Personally for amplicon sequencing I’m a big fan of amplicon sequence variants which essentially become barcodes for very specific parameters. In bacteria, sex is way more complicate and the “species level” approximation is something useful to put things in nice boxes. (So, like even though + can have babies, a liger can’t). My understanding from macroscopic ecology is that a species is something that has independent reproduction that produces fertile offspring. Okay, philosophically, species are kind of hand wavy, especially for bacteria. How can we know then for sure species abundance? Just to be sure, is it possible to always have a few false positives even in standard cultures samples analysis using QIIME2 and yet be doing everything as best as possible in all the steps of the analyses “as of 3 December 2020”? I believe that taking into account all the above said, it is possible that some reads are incorrectly classified into different species due to intracellular diversity of 16S rRNA genes as well as possible other details regarding the process used for classification such as selected quality of reads, length discarded, sequencing platform error rate or other errors of algorithms that have not yet been overcome. One last question, I have used DNA extracted from standard cultures, for the evaluation of sequencing and analyses methods and I have seen that even though I detect all the species I should have, I still get some in very low amounts that shouldn’t be there. Is it then better to use one variable region with a good enough length to be accurate for 16S rRNA studies? How long is your read length and will you be able to scaffold (V13 tends to be longer than a 2x300 Illumina run V4 2x150 tends not to join)įinding appropriate ways to combine multiple reads is really difficult as of 3 December 2020.Specific primers can focus on specific clades of interest, do you need that specificity?.How much you want to compare across enviroments (EMP are designed to work lots of places but may give you lower resolution).Standard for your environment (for example, the vaginal people have primers they really like).Picking your hypervariable region depends on a lot of things, including: There are cases where having that resolution is critical to the biology and cases where we don’t know. Its constrained by read length (longer reads -> more specificity), the region/primers you chose, and what you believe you need. This is one of those generalizability/specificity problems. Or, you can do flow cytometery like the paper above talked about, if you can get the protocol to work and can find a flow cytometer that will let you run bacterial cells.Īnd what about overlaps of specific V regions between closely related species? You can check the number of 16s genes in your original sample using qPCR (which does not directly correlate to the number of cells because cells have multiple 16s genes and copy number variation is always a fun discussion, see posts below). The quality if your extraction affects the final read count, but less os than you’d think. There are low biomass protocols that can help with this issue, but my experience has been its closer to binary. If you don’t have a lot of bacterial cells, you will have trouble getting DNA to amplify. I’d recommend looking at Quantitative microbiome profiling links gut community variation to microbial load and specifically recommend Figure S5. Even if you used a single region, the number of 16s genes are not related to the number of cells as a general rule. How in the end will we be able to know exactly how many cells we had in our sample from each species since we cannot guarantee that they all were sequenced with the same success?
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