A Simple Genetic Algorithm for Optimizing Multiple Sequence Alignment on the Spread of the SARS Epidemic

Q3 Computer Science Open Bioinformatics Journal Pub Date : 2019-02-28 DOI:10.2174/1875036201912010030
S. Amiroch, M. S. Pradana, M. I. Irawan, I. Mukhlash
{"title":"A Simple Genetic Algorithm for Optimizing Multiple Sequence Alignment on the Spread of the SARS Epidemic","authors":"S. Amiroch, M. S. Pradana, M. I. Irawan, I. Mukhlash","doi":"10.2174/1875036201912010030","DOIUrl":null,"url":null,"abstract":"Multiple sequence alignment is a method of getting genomic relationships between 3 sequences or more. In multiple alignments, there are 3 mutation network analyses, namely topological network system, mutation region network and network system of mutation mode. In general, the three analyses show stable and unstable regions that map mutation regions. This area of ​​mutation is described further in a phylogenetic tree which simultaneously illustrates the path of the spread of an epidemic, the Severe Acute Respiratory Syndrome (SARS) epidemic. The process of spreading the SARS viruses, in this case, is described as the process of phylogenetic tree formation, and as a novelty of this research, multiple alignments in the process are analyzed in detail and then optimized with genetic algorithms.The data used to form the phylogenetic tree for the spread of the SARS epidemic are 14 DNA sequences which are then optimized by using genetic algorithms. The phylogenetic tree is constructed by using the neighbor-joining algorithm with a distance matrix that the intended distance is the genetic distance obtained from sequence alignment by using the Needleman Wunsch Algorithm.The results of the analysis obtained 3649 stable areas and 19 unstable areas. The results of phylogenetic tree from the network system analysis indicated that the spread of the SARS epidemic extended from Guangzhou 16/12/02 to Zhongshan 27/12/02, then spread simultaneously to Guangzhou 18/02/03 and Guangzhou hospital. After that, the virus reached Metropole, Zhongshan, Hongkong, Singapore, Taiwan, Hong kong, and Hanoi which then continued to Guangzhou 01/01/03 and Toronto at once. The results of the mutation region network system demonstrate decomposition of orthogonal mutations in the 1st order arc.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open Bioinformatics Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1875036201912010030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 2

Abstract

Multiple sequence alignment is a method of getting genomic relationships between 3 sequences or more. In multiple alignments, there are 3 mutation network analyses, namely topological network system, mutation region network and network system of mutation mode. In general, the three analyses show stable and unstable regions that map mutation regions. This area of ​​mutation is described further in a phylogenetic tree which simultaneously illustrates the path of the spread of an epidemic, the Severe Acute Respiratory Syndrome (SARS) epidemic. The process of spreading the SARS viruses, in this case, is described as the process of phylogenetic tree formation, and as a novelty of this research, multiple alignments in the process are analyzed in detail and then optimized with genetic algorithms.The data used to form the phylogenetic tree for the spread of the SARS epidemic are 14 DNA sequences which are then optimized by using genetic algorithms. The phylogenetic tree is constructed by using the neighbor-joining algorithm with a distance matrix that the intended distance is the genetic distance obtained from sequence alignment by using the Needleman Wunsch Algorithm.The results of the analysis obtained 3649 stable areas and 19 unstable areas. The results of phylogenetic tree from the network system analysis indicated that the spread of the SARS epidemic extended from Guangzhou 16/12/02 to Zhongshan 27/12/02, then spread simultaneously to Guangzhou 18/02/03 and Guangzhou hospital. After that, the virus reached Metropole, Zhongshan, Hongkong, Singapore, Taiwan, Hong kong, and Hanoi which then continued to Guangzhou 01/01/03 and Toronto at once. The results of the mutation region network system demonstrate decomposition of orthogonal mutations in the 1st order arc.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
非典型肺炎传播过程多序列比对优化的简单遗传算法
多序列比对是一种获得3个或更多序列之间基因组关系的方法。在多重比对中,有3种变异网络分析,即拓扑网络系统、变异区域网络和变异模式网络系统。一般来说,这三个分析显示了映射突变区域的稳定和不稳定区域。该区域​​在系统发育树中进一步描述了突变,该树同时说明了一种流行病——严重急性呼吸系统综合征(SARS)流行病的传播路径。在这种情况下,SARS病毒的传播过程被描述为系统发育树的形成过程,作为本研究的一个新颖之处,对该过程中的多重比对进行了详细分析,然后用遗传算法进行了优化。用于形成SARS疫情传播的系统发育树的数据是14个DNA序列,然后使用遗传算法对其进行优化。系统发育树是通过使用具有距离矩阵的邻居连接算法构建的,该距离矩阵的预期距离是通过使用Needleman-Wunsch算法从序列比对中获得的遗传距离。分析结果得到3649个稳定区和19个不稳定区。网络系统分析的系统发育树结果表明,SARS疫情的传播从2002年12月16日的广州蔓延到2002年12日的中山,然后同时蔓延到2003年2月18日的广州和广州医院。此后,病毒抵达大都会、中山、香港、新加坡、台湾、香港和河内,然后于2003年1月1日继续传播到广州和多伦多。突变区域网络系统的结果证明了正交突变在一阶弧中的分解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Open Bioinformatics Journal
Open Bioinformatics Journal Computer Science-Computer Science (miscellaneous)
CiteScore
2.40
自引率
0.00%
发文量
4
期刊介绍: The Open Bioinformatics Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters, clinical trial studies and guest edited single topic issues in all areas of bioinformatics and computational biology. The coverage includes biomedicine, focusing on large data acquisition, analysis and curation, computational and statistical methods for the modeling and analysis of biological data, and descriptions of new algorithms and databases. The Open Bioinformatics Journal, a peer reviewed journal, is an important and reliable source of current information on the developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.
期刊最新文献
Decision-making Support System for Predicting and Eliminating Malnutrition and Anemia Immunoinformatics Approach for the Design of Chimeric Vaccine Against Whitmore Disease A New Deep Learning Model based on Neuroimaging for Predicting Alzheimer's Disease Early Prediction of Covid-19 Samples from Chest X-ray Images using Deep Learning Approach Electronic Health Record (EHR) System Development for Study on EHR Data-based Early Prediction of Diabetes Using Machine Learning Algorithms
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1