I needed the sequences of variable region V1, V2, V3, and V4 of 16s rRNA of Staphylococcus aureus. I solved the problem by manually locating the regions, using the alignment in this article and the positional numbers from the same article:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2562909/
(Supplement 7)
But is there an easier way to do it? Is there any database that would automatically give me the V1 - V4 sequences, or which would automatically highlight them for me inside the whole 16s sequence?
Hi Barbara. We designed a tool exactly for doing this. Check out the paper here http://www.sciencedirect.com/science/article/pii/S0167701210002745 and the tool here http://www.microbiome.ch/web/Tools.html.
mothur has a command to extract a segment of DNA from a fasta file, based on primer pairs. It's easy to use, but there's no graphical output (if that's what you're after). Primers for those regions are pretty well established, so you'd just need to pick whichever ones are best suited for your sample.
http://www.mothur.org/wiki/Pcr.seqs
Article Introducing mothur: Open-Source, Platform-Independent, Commu...
Hi Barbara. We designed a tool exactly for doing this. Check out the paper here http://www.sciencedirect.com/science/article/pii/S0167701210002745 and the tool here http://www.microbiome.ch/web/Tools.html.
Good question and good answers. I will just add a mention of arb software environment often used for rRNA work with sequences & alignments
Hi Barbara,
well, one way of identifying highly variable regions is simply generating a window of nucleotides (or amino acids) and sliding such window along the sequence in an overlapping manner. For each of the windows you can estimate the number of nucleotide or amino acid substitutions using whatever model you like. To determine which of the windows is significantly variable, you can build 1000 windows each of which will include a randomly picked site of the alignment and calculate the diversity of that window. Using these 1000 windows you build a distribution and then you place your real values (those taken from your first analysis) within the distribution to identify those regions that are beyond the 1% tail of the distribution. The good thing about this is that you do not need to know much about the distribution of your data since you are performing a non-parametric search for your significant windows.
Hope this helps,
Mario
Mario, this sounds like an advice for a programmer, not the user. But thanks anyway.
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