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Sequence-Based Predicting Bacterial Essential ncRNAs Algorithm by Machine Learning

Intelligent Automation & Soft Computing 2023年第6期36卷 2731-2741页

作者： Yuan-Nong Ye Ding-Fa Liang Abraham Alemayehu Labena Zhu Zeng Bioinformatics and Biomedical Big data Mining Laboratory Department of Medical InformaticsSchool of Big HealthGuizhou Medical UniversityGuiyang550025China Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical EngineeringSchool of Biology and EngineeringGuizhou Medical UniversityGuiyang550025China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of EducationGuizhou Medical UniversityGuiyang550025China College of Computational and Natural Sciences Dilla UniversityDilla419Ethiopia

Essential ncRNA is a type of ncRNAwhich is indispensable for the sur-vival of *** essential ncRNAs cannot encode proteins,they are as important as essential coding genes in *** have got wide variety of applications such as antimicrobial target discovery,minimal genome construction and evolution *** present,the number of species required for the deter-mination of essential ncRNAs in the whole genome scale is still very few due to the traditional methods are time-consuming,laborious and *** addition,tra-ditional experimental methods are limited by the organisms as less than 1%of bacteria can be cultured in the ***,it is important and necessary to develop theories and methods for the recognition of essential non-coding *** this paper,we present a novel method for predicting essential ncRNA by using both compositional and derivative features calculated by information theory of ncRNA *** method was developed with Support Vector Machine(SVM).The accuracy of the method was evaluated through cross-species cross-vali-dation and found to be between 0.69 and *** shows that the features we selected have good performance for the prediction of essential ncRNA using ***,the method can be applied for discovering essential ncRNAs in bacteria.

关键词： bioinformatics biological information theory biomedical informatics

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TCRosetta:An Integrated Analysis and Annotation Platform for T-cell Receptor Sequences

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Genomics, Proteomics & bioinformatics 2024年第4期22卷 181-190页

作者： Tao Yue Si-Yi Chen Wen-Kang Shen Zhan-Ye Zhang Liming Cheng An-Yuan Guo Center for Artificial Intelligence Biology Hubei Bioinformatics&Molecular Imaging Key LaboratoryCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Laboratory Medicine Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Department of Thoracic Surgery West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu 610041China

T cells and T-cell receptors(TCRs)are essential components of the adaptive immune *** of the TCR repertoire offers a promising and highly informative source for understanding the functions of T cells in the immune response and *** TCR repertoire studies have attracted much attention,there are few online servers available for TCR repertoire analysis,especially for TCR sequence annotation or advanced ***,we developed TCRosetta,a comprehensive online server that integrates analytical methods for TCR repertoire analysis and *** combines general feature analysis,large-scale sequence clustering,network construction,peptide–TCR binding prediction,generation probability calculation,and k-mer motif analysis for TCR sequences,making TCR data analysis as simple as *** TCRosetta server accepts multiple input data formats and can analyze�20,000 TCR sequences in less than 3 *** is the most comprehensive web server available for TCR repertoire analysis and is freely available at https://***/TCRosetta/.

关键词： T-cell receptor TCR repertoire analysis Batch search Annotation Web server.

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A novel copper metabolism-related signature model for predicting the prognosis,target drugs,and immunotherapy in stomach adenocarcinoma

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Genes & Diseases 2024年第5期11卷 21-24页

作者： Kai Zhuang Siqi Tang Haixin Feng Jinying Zhang Ying Liu Yong Liu Yongjian Su Jiaqi Yu Zunnan Huang Key Laboratory of Computer-Aided Drug Design of Dongguan City The First Dongguan Affiliated HospitalGuangdong Medical UniversityDongguanGuangdong 523710China Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University Key Laboratory for Research and Development of Natural Drugs of Guangdong ProvinceSchool of PharmacyGuangdong Medical UniversityDongguanGuangdong 523808China School of Public Health Guangdong Medical UniversityDongguanGuangdong 523808China Marine Biomedical Research Institute of Guangdong Zhanjiang ZhanjiangGuangdong 524203China

