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https://github.com/lcalviell/ORFquant
An R package for Splice-aware quantification of translation using Ribo-seq data
https://github.com/lcalviell/ORFquant
Last synced: about 1 month ago
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An R package for Splice-aware quantification of translation using Ribo-seq data
- Host: GitHub
- URL: https://github.com/lcalviell/ORFquant
- Owner: lcalviell
- License: gpl-3.0
- Created: 2019-09-19T03:47:49.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-12-20T18:11:41.000Z (about 1 year ago)
- Last Synced: 2024-08-05T15:04:29.744Z (5 months ago)
- Language: HTML
- Size: 3.85 MB
- Stars: 14
- Watchers: 2
- Forks: 13
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-riboseq - Code - 020-0450-4) | R; Isoform level translation quantification of ORFs | (Isoform Level Analysis)
README
# ORFquant
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:
### Quantification of translation uncovers the functions of the alternative transcriptome ###
*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!