hello,biostar members:
i have collect about 2000SNPs in my SNPs study. recently i came across a paper, in which the snps are seperated into several parts, that is coding area, 3'UTR, 5'UTR, intergenetic, promoter, TFBS, miRNA, enhancer...
so i want to know how can i use this kind of classification to handle with my SNPs set?
if there is anyone know how to do it, could you offer me a database or scripts to do this jobs?
thank you !
•
written
by
J.F.Jiang
If I understand correctly, this could well be what you are after:
http://snpeff.sourceforge.net/
Also this:
http://www.openbioinformatics.org/annovar/
I'm a newbie and plan to try both but haven't gotten around to it yet.
I have looked up into the two databases, it seems that i did not clearly declare the problem. the 2000SNPs i collected is not the one from NGS platform or array. It is just those i get from papers and databases. So the format of my file only contains the SNPs rs# number, chr#, position, alles, nothing else.
Thus, i want to quickly know which part are they. But it seems that the first database is what i want, i will carefully checked it to see if it is what i want.
considering human genome annotation (you will have to pay attention to the human genome version you've worked with, in order to select the appropriate annotations), I would rather go for any of these 2:
these 2 are valid options for the thousands of variants coming out of a NGS experiment, so I'm pretty sure that if you format your SNP list into a valid format which any of these 2 programs accept then you will end up annotating your SNP list easily.
One might start with GFF-formattted GENCODE annotations:
$ wget -qO- ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_21/gencode.v21.annotation.gff3.gz | gunzip --stdout - > gencode.v21.gff
Using the feature ontology defined here, one can segregate GFF annotations by feature type (see: http://song.cvs.sourceforge.net/viewvc/song/ontology/sofa.obo?revision=1.217). Feature types include keywords like three_prime_UTR, promoter, etc. We can grab a sorted listing of feature types to automate this process. For example:
$ wget -qO- http://song.cvs.sourceforge.net/viewvc/song/ontology/sofa.obo?revision=1.217 | grep '^name:' | sed 's/name: //' | sort > gff_feature_types.txt
We can then segregate the GENCODE annotations by feature type:
$ while read feature_type; do grep ${feature_type} gencode.v21.gff > feature.${feature_type}.gff; done < gff_feature_types.txt
Let's assume that you have your variants in a VCF-formatted file called variants.vcf. Let's convert it to BED with vcf2bed:
$ vcf2bed < variants.vcf > variants.bed
For each smaller annotation file that is of non-zero size, we can convert its annotations to BED elements with gff2bed. We then perform set operations against the variants, separating them into per-feature-type categories based on one or more bases of overlap with the annotation subset:
$ find . -name feature.*.gff ! -size 0 -exec bedops --element-of 1 variants.bed <(gff2bed < {}) > variants.{}.bed \;
Each non-empty file variants.*.bed contains variants that overlap a GENCODE v21 feature by its feature type.
•
written
by
Alex Reynolds
I am trying to use the above code to annotate a few sites I have. I am able to get all the feature.${feature_type}.gff files, but the last line shows some error below.
find . -name feature.*.gff ! -size 0 -exec bedops --element-of 1 sample1.bed <(gff2bed < {}) > variants.{}.bed \;
-bash: {}: No such file or directory
find: paths must precede expression: feature.coding_region_of_exon.gff
Any suggestion how I can modify the code? Thanks
when the -exec option gets complicated I find it easier to build a for loop. in fact I always try to code as visual as possible in order to quickly review it when needed.
for file in `find . -name feature.*.gff ! -size 0`; do
cat $file \
| gff2bed \
| bedops --element-of 1 sample1.bed \
> variants.$file.bed
done
Perhaps try wrapping the command in tick marks:
find . -name feature.*.gff ! -size 0 -exec 'bedops --element-of 1 sample1.bed <(gff2bed < {}) > variants.{}.bed \;'
I think PLINK is well suited for your problem, look at SNP annotation
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