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 …

As a solution we provide a LeafCutter shiny app called leafviz allowing interactive visual interrogation of your results. Go to the Visualization tab to learn how to feed the LeafCutter …

The original version of the app is still within the Leafcutter repository but will eventually be deprecated as all further maintenance and development will be to the standalone version.

For the analysis in the LeafCutter paper we used either OLego, which is designed to be particularly sensitive for finding de novo junctions, or STAR, which is fast as long as you have …

If you just want to get leafcutter quantifications (junction counts for each splicing cluster) e.g. for sQTL analysis, then you just need the python scripts in scripts and clustering. If you want to do …

To perform splicing QTL (sQTL) analysis using LeafCutter you’ll first need to preprocess your RNA-seq data as described in Steps 1 and 2 under Differential Splicing. sQTL analysis is a …

Dirichlet multinomial GLM likelihood ratio test for a single cluster (single overdispersion parameter)

Parallelization across tested clusters is achieved using foreach/doMC, so the number of threads that will be used is determined by the cores argument passed to registerDoMC.

Convert K-1 representation of parameters to realSource: R/differential_splicing.R

We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable intron splicing events from short-read RNA-seq data and finds alternative …