ScSmOP: a universal computational pipeline for single-cell single-molecule multiomics data analysis

Area of Science:

• Genomics
• Epigenomics
• Transcriptomics
• Computational Biology

Background:

• Single-cell multiomics techniques offer single-molecule resolution and spatial localization.
• These methods reveal genomic, epigenomic, and transcriptomic heterogeneity in individual cells.
• Emerging single-cell multiomics methods present significant computational data processing challenges.

Purpose of the Study:

• To introduce Single-cell Single-molecule multiple Omics Pipeline (ScSmOP), a universal computational pipeline.
• To address the computational challenges in analyzing barcode-indexed single-cell single-molecule multiomics data.
• To provide a versatile, efficient, and robust tool for multiomics data analysis.

Main Methods:

• ScSmOP utilizes C language for spaced-seed hash table-based algorithms.
• Algorithms are designed for barcode identification in ligation-based and synthesis-based barcoding data.
• The pipeline includes data mapping and deconvolution steps.

Main Results:

• ScSmOP demonstrates high reproducibility compared to published pipelines.
• Comprehensive analyses cover single-cell omics, single-molecule chromatin interaction, and spatial transcriptomic data.
• ScSmOP processes data from various cell types and species, including scRNA-seq, scATAC-seq, scARC-seq, ChIA-Drop, SPRITE, RD-SPRITE, and scSPRITE.

Conclusions:

• ScSmOP is a versatile, efficient, easy-to-use, and robust pipeline.
• The pipeline exhibits rapid performance for single-cell single-molecule multiomics data analysis.
• ScSmOP facilitates deeper insights into cellular heterogeneity through multiomics data integration.