Hi Alex,
I have done genome guided assembly using StringTie and it also generated a gff file. I also have already reported CDS gff. I want to merge both gff and want to extract consensus sequences from genome.
Hi, I was wondering if anyone know how to merge 2 or more gff files to make a consensus gff file? I have gtf file from tophat/cufflinks pipeline gff file from velvet assembly and i would like to merge these two to the already annotated gff file. The idea is to have one gff file instead of three gff files so that i can load this as track on my Genome Browser. Thanks in advance
One way to do this is to start by converting them to sorted UCSC BED with BEDOPS gtf2bed (link) and sort-bed (link). Here's a quick way to convert a bunch of files if you use a bash shell:
$ for i in `ls *.gtf`; \
do gtf2bed < $i > $i.converted.bed; \
done;
Then do a multiset union set operation with BEDOPS bedops (link) to make a single BED file called answer.bed that can be loaded into your genome browser instance:
$ bedops --everything *.converted.bed | cut -f1-6 - > answer.bed
Hi Alex,
I have done genome guided assembly using StringTie and it also generated a gff file. I also have already reported CDS gff. I want to merge both gff and want to extract consensus sequences from genome.
I know you probably figured this out since it was asked 6 years ago but I ended up using gffcompare in 2019 (for anyone else looking for this): https://ccb.jhu.edu/software/stringtie/gffcompare.shtml
It worked really well.
For installation:
conda create --name gffcompare_env -c bioconda gffcompare -y
(I usually create a separate environment for all my tools to keep things clean. )
The above tool takes in a file with paths to gtf files and then you just specify an output, Se the link for more information on the usage.
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Any reason to not simply load three separate files? Alternatively, you can simply concatenate them and sort by chromosome location.
No particular reason..I want to all three files separately along with a fourth file indicating the consensus annotation which users might feel more convenient dealing with.
Programs such as MAKER combine evidence for gene models