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Ten computational challenges in human virome studies

Virologica Sinica 2024年第6期39卷 845-850页

作者： Yifan Wu Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha 410082China

In recent years,substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and *** this progress,the field of human virome research remains nascent,primarily hindered by the lack of effective methods,particularly in the domain of computational *** perspective systematically outlines ten computational challenges spanning various types of virome *** challenges arise due to the vast diversity of viromes,the absence of a universal marker gene in viral genomes,the low abundance of virus populations,the remote or minimal homology of viral proteins to known proteins,and the highly dynamic and heterogeneous nature of *** each computational challenge,we discuss the underlying reasons,current research progress,and potential *** resolution of these challenges necessitates ongoing collaboration among computational scientists,virologists,and multidisciplinary *** essence,this perspective serves as a comprehensive guide for directing computational efforts in human virome studies.

关键词： Human virome Computational biology Computational viromics Artificial intelligence

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Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms

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Virologica Sinica 2022年第3期37卷 437-444页

作者： Huiting Chen Zhaozhong Zhu Ye Qiu Xingyi Ge Heping Zheng Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha410082China

The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune ***,there is still a lack of effective tools for determining the cleavage sites of the 3CL *** study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein,and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus,Betacoronavirus and *** residue preferences were observed at the neighboring positions of the cleavage sites.A random forest(RF)model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes,and the model achieved an AUC of 0.96 in *** RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus *** achieved an AUC of 0.95 and predicted correctly 80%of the cleavage ***,1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF *** proteins were enriched in several GO terms related to the cytoskeleton,such as the microtubule,actin and ***,a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF ***,the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.

关键词： Coronavirus 3C-like protease Cleavage sites Machine-learning algorithms 3CLP webserver

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EVIHVR: A platform for analysis of expression, variation and identification of human virus receptors

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Infectious Medicine 2022年第1期1卷 59-62页

作者： Zheng Zhang Zena Cai Longfei Mao Xing-Yi Ge Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangshaChina

Motivation:Virus receptors are presented on the cell surfaces of a host and are key for viral infection of host ***,no unified resource for the study of viral receptors is currently ***:To address this problem,we built EVIHVR,a platform for analyzing the expression and variation,and for the identification of human virus *** provides three functions:(1)Receptor expression function for browsing and analyzing the expression of human virus receptors in various human tissues/cells;(2)Receptor gene polymorphism function for analyzing the genetic polymorphism of human virus receptors in different human populations and human tissues;and(3)Predict receptor function for identifying potential virus receptors based on differential expression *** can become a useful tool for the analysis and identification of human virus receptors.

关键词： Virus receptor Genetic polymorphism Receptor identification Human-infecting virus Web services

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Computational Viromics: Applications of the Computational Biology in Viromics Studies

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Virologica Sinica 2021年第5期36卷 1256-1260页

作者： Congyu Lu Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha 410082China

Viruses are a kind of biological entities which rely on host cells for *** on the genetic materials and replication mode,they can be grouped into double-stranded DNA(dsDNA),single-stranded DNA(ssDNA),doublestranded RNA(dsRNA),positive-sense single-stranded RNA(+ssRNA),negative-sense single-stranded RNA(-ssRNA),ssRNA reverse transcriptase viruses(ssRNART)and dsDNA reverse transcriptase viruses(dsDNA-RT)(Walker et al.2020).Viruses can infect most kinds of biological entities,including viruses,bacteria,archaea and eukaryote(La Scola et al.2008;Fermin,2018).

关键词： al. dsDNA stranded

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Ten computational challenges in human virome studies

arXiv

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arXiv 2024年

作者： Yifan, Wu Peng, Yousong Bioinformatics Center College of Biology Hunan Provincial Key Laboratory of Medical Virology Hunan University Changsha China

In recent years, substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and diseases. Despite this progress, the field of human virome research remains nascent, primarily hindered by the absence of effective methods, particularly in the domain of computational tools. This perspective systematically outlines ten computational challenges spanning various phases of virome studies, ranging from virus identification, sequencing quality evaluation, genome assembly, annotation of viral taxonomy, genome and proteins, inference of biological properties, applications of the virome in disease diagnosis, interactions with other microbes, and associations with human diseases. The resolution of these challenges necessitates ongoing collaboration among computational scientists, virologists, and multidisciplinary experts. In essence, this perspective serves as a comprehensive guide for directing computational efforts in human virome studies, aiming to significantly propel the field forward. © 2024, CC BY.

