Single-Cell Indel Detection Enhances Genetic Ancestry and Cellular Lineage Analysis
- 1The University of Alabama at Birmingham
Abstract
Small insertions and deletions (Indels) provide critical information for cancer genomics and clonal evolution, yet their detection from single-cell sequencing (SCS) such as scRNA-seq and scATAC-seq remains challenging due to sparse coverage, alignment artifacts, and RNA editing. Here, we present Monopogen-Indel, a bioinformatics framework for accurate germline and somatic Indel detection through haplotype-aware variant calling, dynamic template matching in repetitive regions, and cell-population- based allele segregation analysis. We validated germline Indel detection in human retina snRNA-seq with matched bulk whole genome sequencing (WGS). Monopogen-Indel detected 41,000-45,000 germline Indels per sample, with >70% precision and >90% genotyping accuracy. Using 65 heart left ventricle snATAC-seq samples, indel-based global ancestry inference segregated genetic ancestry comparably to SNVs, establishing indels as an independent marker of genetic diversity in SCS. In scRNA-seq from 43,717 cells across four anatomic sites of a patient with high grade serous ovarian cancer (HGSOC), Monopogen- Indel identified ~50,000 germline Indels per sample at 86% WGS-validated precision and an average of 1,040 de novo Indels per sample. Approximately 50% of de novo Indels reflected biological sources, including variants WGS variants, RNA editing, and cell-type-specific patterns. Somatic SNVs were correctly restricted to CD45- cells, indicating high specificity in delineating somatic from germline variants. In summary, Monopogen-indel is the first framework dedicated to indel detection from SCS and expands the utility of existing single-cell data for population genetics and cancer evolution. The module is integrated into Monopogen repository https://github.com/KChen-lab/Monopogen.
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BibTeX
@misc{wang2026singlecell,
title = {{Single-Cell Indel Detection Enhances Genetic Ancestry and Cellular Lineage Analysis}},
author = {Z. Wang and K. Chen and J. Dou},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.02.748725},
url = {https://www.biorxiv.org/content/10.64898/2026.09.02.748725v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Single-Cell Indel Detection Enhances Genetic Ancestry and Cellular Lineage Analysis
AU - Z. Wang
AU - K. Chen
AU - J. Dou
PY - 2026
DA - 2026/09/02
PB - bioRxiv
DO - 10.64898/2026.09.02.748725
UR - https://www.biorxiv.org/content/10.64898/2026.09.02.748725v1
AB - Small insertions and deletions (Indels) provide critical information for cancer genomics and clonal evolution, yet their detection from single-cell sequencing (SCS) such as scRNA-seq and scATAC-seq remains challenging due to sparse coverage, alignment artifacts, and RNA editing. Here, we present Monopogen-Indel, a bioinformatics framework for accurate germline and somatic Indel detection through haplotype-aware variant calling, dynamic template matching in repetitive regions, and cell-population- based allele segregation analysis. We validated germline Indel detection in human retina snRNA-seq with matched bulk whole genome sequencing (WGS). Monopogen-Indel detected 41,000-45,000 germline Indels per sample, with >70% precision and >90% genotyping accuracy. Using 65 heart left ventricle snATAC-seq samples, indel-based global ancestry inference segregated genetic ancestry comparably to SNVs, establishing indels as an independent marker of genetic diversity in SCS. In scRNA-seq from 43,717 cells across four anatomic sites of a patient with high grade serous ovarian cancer (HGSOC), Monopogen- Indel identified ~50,000 germline Indels per sample at 86% WGS-validated precision and an average of 1,040 de novo Indels per sample. Approximately 50% of de novo Indels reflected biological sources, including variants WGS variants, RNA editing, and cell-type-specific patterns. Somatic SNVs were correctly restricted to CD45- cells, indicating high specificity in delineating somatic from germline variants. In summary, Monopogen-indel is the first framework dedicated to indel detection from SCS and expands the utility of existing single-cell data for population genetics and cancer evolution. The module is integrated into Monopogen repository https://github.com/KChen-lab/Monopogen.
ER -