[Methods and applications for microbiome data analysis].
Clicks: 431
ID: 52528
2019
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Star Article
77.9
/100
431 views
345 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
Development of high-throughput sequencing stimulates a series of microbiome technologies, such as amplicon sequencing, metagenome, metatranscriptome, which have rapidly promoted microbiome research. Microbiome data analysis involves a lot of basic knowledge, software and databases, and it is difficult for peers to learn and select proper methods. This review systematically outlines the basic ideas of microbiome data analysis and the basic knowledge required to conduct analysis. In addition, it summarizes the advantages and disadvantages of commonly used software and databases used in the comparison, visualization, network, evolution, machine learning and association analysis. This review aims to provide a convenient and flexible guide for selecting analytical tools and suitable databases for mining the biological significance of microbiome data.Reference Key |
liu2019methodsyi
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
---|---|
Authors | Liu, Yongxin;Qin, Yuan;Guo, Xiao-Xuan;Bai, Yang; |
Journal | Yi chuan = Hereditas |
Year | 2019 |
DOI | 10.16288/j.yczz.19-222 |
URL | |
Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.