Plug-in error

[Mariem]

Hi,

I’ve been trying to run the following command, but a plugin error message keeps popping up and I’m not sure what it means as it doesn’t seem to be a memory-related error:

 

I’ve tried changing the –p-n-jobs 4 to –p-n-jobs 3, –p-n-jobs 2, –p-n-jobs 1, but am also wondering if the error has to do with the version of QIIME I’m running?

Any help is appreciated.

Mariem

[Andre Comeau]

Hmm...the blank screen doesn't help us very much. Are you able to see what is inside/post to email the contents of that log file it generated? You should be able to type:

less ​/tmp/qiime2-q2cli-err-jhalgy9x.log

...to get the contents displayed on the screen. The most common problem by far with this step is running out of memory - is this a very large dataset you are running (ie: has a very large number of total ASVs you are attempting to classify)?

	ANDRÉ M. COMEAU, PhD Manager • Integrated Microbiome Resource (IMR) T: 902.494.2684 | E: andre....@dal.ca	​Address for deliveries: Dept. of Pharmacology Tupper Med. Bldg., room 5D Dalhousie University 5850 College St. Halifax NS B3H 4R2
​	Research Associate (Lab Manager) Morgan Langille Lab • Dept. of Pharmacology ResearchGate Profile • GoogleScholar Publications
"Without fantasy, there is no science. Without fact, there is no art." - Nabokov "The good thing about science is that it's true whether or not you believe in it." - Neil deGrasse Tyson

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From: microbio...@googlegroups.com <microbio...@googlegroups.com> on behalf of Mariem <mjwo...@gmail.com>
Sent: Wednesday, March 16, 2022 2:19 PM
To: Microbiome Helper <microbio...@googlegroups.com>
Subject: [microbiome-helper] Plug-in error

 

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[mjwo...@gmail.com]

Ah ok, it does look like it's a memory error!

I'm not too sure what counts as a large dataset (though I think it did take a few days for the DADA2 command to finish running) , but according to my filtered feature table, I have 823 features with a total frequency of 768,536.

[Andre Comeau]

Well, normally I would say 823 features (=ASVs) would not be a huge amount of sequences to have to classify, but if DADA2 took a couple of days just for your 800k sequences, then maybe it is a problem. How much RAM do you have on that machine and have you allocated as much as you can to the image in VBox?

A test you could do would be to try with just 1/4 of your samples and see how many ASV it says you have and, if much less, try the classification on that smaller bunch to see if you can complete (there are also commands to be able to subsample your ASVs to a smaller number directly just for testing instead of having to rerun the 1/4 of samples all the way through)...then you'd at least know it is memory for sure and around how many you could handle on that machine. Best case is to borrow/use a machine with more RAM just for that step - once through it, you can transfer the files back to your machine and continue the rest.

	ANDRÉ M. COMEAU, PhD Manager • Integrated Microbiome Resource (IMR) T: 902.494.2684 | E: andre....@dal.ca	​Address for deliveries: Dept. of Pharmacology Tupper Med. Bldg., room 5D Dalhousie University 5850 College St. Halifax NS B3H 4R2
​	Research Associate (Lab Manager) Morgan Langille Lab • Dept. of Pharmacology ResearchGate Profile • GoogleScholar Publications
"Without fantasy, there is no science. Without fact, there is no art." - Nabokov "The good thing about science is that it's true whether or not you believe in it." - Neil deGrasse Tyson

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From: microbio...@googlegroups.com <microbio...@googlegroups.com> on behalf of mjwo...@gmail.com <mjwo...@gmail.com>
Sent: Friday, March 18, 2022 4:04 PM
To: Microbiome Helper <microbio...@googlegroups.com>
Subject: Re: [microbiome-helper] Plug-in error

 

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[mjwo...@gmail.com]

The computer I'm using has 16 GB RAM, 6 cores and I allocated 4096 MB, 4 cores to the image in Virtual Box. I tried allocating 8 and 12 GB and got a different error of [Errno 12] cannot allocate memory. I tried allocating as much as I could at 14,499 MB but received a critical error message from VirtualBox and the whole thing had to power off.

So I'll go ahead and try re-running 1/4 of the samples and report back. Thank you for all this advice so far!