Here is a brief overview of the study:
• 16s, V4, Primers: 515F/806R
• 80 biological samples (lizard faeces, cloacal swabs) + 22 negative controls (6 blank swabs, 11 extraction blanks, 5 PCR blanks)
• Quantification was performed after DNA extraction, PCR, cleanup, and pooling.
• PCR product was pooled at equal nanomolar concentrations
• Sequencing: Illumina MiSeq 150 x 2
• Data generated by sequencing company with BCL2FASTQ2 conversion software.
• Data was received as demultiplexed fastq files in pairs (read1 and read2) with adapters already trimmed.
• Imported data to qiime2 artifact
• Denoising, reads joined, & ASV table constructed with DADA2
• Various metrics showed not only no difference between treatments, but no difference between the negative controls and the biological samples.
• I filtered the features occurring in the controls from the biological samples, this however resulted in feature frequency dropping by 90% and the remaining features occurring in only a few samples each.
• I repeated this and only filtering out the PCR Blank features, but the results were similar.
rep-seqs.qzv (750.9 KB) rep-seqs-features-allControlsFiltered.qzv (599.8 KB) rep-seqs-features-PCRBlanksFiltered.qzv (715.4 KB) table.qzv (591.1 KB) table-features-allControlsFiltered.qzv (571.6 KB) table-features-PCRBlanksFiltered.qzv (622.5 KB)
I can’t work out why, but it seems like my samples are predominantly comprised of contamination (despite quantification during the labwork indicating this shouldn’t be the case).
• Is there something obvious I’m missing or doing wrong during the bioinformatics that could cause this issue. Any ideas?
• Do the filtered feature tables have any useful data? Or are they just sequencing artifacts?