2023
DOI: 10.1093/procel/pwad024
|View full text |Cite
|
Sign up to set email alerts
|

The best practice for microbiome analysis using R

Abstract: With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a p… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
26
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
9

Relationship

2
7

Authors

Journals

citations
Cited by 48 publications
(26 citation statements)
references
References 64 publications
0
26
0
Order By: Relevance
“…Sequenced reads underwent analysis utilizing R (v4.3.1) and DADA2 (v1.26.0) ( Callahan et al, 2016 ; Wen et al, 2023 ). Initial preprocessing involved the removal of 20 base pairs from both the beginning and the end of each read to eliminate low-quality regions flanking the reads.…”
Section: Methodsmentioning
confidence: 99%
“…Sequenced reads underwent analysis utilizing R (v4.3.1) and DADA2 (v1.26.0) ( Callahan et al, 2016 ; Wen et al, 2023 ). Initial preprocessing involved the removal of 20 base pairs from both the beginning and the end of each read to eliminate low-quality regions flanking the reads.…”
Section: Methodsmentioning
confidence: 99%
“…Unless otherwise specified, statistical analysis and data visualization were performed in R software. The bioinformatics analysis followed the methods of previous research with slight modification (Wen et al., 2023 ), and the main steps are listed below. Prior to alpha‐diversity analyses, we used the R phyloseq (v1.44.0) package to control potential sequencing depth influences by subsampling all samples to 17,098 sequences (McMurdie & Holmes, 2013 ).…”
Section: Methodsmentioning
confidence: 99%
“…In this study, the methods described in references [ 17 , 18 , 19 , 20 , 21 ] were employed for the processing and analysis of sequencing data. This involved merging pair-end reads, trimming primers and barcodes, clustering OTUs (Operational Taxonomic Units), de-replication sequences, reducing abundance noise, removing chimeras, and generating the feature table.…”
Section: Methodsmentioning
confidence: 99%