Hello @Mustafa_Talib ,
Hello,
I am conducting a meta-analysis of multiple 16S metabarcoding studies. These studies cover different regions (V3–V4 and V4 only) and use different primers.
I retrieved the raw sequencing data from all of these studies and conducted the QIIME pipeline separately until merging the tables and sequences after DADA2 using the qiime feature-table merge and qiime feature-table summarize commands. I have several questions about what I have done and the next steps:
Is it OK to merge datasets …
Dear QIIME 2 Community,
I hope this message finds you well. We are currently doing a meta-analysis of rhizosphere microbiome data from banana plants using QIIME 2 and are seeking guidance on our current workflow and best practices for downstream analyses.
Project Overview:
Data Source: Raw reads were obtained from the NCBI SRA database.
Target Region: Our primary target region is the V3-V4 hypervariable region of the bacterial 16S rRNA gene.
Challenge: Due to the limited availability of Biop…
Hi QIime team,
I am running a large meta-analysis of microbiome data with datasets that include the 341F primers and then the EMP 515F primers and I would like to cut at 515F to "standardize" them. I have seen a couple posts (Q2-cutadapt of primer sequences flag verification? & Reads processing with different primers ) discussing the pros and cons of either fragment insertion or trimming prior to denoising.
Personally, I would love to trim my dataset in one go on one large file. There are many…
Context & Motivation
I am currently working on a large-scale meta-analysis involving hundreds of projects and over 10,000 samples. My dataset is a mixture of:
V3-V4 region (e.g., amplicons from 341F/805R)
V4 region (e.g., amplicons from 515F/806R)
The primary goal is to perform a unified analysis across all studies, including alpha/beta diversity and taxonomic comparisons.
The Challenge
As these datasets target different (though overlapping) hypervariable regions, simply merging the fea…
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