Sign in
BioinformaticsNature Communications
Peer-reviewed journal article

scReadSim: a single-cell RNA-seq and ATAC-seq read simulator

Guanao YaniD, Dongyuan SongiD, Jingyi Jessica LiiD

Abstract

Benchmarking single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) computational tools demands simulators to generate realistic sequencing reads. However, none of the few read simulators aim to mimic real data. To fill this gap, we introduce scReadSim, a single-cell RNA-seq and ATAC-seq read simulator that allows user-specified ground truths and generates synthetic sequencing reads (in FASTQ and BAM formats) by mimicking real data. At both read-sequence and read-count levels, scReadSim mimics real scRNA-seq and scATAC-seq data. Moreover, scReadSim provides ground truths, including unique molecular identifier counts for scRNA-seq and open chromatin regions for scATAC-seq. In particular, scReadSim allows users to design cell-type-specific ground-truth open chromatin regions for scATAC-seq data generation. In benchmark applications of scReadSim, we show that cell-ranger is a preferred scRNA-seq UMI deduplication tool, and HMMRATAC and MACS3 achieve top performance in scATAC-seq peak calling.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISChen '21Navidi '21Crowell '21Chugh '26Cao '21Li '23
72% semantic matchBioinformatics
Zhanlin Chen, Jing Zhang, Jason J. Liu+5 more·Bioinformatics·Jan 14, 2021· 15 citations