Annotating a genome based on CNV calls.

[pepsimax]

I have a bed file of read locations that looks like

chr1 193244 193246
chr2 293244 293246
etc.

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.

[stqa8350]

Hi

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

Thanks

Uma

[adamyao]

VarioWatch (http://genepipe.ncgm.sinica.edu.tw/variowatch/main.do) 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.

[lgmSeq]

I can recommend an easy way to cover CNV overlap with human genes.

I - Create an initial genetrack.RefSeq.GRCh37.txt file
Go to the UCSC genome table browser:

http://gatkforums.broadinstitute.org/discussion/1329/using-refseq-data

There are many output options, here are the changes that you'll need to make:
clade: Mammal
genome: Human
assembly: ''choose the appropriate assembly for the reference you're using''
group: Genes abd Gene Prediction Tracks
track: RefSeq Genes
table: refGene
region: ''choose the genome option''

Choose the output filename:
genetrack.RefSeq.GRCh37.txt

Click the get output button.

You now have your initial RefSeq file, which will not be sorted, and will contain non-standard contigs (contigs other than the standard 1-22,X,Y,MT)



II - Remove non-standard contigs and sorting the file in karyotypic order:
Create the extract.tcl. This file looks like so:

#!/usr/bin/env tclsh

# Remove contigs other than the standard 1-22,X,Y,MT
# and sort the file in karyotypic order.

proc ContentFromFile {{Fichier ""}} {
if {[string equal $Fichier ""]} {return ""}
set f [open $Fichier r]
set Texte [read -nonewline $f]
close $f
return $Texte
}

proc LinesFromFile {{Fichier ""}} {
return [split [ContentFromFile $Fichier] "\n"]
}

proc WriteTextInFile {texte fichier} {
set fifi [open $fichier a]
puts $fifi $texte
close $fifi
return 1
}

proc IncreasingSortOnElement4 {X Y {N 4}} {
return [expr {[lindex $X $N]>[lindex $Y $N]}]
}

## Checking and displaying parameter
set geneFile [lindex $argv 0]
if {![file exists $geneFile]} {
puts "$geneFile doesn't exist. Exit"
exit
}

## Defining output file
regsub ".txt" $geneFile "" outputFile
set outputFile "$outputFile.sorted.txt"
file delete -force $outputFile
puts "...creation of $outputFile"

foreach L [LinesFromFile $geneFile] {
set Ls [split $L "\t"]
if {[regexp "^#" $L]} {
WriteTextInFile $L $outputFile
set i_chr [lsearch -exact $L "chrom" ]; if {$i_chr == -1} {puts "Bad header line syntax. chrom column not found - Exit"; exit}
continue
}

regsub -all " " [lindex $Ls $i_chr] "" chrom
lappend linelist($chrom) "$L"
}

foreach val {1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y M MT} {
if {![info exists linelist(chr$val)]} {continue}
WriteTextInFile [join [lsort -command IncreasingSortOnElement4 $linelist(chr$val)] "\n"] $outputFile
}


Then run the extract.tcl file:
extract.tcl genetrack.RefSeq.GRCh37.txt
-> create genetrack.RefSeq.GRCh37.sorted.txt


III - Create the genetrack.RefSeq.GRCh37.sorted.bed file:
cat genetrack.RefSeq.GRCh37.sorted.txt | awk -F"\t" '{print $3"\t"$5"\t"$6"\t"$13"\t"}' > genetrack.RefSeq.GRCh37.sorted.bed


IV - Annotate your CNV bed file:
Sample CNV bed file = CNVsample.bed
chr7 5952473 5978460

Using Bedtools, run the intersection like so:
intersectBed -a CNVsample.bed -b genetrack.RefSeq.GRCh37.sorted.bed -wb > CNVsample.annotated

chr7 5952473 5965603 chr7 5938340 5965603 CCZ1
chr7 5965776 5978460 chr7 5965776 6010314 RSPH10B
chr7 5965776 5978460 chr7 5965776 6010314 RSPH10B2


Of course, you can use Bedtools to run the intersection with other bed files!

[lgmSeq]

AnnotSV: An integrated tool for Structural Variations annotation

Hi,

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).