Found contaminated negative controls - am I denoising correctly with DADA2?

[Nicholas_Bokulich]

Hi @jnie93,
Welcome to the community

Let's start with this question, and some explanation:

@jnie93

• Am I using the right parameters for denoising?

Yes, these look fine to me.

@jnie93

From my research online, I took this to mean that our amplicon is 507 bp (534-507) long.

That would be without primers included (as far as I know) — looks like you are using EMP format, so that should be correct, but just a caveat to keep in mind.

@jnie93

However, when I looked at our interactive quality plot (demux.qzv), our median quality score dropped below 20 at positions 263 for forward and 202 for the reverse, respectively
• 263+202-507 = -38, which means we get no overlap

Yeah, those data look very noisy, but I think what you did is fine:

@jnie93

• To get some overlap, I lowered the median quality score cutoff to the point where we could get some overlap and ended up running the following command:

Doing so means that many more noisy reads are included, but dada2 is filtering these out. This is why you are seeing very high proportions (~75-90% of reads) being dropped at the filtering step (see the stats summary). However, you have very high sequence coverage for most samples, so not a problem. You are getting very good merging rates and usable sequence counts in the output.

@jnie93

1. Do I have contamination in my negative controls?

Yes, I am afraid so, but do not despair:

@jnie93

Someone in my lab told me that we should have sequence counts of 0 for the negative controls after denoising, otherwise, this means we have contamination.

That's in an ideal world... but there are other reasons for reads appearing in the negative controls (e.g., index hopping, cross-talk). However, given the number of reads in the negative controls I would bet that this is mostly due to cross-contamination.

Cross-contamination is a common issue... but how to fix this is an open area of research. This discussion may be helpful for you, and give you some ideas for strategies to address this issue in your data, rather than tossing out your data and starting again. It takes a very small amount of cross-contamination to mar a negative control... levels far below what it would take to impact the composition of a real sample (unless if it is a low-biomass sample). There are some tools out there for addressing contamination, but no easy solutions. Discuss with your PI... I think that tossing out your data would be rash (unless if you are sequencing low-biomass samples), but with reads counts on par with your real samples you should look very carefully to see what contaminants are present, whether they are cross-contaminants or reagent contaminants, and what next steps you want to take. I don't have easy suggestions, but will say that you are definitely not alone in this problem, and (unless you have low-biomass samples!) you should not just toss the data.

If you find other strategies for contaminant removal, please feel free to add to that discussion!

Good luck!