Hey, does anyone have any pointers, advice, or experience on modifying GTF files for use with cufflinks??? (v 2.0.2)
In the course of examining RNA-seq data and performing RNA-seq data analysis, an issue I've run into (using the "tuxedo" software/pipeline of tophat->cufflinks) is that tophat maps to apparent non-coding regions (possibly regulatory) but that cufflinks won't indicate FPKM expressions for the pileups! So a strategy we are trying, whose goal is to trigger cufflinks to tell FPKM expression values, is to either modify or create GTF annotation data and tell tophat/cufflinks to *not* try to find novel transcripts while using the created/modified GTF so that cufflinks might give FPKM values!
One strategy we tried is to create a GTF with features/annotations corresponding to the regions of interest. Created as "pseudogene exons" (in columns 2 and 3), and using exsiting ensemble geneIDs, but custom transcript_ids we fed the GTF to cufflinks. When cufflinks program execution got to the "Loading Annotation" part (at the beginning of the run) it crashed with a segmentation fault! In the attribute column (#9), no information besides the gene_id and transcript_id was provided! cufflinks may have crashed because no gene_name was given. We really don't know however!
Another strategy we are currently trying is to *modify* an existing GTF (from illumina/igenomes/ensemble) that *modify* work with cufflinks. This time, to capture regions upstream and downstream of genes, for each geneid, we modify the lowest start-value over all annotations by decreasing it by 1000 (to *hopefully* capture expressions of regions upstream). Similarly, we modify the highest end-value by increasing it by 1000 to *hopefully* capture expressions of regions downstream. This is currently going on now, so I don't know if the run will work, end successfully, and give us the expression/FPKM values/numbers we are looking for....
Any pointers, advice, experience, knowledge, insight, etc. with GTF file tweaking for cufflinks would be appreciated!
We are using tophat v2.0.4 and cufflinks v2.0.2 by the way.
thanks
-Eddie
In the course of examining RNA-seq data and performing RNA-seq data analysis, an issue I've run into (using the "tuxedo" software/pipeline of tophat->cufflinks) is that tophat maps to apparent non-coding regions (possibly regulatory) but that cufflinks won't indicate FPKM expressions for the pileups! So a strategy we are trying, whose goal is to trigger cufflinks to tell FPKM expression values, is to either modify or create GTF annotation data and tell tophat/cufflinks to *not* try to find novel transcripts while using the created/modified GTF so that cufflinks might give FPKM values!
One strategy we tried is to create a GTF with features/annotations corresponding to the regions of interest. Created as "pseudogene exons" (in columns 2 and 3), and using exsiting ensemble geneIDs, but custom transcript_ids we fed the GTF to cufflinks. When cufflinks program execution got to the "Loading Annotation" part (at the beginning of the run) it crashed with a segmentation fault! In the attribute column (#9), no information besides the gene_id and transcript_id was provided! cufflinks may have crashed because no gene_name was given. We really don't know however!
Another strategy we are currently trying is to *modify* an existing GTF (from illumina/igenomes/ensemble) that *modify* work with cufflinks. This time, to capture regions upstream and downstream of genes, for each geneid, we modify the lowest start-value over all annotations by decreasing it by 1000 (to *hopefully* capture expressions of regions upstream). Similarly, we modify the highest end-value by increasing it by 1000 to *hopefully* capture expressions of regions downstream. This is currently going on now, so I don't know if the run will work, end successfully, and give us the expression/FPKM values/numbers we are looking for....
Any pointers, advice, experience, knowledge, insight, etc. with GTF file tweaking for cufflinks would be appreciated!
We are using tophat v2.0.4 and cufflinks v2.0.2 by the way.
thanks
-Eddie
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