Ion Torrent data denoise with dada2

Hi @Marek_Koutny!

We can’t offer any hypotheses about any differences you might be experiencing or observing unless you provide us with more details about what you did previously and what you are doing now.

This error is because you do not have enough RAM (memory) available in your computing environment. Can you provide some details about your computing environment? Thanks.

Hello. I understand that you cannot know what is happening. We analysed the same DNA samples in DGGE and get a similar basic pattern of bands in every sample. DGGE should overrate most abundant sequences. I expected the same with NGS. This is my first experience with NGS.

For the error described, I run qiime2 in Virtual box. The error occurred with your suggested setting (2GB RAM). After your hint that low RAM could be the problem I increased RAM to 5GB (the maximum I can). The error occurred again with both SILVA classifiers provided.

If you need any further data to see the solution I will provide them immediately.


Hi @Marek_Koutny,
Are you using the same PCR primers for sequencing as you used for DGGE? I agree with you that the general patterns should be consistent between the two approaches, but one reason for a difference could be differences in the PCR primers. Each pair of primers will have biases for different microbial taxa, so you’ll get a different view of the community depending on which primers you’re using. Also, at what taxonomic level are you seeing a lot of differences? At the species level, a lot of differences between the approaches isn’t very surprising, as sequencing 16S doesn’t give very accurate species-level assignments (I think the same is true of DGGE, but I know less about DGGE) - this pre-print has some information on this. At the family level, for example, I’d expect to see a lot more similarity in the profiles derived from DGGE and 16S sequencing.

Regarding your memory error, unfortunately the only way around this will be to run the analysis on a system with more memory. I would recommend trying for 16GB (some discussion of this occurred in this topic).

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