- 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.
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The Challenge
As these datasets target different (though overlapping) hypervariable regions, simply merging the feature tables leads to massive batch effects. Previously, the "standard" approach was to use qiime feature-classifier extract-reads to trim V3-V4 sequences down to the V4 region. However, I am concerned about the potential loss of taxonomic resolution and the strictness of primer-based trimming for such a diverse dataset. -
Proposed Strategy: Greengenes2
I am planning to use the Greengenes2 (GG2) framework (via q2-greengenes2). My understanding is that the non-v4-v4-asvs pipeline can perform phylogenetic placement of these disparate fragments into a single unified backbone tree.
My logic is: By anchoring both V3-V4 and V4 ASVs to the same reference phylogeny, they should become comparable in a shared coordinate system without manual trimming.
- Specific Questions
Is Greengenes2 currently considered the "Gold Standard" for handling mixed-region 16S meta-analyses?
Taxonomic Consistency: In your experience, how well do V3-V4 and V4 reads from the same biological source cluster together once placed on the GG2 tree?
Computational Performance: Given the scale of ~10,000 samples and potentially hundreds of thousands of ASVs, are there specific memory or threading considerations for the non-v4-v4-asvs or fragment-insertion steps?
Thank you for your time and for developing these incredible tools!