Aspects of the project I find fascinating
I really like that your overview contains personal goals for you to gain practical experience, in addition to generating the figure. Also, that's a lot of genomes to align! I admire you both taking on such a robust project.
Questions I have about the approach or results
What is the purpose of aligning multiple genera to multiple genomes, as opposed to picking one genus? As it stands you are essentially asking/answering three separate questions because the genomes from different genera cannot be compared to each other. This is totally fine- but how will it be represented all in one figure? And how will it be interpreted?
Suggestions on next steps, project organization, etc.
For our Ebola alignment we've been using ggmsa and ggseqlogo in R. You could also consider a phylogenetic tree with ggtree in R! ggmsa and ggseqlogo work best with a smaller portion of the genome, so I think a phylogenetic tree could be a great asset.
As a "reach" goal I recommend a heat map to show variation across your aligned genomes. This always looks really cool. You could use pheatmap package for this in R. Another option is to look deeper at the proportion of amino acid substitutions - purines, pyrimidines, etc. - and what effect these may have.
Aspects of the project I find fascinating
I really like that your overview contains personal goals for you to gain practical experience, in addition to generating the figure. Also, that's a lot of genomes to align! I admire you both taking on such a robust project.
Questions I have about the approach or results
What is the purpose of aligning multiple genera to multiple genomes, as opposed to picking one genus? As it stands you are essentially asking/answering three separate questions because the genomes from different genera cannot be compared to each other. This is totally fine- but how will it be represented all in one figure? And how will it be interpreted?
Suggestions on next steps, project organization, etc.
For our Ebola alignment we've been using ggmsa and ggseqlogo in R. You could also consider a phylogenetic tree with ggtree in R! ggmsa and ggseqlogo work best with a smaller portion of the genome, so I think a phylogenetic tree could be a great asset.
As a "reach" goal I recommend a heat map to show variation across your aligned genomes. This always looks really cool. You could use pheatmap package for this in R. Another option is to look deeper at the proportion of amino acid substitutions - purines, pyrimidines, etc. - and what effect these may have.