chem16S: community-level chemical metrics for exploring genomic adaptation to environments.

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad564
Jeffrey M Dick, Xun Kang
{"title":"chem16S: community-level chemical metrics for exploring genomic adaptation to environments.","authors":"Jeffrey M Dick,&nbsp;Xun Kang","doi":"10.1093/bioinformatics/btad564","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>The chem16S package combines taxonomic classifications of 16S rRNA gene sequences with amino acid compositions of prokaryotic reference proteomes to generate community reference proteomes. Taxonomic classifications from the RDP Classifier or data objects created by the phyloseq R package are supported. Users can calculate and visualize a variety of chemical metrics in order to explore the effects of redox, salinity, and other physicochemical variables on the genomic adaptation of protein sequences at the community level.</p><p><strong>Availability and implementation: </strong>Development of chem16S is hosted at https://github.com/jedick/chem16S. Version 1.0.0 is freely available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/package=chem16S.</p>","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505500/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btad564","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

Summary: The chem16S package combines taxonomic classifications of 16S rRNA gene sequences with amino acid compositions of prokaryotic reference proteomes to generate community reference proteomes. Taxonomic classifications from the RDP Classifier or data objects created by the phyloseq R package are supported. Users can calculate and visualize a variety of chemical metrics in order to explore the effects of redox, salinity, and other physicochemical variables on the genomic adaptation of protein sequences at the community level.

Availability and implementation: Development of chem16S is hosted at https://github.com/jedick/chem16S. Version 1.0.0 is freely available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/package=chem16S.

Abstract Image

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
chem16S:用于探索基因组对环境适应的社区级化学指标。
摘要:chem16S包将16S rRNA基因序列的分类分类与原核参考蛋白质组的氨基酸组成相结合,生成群落参考蛋白质组。支持RDP分类器中的分类或phyloseq R包创建的数据对象。用户可以计算和可视化各种化学指标,以便在群落水平上探索氧化还原、盐度和其他物理化学变量对蛋白质序列基因组适应的影响。可用性和实施:chem16S的开发位于https://github.com/jedick/chem16S.1.0.0版本可从综合R档案网络(CRAN)免费获得,网址为https://cran.r-project.org/package=chem16S.
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
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
期刊最新文献
MEHunter: Transformer-based mobile element variant detection from long reads Metabolic syndrome may be more frequent in treatment-naive sarcoidosis patients. Coracle—A Machine Learning Framework to Identify Bacteria Associated with Continuous Variables CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism
×
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