关键词： Association reactions

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The OsWRKY72-OsAAT30/OsGSTU26 module mediates reactive oxygen species scavenging to drive heterosis for salt tolerance in hybrid rice

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Journal of Integrative Plant Biology 2024年第4期66卷 709-730页

作者： Citao Liu Bigang Mao Yanxia Zhang Lei tian Biao Ma Zhuo Chen Zhongwei Wei Aifu Li Ye Shao Gongye Cheng Lingling Li Wenyu Li Di Zhang Xiaoping Ding Jiangxiang Peng Yulin Peng Jiwai He Nenghui Ye Dingyang Yuan Chengcai Chu Meijuan Duan Hunan Provincial Key Laboratory of Stress Biology College of AgricultureHunan Agricultural UniversityChangsha 410128China State Key Laboratory of Hybrid Rice Hunan Hybrid Rice Research CenterChangsha 410125China College of Agriculture South China Agricultural UniversityGuangzhou 510642China State Key Laboratory of Wheat and Maize Crop Science Center for Crop Genome EngineeringCollege of AgronomyHenan Agricultural UniversityZhengzhou 450002China College of Horticulture Fujian Agriculture and Forestry UniversityFuzhou 350002China Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48109 USA Biobin Data Science Co. Ltd.Changsha 410221China

Hybrid rice(Oryza sativa)generally outperforms its inbred parents in yield and stress tolerance,a phenomenon termed heterosis,but the underlying mechanism is not completely ***,we combined transcriptome,proteome,physiological,and heterosis analyses to examine the salt response of super hybrid rice Chaoyou1000(CY1000).In addition to surpassing the mean values for its two parents(mid-parent heterosis),CY1000 exhibited a higher reactive oxygen species scavenging ability than both its parents(over-parent heterosis or heterobeltiosis).Nonadditive expression and allele-specific gene expression assays showed that the glutathione S-transferase gene OsGSTU26 and the amino acid transporter gene OsAAT30 may have major roles in heterosis for salt tolerance,acting in an overdominant fashion in ***,we identified OsWRKY72 as a common transcription factor that binds and regulates OsGSTU26 and *** salt-sensitive phenotypes were associated with the OsWRKY72^(paternal)genotype or the OsAAT30^(maternal)genotype in core rice germplasm ***72^(paternal)specifically repressed the expression of OsGSTU26 under salt stress,leading to salinity sensitivity,while OsWRKY72^(maternal)specifically repressed OsAAT30,resulting in salinity *** results suggest that the OsWRKY72-OsAAT30/OsGSTU26 module may play an important role in heterosis for salt tolerance in an overdominant fashion in CY1000 hybrid rice,providing valuable clues to elucidate the mechanism of heterosis for salinity tolerance in hybrid rice.

关键词： allele-specific gene expression(ASE) heterosis hybrid rice nonadditive effect(NAE) salt tolerance transcriptome and proteome

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Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data

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IEEE/ACM Transactions on Computational Biology and bioinformatics 2024年 PP卷 1-11页

作者： Lan, Wei Chen, Jianwei Liu, Mingyang Chen, Qingfeng Liu, Jin Wang, Jianxin Chen, Yi-Ping Phoebe Wei Lan is School of Computer Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning Guangxi China School of Computer Electronic and Information Guangxi University Nanning Guangxi China School of Computer Electronic and Information and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources Guangxi University Nanning Guangxi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Department of Computer Science and Information Technology La Trobe University Melbourne Victoria Australia

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE

关键词： RNA

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Development of PREDAC-H1pdm to model the antigenic evolution of influenza A/(H1N1)pdm09 viruses

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Virologica Sinica 2023年第4期38卷 541-548页

