Hi,
I am interested in data mining bacterial genomes to check for trytophan metabolizing genes. Also, I would like to predict the pathways based on the presence of genes/proteins homologous to the experimentally identified genes/proteins involved in the particular pathway.
I am thinking of taking the genes from bacterial genomes and performing nucleotide BLAST.
Is there any other better methods to accomplish this task?
Any existing tools which is already developed to do this task. Kindly share its information. Thanks in advance.
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I am thinking of taking the genes from bacterial genomes and performing nucleotide BLAST.
There are definitely better ways of doing this task. The way you posed the question makes it general enough that it could be enough work to defend a dissertation, or just a four-week project. Since I have no idea which one it is, my suggestions will be general.
Searching for similarity based on nucleotide identity is not the best way unless you are interested only for very related organisms. Searching for protein similarity is more likely to catch somewhat distant relationships, and specifically using hidden Markov models (HMMs). A well-known database of HMMs is Pfam, and they have quite a few tryptophan-related HMMs that may be useful to you. The idea is to take HMMs that are of interest and search protein databases using HMMer.
For pathway integration, or for global queries regarding the presence or absence of pathways and their components, I suggest KEGG. They have lots of info on tryptophan metabolism.
Apologies for the delayed reply situation changed a lot during this month. So I didn't have access to the lab due to lockdown.
I agree it is a big project. I have been asked to work on this requirement for summer activity (I can say 3 to 4 weeks project). Can you recommend some ideas to proceed?
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