clinical samples. It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples, thereby enhancing the reliability of transcriptomic meta-analysis. A total of 182,832 cells and 69,352 spatial spots were added to the single-cell and spatial transcriptomics sections, respectively. A novel single-cell level and 2-dimension (sc-2D) metric was proposed as well to summarize cell type-specific and dysregulated gene expression patterns. Results are all graphically visualized in our online portal, allowing users to easily retrieve data through a user-friendly interface and navigate between different views. With extensive clinical phenotypes and transcriptomic data in the database, we show two applications for identifying prognosis-associated cells and tumor microenvironment. HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808170","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

NeoTCR: an immunoinformatic database of experimentally-supported functional neoantigen-specific TCR sequences NeoTCR：实验支持的功能性新抗原特异性 TCR 序列免疫形式数据库

Genomics, Proteomics & Bioinformatics Pub Date : 2024-02-03 DOI: 10.1093/gpbjnl/qzae010

Weijun Zhou, Wenting Xiang, Jinyi Yu, Zhihan Ruan, Yichen Pan, Kankan Wang, Jian Liu

{"title":"NeoTCR: an immunoinformatic database of experimentally-supported functional neoantigen-specific TCR sequences","authors":"Weijun Zhou, Wenting Xiang, Jinyi Yu, Zhihan Ruan, Yichen Pan, Kankan Wang, Jian Liu","doi":"10.1093/gpbjnl/qzae010","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae010","url":null,"abstract":"\u0000 Neoantigen-based immunotherapy has demonstrated long-lasting antitumor activity. The recognition of neoantigens by T cell receptors (TCRs) is considered a trigger for antitumor responses. Due to the overwhelming number of TCR repertoires in the human genome, pinpointing neoantigen-specific TCRs is a formidable challenge. Recent studies have identified a number of functional neoantigen-specific TCRs, but the corresponding information is scattered across published literature and is difficult to retrieve. To improve access to these data, we developed an immunoinformatic database (NeoTCR) containing a unified description of publicly available neoantigen-specific TCR sequences, as well as relevant information on targeted neoantigens, from experimentally supported studies across 18 cancer subtypes. A user-friendly web interface allows interactive browsing and running of complex database queries. To facilitate rapid identification of neoantigen-specific TCRs from raw sequencing data, NeoTCR offers a one-stop analysis for annotation and visualization of TCR clonotypes, discovery of existing neoantigen-specific TCRs, and exclusion of bystander viral-associated TCRs. NeoTCR represents a unique tool to expedite future studies of neoantigen-specific TCRs and the development of neoantigen-based immunotherapy. NeoTCR is available at http://neotcrdb.bioaimed.com/ and https://github.com/lyotvincent/NeoTCR.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808577","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

NeoTCR: an immunoinformatic database of experimentally-supported functional neoantigen-specific TCR sequences NeoTCR：实验支持的功能性新抗原特异性 TCR 序列免疫形式数据库

Genomics, Proteomics & Bioinformatics Pub Date : 2024-02-03 DOI: 10.1093/gpbjnl/qzae010

Weijun Zhou, Wenting Xiang, Jinyi Yu, Zhihan Ruan, Yichen Pan, Kankan Wang, Jian Liu

{"title":"NeoTCR: an immunoinformatic database of experimentally-supported functional neoantigen-specific TCR sequences","authors":"Weijun Zhou, Wenting Xiang, Jinyi Yu, Zhihan Ruan, Yichen Pan, Kankan Wang, Jian Liu","doi":"10.1093/gpbjnl/qzae010","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae010","url":null,"abstract":"\u0000 Neoantigen-based immunotherapy has demonstrated long-lasting antitumor activity. The recognition of neoantigens by T cell receptors (TCRs) is considered a trigger for antitumor responses. Due to the overwhelming number of TCR repertoires in the human genome, pinpointing neoantigen-specific TCRs is a formidable challenge. Recent studies have identified a number of functional neoantigen-specific TCRs, but the corresponding information is scattered across published literature and is difficult to retrieve. To improve access to these data, we developed an immunoinformatic database (NeoTCR) containing a unified description of publicly available neoantigen-specific TCR sequences, as well as relevant information on targeted neoantigens, from experimentally supported studies across 18 cancer subtypes. A user-friendly web interface allows interactive browsing and running of complex database queries. To facilitate rapid identification of neoantigen-specific TCRs from raw sequencing data, NeoTCR offers a one-stop analysis for annotation and visualization of TCR clonotypes, discovery of existing neoantigen-specific TCRs, and exclusion of bystander viral-associated TCRs. NeoTCR represents a unique tool to expedite future studies of neoantigen-specific TCRs and the development of neoantigen-based immunotherapy. NeoTCR is available at http://neotcrdb.bioaimed.com/ and https://github.com/lyotvincent/NeoTCR.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"77 9-10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868637","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

FP-Zernike: an open-source structural database construction toolkit for fast structure retrieval FP-Zernike：用于快速结构检索的开源结构数据库构建工具包

