从蛋白质家族水平探索微生物功能生物多样性--从元基因组序列读数到注释蛋白质群。

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in bioinformatics Pub Date : 2023-03-03 eCollection Date: 2023-01-01 DOI:10.3389/fbinf.2023.1157956
Fotis A Baltoumas, Evangelos Karatzas, David Paez-Espino, Nefeli K Venetsianou, Eleni Aplakidou, Anastasis Oulas, Robert D Finn, Sergey Ovchinnikov, Evangelos Pafilis, Nikos C Kyrpides, Georgios A Pavlopoulos
{"title":"从蛋白质家族水平探索微生物功能生物多样性--从元基因组序列读数到注释蛋白质群。","authors":"Fotis A Baltoumas, Evangelos Karatzas, David Paez-Espino, Nefeli K Venetsianou, Eleni Aplakidou, Anastasis Oulas, Robert D Finn, Sergey Ovchinnikov, Evangelos Pafilis, Nikos C Kyrpides, Georgios A Pavlopoulos","doi":"10.3389/fbinf.2023.1157956","DOIUrl":null,"url":null,"abstract":"<p><p>Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10029925/pdf/","citationCount":"0","resultStr":"{\"title\":\"Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters.\",\"authors\":\"Fotis A Baltoumas, Evangelos Karatzas, David Paez-Espino, Nefeli K Venetsianou, Eleni Aplakidou, Anastasis Oulas, Robert D Finn, Sergey Ovchinnikov, Evangelos Pafilis, Nikos C Kyrpides, Georgios A Pavlopoulos\",\"doi\":\"10.3389/fbinf.2023.1157956\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.</p>\",\"PeriodicalId\":73066,\"journal\":{\"name\":\"Frontiers in bioinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2023-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10029925/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fbinf.2023.1157956\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fbinf.2023.1157956","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

元基因组学使人们能够获得自然微生物群落的基因库。元基因组枪式测序已成为研究和分类各种环境中微生物的首选方法。为此,人们开发了多种方法来处理和分析从原始读数到最终产品(如预测的蛋白质序列或家族)的序列数据。在本文中,我们将对这些方法进行全面回顾,以简化处理过程,并讨论可供选择的方法,以便在蛋白质家族水平上探索生物多样性。我们提供了分析工具的详细信息,并对其可扩展性及其优缺点进行了评论。最后,我们报告了可用的数据存储库,并推荐了与系统发育分布、结构预测和元数据富集有关的蛋白质家族注释的各种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

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
Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters.

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.60
自引率
0.00%
发文量
0
期刊最新文献
The quantum hypercube as a k-mer graph. A review of model evaluation metrics for machine learning in genetics and genomics. Visual analysis of multi-omics data. Molecular docking and molecular dynamic simulation studies to identify potential terpenes against Internalin A protein of Listeria monocytogenes. PhIP-Seq: methods, applications and challenges.
×
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