Alternative splicing quantification, differential splicing,

outlier splicing detection, and splicing QTL mapping ([original](http://davidaknowles.github.io/leafcutter/index.html)) ([raw](?raw))

LeafCutter: Annotation-free quantification of RNA splicing

Yang I. Li1, David A. Knowles1, Jack Humphrey, Alvaro N. Barbeira, Scott P. Dickinson, Hae Kyung Im, Jonathan K. Pritchard

1_Equal contribution_

Leafcutter quantifies RNA splicing variation using short-read RNA-seq data. The core idea is to leverage spliced reads (reads that span an intron) to quantify (differential) intron usage across samples. The advantages of this approach include

• easy detection of novel introns
• modeling of more complex splicing events than exonic PSI
• avoiding the challenge of isoform abundance estimation
• simple, computationally efficient algorithms scaling to 100s or even 1000s of samples

For details please see our bioRxiv preprint and corresponding Nature Genetics publication.

Additionally, for full details on the leafcutter for Mendelian Diseases (leafcutterMD) method that performs outlier splicing detection, see our Bioinformatics publication.

• Installation
• Differential splicing
• Outlier splicing
• Visualization
• SplicingQTL

Check out a demo leafcutter shiny app here: 10 brain vs. 10 heart samples from GTEx.

We have a Google group for user questions at https://groups.google.com/forum/#!forum/leafcutter-users