Scalable and unsupervised discovery from raw sequencing reads using SPLASH2.

Abstract

We introduce SPLASH2, a fast, scalable implementation of SPLASH based on an efficient k-mer counting approach for regulated sequence variation detection in massive datasets from a wide range of sequencing technologies and biological contexts. We demonstrate biological discovery by SPLASH2 in single-cell RNA sequencing (RNA-seq) data and in bulk RNA-seq data from the Cancer Cell Line Encyclopedia, including unannotated alternative splicing in cancer transcriptomes and sensitive detection of circular RNA.