High-fidelity, large-scale targeted profiling of microsatellites.

IF 6.2 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Caitlin A Loh, Danielle A Shields, Adam Schwing, Gilad D Evrony
{"title":"High-fidelity, large-scale targeted profiling of microsatellites.","authors":"Caitlin A Loh, Danielle A Shields, Adam Schwing, Gilad D Evrony","doi":"10.1101/gr.278785.123","DOIUrl":null,"url":null,"abstract":"<p><p>Microsatellites are highly mutable sequences that can serve as markers for relationships among individuals or cells within a population. The accuracy and resolution of reconstructing these relationships depends on the fidelity of microsatellite profiling and the number of microsatellites profiled. However, current methods for targeted profiling of microsatellites incur significant \"stutter\" artifacts that interfere with accurate genotyping, and sequencing costs preclude whole-genome microsatellite profiling of a large number of samples. We developed a novel method for accurate and cost-effective targeted profiling of a panel of more than 150,000 microsatellites per sample, along with a computational tool for designing large-scale microsatellite panels. Our method addresses the greatest challenge for microsatellite profiling-\"stutter\" artifacts-with a low-temperature hybridization capture that significantly reduces these artifacts. We also developed a computational tool for accurate genotyping of the resulting microsatellite sequencing data that uses an ensemble approach integrating three microsatellite genotyping tools, which we optimize by analysis of de novo microsatellite mutations in human trios. Altogether, our suite of experimental and computational tools enables high-fidelity, large-scale profiling of microsatellites, which may find utility in diverse applications such as lineage tracing, population genetics, ecology, and forensics.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"1008-1026"},"PeriodicalIF":6.2000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368184/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome research","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1101/gr.278785.123","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Microsatellites are highly mutable sequences that can serve as markers for relationships among individuals or cells within a population. The accuracy and resolution of reconstructing these relationships depends on the fidelity of microsatellite profiling and the number of microsatellites profiled. However, current methods for targeted profiling of microsatellites incur significant "stutter" artifacts that interfere with accurate genotyping, and sequencing costs preclude whole-genome microsatellite profiling of a large number of samples. We developed a novel method for accurate and cost-effective targeted profiling of a panel of more than 150,000 microsatellites per sample, along with a computational tool for designing large-scale microsatellite panels. Our method addresses the greatest challenge for microsatellite profiling-"stutter" artifacts-with a low-temperature hybridization capture that significantly reduces these artifacts. We also developed a computational tool for accurate genotyping of the resulting microsatellite sequencing data that uses an ensemble approach integrating three microsatellite genotyping tools, which we optimize by analysis of de novo microsatellite mutations in human trios. Altogether, our suite of experimental and computational tools enables high-fidelity, large-scale profiling of microsatellites, which may find utility in diverse applications such as lineage tracing, population genetics, ecology, and forensics.

高保真、大规模的微卫星定向剖析。
微卫星是高度易变的序列,可作为群体中个体或细胞间关系的标记。重建这些关系的准确性和分辨率取决于微卫星剖析的保真度和剖析的微卫星数量。然而,目前有针对性的微卫星分析方法会产生明显的 "滞后 "伪影,干扰准确的基因分型,而且测序成本高,无法对大量样本进行全基因组微卫星分析。我们开发了一种新方法,可对每个样本中大于 15 万个微卫星进行准确且经济高效的靶向分析,同时还开发了一种用于设计大规模微卫星面板的计算工具。我们的方法解决了微卫星图谱分析的最大挑战--"停顿 "伪影--低温杂交捕获可显著减少这些伪影。我们还开发了一种计算工具,用于对得到的微卫星测序数据进行准确的基因分型,该工具采用了一种集合方法,整合了三种微卫星基因分型工具,我们通过分析人类三组微卫星的新突变对其进行了优化。总之,我们的这套实验和计算工具能够对微卫星进行高保真、大规模的分析,这可能会在世系追踪、群体遗传学、生态学和法医学等多种应用中找到用武之地。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Genome research
Genome research 生物-生化与分子生物学
CiteScore
12.40
自引率
1.40%
发文量
140
审稿时长
6 months
期刊介绍: Launched in 1995, Genome Research is an international, continuously published, peer-reviewed journal that focuses on research that provides novel insights into the genome biology of all organisms, including advances in genomic medicine. Among the topics considered by the journal are genome structure and function, comparative genomics, molecular evolution, genome-scale quantitative and population genetics, proteomics, epigenomics, and systems biology. The journal also features exciting gene discoveries and reports of cutting-edge computational biology and high-throughput methodologies. New data in these areas are published as research papers, or methods and resource reports that provide novel information on technologies or tools that will be of interest to a broad readership. Complete data sets are presented electronically on the journal''s web site where appropriate. The journal also provides Reviews, Perspectives, and Insight/Outlook articles, which present commentary on the latest advances published both here and elsewhere, placing such progress in its broader biological context.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信