We used LeafCutter20 to obtain clusters of variably spliced introns. Leafcutter allows the identification of splicing events without relying on existing annotations, which are typically incomplete, especially in the setting of large genes or individual/population-specific isoforms. Leafcutter defines “clusters” of introns that represent alternative splicing choices. To do this, it first groups together overlapping introns (defined by spliced reads). For each of these groups, Leafcutter constructs a graph where nodes are introns and edges represent overlapping introns. The connected components of this graph define the intron clusters. Singleton nodes (introns) are discarded. For each intron cluster, it iteratively (1) removed introns that were supported with fewer than 100 reads or fewer than 5% of the total number of intronic read counts for the entire cluster, and (2) re-clustered introns according to the procedure above. The intron usage ratio for each clusters was next computed and standardized (across individuals) and quantile normalized (across sample) as in Li et al.20. LeafCutter was carefully benchmarked against other methods (see Li et al.20), and was able, for example, to identify as many or more differentially spliced events d than compared to other methods.