Stomach adenocarcinoma(STAD)is one of the most common gastric neoplasms with a high death ***,there is an urgent need to propose an efficient therapy for *** plays key roles in regulating the distribution of immune cells and affecting the tumor immune escape,and may be a novel indicator of immunotherapy in ***,the specific impact of copper metabolism-related genes(CMRGs)on the patient’s prognosis,tumor microenvironment,and immunotherapeutic response remains unelucidated.

关键词： metabolism stomach adenocarcinoma

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An automatic annotation tool and reference database for T cell subtypes and states at single-cell resolution

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Science Bulletin 2025年第10期70卷 1659-1672页

作者： Shen, Wen-Kang Zhang, Chu-Yu Gu, Yi-Min Luo, Tao Chen, Si-Yi Yue, Tao Xie, Gui-Yan Liao, Yu Yuan, Yong Lei, Qian Guo, An-Yuan Department of Thoracic Surgery West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610041 China Hubei Bioinformatics & Molecular Imaging Key Laboratory College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China College of Life Sciences University of Chinese Academy of Sciences Beijing101408 China Department of Rheumatology & Immunology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430074 China

T cells have various subtypes and states with different functions. However, a reference list and automated annotation tool for T cell subtypes and states are lacking, which is critical for analyzing and comparing T cells under various conditions. We constructed the largest human T cell reference, containing 1,348,268 T cells from 35 conditions and 16 tissues. We classified T cells into 33 subtypes and further stratified them into 68 categories according to subtype and state. Based on this reference, we developed a tool named STCAT to automatically annotate T cells from scRNA-seq data by hierarchical models and marker correction. The accuracy of STCAT was 28% higher than that of existing tools validated on six independent datasets, including cancer and healthy samples. Using STCAT, we consistently discovered that CD4+ Th17 cells were enriched in late-stage lung cancer patients in multiple datasets, whereas MAIT cells were prevalent in milder-stage COVID-19 patients. We also confirmed a decrease in Treg cytotoxicity in post-treatment ovarian cancer. Systematic landscape analyses of CD4+ and CD8+ T cell references revealed that CD4+ Treg cells were enriched in tumor samples and that CD8+ naive-related cells were abundant in healthy individuals. Finally, we deposited all the T cell references and annotations into a TCellAtlas (https://***/TCellAtlas) database, which allows users to browse T cell expression profiles and analyze customized scRNA-seq data by STCAT. In conclusion, comprehensive human T cell subtypes and states reference, automated annotation tool, and database will greatly facilitate research on T cell immunity and tumor immunology. © 2025 Science China Press

关键词： T-cells

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Multi-omics integration reveals potential stage-specific druggable targets in T-cell acute lymphoblastic leukemia

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Genes & Diseases 2024年第5期11卷 374-389页

作者： Zijun Yan Jie Xia Ziyang Cao Hongyang Zhang Jinxia Wang Tienan Feng Yi Shu Lin Zou Clinical Research Unit Shanghai Children’s HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai 200062China Institute of Pediatric Infection Immunityand Critical Care MedicineShanghai Jiao Tong University School of MedicineShanghai 200062China Center for Clinical Molecular Laboratory Medicine of Children’s Hospital of Chongqing Medical University Chongqing 400014China Clinical Research Institute Shanghai Jiao Tong University School of MedicineShanghai 200025China Bioinformatics and BioMedical Bigdata Mining Laboratory School of Big HealthGuizhou Medical UniversityGuiyangGuizhou 554300China

