To assess the clinical impact of splice-altering noncoding mutations in autism spectrum disorder (ASD), we used a deep learning framework (SpliceAI) to predict the splice-altering potential of de novo mutations in 3,953 individuals with ASD from the Simons Simplex Collection. To validate these predictions, we selected 36 individuals that harbored predicted de-novo cryptic splice mutations; each individual represented the only case of autism within their immediate family. We obtained peripheral blood-derived lymphoblastoid cell lines (LCLs) and performed high-depth mRNA sequencing (approximately 350 million 150 bp single-end reads per sample). We used OLego to align the reads against a reference created from hg19 by substituting de novo variants of each individual with the corresponding alternate allele.