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Curated Papers > Recent high-impact research on "single-cell sequencing" (IF≥30, last 5 years)

Single-nucleotide variant calling in single-cell sequencing data with Monopogen. ​

Literature Information ​

DOI10.1038/s41587-023-01873-x
PMID37592035
JournalNature biotechnology
Impact Factor41.7
JCR QuartileQ1
Publication Year2024
Times Cited17
Keywordssingle-cell sequencing, single-nucleotide variant, computational tool, population genetics, clonal lineage tracing
Literature TypeJournal Article
ISSN1087-0156
Pages803-812
Issue42(5)
AuthorsJinzhuang Dou, Yukun Tan, Kian Hong Kock, Jun Wang, Xuesen Cheng, Le Min Tan, Kyung Yeon Han, Chung-Chau Hon, Woong-Yang Park, Jay W Shin, Haijing Jin, Yujia Wang, Han Chen, Li Ding, Shyam Prabhakar, Nicholas Navin, Rui Chen, Ken Chen

TL;DR ​

The study introduces Monopogen, a computational tool that detects single-nucleotide variants (SNVs) from single-cell sequencing data, revealing how genetic backgrounds influence transcriptional and epigenetic profiles. This tool enhances understanding of cellular processes by enabling accurate identification of germline and somatic SNVs, facilitating ancestry inference, and linking genetic variants to cardiomyocyte functions and clonal hematopoiesis.

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single-cell sequencing · single-nucleotide variant · computational tool · population genetics · clonal lineage tracing

Abstract ​

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

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Primary Questions Addressed ​

  1. How does Monopogen compare to other existing tools for single-nucleotide variant calling in terms of accuracy and efficiency?
  2. What are the specific advantages of using linkage disequilibrium from external reference panels in detecting germline SNVs with Monopogen?
  3. In what ways can Monopogen's capabilities in identifying somatic SNVs enhance our understanding of clonal evolution in cancer?
  4. How does Monopogen facilitate the integration of single-cell omics data with population genetics for studying complex traits?
  5. What implications do the findings from Monopogen have for future research in personalized medicine and targeted therapies?

Key Findings ​

Research Background and Objectives ​

The study focuses on the development of Monopogen, a novel tool for single-nucleotide variant (SNV) calling in single-cell sequencing data. Traditional SNV calling methods often struggle with the complexity of single-cell data, which can contain noise and errors due to low sequencing depth and cell heterogeneity. The objective of this research is to enhance the accuracy of SNV detection in single-cell sequencing, enabling better insights into genetic variations at the cellular level.

Main Methods/Materials/Experimental Design ​

The Monopogen framework utilizes a multi-step process for SNV detection, which includes:

  1. SNV Classification: SNVs are categorized based on their overlap with the 1,000 Genomes Project (1KG) data, identifying germline and de novo variants.
  2. Feature Extraction: Various metrics (e.g., quality score, variant distance bias) are computed for SNV calling.
  3. LD Refinement: Linkage disequilibrium (LD) refinement is applied to improve the accuracy of variant phasing.
  4. Multi-threaded Implementation: The framework is designed for high performance, leveraging parallel processing to handle large genomic datasets.

The workflow can be represented in a flowchart format using Mermaid code:

Mermaid diagram

Key Results and Findings ​

  • Improved Accuracy: Monopogen demonstrated superior performance compared to existing SNV callers, particularly in single-cell RNA and DNA sequencing data.
  • Benchmarking: The tool was benchmarked against several other SNV callers (e.g., Samtools, GATK), showing higher precision and recall rates in identifying true variants.
  • Applications: The tool was successfully applied to various datasets, including retina and heart samples, revealing novel somatic variants that may contribute to disease understanding.

Main Conclusions/Significance/Innovation ​

Monopogen represents a significant advancement in the field of genomics, specifically in the analysis of single-cell sequencing data. Its innovative approach to SNV calling, characterized by its robust LD refinement and multi-threaded processing, enhances the reliability of genetic variant detection. This tool could facilitate more accurate genetic studies, potentially leading to breakthroughs in precision medicine and understanding complex diseases.

Research Limitations and Future Directions ​

  • Limitations: While Monopogen improves SNV detection, it still faces challenges with very low coverage data and may not be fully optimized for all types of genomic variations.
  • Future Directions: Further development could focus on integrating Monopogen with other genomic analysis tools and expanding its application to larger and more diverse datasets. Additionally, enhancing its capabilities to detect structural variants and copy number variations could broaden its utility in genomic research.

References ​

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Literatures Citing This Work ​

  1. Gene regulatory network inference in the era of single-cell multi-omics. - Pau Badia-I-Mompel;Lorna Wessels;Sophia Müller-Dott;Rémi Trimbour;Ricardo O Ramirez Flores;Ricard Argelaguet;Julio Saez-Rodriguez - Nature reviews. Genetics (2023)
  2. Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors. - Leah L Weber;Chuanyi Zhang;Idoia Ochoa;Mohammed El-Kebir - PLoS computational biology (2023)
  3. Inferring clonal somatic mutations directed by X chromosome inactivation status in single cells. - Ilke Demirci;Anton J M Larsson;Xinsong Chen;Johan Hartman;Rickard Sandberg;Jonas Frisén - Genome biology (2024)
  4. Computational methods for allele-specific expression in single cells. - Guanghao Qi;Alexis Battle - Trends in genetics : TIG (2024)
  5. Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data. - Valérie Marot-Lassauzaie;Sergi Beneyto-Calabuig;Benedikt Obermayer;Lars Velten;Dieter Beule;Laleh Haghverdi - Bioinformatics (Oxford, England) (2024)
  6. demuxSNP: supervised demultiplexing single-cell RNA sequencing using cell hashing and SNPs. - Michael P Lynch;Yufei Wang;Shannan Ho Sui;Laurent Gatto;Aedin C Culhane - GigaScience (2024)
  7. Dissecting cardiovascular disease-associated noncoding genetic variants using human iPSC models. - Saif F Dababneh;Hosna Babini;Verónica Jiménez-Sábado;Sheila S Teves;Kyoung-Han Kim;Glen F Tibbits - Stem cell reports (2025)
  8. Evaluating genetic-ancestry inference from single-cell RNA-seq data. - Jianing Yao;Steven Gazal - bioRxiv : the preprint server for biology (2025)
  9. Identifying genetic errors of immunity due to mosaicism. - Elizabeth G Schmitz;Malachi Griffith;Obi L Griffith;Megan A Cooper - The Journal of experimental medicine (2025)
  10. scAI-SNP: a method for inferring ancestry from single-cell data. - Sung Chul Hong;Francesc Muyas;Isidro Cortés-Ciriano;Sahand Hormoz - BMC methods (2025)

... (7 more literatures)

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