An R package for Splice-aware quantification of translation using Ribo-seq data
ORFquant is an R package that aims at detecting and quantifiying ORF translation on complex transcriptomes using Ribo-seq data. This package uses syntax and functions present in Bioconductor packages like GenomicFeatures, rtracklayer or BSgenome. ORFquant aims at quantifying translation at the single ORF level taking into account the presence of multiple transcripts expressed by each gene. To do so, the ORFquant pipeline consists of transcript filtering, de-novo ORF finding, ORF quantification and ORF annotation. A variety of annotation methods, both in transcript and genomic space, is performed for each ORF, to yield a more complete picture of alternative splice sites usage, uORF translation, translation on NMD candidates and more.
More details can be found in our manuscript:
Lorenzo Calviello^, Antje Hirsekorn, Uwe Ohler^
biorXiv (2019), doi: https://doi.org/10.1101/608794
https://www.biorxiv.org/content/10.1101/608794v2
Now published in Nature Structural and Molecular Biology:
https://www.nature.com/articles/s41594-020-0450-4
We recommend users to have a look at the vignette: https://htmlpreview.github.io/?https://github.com/lcalviell/ORFquant/blob/master/ORFquant_vignette.html, or our manual (ORFquant_manual.pdf).
To install ORFquant:
library("devtools")
install_github(repo = "lcalviell/ORFquant")
library("ORFquant")
Three steps are required to use ORFquant on your data:
?prepare_annotation_files
parses a .gtf and a .2bit file. (this need to be done once per each annotation-genome combination, a .2bit file can be obtained from a fasta file using the faToTwoBit software from UCSC: https://genome.ucsc.edu/goldenpath/help/twoBit.html - http://hgdownload.soe.ucsc.edu/admin/exe/ )
?prepare_for_ORFquant
or (recommended) the Ribo-seQC package (https://github.com/lcalviell/Ribo-seQC) can create input files for ORFquant using a Ribo-seq .bam file.
?run_ORFquant
is the master function used to perform the entire analysis workflow, for single genes or (recommended) entire transcriptomes. Please check the vignette for an example workflow.
For any question, please email:
[email protected] or [email protected]
Enjoy!