MaltSci 麦伴科研

Appearance

文献精选 > 近年高分"单细胞测序"研究（IF≥30，近5年）

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

文献信息 ​

DOI10.1038/s41587-023-01873-x
PMID37592035
期刊Nature biotechnology
影响因子41.7
JCR 分区Q1
发表年份2024
被引次数17
关键词单细胞测序, 单核苷酸变异, 计算工具, 群体遗传学, 克隆谱系追踪
文献类型Journal Article
ISSN1087-0156
页码803-812
期号42(5)
作者Jinzhuang 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

一句话小结 ​

本研究开发了Monopogen工具，能够从单细胞测序数据中准确识别生殖系和假定体细胞SNVs，提升了基因分型的准确率，并支持祖先推断和克隆谱系追踪。该工具结合群体遗传学和单细胞组学，揭示了遗传背景对细胞特征的影响，具有重要的研究意义。

在麦伴科研 (maltsci.com) 搜索更多文献 ​

单细胞测序 · 单核苷酸变异 · 计算工具 · 群体遗传学 · 克隆谱系追踪

摘要 ​

单细胞组学技术能够对多样的细胞类型和状态进行分子特征分析，但由此产生的转录组和表观遗传组如何依赖于细胞的遗传背景仍然研究不足。我们描述了Monopogen，这是一种从单细胞测序数据中检测单核苷酸变异（SNVs）的计算工具。Monopogen利用外部参考面板中的连锁不平衡信息来识别生殖系SNVs，并通过细胞群体水平的等位基因共分离模式检测假定的体细胞SNVs。它能够识别10万到300万个生殖系SNVs，达到95%的基因分型准确率，同时识别数百个假定的体细胞SNVs。Monopogen衍生的基因型支持全球和地方的祖先推断以及混合样本的识别。它还识别与心肌细胞代谢水平和表观基因组程序相关的变异。此外，Monopogen提高了假定体细胞SNV的检测能力，从而实现了对人类原代克隆造血过程中的克隆谱系追踪。Monopogen将群体遗传学、细胞谱系追踪和单细胞组学结合在一起，以揭示细胞过程中遗传决定因素。

英文摘要 ​

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.

麦伴智能科研服务 ​

智能阅读回答你对文献的任何问题，帮助理解文献中的复杂图表和公式
定位观点定位某个观点在文献中的蛛丝马迹
加入知识库完成数据提取，报告撰写等更多高级知识挖掘功能

主要研究问题 ​

  1. Monopogen如何与其他单细胞测序变异检测工具进行比较，特别是在准确性和速度方面？
  2. 在使用Monopogen进行单核苷酸变异检测时，如何选择合适的外部参考面板以优化结果？
  3. Monopogen在识别与心肌细胞代谢水平相关的变异时，采用了哪些特定的算法或策略？
  4. 除了单核苷酸变异，Monopogen是否能够检测其他类型的遗传变异，例如插入或缺失变异（indels）？
  5. Monopogen在分析混合样本时的表现如何，是否有特别的挑战或考虑因素？

核心洞察 ​

研究背景和目的 ​

随着单细胞测序技术的快速发展，识别单核苷酸变异(SNV)变得尤为重要。传统的SNV调用方法在单细胞数据中的表现往往不佳，因此本研究的目的是开发一种新的SNV调用工具Monopogen，以提高在单细胞测序数据中的SNV检测精度和效率。

主要方法/材料/实验设计 ​

本研究采用Monopogen工具，具体方法如下：

  1. 基因组划分：将基因组划分为小块，便于进行变异扫描和连锁不平衡(LD)精炼。
  2. 并行计算：对每个小块进行并行化处理，以加快计算速度。
  3. SNV检测流程：
    • 步骤1：根据与1KG3数据库的重叠情况将SNV分类为两类（种系SNV和去新SNV）。
    • 步骤2：使用Bcftools提供的七个变异调用指标作为特征，过滤掉预测概率低于0.5的SNV。
    • 步骤3：对未在1KG3中检测到的SNV进行LD精炼，通过分析其邻近的种系SNV来推测基因型。
    • 步骤4：在细胞群体水平进行LD精炼，区分种系SNV和去新SNV。
Mermaid diagram

