Hi,

I am fairly new to bioinformatics. I have the genome sequences of two closely related bacteria (or, more accurately, both genome sequences are publicly available). One bacterium grows much faster under a particular set of growth conditions than the other. I'm trying to determine what genes may be responsible for this improved growth. How do I compare these two genomes to find out what genes are different between the two? And is there a way to find out what the functions (e.g. GO terms) for the different genes are or will I just get a list of gene names as output?

Thanks for any help you can offer.
Fast option is to look up DB that store informations about microbial genomes, i.e. [IMG][2]

A more detailed analysis would require a bit more time and some command line options.
If your strains are related (i.e. same Genus or Species), I would say that the first thing to do is to calculate the genes that these two bacteria shares and the one that are unique. This means to identify orthologous genes. There are plenty of programs to do that out there. You can take a look at this tools: https://omictools.com/pangenomics-category. They are intended to study pan-genomes, but most of them will perfectly suit your use. I personally use GET_HOMOLOGUES, but I read that [OrthoFINDER][1] is really good and reliable.

After this analysis, you will have a list of genes that are shared between the two bacteria and a list of genes that are unique. Also, you will see which genes are present in duplicate (paralogues) in your genomes. 



However, I am not sure that this analysis will answer your question. Especially if the strain are extremely closely related, you might need to do a more detailed analysis considering gene variants, i.e. SNPs. You might then see that some shared genes show differences in the two strains. You might find that these differences will influence the protein sequences as well (they are non synonymous mutations), indicating that the two strains have different version of shared proteins.

Hope this helps.


  [1]: http://www.stevekellylab.com/software/orthofinder
  [2]: https://img.jgi.doe.gov/