QIIME 1.4.0 is live!

14 12 2011

We’re very excited to announce the 1.4.0 release of QIIME. You can find the new version here. We’ll be posting the new EC2 images (release and development versions) tomorrow, and you’ll be able to find the AMI identifiers on the “Resources” page of the new QIIME website.

This release contains a fix for the make_distance_histograms.py bug that we announced last week. Again, we’re sorry for any inconvenience that that may have caused. This release is additionally packed with a lot of new features – some key ones are:

* A lot of new tutorials including retraining of the RDP classifier, working with Amazon Web Services, coverage of basic unix/linux commands, and others.

* Addition of the OTUPIPE workflow for chimera detection, quality filtering, and OTU picking. This is now available via the pick_otus.py module, and will require you to install the usearch software (even in EC2 and VirtualBox, due to licensing restrictions).

* Addition of code to support plotting comparisons of raw distance data in QIIME. This is available in the new scripts make_distance_comparison_plots.py and make_distance_boxplots.py, and covered in a new tutorial which includes some examples of the plots that can be generated.

* Added new script nmds.py to support Non-Metric Multidimensional Scaling analysis.

* Support in the pick_otus_through_otu_table.py scipt for running uclust_ref in parallel with creation of new clusters (i.e., open-reference OTU picking with the reference step running in parallel and the de novo step running serially).

* assign_taxonomy_reference_seqs_fp and assign_taxonomy_id_to_taxonomy_fp are new qiime_config values, allowing users to set defaults for the dataset they’d like to perform taxonomy assignment against. This works for the serial and parallel versions of assign_taxonomy for both BLAST and RDP.

* Added option (-e/–max_rare_depth) to the command line of alpha_rarefaction.py. This provides a convenient way for users to specify the maximum rarefaction depth on the command line, and is useful for when it needs to be set to something other than the median rarefaction depth. Also added option to control minimum rarefaction depth from the alpha_rarefaction.py command line.

* Added support for 5- and 10-fold and leave-one-out cross-validation to supervised_learning.py.

* Added subsample_fasta.py module for randomly subsampling fasta files.

Plus lots of additional new features: the list continues in the ChangeLog for this release.

Thanks, and have fun!





%d bloggers like this: