understanding the trade-off between dada2 single vs paired ends

[Nicholas_Bokulich]

Hi @e_flat_minor ,

Sounds like an all-too-familiar problem

@e_flat_minor

1.) What kind/ amount of information am I losing if I use denoise-single instead of paired?

You will lose some amount of taxonomic resolution and ASV resolution (impacting diversity, etc), but probably not an enormous amount.

If you want to run a test to see what kind of information you lose, you could follow this tutorial but trimming with two different primer sets (V3 only vs. V3-V4).

Processing, filtering, and evaluating the SILVA database (and other reference sequence data) with RESCRIPt

Please consider this tutorial a living document, which may change based upon community feedback and ongoing plugin development. RESCRIPt RESCRIPt (REference Sequence annotation and CuRatIon Pipeline) is a python package and QIIME 2 plugin for formatting, managing, and manipulating sequence reference databases. This package was designed for compiling, manipulating, and evaluating sequence reference databases from SILVA, NCBI, Greengenes, GTDB, and other sources, and for construc…

@e_flat_minor

2.) Can I use denoise-single on the first run and denoise-paired on the later runs (where reverse reads look better) and later merge the table? Or will this result in a bias in downstream analyses?

This will certainly lead to biases, as the paired-end reads will be longer and somewhat taxonomically different from the single-end reads. If using ASV (denoising) methods, your ASVs will all have different IDs and so no ASVs will be shared between the single and paired-end runs!

On the other hand, if you do want to follow an approach like this you could use closed-reference OTU clustering to map all of the single- and paired-end reads against full-length reference sequences, then it will be possible to compare across runs. But then you lose other information (e.g., ASV-level variation, reads that do not map to a reference) so it is still a trade-off.

All in all, I'd suggest seeing what the single-end reads get you in terms of diversity and taxonomy results, then circle back if you think that you are losing too much.

Good luck!