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MAINT: Update old classifier URLs #587

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Jun 26, 2024
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MAINT: Update old classifier URLs #587

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08e5e8c
MAINT: updates old classifier URLs to be non-release specific
lizgehret Jun 26, 2024
6ac63de
updates sepp ref DBs
lizgehret Jun 26, 2024
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4 changes: 2 additions & 2 deletions source/data-resources.rst
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Expand Up @@ -79,5 +79,5 @@ The following databases are intended for use with q2-fragment-insertion, and
are constructed directly from the
`SEPP-Refs project <https://github.com/smirarab/sepp-refs/>`_.

- `Silva 128 SEPP reference database <https://data.qiime2.org/2024.5/common/sepp-refs-silva-128.qza>`_ (MD5: ``7879792a6f42c5325531de9866f5c4de``)
- `Greengenes 13_8 SEPP reference database <https://data.qiime2.org/2024.5/common/sepp-refs-gg-13-8.qza>`_ (MD5: ``9ed215415b52c362e25cb0a8a46e1076``)
- `Silva 128 SEPP reference database <https://data.qiime2.org/classifiers/sklearn-0.21.2/sepp-ref-dbs/sepp-refs-silva-128.qza>`_ (MD5: ``7879792a6f42c5325531de9866f5c4de``)
- `Greengenes 13_8 SEPP reference database <https://data.qiime2.org/classifiers/sklearn-0.21.2/sepp-ref-dbs/sepp-refs-gg-13-8.qza>`_ (MD5: ``9ed215415b52c362e25cb0a8a46e1076``)
2 changes: 1 addition & 1 deletion source/tutorials/moving-pictures-usage.rst
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Expand Up @@ -798,7 +798,7 @@ from sequence to taxonomy.
from urllib import request
from qiime2 import Artifact
fp, _ = request.urlretrieve(
'https://data.qiime2.org/2024.5/common/gg-13-8-99-515-806-nb-classifier.qza',
'https://data.qiime2.org/classifiers/sklearn-1.4.2/greengenes/gg-13-8-99-515-806-nb-classifier.qza',
)

return Artifact.load(fp)
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2 changes: 1 addition & 1 deletion source/tutorials/moving-pictures.rst
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Expand Up @@ -383,7 +383,7 @@ In the next sections we'll begin to explore the taxonomic composition of the sam


.. download::
:url: https://data.qiime2.org/2024.5/common/gg-13-8-99-515-806-nb-classifier.qza
:url: https://data.qiime2.org/classifiers/sklearn-1.4.2/greengenes/gg-13-8-99-515-806-nb-classifier.qza
:saveas: gg-13-8-99-515-806-nb-classifier.qza

.. command-block::
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2 changes: 1 addition & 1 deletion source/tutorials/pd-mice.rst
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Expand Up @@ -528,7 +528,7 @@ Up until now we have been performing diversity analyses directly on ASVs; in oth
For this analysis, we'll use a pre-trained naive Bayes machine-learning classifier that was trained to differentiate taxa present in the 99% Greengenes 13_8 reference set trimmed to 250 bp of the V4 hypervariable region (corresponding to the 515F-806R primers). `This classifier works`_ by identifying k-mers that are diagnostic for particular taxonomic groups, and using that information to predict the taxonomic affiliation of each ASV. We can download the pre-trained classifier here:

.. download::
:url: https://data.qiime2.org/2024.5/common/gg-13-8-99-515-806-nb-classifier.qza
:url: https://data.qiime2.org/classifiers/sklearn-1.4.2/greengenes/gg-13-8-99-515-806-nb-classifier.qza
:saveas: gg-13-8-99-515-806-nb-classifier.qza

It's worth noting that Naive Bayes classifiers perform best when they're trained for the specific hypervariable region amplified. You can train a classifier specific for your dataset based on the :doc:`training classifiers tutorial <feature-classifier>` or download classifiers for other datasets from the :doc:`QIIME 2 resource page <../data-resources>`. Classifiers can be re-used for consistent versions of the underlying packages, database, and region of interest.
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