My name is Anas, i am currently a trainee and i have been given 16s sequences (fastq.gz) to analyze (they came from healthy vs diseased mussels). Qiime2 seemed to be the best way for me to perform this analysis, so i went through the "moving pictures" and the atacama soil microbiome tutorials with success.
I have very basic knowledge about microbiome analysis but no experience in this topic. Therefore i am asking for help in order to guide me through the workflow of the analysis.
From my sequences (paired end demultiplexed), i would like to generate figures as follows:
My name is Anas, i am currently a trainee and i have been given 16s sequences (fastq.gz) to analyze (they came from healthy vs diseased mussels). Qiime2 seemed to be the best way for me to perform this analysis, so i went through the "moving pictures" and the atacama soil microbiome tutorials with success.
I have very basic knowledge about microbiome analysis but no experience in this topic. Therefore i am asking for help in order to guide me through the workflow of the analysis.
From my sequences (paired end demultiplexed), i would like to generate figures as follows:
Welcome to the :qiime2: forum! Just so you know, there's often a delay for the first couple of posts, so be patient, and it will appear soon.
The place I would recommend starting is with one of the workflow tutorials. This presents a relatively complete analysis. My favorite is the parkinson's mouse tutorial, which will gety ou from raw fastq sequences to alpha diversity and a PCoA. If you have paired end sequences, you may want to look at denoising in either the Atacama soil microbiome tutorial or Alternative methods of read joining. Chao1 is not recommended with denoising, so you may want to consider an alternative metric. The data can either be plotted in QIIME and modified, or you can export to your favorite plotting software and make your own bespoke figures.
The second figure uses LefSe for their analysis, which isn't implemented in QIIME. My recommendation is that you use ANCOM, ALDeX, or Songbird for your differential abundance since those are most appropriate. (My favorite paper on the topic is Microbiome Datasets are Compositional and this is not optional, the doi of which Im seriously debating getting tattooed on my knuckles.)
And, of course, you're welcome tocome back and ask questions.
