A comprehensive analysis tool for Ribo-seq and small RNA-seq data
Ribo-seQC (RiboseQC) is an R package that performs quality control analysis of small RNA-seq data, with a focus on Ribo-seq and related techniques. Thanks to syntax and functions present in Bioconductor packages like GenomicFeatures, rtracklayer or BSgenome, this package can perform comprehensive analyses on a variety of genomic regions. In addition, Ribo-seQC allows to automatically generate an html report for each analyzed sample, allowing for quick and interactive comparison of multiple samples at once.
This tools focuses on the analysis of different read lengths, taking into account the genomic regions they map to (e.g. coding sequence, UTRs, non-coding RNAs, mitochondria or chloroplasts, etc...). Other useful features, such as automatic P-sites position calculation or analysis the top mapping positions, are available in the Ribo-seQC package, and we encourage to donwload and have a look at the vignette (RiboseQC.html https://htmlpreview.github.io/?https://github.com/lcalviell/Ribo-seQC/blob/master/RiboseQC.html ), our manual (RiboseQC-manual.pdf), and our manuscript:
Lorenzo Calviello*, Dominique Sydow*, Dermot Harnett, Uwe Ohler^
biorXiv (2019), doi: https://doi.org/10.1101/601468
https://www.biorxiv.org/content/10.1101/601468v1
An example Ribo-seQC html report for Ribo-seq data from Arabodopsis roots and shoots (Hsu et al, PNAS 2016) is available here, and can be downloaded and opened using a browser like Chrome, Firefox or others (Warning, file size is ~120Mb):
https://drive.google.com/open?id=13OBuS4CG0GA6j3Dt68zRtC8KBzNkmO7J
Here another report created using data from a TCP-seq (Archer et al, Nature 2016) experiment in yeast (Warning, file size is ~110Mb):
https://drive.google.com/open?id=1568rBoFgYBREhrNeHQr1-66nQVKA8WEZ
To install Ribo-seQC:
library("devtools")
install_github(repo = "lcalviell/Ribo-seQC")
library("RiboseQC")
Two simple steps are required to use Ribo-seQC 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/ )
and
?RiboseQC_analysis
the master function used to perform the entire analysis workflow. Please check the vignette for some example workflows.
For any question, please email:
calviello.l.bio@gmail.com, dominique.sydow@posteo.de (analysis and data visualization), Dermot.Harnett@mdc-berlin.de (package mantainer), Uwe.ohler@mdc-berlin.de (project supervisor).
Enjoy!