How to choose a reference set of highly expressed genes for calculating Codon Adaptation Index? What if I do not have any experimental evidence related to their expression levels or on the other hand what if I have a newly annotated genome? Is it still possible to compute CAI?
Why do you think that highly expressed genes in particular would make a good CAI reference? Personally, I would go for a highly conserved (between taxa) set of genes.
Codon Adaptation Index (CAI) was first introduced by Sharp and Li to measure synonymous codon usage bias for a DNA or RNA sequence.It also measures the resemblance between the synonymous codon usage of a gene and the synonymous codon frequencies of a reference set.CAI was originally proposed to provide an estimate that can be used across genes and species, ranging from 0 to 1.
If a gene always uses the most frequently used synonymous codon in the reference set,then CAI=1. If a gene always uses the least frequently used synonymous codon in the reference set,then CAI=0.
Click on this link to explore more:Implementation Of Codon Adaptation Index (CAI) Using Biopython
abdulwrs7 if you want to promote your website (given it has bioinformatics-related relevance) then please open a new question and choose type Blog. Adding the exact same answer to five old threads that already received (accepted) answers should not really be the way to go here.
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Thanks for the reply. Yes. It might be a good idea if I want to see deviation of a coding sequence between taxa. But, in my case I would want to see the similarities between coding sequences within the same organism/taxon so as to find out genes which have some correlation like they are similarly expressed, or like they belong to same pathway, or they are functioning towards a defined biological objective, etc.