作者： Mi Liu Jingze Liu Wenjun Song Yousong Peng Xiao Ding Lizong Deng Taijiao Jiang Jiangsu Institute of Clinical Immunology The First Affiliated Hospital of Soochow UniversitySuzhou215006China Institute of Systems Medicine Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing100005China Suzhou Institute of Systems Medicine Suzhou215123China Guangzhou Laboratory Guangzhou510005China Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha410082China State Key Laboratory of Respiratory Disease the First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou Medical UniversityGuangzhou510120China

The Influenza A(H1N1)pdm09 virus caused a global pandemic in 2009 and has circulated seasonally ever *** the continual genetic evolution of hemagglutinin in this virus leads to antigenic drift,rapid identification of antigenic variants and characterization of the antigenic evolution are *** this study,we developed PREDAC-H1pdm,a model to predict antigenic relationships between H1N1pdm viruses and identify antigenic clusters for post-2009 pandemic H1N1 *** model performed well in predicting antigenic variants,which was helpful in influenza *** mapping the antigenic clusters for H1N1pdm,we found that substitutions on the Sa epitope were common for H1N1pdm,whereas for the former seasonal H1N1,substitutions on the Sb epitope were more common in antigenic ***,the localized epidemic pattern of H1N1pdm was more obvious than that of the former seasonal H1N1,which could make vaccine recommendation more ***,the antigenic relationship prediction model we developed provides a rapid determination method for identifying antigenic variants,and the further analysis of evolutionary and epidemic characteristics can facilitate vaccine recommendations and influenza surveillance for H1N1pdm.

关键词： H1N1pdm virus Antigenic relationship prediction Antigenic evolution Vaccine recommendation

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A PTAS Framework for Clustering Problems in Doubling Metrics 29th

A PTAS Framework for Clustering Problems in Doubling Metri...

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29th International Computing and Combinatorics Conference, COCOON 2023

作者： Wu, Di Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Engineering State University of New York at Buffalo BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha China

ISBN: (纸本)9783031491894

Constrained clustering problems have been studied extensively in recent years. In this paper, we focus on a class of constrained k-median problems with general constraints on facilities, denoted as GCF k-CMedian problems. We present a randomized polynomial-time approximation scheme (PTAS) framework based on the split-tree decomposition and dynamic programming process for GCF k-CMedian problems, such as k-median with service installation costs, k-facility location and priority k-median problems in doubling metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Dynamic programming

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Collaborative Matrix Factorization with Soft Regularization for Drug-Target Interaction Prediction

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Journal of Computer Science & Technology 2021年第2期36卷 310-322页

作者： Li-Gang Gao Meng-Yun Yang Jian-Xin Wang School of Computer Science and Engineering Central South UniversityChangsha 410083China Hunan Provincial Key Laboratory of Bioinformatics Central South UniversityChangsha 410083China School of Science Shaoyang UniversityShaoyang 422000China

Identifying the potential drug-target interactions(DTI)is critical in drug *** drug-target interaction prediction methods based on collaborative filtering have demonstrated attractive prediction ***,many corresponding models cannot accurately express the relationship between similarity features and DTI *** order to rationally represent the correlation,we propose a novel matrix factorization method,so-called collaborative matrix factorization with soft regularization(SRCMF).SRCMF improves the prediction performance by combining the drug and the target similarity information with matrix *** contrast to general collaborative matrix factorization,the fundamental idea of SRCMF is to make the similarity features and the potential features of DTI approximate,not ***,SRCMF obtains low-rank feature representations of drug similarity and target similarity,and then uses a soft regularization term to constrain the approximation between drug(target)similarity features and drug(target)potential features of *** comprehensively evaluate the prediction performance of SRCMF,we conduct cross-validation experiments under three different *** terms of the area under the precision-recall curve(AUPR),SRCMF achieves better prediction results than six state-of-the-art ***,under different noise levels of similarity data,the prediction performance of SRCMF is much better than that of collaborative matrix *** conclusion,SRCMF is robust leading to performance improvement in drug-target interaction prediction.

关键词： drug-target interaction collaborative matrix factorization soft regularization noisy data

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