{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "The University of Alabama at Birmingham"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Z. Wang"
    },
    {
      "name": "K. Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Dou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fr1xfxk83t5q1.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.02.748725",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.02.748725v1.full",
  "id": "fr1xfxk83t5q1",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.02.748725v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "genomics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7211874860",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.02.748725v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fr1xfxk83t5q1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:00Z",
      "native_id": "10.64898/2026.09.02.748725",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.02.748725v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:genomics",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Single-Cell Indel Detection Enhances Genetic Ancestry and Cellular Lineage Analysis",
  "title_text": "Single-Cell Indel Detection Enhances Genetic Ancestry and Cellular Lineage Analysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:00Z",
  "url": "https://research.draconitic.com/paper/fr1xfxk83t5q1",
  "venue": "bioRxiv",
  "versions": []
}