2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV

Xiao, Ming; Liu, Guangdi; Xie, Jianghang; Dai, Zichun; Wei, Zihao; Ren, Ziyao; Yu, Jun; Zhang, Le

doi:10.1109/tcbb.2021.3049617

Cited by 22 publications

(17 citation statements)

References 48 publications

(57 reference statements)

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“…To overcome these shortcomings, we will employ natural language processing technology 58 , 59 to improve manual update efficiency and use high-performance computing 60 , 61 , 62 to reduce the processing time for similarity computing. Finally, we will make MDCB as a highly integrated web-based MC-related data platform by integrating more advanced bioinformatics applications and algorithms 27 , 63 , 64 , 65 , 66 , 67 , 68 , 69 in the future.…”

Section: Discussionmentioning

confidence: 99%

MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

Zhang

Guo

Xiao

et al. 2021

Acta Pharmaceutica Sinica B

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Mitotic catastrophe (MC) is a form of programmed cell death induced by mitotic process disorders, which is very important in tumor prevention, development, and drug resistance. Because rapidly increased data for MC is vigorously promoting the tumor-related biomedical and clinical study, it is urgent for us to develop a professional and comprehensive database to curate MC-related data. Mitotic Catastrophe Database (MCDB) consists of 1214 genes/proteins and 5014 compounds collected and organized from more than 8000 research articles. Also, MCDB defines the confidence level, classification criteria, and uniform naming rules for MC-related data, which greatly improves data reliability and retrieval convenience. Moreover, MCDB develops protein sequence alignment and target prediction functions. The former can be used to predict new potential MC-related genes and proteins, and the latter can facilitate the identification of potential target proteins of unknown MC-related compounds. In short, MCDB is such a proprietary, standard, and comprehensive database for MC-relate data that will facilitate the exploration of MC from chemists to biologists in the fields of medicinal chemistry, molecular biology, bioinformatics, oncology and so on. The MCDB is distributed on http://www.combio-lezhang.online/MCDB/index_html/ .

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Section: Discussionmentioning

confidence: 99%

MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

Zhang

Guo

Xiao

et al. 2021

Acta Pharmaceutica Sinica B

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“…Furthermore, the cosine similarity function is being employed to compute the pairwise distance between composition vectors of the sequences. The distance matrix acquired by this process can be used to construct a phylogenetic tree using algorithms like UPGMA and neighbour joining [15].…”

Section: Literature Surveymentioning

confidence: 99%

Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes

et al. 2021

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Virus attacks have had devastating effects on mankind. The prominent viruses such as Ebola virus ( 2012), SARS-CoV or Severe acute respiratory syndrome, Middle East respiratory syndrome-related coronavirus called as the MERS (EMC/2012), Spanish flu (H1N1 virus-1918) and the most recent COVID-19(SARS-CoV-2) are the ones that have created a difficult situation for the survival of the human race. Currently, throughout the world, a global pandemic situation has put economy, livelihood and human existence in a very pathetic situation. Most of the above-mentioned viruses exhibit some similar characteristics and genetic pattern. Analysing such characteristics and genetic pattern can help the researchers to get a deeper insight into the viruses and helps in finding appropriate medicine or cure. To address these issues, this paper proposes an experimental analysis of the above-mentioned viruses data using correlation methods. The virus data considered for the experimental analysis include the distribution of various amino acids, protein sequences, 3D modelling of viruses, pairwise alignment of proteins that comprise the DNA genome of the viruses. Furthermore, this comparative analysis can be used by the researchers and organizations like WHO(World Health Organization), computational biologists, genetic engineers to frame a layout for studying the DNA sequence distribution, percentage of GC (guanine-cytosine) protein which determines the heat stability of viruses. We have used the Biopython to illustrate the gene study of prominent viruses and have derived results and insights in the form of 3D modelling. The experimental results are more promising with an accuracy rate of 96% in overall virus relationship calculation.

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“…The established approach for streamline analysis is well recognized. First, bacterial genomic DNA is extracted and subjected to sequencing according to the guidelines of the commercial platform; then, the obtained results are analyzed using bioinformatics software ( Zhang et al, 2019a ; Zhang et al, 2019d ; Lei et al, 2020 ; Wu et al, 2020 ; You et al, 2020 ; Zhang et al, 2021a ; Gao et al, 2021 ; Lei Zhang et al, 2021 ) or suitable platform databases ( Xiao et al, 2020 ; Zhang et al, 2021b ; Xiao et al, 2021 ) for the assembly and annotation of genomes, determination of SNPs, identification of antimicrobial resistance genes ( Lv et al, 2021 ) and virulence-associated factors, building phylogenetic trees, etc. ( Seemann, 2014 ; Johansen et al, 2018 ; Yasuhiro et al, 2018 ; Ledwaba et al, 2021 ; Liu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection

Xiao

Zhu

et al. 2022

Front. Genet.

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Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions.Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.

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2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV

Cited by 22 publications

References 48 publications

MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes

An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection

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