The QIIME 1.2.0 release is now live and contains lots of exciting new features, some bug fixes, cleaned up code and interfaces, and new documentation. Grab QIIME-1.2.0 here.
New features include:
* Addition of the new PyCogent SFF tools. Converting SFF files into fasta, qual and flowgram files is now supported through process_sff.py without dependence on Roche’s sff tools.
* Ability to add jackknife support to 2D and 3D PCoA plots as seen in Figure 2 of this article. This feature is now accessible via the jackknifed_beta_diversity.py script, which has replaced jackknifed_upgma.py.
* Added a parallel version of the uclust_ref OTU picker (parallel_pick_otus_uclust_ref.py).
* Added ability to run beta diversity calculations in parallel at the single OTU table level to improve performance when computing diversity on very large collections of samples. This functionality is now hooked up to the beta_diversity_through_3d_plots.py workflow script.
* Added several new pages to the documentation including a Procrustes analysis/plotting tutorial; a tutorial for identifying and removing chimeric sequences; and improved documentation on common Qiime input file formats.
* Added new script (shared_phylotypes.py) for computing shared OTUs between pairs of samples.
* Add script (plot_rank_abundance_graph.py) to draw rank abundance graphs.
* Added script (quality_scores_plot.py) to plot quality score by position given a .qual file. This is useful with another new script (truncate_fasta_qual_files.py) to truncate fasta and qual files at the point where quality begins to decrease. This has been useful in controlling for quality issues on 454 Ti runs.
* Added capability to perform supervised classification of metadata categories using the Random Forests classifier (supervised_learning.py). Outputs include a ranking of OTUs by discriminatory power, and the estimated probability of each metadata category for each sample. The latter may be useful for detecting potentially mislabeled samples. The technique was recently used here.
We’re also excited to announce that we’ve created a QIIME EC2 image that will be going live this week. This will allow Amazon Web Services run their QIIME analyses in the cloud. This has been particularly useful so far for denoising data as this is typically the performance bottleneck in QIIME analyses. We hope that this will generally be useful for users who need additional compute resources to support their big analyses.
As the majority of our users are working on 64-bit machines, we’ve dropped support for the 32-bit QIIME VirtualBox in QIIME 1.2.0. Supporting both versions was becoming too time-consuming. Some notes on this are available here. We’re sorry for any inconvenience this may cause.
For QIIME VB users, we’ve created a VB upgrade script for upgrading the 64-bit Qiime VirtualBox. You can find information on obtaining and running this script here.
For this release of the QIIME VB and EC2 image we used the CloVR package to build our virtual machine images. We’d like to thank Samuel Angiuoli and James White of the Institute for Genome Sciences for their help in getting us up and running with CloVR! We’d also like to thank our users for the continued feedback on QIIME: your input is critical to making QIIME a useful and usable toolkit.
As always, get in touch on the QIIME forum with any questions or comments. Have fun!