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Old 01-15-2015, 03:40 AM   #1
nouse
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Location: Germany

Join Date: Sep 2013
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Default Number of clusters are way too high in my QIIME analysis

Hi there,

I am using QIIME to process my dataset of 2 million joined MiSeq PE-reads (~500 bp) from a 16S rDNA community survey.

I do have the output of the proprietory pipeline of the sequencing vendor, but my PI asked me to simulate every step, just to know whats going on.
I was able to mimick the joining and the demultiplexing with QIIME, as well with some quality checks.
My last step was a standalone chimera check with uchime.

Now, on for the OTU picking.
My input file is about 2 million reads in 38 samples, and the proprietory pipeline of the sequencing vendor yielded about 14.000 OTUs (biologically, this makes sense, as the 38 samples are coming from different sites and different treatments).

However, when i was using QIIME with de_novo_clustering at 97% similarity and methods...

....uclust with the default command

i got 320.000 clusters!

...usearch with an additional chimera slaying step and the removal of single- and doubletons:

pick_otus.py -i in.fasta -m usearch -o out -s 0.97 --db_filepath path_to_reference_chimera_db --minsize 3

i received 800.000 clusters!

With cd-hit i failed, because my fasta input doesnt contain the amplification primers anymore (see my other thread).

The files the sequencing vendor provided me made me believe that usearch was indeed used (there is a sortlen in the output, and a *.uc file as well).



Questions:
Where did i fail?
How can i be less stringent?
How do you cluster your 16S illumina data?
Is there a denoising step in the QIIME for illumina, similar to the 454 denoising?

Thank you very much!

I am stuck, and its kinda urgent.


PS: I cannot use the qiime tutorial for illumina because i cannot run iPython on my machine.
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Old 01-16-2015, 11:45 AM   #2
gblanchard4
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Location: New Orleans

Join Date: May 2013
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You may get better help in the Qiime Forums

The sequencing vendor may have used UPARSE, built into USEARCH v7, before jumping into Qiime. It generally gives lower OTU counts.

You don't need ipython to run the tutorial. Any command they pass with the exclamation mark is just a regular qiime command, you should just need to pass the output folder.

You do not need to denoise Illumina data.
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Old 01-21-2015, 11:29 AM   #3
cliffbeall
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Location: Ohio

Join Date: Jan 2010
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We found with similar data (not using QIIME) that it helped to exclude reads where the mean quality is below a threshold - we used Phred 28.

Also it is common to exclude OTUs that have less than a threshold number of members.
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