Hi friends,
I have got two excelsheets containing the position of synonmous and non-synonmous mutations between human and chimp. And I have found synonmous mutations are less than that of non-synonmous mutations. Now how can I interpret the result from the given output? Please help.
Sorry, but these most likely are not SNPs in the sense of the definiton, if they are not the result of a population study, but the comparison of the genome sequences.
There are no SNPs shared between human and chimp. See How To Find Out The Snps Which Are Present In Human And Are Also Present In Other Organisms?
Edited title, question, and tags to reflect that this question is NOT about SNPs but about synonymous vs. non-synonymous mutations between human and chimp.
You can interpret there are less synonymous SNPs than non-synonymous SNPs. You will need to provide more information about the question you want answered before anyone can help you.
What kind of experiment has this come from?
What results were you expecting?
You can interpret there are more non-synonymous SNPs than synonymous SNPs. You will need to provide more information about the question you want answered before anyone can help you.
the data obtained is form comparative analysis of human and chimpanzee's sequences.
So do you want to predict the effect of the changes? What do you want to do with the data set?
@Giovanni - your claim that there are no SNPs shared between human and chimp is not strictly true -- there is evidence for trans-specific polymorphism at HLA: http://www.ncbi.nlm.nih.gov/pubmed/2460344
@Casey, I was wondering how much confidence one could have in the findings of this article, as it is from 1988 long before the availability of any full genome sequence. No, criticism, just curiosity.
@Michael - my feeling is that it is probably correct. It's a textbook example of trans-specific polymorphism that has not been overturned in over two decades. But I agree that it is probably worth revisiting this issue and perhaps looking for any other loci that show shared human-chimp alleles in the 1000+ genomes era.