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 Bioprojects on NCBI SRA, our dataset contains samples with varying target regions: some are V3-V4, and others are V4 only. Additionally, sequence lengths vary across samples.
Current Workflow:
- Data Import: Raw reads were imported as FASTQ files into QIIME 2 as artifacts. They are a mix of paired and single end reads.
- Preprocessing:
- Following recommendations from this forum, we decided to use only the forward reads.
- Primer and adapter sequences were trimmed using [Specify the tool/plugin used for trimming, e.g., cutadapt].
- Denoising was performed using DADA2, resulting in a feature table and representative sequences.
- All representative sequences were trimmed to a uniform length of 231 base pairs.
- We have information on the primers used for the bioprojects from their published articles and they used different primers.
Specific Questions and Concerns:
- Validity of Using Mixed V3-V4 and V4 Data:
- Given the inherent differences in the amplified regions, is it statistically sound to combine V3-V4 and V4 datasets for alpha and beta diversity analyses?
- Are there specific considerations or potential biases we should be aware of?
- We have one bioproject that only has the V4 region so we would lose these samples if we only analyze the bioprojects with V3-V4. Is it possible for us to only analyze the V4 region and trim the other samples as well?
- Uniform Sequence Length Trimming:
- Trimming all sequences to 231 bp was done to ensure consistency. Is this the most appropriate approach, or are there alternative methods (e.g., using a minimum overlap during merging in DADA2) that might preserve more information?
- What are the potential drawbacks of aggressively trimming to a short uniform length?
- Downstream Analysis Suitability:
- We plan to perform the following downstream analyses:
- Taxonomic classification with Greengenes2.
- Possibly fragment-insertion as well, though we are still not sure how to incorporate this into the workflow, so any advice would help.
- Alpha and beta diversity analyses to assess richness and abundance.
- Differential abundance analysis for biomarker identification.
- Co-occurrence network analysis.
- Functional prediction (e.g., using PICRUSt2).
- Given the potential biases from mixed target regions and uniform trimming, are these downstream analyses still reliable?
- Are there specific plugins or parameters in QIIME 2 that we should pay close attention to for these analyses, especially given the mentioned data variations?
- Alternative Strategies:
- Are there alternative preprocessing or analysis strategies you would recommend to mitigate the challenges posed by our dataset?
- Is there a way to computationally correct for the difference in the amplified regions?
- Metadata Consideration:
- Should the hypervariable target region be included as metadata to account for the variation in the statistical analysis?
We appreciate any insights, criticisms, and recommendations you can provide. We are eager to learn and ensure the robustness of our analysis.
Thank you for your time and expertise.
Sincerely,
John_Kim