T-cell acute lymphoblastic leukemia(T-ALL),a heterogeneous hematological malignancy,is caused by the developmental arrest of normal T-cell *** development of targeted therapeutic regimens is impeded by poor knowledge of the stage-specific aberrances in this *** this study,we performed multi-omics integration analysis,which included mRNA expression,chromatin accessibility,and gene-dependency database analyses,to identify potential stage-specific druggable targets and repositioned drugs for this *** multi-omics integration helped identify 29 potential pathological genes for *** genes exhibited tissue-specific expression profiles and were enriched in the cell cycle,hematopoietic stem cell differentiation,and the AMPK signaling *** these,four known druggable targets(CDK6,TUBA1A,TUBB,and TYMS)showed dysregulated and stage-specific expression in malignant T cells and may serve as stage-specific targets in *** TUBA1A expression level was higher in the early T cell precursor(ETP)-ALL cells,while TUBB and TYMS were mainly highly expressed in malignant T cells arrested at the CD4 and CD8 double-positive or single-positive ***6 exhibited a U-shaped expression pattern in malignant T cells along the naıve to maturation ***,mebendazole and gemcitabine,which target TUBA1A and TYMS,respectively,exerted stage-specific inhibitory effects on T-ALL cell lines,indicating their potential stage-specific antileukemic role in ***,our findings might aid in identifying potential stage-specific druggable targets and are promising for achieving more precise therapeutic strategies for T-ALL.

关键词： Multi-omics Stage-specific druggable targets Targeted therapeutic strategies T-cell acute lymphoblastic leukemia Drug repositioning

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bioinformatics tools and resources for cancer and application

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Chinese Medical Journal 2024年第17期137卷 2052-2064页

作者： Jin Huang Lingzi Mao Qian Lei An-Yuan Guo Department of Thoracic Surgery West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengduSichuan 610041China Hubei Bioinformatics&Molecular Imaging Key Laboratory College of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanHubei 430074China

Tumor bioinformatics plays an important role in cancer research and precision *** primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and *** recent years,driven by breakthroughs in high-throughput technologies,large-scale cancer omics data have accumulated *** to effectively utilize and share these data is particularly *** address this crucial task,many computational tools and databases have been developed over the past few *** help researchers quickly learn and understand the functions of these tools,in this review,we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis,regulatory analysis of tumorigenesis,tumor treatment and prognosis,immune infiltration analysis,immune repertoire analysis,cancer driver gene and driver mutation analysis,and cancer single-cell analysis,which may further help researchers find more suitable tools for their research.

关键词： Tumor bioinformatics Cancer research Tools databases

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Integrated Helicobacter Pylori Genome database and Its Analysis 11th

Integrated Helicobacter Pylori Genome Database and Its Analy...

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11th International Conference on Computer Engineering and Networks, CENet2021

作者： Zheng, Liufeng Guo, Mujuan Liang, Dingfa Ye, Yuannong Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9789811665530

The shape of helicobacter pylori (HP) is usually helical or S-shaped, which makes it to have the feature of highly divergent. Besides, its distribution also is different greatly in space and time, which is the factor to lead to multiple infection. In addition, in the same infected person, multiple infection also appeared in different regions of strain gene infection origin. Therefore, it is of great significance to study the genomic diversity and evolutionary analysis of Helicobacter pylori. In this paper, we are to analyze the homology of multiple sequences using ClustalX1.83 and meanwhile to construct the homologous genome evolutionary tree by MEGA7.0 to analyze the its function. The purpose of this study is to compare the genomic evolutionary differences of Helicobacter pylori among populations in different regions, and to discuss the significance of its infection prevalence and genomic variation in geographical changes, so as to conduct further discussion on the molecular level research mechanism of Helicobacter pylori. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genome

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Optimization of Gene Translation Using SD Complementary Sequences and Double Codons 11th

Optimization of Gene Translation Using SD Complementary Sequ...

