How to differentiate snps into missense, none and silent using Annovar?
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7.3 years ago

Hi,

I have list of snps and trying to annotate them using Annovar. How can I use Annovar for classifying SNPs into Missense, Silent and Others using Annovar tool? Can we calculate hom/het ratio also using Annovar?

Thanks

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7.3 years ago
Sam ★ 4.4k

You can just follow the Annovar manual

They have a detail tutorial section where you can just follow to get the Snps to be annotated. For the hom/het ratio, I am not sure if annovar can do it for you or not. But it is very easy for you to calculate based on the Annovar output (or even, the original vcf)

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I checked already but could not find any function or program classifying snps into Homo/Hetero and missense/nonsense/others. I already used annotation program but it generates two files variant_function and exonic_variant_function and non of them having such infomation. Can you pls tell me which program can be useful for me.

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If you look carefully to the link I provided, you can see that once you perform the refSeq gene annotation file, you should get a <prefix>.exonic_variant_function file which will have the snp annotation with information such as

Annotation           Precedence     Explanation

frameshift           1              an insertion of one or more nucleotides that cause
insertion                           frameshift changes in protein coding sequence

frameshift           2              a deletion of one or more nucleotides that cause
deletion                            frameshift changes in protein coding sequence

frameshift block     3              a block substitution of one or more nucleotides that
substitution                        cause frameshift changes in protein coding sequence

stopgain             4              a nonsynonymous SNV, frameshift insertion/deletion,
                                    nonframeshift insertion/deletion or block substitution
                                    that lead to the immediate creation of stop codon at
                                    the variant site. For frameshift mutations, the
                                    creation of stop codon downstream of the variant
                                    will not be counted as "stopgain"!

stoploss             5              a nonsynonymous SNV, frameshift insertion/deletion,
                                    nonframeshift insertion/deletion or block substitution
                                    that lead to the immediate elimination of stop codon
                                    at the variant site

nonframeshift        6              an insertion of 3 or multiples of 3 nucleotides that do
insertion                           not cause frameshift changes in protein coding sequence

nonframeshift        7              a deletion of 3 or mutliples of 3 nucleotides that do
deletion                            not cause frameshift changes in protein coding sequence

nonframeshift block  8              a block substitution of one or more nucleotides that do
substitution                        not cause frameshift changes in protein coding sequence

nonsynonymous        9              a single nucleotide change that causes an amino acid
SNV                                 change

synonymous SNV       10             a single nucleotide change that does not cause an
                                    amino acid change

unknown              11             unknown function (due to various errors in the gene
                                    structure definition in the database file)

So you should get most of that information. However, for the het and hom, it all depends on your input file. What is your input format?

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Thanks for reply but it explains about synonymous and non-synonymous. While synonymous mutations only occur within exons, and are not always silent mutations. Similarly missense is a type of non synonymous. (I am not really expert in biology can you pls explain here).

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Only exonic variants will have those annotation as synonymous and non-synonymous is about the amino acid changes. If you want to know more about those, you can easily find a lot of information on the internet.

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