Property-Dependent Analysis of Aligned Proteins from Two Or More Populations

Steinar Thorvaldsen, E. Ytterstad, T. Flå
{"title":"Property-Dependent Analysis of Aligned Proteins from Two Or More Populations","authors":"Steinar Thorvaldsen, E. Ytterstad, T. Flå","doi":"10.1142/9781860947292_0020","DOIUrl":null,"url":null,"abstract":"Multiple sequence alignments can provide information for comparative analyses of proteins and protein populations. We present some statistical trend-tests that can be used when an aligned data set can be divided into two or more populations based on phenotypic traits such as preference of temperature, pH, salt concentration or pressure. The approach is based on estimation and analysis of the variation between the values of physicochemical parameters at positions of the sequence alignment. Monotonic trends are detected by applying a cumulative Mann-Kendall test. The method is found to be useful to identify significant physicochemical mechanisms behind adaptation to extreme environments and uncover molecular differences between mesophile and extremophile organisms. A filtering technique is also presented to visualize the underlying structure in the data. All the comparative statistical methods are available in the toolbox DeltaProt.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... Asia-Pacific bioinformatics conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/9781860947292_0020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

Multiple sequence alignments can provide information for comparative analyses of proteins and protein populations. We present some statistical trend-tests that can be used when an aligned data set can be divided into two or more populations based on phenotypic traits such as preference of temperature, pH, salt concentration or pressure. The approach is based on estimation and analysis of the variation between the values of physicochemical parameters at positions of the sequence alignment. Monotonic trends are detected by applying a cumulative Mann-Kendall test. The method is found to be useful to identify significant physicochemical mechanisms behind adaptation to extreme environments and uncover molecular differences between mesophile and extremophile organisms. A filtering technique is also presented to visualize the underlying structure in the data. All the comparative statistical methods are available in the toolbox DeltaProt.
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
来自两个或多个群体的对齐蛋白的特性依赖分析
多序列比对可以为蛋白质和蛋白质群体的比较分析提供信息。我们提出了一些统计趋势测试,当一个排列的数据集可以根据表型特征(如温度、pH值、盐浓度或压力的偏好)划分为两个或多个种群时,可以使用这些趋势测试。该方法基于对序列比对位置上理化参数值变化的估计和分析。单调趋势是通过应用累积曼-肯德尔检验来检测的。该方法有助于识别极端环境适应背后的重要物理化学机制,揭示中温生物和极端生物之间的分子差异。提出了一种过滤技术,使数据的底层结构可视化。所有的比较统计方法都可以在工具箱DeltaProt中找到。
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
自引率
0.00%
发文量
0
期刊最新文献
Tuning Privacy-Utility Tradeoff in Genomic Studies Using Selective SNP Hiding. The Future of Bioinformatics CHEMICAL COMPOUND CLASSIFICATION WITH AUTOMATICALLY MINED STRUCTURE PATTERNS. Predicting Nucleolar Proteins Using Support-Vector Machines Proceedings of the 6th Asia-Pacific Bioinformatics Conference, APBC 2008, 14-17 January 2008, Kyoto, Japan
×
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