SPLASH2 provides ultra-efficient, scalable, and unsupervised discovery on raw sequencing reads

Marek Kokot; Roozbeh Dehghannasiri; Tavor Baharav; Julia Salzman; Sebastian Deorowicz

doi:10.1101/2023.03.17.533189

SPLASH2 provides ultra-efficient, scalable, and unsupervised discovery on raw sequencing reads

bioRxiv [Preprint]. 2024 Mar 30:2023.03.17.533189. doi: 10.1101/2023.03.17.533189.

Authors

Marek Kokot¹, Roozbeh Dehghannasiri^{2

3}, Tavor Baharav⁴, Julia Salzman^{2

3

5}, Sebastian Deorowicz¹

Affiliations

¹ Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland.
² Department of Biomedical Data Science, Stanford University, Stanford, 94305, USA.
³ Department of Biochemistry, Stanford University, Stanford, 94305, USA.
⁴ Department of Electrical Engineering, Stanford University, Stanford, 94305, USA.
⁵ Department of Statistics (by courtesy), Stanford University, Stanford, 94305, USA.

Abstract

SPLASH is an unsupervised, reference-free, and unifying algorithm that discovers regulated sequence variation through statistical analysis of k-mer composition, subsuming many application-specific methods. Here, we introduce SPLASH2, a fast, scalable implementation of SPLASH based on an efficient k-mer counting approach. SPLASH2 enables rapid analysis of massive datasets from a wide range of sequencing technologies and biological contexts, delivering unparalleled scale and speed. The SPLASH2 algorithm unveils new biology (without tuning) in single-cell RNA-sequencing data from human muscle cells, as well as bulk RNA-seq from the entire Cancer Cell Line Encyclopedia (CCLE), including substantial unannotated alternative splicing in cancer transcriptome. The same untuned SPLASH2 algorithm recovers the BCR-ABL gene fusion, and detects circRNA sensitively and specifically, underscoring SPLASH2's unmatched precision and scalability across diverse RNA-seq detection tasks.

Publication types

Preprint

Grants and funding

R35 GM139517/GM/NIGMS NIH HHS/United States