关键结果和发现 ​

  • Monopogen在多个数据集上的表现优于现有的SNV调用工具，如Samtools和GATK。
  • 通过LD精炼，Monopogen能够有效区分种系和去新SNV，显著提高了SNV的检测准确性。
  • 研究中检测到的新SNV在不同样本中的分布显示出高度的可重复性和一致性。

主要结论/意义/创新性 ​

Monopogen作为一种新型SNV调用工具，展示了在单细胞测序数据中有效识别变异的潜力。其创新性体现在以下几个方面：

  • 引入LD精炼方法，有效提高了SNV检测的准确性。
  • 采用并行计算策略，显著提升了计算效率。
  • 提供了一种新的思路来处理复杂的单细胞基因组数据，具有广泛的应用前景。

研究局限性和未来方向 ​

尽管Monopogen在本研究中表现良好，但仍存在一些局限性：

  • 对于极低频率的SNV，检测的可靠性仍需进一步验证。
  • 在不同物种或不同类型的细胞中，Monopogen的适用性尚待评估。

未来的研究可以集中在以下几个方向：

  • 扩展Monopogen的应用范围，包括其他类型的基因组数据。
  • 进一步优化算法，提高对低频SNV的检测能力。
  • 结合其他生物信息学工具，增强分析的全面性和深度。

