QIIME 2 2018.6 has been release!
Please see the official changelog for more details.
Original content of post
Important
The following is an early developer preview of the changes expected in 2018.6 This post is a live-document which will be updated throughout our development cycle. Any links will in this topic will be broken until the release is officially published. When we are ready for release, we’ll copy this changelog and create a new post in the Announcements category.
Important Developer Dates:
- PRs must be submitted by: 06/15
- PRs must be merged by: 06/19
- Release Day: 06/21
The QIIME 2 2018.6 release is now live! There are lots of exciting new changes packed into this release that are described below.
Our next planned QIIME 2 release is tentatively scheduled for July or August 2018 (QIIME 2 2018.7 / 2018.8), but please stay tuned.
Check out the QIIME 2 2018.6 docs for details on installing the latest QIIME 2 release, as well as tutorials and other resources. Get in touch on the QIIME 2 forum 2 if you run into any issues!
Virtual machine builds are also available today - check out the docs for info on getting set up!
Important
We recently discovered a bug related to the q2-cutadapt plugin’s trim-paired command that, in certain situations, can produce incorrect results. In particular, the forward and reverse reads are swapped, causing the underlying cutadapt command to apply the forward read trimming parameters on the reverse reads (and vice versa).
Check out this post for more detail:
Incorrect results produced by q2-cutadapt's trim-paired
Here’s the highlights of the release:
-
- For developers: fixed a bug where the
util.duplicate
function failed due to lack of ownership/write-permissions.util.duplicate
now copies files if these permission errors prevent hard linking. Thanks @ChrisKeefe! - For interface developers: a new utility
actions_by_input_type
has been added toqiime2.sdk.util
which can list plugin-actions by an input semantic type. Thanks @antgonza!
- For developers: fixed a bug where the
-
- Check out the new overview tutorial, intended to give a broad overview of the workflows employed in QIIME 2 for marker gene amplicon sequence data analysis, and different options therein! This guide is meant as a somewhat non-technical primer for beginners, as well as a handy desk reference for expert users who are untroubled by excessive use of emoji. Most importantly, it is chock full of BEAUTIFUL FLOWCHARTS. Enjoy!
- The q2-longitudinal tutorial has been updated to include details on using fit vs. residual plots to evaluate
linear-mixed-effects
model quality and identify outliers. - The q2-longitudinal tutorial has been updated to include recommendations on choosing random vs. fixed effects.
- The q2-longitudinal tutorial has been endowed with a BEAUTIFUL FLOWCHART of its own to give an overview of this plugin’s actions.
- Added a link to the draft developer docs - thanks @cduvallet!
-
- Fixed a typo in
inspect-metadata
- thanks@WottatoParrior
!
- Fixed a typo in
-
- Upgraded to maintain compatibility with latest release of
pandas
, version 0.23 !
- Upgraded to maintain compatibility with latest release of
-
- Raw alpha diversity and metadata values plotted by the
alpha-group-significance
visualizer can now be downloaded to TSV in the resulting visualization! - Raw alpha diversity and metadata values plotted by the
alpha-correlation
visualizer can now be downloaded to TSV in the resulting visualization! - Raw pairwise distances and sample metadata values plotted in the
beta-group-significance
visualizer can now be downloaded in the resulting visualization!
- Raw alpha diversity and metadata values plotted by the
-
- Updated default parameter settings for all methods to match the recommendations reported in the q2-feature-classifier article. Be sure to download the latest pre-trained feature classifiers!
- Minor fix to prevent a redundant
memory
parameter from sneaking into the interface from the newest scikit-learn release.
-
- Fixed a minor typo in the
correlation-clustering
docstring. - Upgraded to maintain compatibility with latest release of
statsmodels
, version 0.9.0 !
- Fixed a minor typo in the
-
- The
linear-mixed-effects
visualizer now generates fit vs. residual plots for assessing model quality and identifying outliers! See the updated q2-longitudinal tutorial for more details. - Upgraded to maintain compatibility with latest release of
statsmodels
, version 0.9.0 ! - Fixed a bug in
linear-mixed-effects
that caused an error when attempting to use feature names beginning with numerals as a metric. Thanks @JenKelly for spotting and diagnosing this bug!
- The
-
- Added two new methods for tree-building, based on the RAxML tool! Thanks @SoilRotifer!! Check out the new draft Community Tutorial!
-
- Added a new visualizer,
evaluate-taxonomy
! This visualizer calculates precision/recall/F-measure for a list of observed taxonomy assignments, compared to a list of expected taxonomy assignments for those features, and creates a simple plot for p/r/f at each taxonomic level. -
evaluate-composition
now calculates Bray-Curtis and Jaccard distance between expected and observed results.
- Added a new visualizer,
-
-
Classifiers and regressors can now be saved as artifacts! Two new methods —
fit_classifier
andfit_regressor
— allow users to train classification and regression models (similar toclassify-samples
andregress-samples
), and save those models as artifacts for re-use. These methods also identify and select predictive features. - The new
predict
method allows users to predict metadata values for samples in a feature table, using a trained classifier (fromfit_classifier
orfit_regressor
) as input! - Two new methods,
classify-samples-ncv
andregress-samples-ncv
, support fully nested cross-validated sample prediction. These methods work similarly toclassify-samples
andregress-samples
, except that metadata values are predicted for all input samples, and accuracy and feature importance scores are averaged across each iteration. Among other uses, these methods can be used to predict mislabeled or abnormal samples within a dataset. - Most actions now have the option to either ignore or raise an error (default) when samples present in the feature table are missing from the sample metadata. The result of ignoring is that these missing samples will be silently dropped from the analysis…
- Feature tables are no longer transformed to
pandas.DataFrame
s. Short story for users:q2-sample-classifier
actions should now be faster and more efficient! - The new
split_table
method randomly subsamples a feature table into training and test sets at a specified ratio. Splitting can be stratified on a specific metadata column. This method is used for splitting feature tables prior to training predictive models.
-
Classifiers and regressors can now be saved as artifacts! Two new methods —
-
- Fixed a UX bug by improving error transparency in the
collapse
method. Users are now notified if the Feature IDs in theirFeatureTable[Frequency]
are missing from theirFeatureData[Taxonomy]
. Thanks @ChrisKeefe!
- Fixed a UX bug by improving error transparency in the
-
- A new transformer from
BIOMV210Format
toBIOMV100Format
was added. Thanks @turanoo! - A new transformer from
FastqManifestFormat
topandas.DataFrame
was added. This should prevent future occurrences of paired-end file ordering issues.
- A new transformer from
-
- Added a new sequence length 7-number-distribution table to
summarize
- this is helpful when working with pre-joined reads. Thanks @jakereps!
- Added a new sequence length 7-number-distribution table to
Happy QIIME-ing!