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Related Experiment Video

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

Kai Jing1,2, Yewen Xu1,2, Yang Yang1,2

  • 1Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.

Briefings in Bioinformatics
|October 2, 2023
PubMed
Summary

A new computational pipeline, ScSmOP, analyzes single-cell, single-molecule multiomics data. It offers reproducible, rapid, and versatile processing for genomic, epigenomic, and transcriptomic analyses across various species and cell types.

Keywords:
barcode identificationmultiomicspipelinesingle cellsingle moleculespaced seed hash

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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.