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11th International Conference on Computer Engineering and Networks, CENet2021

作者： Liang, Dingfa Huang, Zhumian Zheng, Liufeng Ye, Yuannong Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9789811665530

Studies have confirmed that the growth, development and maintenance structure and function of all living organisms are determined by their genes, but the speed of gene translation products is affected by various internal and external factors. Therefore, studying the gene translation speed is extremely important for production and improving the speed through a variety of ways has always been an important subject. In recent years, the found of codon usage bias, tRNA recycling model and SD sequence-like structure provide another way to optimize the translation efficiency. These methods are almost based on codon usage theory to optimize gene sequence to speed up or slow down the translation speed so as to regulate the cell expression and metabolism. This paper is combined the three theories above, aimed at *** to design some reasonable algorithm to optimize gene sequence and thus using codon adaptation index (CAI) to evaluate the optimized effect. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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The Algorithms of Predicting Bacterial Essential Genes and NcRNAs by Machine Learning 11th

The Algorithms of Predicting Bacterial Essential Genes and N...

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11th International Conference on Computer Engineering and Networks, CENet2021

作者： Ye, Yuannong Liang, Dingfa Zeng, Zhu Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9789811665530

Essential genes are indispensable for biological survival. Thus it is of great significance to identify and study essential genes. A machine learning method, K-Nearest Neighbor, is used for development of predicting essential bacterial genes. The homologous features, including sequence homology and functional homology, of the bacterial genomes are extracted for determining essential genes. Based on the features, we use K-Nearest Neighbor algorithm for determining of gene function. And we tune the minimum matching parameter (K) in the essential gene predicted model for building an optimal model of the Escherichia coli specificity model. The corresponding optimal parameter (K) is then extended to other bacterial essential genes predicting models. After cross validation, the highest accuracy is 0.89 while K between 5 and 7. Therefore, the features we extracted can increase the accuracy of the bacterial essential gene prediction. In the premise, we found that the prediction accuracy of the prediction model based on K-Nearest Neighbor was not significantly different in different evolutionary distances between organisms in the database and the investigated species. That means the machine learning model can be extended to more distant species. It wills have a better predictive performance for predicting essential genes of distant species than the usual sequence-based methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Escherichia coli

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Novel loci and potential mechanisms of major depressive disorder,bipolar disorder,and schizophrenia

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Science China(Life Sciences) 2022年第1期65卷 167-183页

作者： He Wang Zhenghui Yi Tieliu Shi Center for Bioinformatics and Computational Biology Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Shanghai Mental Health Center Shanghai Jiao Tong University School of MedicineShanghai 200030China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghai 200062China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University&Capital Medical UniversityBeijing 100083China

Different psychiatric disorders share genetic relationships and pleiotropic loci to certain *** integrated and analyzed datasets related to major depressive disorder(MDD),bipolar disorder(BIP),and schizophrenia(SCZ)from the Psychiatric Genomics Consortium using multitrait analysis of genome-wide association analysis(MTAG).MTAG significantly increased the effective sample size from 99,773 to 119,754 for MDD,from 909,061 to 1,450,972 for BIP,and from 856,677 to 940,613 for *** discovered 7,32,and 43 novel lead single nucleotide polymorphisms(SNPs)and 1,6,and 3 novel causal SNPs for MDD,BIP,and SCZ,respectively,after *** identified rs8039305 in the FURIN gene as a novel pleiotropic locus across the three *** performed marker analysis of genomic annotation(MAGMA)and Hi-C-coupled MAGMA(H-MAGMA)based gene-set analysis and identified 101 genes associated with the three disorders,which were enriched in the regulation of postsynaptic membranes,postsynaptic membrane dopaminergic synapses,and Notch signaling ***,we performed Mendelian randomization analysis using different tools and detected a causal effect of BIP on ***,we demonstrated the usage of combined genome-wide association studies summary statistics for exploring potential novel mechanisms of the three psychiatric disorders,providing an alternative approach to integrate publicly available summary data.

关键词： psychiatric disorders multitrait analysis of genome-wide association analysis novel loci Mendelian randomization causal effects

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