参考文献 ​

  1. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. - Urmo Võsa;Annique Claringbould;Harm-Jan Westra;Marc Jan Bonder;Patrick Deelen;Biao Zeng;Holger Kirsten;Ashis Saha;Roman Kreuzhuber;Seyhan Yazar;Harm Brugge;Roy Oelen;Dylan H de Vries;Monique G P van der Wijst;Silva Kasela;Natalia Pervjakova;Isabel Alves;Marie-Julie Favé;Mawussé Agbessi;Mark W Christiansen;Rick Jansen;Ilkka Seppälä;Lin Tong;Alexander Teumer;Katharina Schramm;Gibran Hemani;Joost Verlouw;Hanieh Yaghootkar;Reyhan Sönmez Flitman;Andrew Brown;Viktorija Kukushkina;Anette Kalnapenkis;Sina Rüeger;Eleonora Porcu;Jaanika Kronberg;Johannes Kettunen;Bernett Lee;Futao Zhang;Ting Qi;Jose Alquicira Hernandez;Wibowo Arindrarto;Frank Beutner; ; ;Julia Dmitrieva;Mahmoud Elansary;Benjamin P Fairfax;Michel Georges;Bastiaan T Heijmans;Alex W Hewitt;Mika Kähönen;Yungil Kim;Julian C Knight;Peter Kovacs;Knut Krohn;Shuang Li;Markus Loeffler;Urko M Marigorta;Hailang Mei;Yukihide Momozawa;Martina Müller-Nurasyid;Matthias Nauck;Michel G Nivard;Brenda W J H Penninx;Jonathan K Pritchard;Olli T Raitakari;Olaf Rotzschke;Eline P Slagboom;Coen D A Stehouwer;Michael Stumvoll;Patrick Sullivan;Peter A C 't Hoen;Joachim Thiery;Anke Tönjes;Jenny van Dongen;Maarten van Iterson;Jan H Veldink;Uwe Völker;Robert Warmerdam;Cisca Wijmenga;Morris Swertz;Anand Andiappan;Grant W Montgomery;Samuli Ripatti;Markus Perola;Zoltan Kutalik;Emmanouil Dermitzakis;Sven Bergmann;Timothy Frayling;Joyce van Meurs;Holger Prokisch;Habibul Ahsan;Brandon L Pierce;Terho Lehtimäki;Dorret I Boomsma;Bruce M Psaty;Sina A Gharib;Philip Awadalla;Lili Milani;Willem H Ouwehand;Kate Downes;Oliver Stegle;Alexis Battle;Peter M Visscher;Jian Yang;Markus Scholz;Joseph Powell;Greg Gibson;Tõnu Esko;Lude Franke - Nature genetics (2021)
  2. Identification of context-dependent expression quantitative trait loci in whole blood. - Daria V Zhernakova;Patrick Deelen;Martijn Vermaat;Maarten van Iterson;Michiel van Galen;Wibowo Arindrarto;Peter van 't Hof;Hailiang Mei;Freerk van Dijk;Harm-Jan Westra;Marc Jan Bonder;Jeroen van Rooij;Marijn Verkerk;P Mila Jhamai;Matthijs Moed;Szymon M Kielbasa;Jan Bot;Irene Nooren;René Pool;Jenny van Dongen;Jouke J Hottenga;Coen D A Stehouwer;Carla J H van der Kallen;Casper G Schalkwijk;Alexandra Zhernakova;Yang Li;Ettje F Tigchelaar;Niek de Klein;Marian Beekman;Joris Deelen;Diana van Heemst;Leonard H van den Berg;Albert Hofman;André G Uitterlinden;Marleen M J van Greevenbroek;Jan H Veldink;Dorret I Boomsma;Cornelia M van Duijn;Cisca Wijmenga;P Eline Slagboom;Morris A Swertz;Aaron Isaacs;Joyce B J van Meurs;Rick Jansen;Bastiaan T Heijmans;Peter A C 't Hoen;Lude Franke - Nature genetics (2017)
  3. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. - Monique G P van der Wijst;Harm Brugge;Dylan H de Vries;Patrick Deelen;Morris A Swertz; ; ;Lude Franke - Nature genetics (2018)
  4. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. - Alexandra-Chloé Villani;Rahul Satija;Gary Reynolds;Siranush Sarkizova;Karthik Shekhar;James Fletcher;Morgane Griesbeck;Andrew Butler;Shiwei Zheng;Suzan Lazo;Laura Jardine;David Dixon;Emily Stephenson;Emil Nilsson;Ida Grundberg;David McDonald;Andrew Filby;Weibo Li;Philip L De Jager;Orit Rozenblatt-Rosen;Andrew A Lane;Muzlifah Haniffa;Aviv Regev;Nir Hacohen - Science (New York, N.Y.) (2017)
  5. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. - Anna S E Cuomo;Daniel D Seaton;Davis J McCarthy;Iker Martinez;Marc Jan Bonder;Jose Garcia-Bernardo;Shradha Amatya;Pedro Madrigal;Abigail Isaacson;Florian Buettner;Andrew Knights;Kedar Nath Natarajan; ;Ludovic Vallier;John C Marioni;Mariya Chhatriwala;Oliver Stegle - Nature communications (2020)
  6. Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants. - Margaret K R Donovan;Agnieszka D'Antonio-Chronowska;Matteo D'Antonio;Kelly A Frazer - Nature communications (2020)
  7. An integrative approach for building personalized gene regulatory networks for precision medicine. - Monique G P van der Wijst;Dylan H de Vries;Harm Brugge;Harm-Jan Westra;Lude Franke - Genome medicine (2018)
  8. Population genetics meets single-cell sequencing. - Tomokazu S Sumida;David A Hafler - Science (New York, N.Y.) (2022)
  9. The Human Cell Atlas: from vision to reality. - Orit Rozenblatt-Rosen;Michael J T Stubbington;Aviv Regev;Sarah A Teichmann - Nature (2017)
  10. The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution. - Orit Rozenblatt-Rosen;Aviv Regev;Philipp Oberdoerffer;Tal Nawy;Anna Hupalowska;Jennifer E Rood;Orr Ashenberg;Ethan Cerami;Robert J Coffey;Emek Demir;Li Ding;Edward D Esplin;James M Ford;Jeremy Goecks;Sharmistha Ghosh;Joe W Gray;Justin Guinney;Sean E Hanlon;Shannon K Hughes;E Shelley Hwang;Christine A Iacobuzio-Donahue;Judit Jané-Valbuena;Bruce E Johnson;Ken S Lau;Tracy Lively;Sarah A Mazzilli;Dana Pe'er;Sandro Santagata;Alex K Shalek;Denis Schapiro;Michael P Snyder;Peter K Sorger;Avrum E Spira;Sudhir Srivastava;Kai Tan;Robert B West;Elizabeth H Williams; - Cell (2020)

引用本文的文献 ​

  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 更多 篇文献)

© 2025 MaltSci 麦伴科研 - 我们用人工智能技术重塑科研