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q2-dbotu

QIIME 2 plugin for distribution-based clustering.

To learn more about distribution-based clustering, check out the original publication or the python implementation, dbOTU3 (and its associated publication). This plugin is essentially a QIIME 2 wrapper around this new implementation.

Installation

Distribution-based clustering is available on conda:

conda install -c cduvallet -c conda-forge dbotu_q2

If that doesn't work, you can clone or download this repo to your computer, activate your qiime environment, and then run:

python setup.py install

Once you install this plugin, it becomes part of the qiime distribution in the environment you're in (so you don't need to re-install it every time you re-activate the environment). If you install a new version of qiime, however, you'll need to re-install the plugin in that new environment. Note that you need a 2019 QIIME 2 release (version 2019.1) or later for this plugin to work.

Also, if the plugin installation doesn't work for some reason (i.e. it installs, but throws an error if you try running it), note that this will likely "break" your entire qiime distribution as well. Basically, when you load up qiime it automatically tries to load up all installed plugins, and if one of those has an error then it will break the whole thing. If this happens, you can uninstall the q2-dbotu plugin from your qiime environment and your qiime should go back to working normally.

Usage

Currently, the dbOTU plugin has only one function, the distribution-based OTU caller in call-otus. From within a QIIME 2 environment (i.e. after doing source activate qiime2-2019.1), run:

qiime dbotu-q2 call-otus \
	--i-table test_data/counts.qza \
	--i-sequences test_data/seq.qza \
	--o-representative-sequences dbotu_seqs.qza \
	--o-dbotu-table dbotu_table.qza

Genetic, abundance, and p-value criteria parameters

There are optional parameters that you can change to improve the performance of clustering. You can see these parameters by typing qiime dbotu-q2 call-otus --help, and you can learn more about how to choose them by reading the original publication and the dbotu3 update.

Note that this plugin wraps the dbotu3 version of distribution-based clustering, which recommends using slightly different parameters than the original version.

Membership file

Currently, the membership information can be printed using the --verbose flag. The first column of the membership file has the representative sequence for each OTU, and all subsequent columns have the sequences which are grouped into that OTU. If you want to see the membership file (which shows which sequences are grouped into which "OTU"), use the --verbose flag (and optionally pipe the output to a separate file):

qiime dbotu-q2 call-otus \
    ...
    --verbose > membership_file.txt

If you're using this plugin a lot and would like a different functionality for the membership info, get in touch by raising an issue or emailing me and we'll figure out how to best implement that.

Data

To run distribution-based clustering, you need (1) some dereplicated sequences and (2) a table of counts indicating how many times each of those sequences is in each of your samples. Dereplicated sequences can be:

  • exact sequence variants output by DADA2 or deblur's denoise commands,
  • dereplicated sequences, which you can get by clustering at 100% identity with vsearch,
  • some other set of representative sequences, perhaps output from clustering with vsearch at a different identity threshold (e.g. 97% OTUs).

The important thing is that the input sequence file contains only non-duplicated sequences (i.e. it is not just all the raw reads present in your dataset). The sequence IDs in the counts table should match the IDs in the input sequences file, and every sequence ID in your dereplicated sequences file must be present in the table.

Using non-QIIME 2 artifact data

If you want to use this plugin but you're not using QIIME 2 for any of your other steps, you'll need to first import your data (a feature table of counts and a fasta file of dereplicated sequences) into qiime. From within the qiime environment, you can do:

qiime tools import \
  --input-path your_sequence_file.fasta \
  --output-path your_sequence_file.qza \
  --type 'FeatureData[Sequence]'

  qiime tools import \
    --input-path your_table.biom \
    --output-path your_table.qza \
    --type 'FeatureTable[Frequency]'

To do

  • output membership info
    • first attempt will be just to print membership to stdout [done]
    • in the future, will want to define a new file format and write membership to that
    • in the more distant future, perhaps there could even be a way to visualize that membership

Notes

  • The repo is called q2-dbotu to keep in line with other qiime 2 plugin repo names, but the function is called dbotu_q2 so that it (1) does not conflict with the existing pip package called dbotu and (2) shows up alphabetically under dbotu which is where I assume most mortal humans will be looking for it in the qiime plugin listings. Sorry for any confusion (and I assure you it has confused me more than you!)
  • The call_otus() function is basically just a QIIME 2-compatible wrapper for the dbotu.DBCaller().run() function in the dbotu package. The original dbotu package was available on pip, but now we have migrated it to conda to play more nicely with this package. The dbotu package source code is: https://github.com/almlab/dbotu3 . The conda package is available on anaconda.org: https://anaconda.org/cduvallet/dbotu .

Versions

  • 2019.1 - re-build package with Python 3.6 for newer versions of QIIME 2
  • 2018.4.2 - output membership info with the --verbose flag
  • 2018.4.1 - fixed bug: transpose table before and after calling dbotu3, so that dbotu3 gets data in expected format (sequences in rows) despite input qiime2 format (sequences in columns)
  • 2018.4.0 - original "working" plugin, uploaded to conda

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QIIME 2 plugin for distribution-based clustering

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