Genomics, Proteomics & Bioinformatics Pub Date : 2024-01-19 DOI: 10.1093/gpbjnl/qzae007

Junhai Qi, Chenjie Feng, Yulin Shi, Jianyi Yang, Fa Zhang, Guojun Li, Renmin Han

{"title":"FP-Zernike: an open-source structural database construction toolkit for fast structure retrieval","authors":"Junhai Qi, Chenjie Feng, Yulin Shi, Jianyi Yang, Fa Zhang, Guojun Li, Renmin Han","doi":"10.1093/gpbjnl/qzae007","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae007","url":null,"abstract":"\u0000 The release of AlphaFold2 has sparked a rapid expansion in protein model databases. Efficient protein structure retrieval is crucial for the analysis of structure models, while measuring the similarity between structures is the key challenge in structural retrieval. Although existing structure alignment algorithms can address this challenge, they are often time-consuming. Currently, the state-of-the-art approach involves converting protein structures into three-dimensional (3D) Zernike descriptors and assessing similarity using Euclidean distance. However, the methods for computing 3D Zernike descriptors mainly rely on structural surfaces and are predominantly web-based, thus limiting their application in studying custom datasets. To overcome this limitation, we developed FP-Zernike, a user-friendly toolkit for computing different types of Zernike descriptors based on feature points. Users simply need to enter a single line of command to calculate the Zernike descriptors of all structures in customized datasets. FP-Zernike outperforms the leading method in terms of retrieval accuracy and binary classification accuracy across diverse benchmark datasets. In addition, we showed the application of FP-Zernike in the construction of the descriptor database and the protocol used for the Protein Data Bank (PDB) dataset to facilitate the local deployment of this tool for interested readers. Our demonstration contained 590,685 structures, and at this scale, our system required only 4–9 seconds to complete a retrieval. The experiments confirmed that it achieved the state-of-the-art accuracy level. FP-Zernike is an open-source toolkit, with the source code and related data accessible at https://ngdc.cncb.ac.cn/biocode/tools/BT007365/releases/0.1, as well as through a webserver at http://www.structbioinfo.cn/.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"16 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139524937","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

A Two-color Single-molecule Sequencing Platform and Its Clinical Applications 双色单分子测序平台及其临床应用

Genomics, Proteomics & Bioinformatics Pub Date : 2024-01-11 DOI: 10.1093/gpbjnl/qzae006

Fang Chen, Bin Liu, Meirong Chen, Zefei Jiang, Zhiliang Zhou, Ping Wu, Meng Zhang, Hua Jin, Linsen Li, Liuyan Lu, H. Shang, Lei Liu, Weiyue Chen, Jianfeng Xu, Ruitao Sun, Guangming Wang, Jiao Zheng, Jifang Qi, Bo Yang, Lidong Zeng, Yan Li, Hui Lv, Nannan Zhao, Wen Wang, Jinsen Cai, Yongfeng Liu, Weiwei Luo, Juan Zhang, Yanhua Zhang, Jicai Fan, Haitao Dan, Xuesen He, Wei-wei Huang, Lei Sun, Qin Yan

{"title":"A Two-color Single-molecule Sequencing Platform and Its Clinical Applications","authors":"Fang Chen, Bin Liu, Meirong Chen, Zefei Jiang, Zhiliang Zhou, Ping Wu, Meng Zhang, Hua Jin, Linsen Li, Liuyan Lu, H. Shang, Lei Liu, Weiyue Chen, Jianfeng Xu, Ruitao Sun, Guangming Wang, Jiao Zheng, Jifang Qi, Bo Yang, Lidong Zeng, Yan Li, Hui Lv, Nannan Zhao, Wen Wang, Jinsen Cai, Yongfeng Liu, Weiwei Luo, Juan Zhang, Yanhua Zhang, Jicai Fan, Haitao Dan, Xuesen He, Wei-wei Huang, Lei Sun, Qin Yan","doi":"10.1093/gpbjnl/qzae006","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae006","url":null,"abstract":"\u0000 DNA sequencers have become increasingly important research and diagnostic tools over the past 20 years. We have developed a single-molecule desktop sequencer, GenoCare 1600 (GenoCare), which utilizes amplification-free library preparation and two-color sequencing-by-synthesis chemistry, making it more user-friendly compared to previous single-molecule sequencing platforms for clinical use. Here, we report sequencing data of an Escherichia coli standard sample by GenoCare, with a consensus accuracy reaching 99.99%. We also evaluated the sequencing performance of this platform in microbial mixtures and coronavirus disease 2019 (COVID-19) samples from throat swabs. Our findings indicate that the GenoCare platform allows for microbial quantitation, sensitive identification of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and accurate detection of virus mutations, as confirmed by Sanger sequencing, demonstrating remarkable potential in clinical application.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"37 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533485","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

On the Responsible Use of Chatbots in Bioinformatics 在生物信息学中负责任地使用聊天机器人

Genomics, Proteomics & Bioinformatics Pub Date : 2024-01-05 DOI: 10.1093/gpbjnl/qzae002

Gangqing Hu, Li Liu, Dong Xu

引用次数: 0

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