QIIME 1.8.0 is live!

12 12 2013

Hello QIIME users,
Today we’re very excited to announce the release of QIIME 1.8.0, which is packed with new features that expand the functionality and usability of QIIME. The QIIME 1.8.0 Virtual Box and EC2 images will be ready later this week. Here are some of the highlights in 1.8.0:

First, one of the most frequent comments that we get about QIIME (which we completely agree with) is that it is difficult to install. To address this, we’ve defined a QIIME “base install” package, which is installable with pip so greatly reduces the complexity of QIIME installation. The QIIME base install package supports running the most commonly used QIIME commands with default parameters, and in most cases will be sufficient for your entire QIIME analyses. You can find discussion of this in the updated QIIME Installation Guide.

Next, our PCoA plots are no longer based on KiNG, but on the new Emperor 3D plotting package. This enables more advanced analysis of PCoA plots in the context of sample metadata, and is one step toward our larger goal of improving QIIME’s interactive visualization capabilities.

We’ve added support for assembling Illumina paired end reads in the new join_paired_ends.py script, which wraps  ea-utils and SeqPrep,  and for working with alternative barcoding schemes used on the Illumina platform with the new extract_barcodes.py. A new tutorial has been added for working with alternative barcoding schemes.

We’ve updated QIIME’s default taxonomy assigner to be the new uclust-based consensus taxonomy assigner. This was shown to be more accurate and faster than the existing methods (Bokulich, Rideout et al. (submitted)). Important: the RDP Classifier is no longer the default taxonomy assigner used in QIIME.

otu_category_significance.py has been removed in favor of group_significance.py, which supports additional types of tests, and is more maintainable and extensible than otu_category_significance.py.

core_diversity_analysis.py has a new parameter, --recover_from_failure, that allows the user to re-run the script on an existing output directory and will only re-run analyses that haven’t already been run. This supports rapid recovery from failed runs, and additionally allows the user to add categories to a previous run, which is very common and previously required a full re-run.

We’ve added new script, estimate_observation_richness.py, which implements some of the interpolation and extrapolation richness estimators in Colwell et al. (2012). Important: This script should be considered beta software; it is currently an experimental feature in QIIME.

Finally, we’ve updated our documentation on contributing to QIIME. If you’re interested in helping us make QIIME better there are lots of ways that you can get involved. See our new Contributing to QIIME document.

And this is all just scratching the surface. There are many more features in QIIME 1.8.0 – for more details you should review the full ChangeLog.


Greg (on behalf of the expanding list of QIIME developers)




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