leafcutter

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

This branch has a Python/pytorch/pyro reimplementation of the LeafCutter differential splicing test. Users (including us!) often had problems installing the RStan based package.

You should be able to install with python -m pip install leafcutter.

The installation process will install leafcutter-cluster and leafcutter-ds as command line tools.

Annotation-free quantification of RNA splicing.

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.

Full documentation is available at http://davidaknowles.github.io/leafcutter/

If you have usage questions we've setup a Google group here: https://groups.google.com/forum/#!forum/leafcutter-users

We've developed a leafcutter shiny app for visualizing leafcutter results: you can view an example here. This shows leafcutter differential splicing results for a comparison of 10 brain vs. 10 heart samples (5 male, 5 female in each group) from GTEx.