Originally posted by cliffbeall
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Rtfp
Read the papers.
As already pointed out, it depends on what you want.
Certain regions work better than others to distinguish among different species, but cannot resolve among others. The best way to know is to find out studies about your target community and see whether they did work. For instance, v6 will not distinguish E coli from Salmonella, which is serious if you want to analyze health data, but may be irrelevant for soil data.
A second factor is the technology: if you are going to generate long reads then your target may be different from the one you'd select for short reads. Again, the literature is the fastest resource.
Another option is to download VAMPS datasets and mine them: you can see which regions distinguish among which species, the lengths they cover, etc... That is more laborious and difficult.
And in case of doubt, the worst choice, but one that often works reasonably well, is to simply repeat blindly what others have done on similar experiments without trying to understand why they did. You risk making huge mistakes and that a careful reviewer may turn your work down, but you also get chances no one will notice your carelessness.
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What environment? What are you trying to find or distinguish? What platform will you be sequencing with? The "best" region is the one that will work for the questions you want to answer. If, for example, you are trying to distinguish two species then one variable region might have more distinguishing power than another.
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One thing you might want to think about is doing more than one region. We looked at V1-2 and V4 on oral bacteria (pre-titanium) and found that V4 significantly undercounted the Bacteroidetes phylum, despite that the primers match Bacteroidetes perfectly well.
The Human Microbiome Project followed a similar tack, with their V1-3 and V3-5 primers. Our current strategy is to use their sequences, on the thought they will become a standard (at least for a few months until the technology changes again).
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There has actually been a fair bit of work done on this in the literature. I'd look at some papers out of Rob Knight's group.
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best 16s region to amplify with NGS ?
Does someone have an idea of the best 16s gene region to amplify with NGS in order to assess microbial (bacterial) diversity in environmental samples ?
cheers
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