There are many methods for phylogenetic tree reconstruction, out of these is there any best or mostly used method for identification of bacteria using 16s rRNA sequence.
Thanks
The best method is to use multiple methods at high bootstrap values and then incorporate findings from each into a hybrid tree as a representation of consensus tree. That can be as easy is running the different versions after optimization and then indicating on one of the outputs which of the branches are conserved in other methods.
We used it in one of our papers here: Article Bacillus glennii sp. nov. and Bacillus saganii sp. nov., iso...
The SILVA 16S rRNA database is a good place to start.
https://www.arb-silva.de/
For most bacteria (and many other organisms too) the ribosomal RNA sequences alone are not sufficient for good classification below the Genus level. But they certainly are enough to tell you something about the species of bacteria you have in a sample. In some cases this may be all you need to know.
As an example, I am attaching a phylogeny built from 16S rRNA sequences from various Escherichia and Shigella isolates plus Salmonella enterica and some others as outgroup. It is easy to see that the sequences are intermingled on the tree so this gene alone is not telling us all we might like to know about the history or classification these bacteria. It's good enough to get us "in the Shigella/Escherichia ballpark", but not to tell us if our isolate is a Shigella or not.
For species level BI is recommend, ML, MP and NJ also used for the construction of phylogeny
Yeah, I agree with Sanjeet KUMAR Verma. Most publications are applying BI and ML (which is done by RaxML) Methods. MP method can be useful but sometimes it goes statistically wrong.
Bayesian Inference method in MrBays or Maximum likelihood method in RaXML or IQTree methods can be used.
IQTree offers an online platform to construct phylogenetic trees. You can access to that service via following link.
http://iqtree.cibiv.univie.ac.at/
- Badges
- Science topic
- Similar topics
- Psychology
- Mental Processes
- Learning