I was largely reintroduced to the linux world and bioinformatics through the use of qiime. I have found it very helpful in learning how to do command-line operations and to gain confidence in my rather coarse bioinformatic skills. I have some data that I keep meaning to process via each pipeline, but just haven't gotten around to it yet. At conferences I have seen fantastic visualizations produced (at least from the data) through each package (qiime,mothur,uparse), so my sense is that they all perform the desired functions, though I agree it is debatable which really is best. One is slower here, one is faster there, and often there is a trade-off between speed and accuracy.
The qiime forum is very active and users generally get their questions answered rather quickly. Some of the moderators are extremely quick to identify a users problem and help them get to resolution.
I do like the ability to only run the scripts you want/need for a given dataset. Such freedom can be wielded very well or very poorly, depending on the user (eg, not bothering to do chimera checking).
I ran qiime a few years ago in a virtual box, which was a super lame experience since my laptop was already old and slow. Then I installed it natively on a desktop in lab, which was also a super lame experience because it took me so long to get all the dependencies installed and in the right places. Now we have a linux-based dual xeon workstation (24 cores) with a bunch of ram and disk space and I maintain the qiime distribution with qiime-deploy tool. Installation and upgrading couldn't be easier, and there is no point trying to run qiime on a wimpy laptop if you intend to process a miseq run in an afternoon. We also recently gained access to a cluster and a sysadmin takes care of all that baloney for us.
You can also submit requests to the qiime team to add functionality. In fact, after asking a question about filtering OTUs from a raw OTU table, one of the coders wrote me a custom script to try to improve my filtering based on my concerns with existing methods. When it didn't work quite right, he fixed it within a day. Still evaluating its utility, but I think it shows the strong support of the qiime community.
When it comes down to it, I think the Knight and Schloss labs both really just want to be able to do sound science. I think parallel development of the different platforms is essential so that we don't get stuck in one paradigm when someone else can see a flaw and provide an effective alternative.
Just to muddy the waters, there is uParse as well: http://www.nature.com/nmeth/journal/v10/n10/full/nmeth.2604.html
cdhit, etc) is a clustering algorithm and not really a "stand alone" analysis program/pipeline like mothur and QIIME. In fact, the are lots of options for clustering from within QIIME and mothur, and both can run
Interesting, we're currently running a few large classifications, so we'll take a close look. Thanks for the link.