There are basically two tools for 16S rRNA which both do everything you'd need to get started. Choose one and stick with it: Qiime or Mothur. I prefer qiime personally, lots of good tutorials to get started.
Make sure you don't get confused with RNA-seq analysis, which is totally different to amplicon sequencing using the rRNA gene.
I'm disappointed with your claim that there are, essentially, two tools for rRNA analysis and scientists should pick one or the other and stick with it, which is not what I would consider a scientific approach. There are, in fact, a lot of tools for rRNA sequence analysis. Neither Qiime nor Mothur even try to do "everything you'd need", nor are they optimal for some of the things they do try to do. The world is a big place, with many tools, and you will never produce optimal (or even original) research by picking a single pipeline or toolset at random and sticking with it "every step of the way".
Sorry if it came across bluntly. I think when someone's getting started with a new data type, maybe even bioinformatics at all, it's important to know which are the figurehead tools of the field, of which qiime/mother are basically the ones. Suggesting to someone who is brand new to the datatype or the processing that they should be trying out the latest tools and pipelines when probably 90% of primary amplicon processing is done with one of these tools does a disservice to the amount of work that qiime/mothur have done in allowing you to make your data accessible in the first place and the amount of tutorials and published work on them.
Plus, it'll only serve to confuse the user with the amount of poorly documented one-upping software they'll stumble upon. When you want to ID your first gene you get told to put it in blast. There's a million better tools, but you need to know the basics before jump headlong into it.
I wholeheartedly stand by the suggestion to use one of them to get started, get to grips with the data and do the fundamental processing. When you find something you can't do, or want to go further in your analysis, at that point you wont be asking "how do I analyse rRNA" but a directed question based on an actual understanding of the data.
I would strongly suggest using QIIME
Start here:
a. http://www.wernerlab.org/teaching/qiime [Tutorial with explanation]
b. [Youtube videos - extensive explanation]
c. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3249058/ [Research paper]
QIIME Homepage here
Video Tutorial for Virtualbox installtion of QIIME on Youtube
For differential abundance, you can use metagenomeSeq. You may also get similar results using limma for abundance/frequency or limma-voom for counts.
mothur also has a related metastats function, as well as a random forest classification function in classify.rf.
If you are using Illumina reads, you may want to take a look at the mothur MiSeq SOP, and there is also a link to an Illumina tutorial on the QIIME website
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version 2.3.6
Look at the most recent rna-seq tools here. Maybe somebody have some additional own experiences.
Best.
Moving to a comment since this
answeris incorrect as far as the original question is concerned.Thank you for moving to comment. I have overlook its rRNA.
The question is on rRNA, not RNA-seq