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Old 06-15-2012, 09:28 AM   #1
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Location: London

Join Date: May 2012
Posts: 9
Default Annotating hg19 based on chr position.

I have a bed file of read locations that looks like

chr1 193244 193246
chr2 293244 293246

I want to identify where they lie on hg19 (what gene, intronic or exonic etc.) How do I automate this? The data spans a number of chr's, but I would still like a visual UCSC like output, as well as a txt one if possible.

Last edited by pepsimax; 06-15-2012 at 09:40 AM.
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Old 08-20-2012, 05:31 AM   #2
Location: cambridge, ma

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Posts: 13


Did you find a solution to this problem. I am having the same at the moment and would like to know.


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Old 08-20-2012, 06:39 PM   #3
Location: Taiwan

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Posts: 19

VarioWatch ( provides visual output up to one thousand variants online in real time. It also provides text output for millions of variants. However, it does not take the bed file format. You can try it if the bed file format can be converted.
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Old 07-09-2018, 08:36 AM   #4
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Default AnnotSV: An integrated tool for Structural Variations annotation


I'm annotating my CNV/SV human events with the AnnotSV tool.
PMID: 29669011 DOI: 10.1093/bioinformatics/bty304

It associates a complete panel of different datasets to provide high quality structural variations (SV) / CNV annotation :
- Gene annotations
- Promoters annotations
- DGV Gold Standard annotations
- DECIPHER gene annotations
- 1000 genomes annotations
- GC content annotations
- Repeated sequences annotations
- TAD annotations
- OMIM annotations
- Gene intolerance annotations
- Haploinsufficiency annotations
- Homozygous and heterozygous SNV/indel annotations
- ...

AnnotSV starts by detecting the genomic overlaps between the input and the annotation features.

Moreover, interesting information, this tool constructs an annotation based on the full-length SV but also an annotation for each gene within the SV.

Really easy to install and to use!

Input format: VCF or BED

Else, if you have CNV calls from different CNV callers, I advise you (before to annotate) to identify/merge the common CNV detected by your different callers. For that, I would consider CNV that share a 70% reciprocal overlap measured by length and position (> 70% shared length) (as done in DGV).

Last edited by lgmSeq; 07-09-2018 at 10:21 PM.
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