So my questions are: how QIIME2 handle mt16S? Might it be assigned as a bacterium? Does it make an “other”? Is it handled the same way in QIIME(1)?
This paper states : (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4133986/)
Also, contaminant sequences must be removed from the dataset. Due to the nature of the 16S rRNA gene, mitochondria, chloroplast (96), and other eukaryotic sequences are likely to be amplified and should be identified and discarded, along with sequences unclassified at the domain level; according to the scope of the study and the primers used, bacterial or archaeal sequences would also be needed to get removed.
Hi @Alban_OTT,
It is definitely true that you will sometimes see organelle rRNA sequences in your data, and they will often be classified during taxonomy classification. For example, in Greengenes, mitochondrial rRNA are often classified as:
(Chloroplasts descend from Cyanobacteria, and mitochondria from Rickettsiales - hence their placement in these lineages in this taxonomy.)
In general, these don’t seem to cause much of a problem, and I don’t consistently remove these sequences from my data sets. In some cases (for example, in this study) since plant surfaces were being sampled we had an overwhelming amount of chloroplast sequences (somewhere around 50% of the sequences were chloroplast sequences, if I remember correctly), and we filtered these from the dataset based on their taxonomic assignments.
In the next release of QIIME 2 we will be improving support for removing these types of sequences based on their taxonomic assignments. We can follow up with you on this post to let you know when that is ready.
Hi @Alban_OTT,
Just to add to @gregcaporaso’s advice: if chloroplast or mitochondrial DNA sequences are more/less abundant in different experimental groups that you are comparing, or if you suspect that this would be the case (e.g., because you are comparing host-associated vs. environmental microbial communities or samples from different hosts or tissue types or different DNA extraction methods), you may want to remove them prior to running other analyses (e.g., beta diversity) even if they are at low abundance.
@gregcaporaso@Nicholas_Bokulich
I confirm that I have some “k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__Rickettsiales; f__mitochondria;” OTUs in my studies.
None of the studies I analyzed had differentially abundant “f_mitochondria[…]”. So I will just left it in the datasets and keep in mind to check it when I interpret deseq2 results.
I don’t expect chloroplasts but I will check it anyway.