Computational framework for identifiable and phase-consistent allele-specific expression quantification

可识别且相位一致的等位基因特异性表达定量的计算框架

基本信息

  • 批准号:
    2154089
  • 负责人:
  • 金额:
    $ 70.17万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-09-01 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

Haplotype inference and allele-specific transcript expression quantification are two fundamentalproblems in genetics and genomics. Haplotype inference aligns maternal and paternal alleles ofgenetic variants along two diploid chromosomes, whereas allele-specific expressionquantification obtains the expression levels of transcripts of maternal and paternal origins fromRNA-seq reads. These two problems are coupled in that one can affect the accuracy of theother: accurate allele-specific expression quantification requires accurate haplotypes to mapRNA-seq reads to and the accuracy of haplotype inference can be enhanced by allele-specificRNA-seq reads. While existing works have considered these two problems separately, this projectdevelops a computational framework to address these two fundamental problems jointly in asingle statistical framework to enhance the accuracy of both inferred haplotypes andallele-specific expression quantification. The computational methods to be developed in thisresearch will advance various aspects of biological research that require accurate allele-specificexpression estimates and haplotypes, including mapping allele-specific eQTLs, detectingimprinted genes, imputing untyped variants, finding signatures of natural selection, anddetecting recombination events. The outcome of the research will be used in outreach activitiesin minority serving institutions to recruit graduate students.The project develops a computational framework for obtaining accurate allele-specificexpression measurements and haplotypes from RNA-seq and genotype data. Two existingframeworks, one for transcript expression quantification and the other for haplotype inference(e.g., Beagle), are combined into a single framework, while keeping the computational efficiencyof the original frameworks. Each of these two existing frameworks is modified to address twopreviously-unmet challenges regarding allele-specific reads: for the RNA-seq quantification, theproject develops a mathematically rigorous approach to obtaining identifiable allele-specificexpression estimates at gene level, at transcript-set level, or at individual transcript level,whereas for haplotype inference, the project couples the model in Beagle with RNA-seqquantification methods of these investigators to jointly estimate identifiable allele-specific expression levels andhaplotypes that are consistent with each other. The computational methods are benchmarkedon allele-specific eQTL mapping, using genotypes and RNA-seq reads from human trios andLG/SM intercross mice with known haplotypes. The outcome of the research is available athttp://www.cs.cmu.edu/~sssykim.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
单倍型推断和等位基因特异性转录本表达定量是遗传学和基因组学中的两个基本问题。单倍型推断将遗传变异的母本和父本等位基因沿着两条二倍体染色体排列,而等位基因特异性表达定量从RNA-seq读数获得母本和父本来源的转录本的表达水平。这两个问题是耦合的,因为一个可以影响另一个的准确性:准确的等位基因特异性表达定量需要精确的单倍型来映射RNA-seq读数,并且单倍型推断的准确性可以通过等位基因特异性RNA-seq读数来增强。虽然现有的工作分别考虑了这两个问题,本项目开发了一个计算框架,以解决这两个基本问题,在一个单一的统计框架,以提高推断的单倍型和等位基因特异性表达定量的准确性。在这项研究中开发的计算方法将推进生物学研究的各个方面,这些研究需要精确的等位基因特异性表达估计和单倍型,包括定位等位基因特异性eQTL,检测印记基因,估算未分型的变体,寻找自然选择的特征,以及检测重组事件。研究结果将用于少数民族服务机构的外展活动,以招收研究生。该项目开发了一个计算框架,用于从RNA-seq和基因型数据中获得准确的等位基因特异性表达测量和单体型。两个现有的框架,一个用于转录表达定量,另一个用于单体型推断(例如,Beagle),合并成一个框架,同时保持了原始框架的计算效率。这两个现有框架中的每一个都被修改以解决关于等位基因特异性读段的两个先前未满足的挑战:对于RNA-seq定量,该项目开发了一种数学上严格的方法来获得基因水平、转录组水平或单个转录水平上可识别的等位基因特异性表达估计,而对于单体型推断,该项目将Beagle模型与这些研究人员的RNA测序定量方法结合起来,共同估计可识别的等位基因特异性表达水平和相互一致的单倍型。这些计算方法以等位基因特异性eQTL定位为基准,使用来自具有已知单倍型的人类trios和LG/SM互交小鼠的基因型和RNA-seq读数。该研究成果可在http:www.cs.cmu.edu/~sssykim.This奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(0)
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Seyoung Kim其他文献

A Novel Chemoentrapment Approach for Supportless Recycling of a Catalyst: Catalytic Asymmetric Dihydroxylation
催化剂无载体回收的新型化学捕获方法:催化不对称二羟基化
  • DOI:
    10.1002/adsc.200505351
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    5.4
  • 作者:
    Daewon Lee;Honggeun Lee;Seyoung Kim;Chang;B. Moon Kim
  • 通讯作者:
    B. Moon Kim
Publisher Correction: Alloying conducting channels for reliable neuromorphic computing
出版商更正:合金化传导通道以实现可靠的神经形态计算
  • DOI:
    10.1038/s41565-020-0735-0
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    38.3
  • 作者:
    Hanwool Yeon;Peng Lin;Chanyeol Choi;S. H. Tan;Yongmo Park;Doyoon Lee;Jaeyong Lee;Feng Xu;B. Gao;Huaqiang Wu;He Qian;Y. Nie;Seyoung Kim;Jeehwan Kim
  • 通讯作者:
    Jeehwan Kim
Predictive power of grit, professor support for autonomy and learning engagement on perceived achievement within the context of a flipped classroom
在翻转课堂的背景下,毅力的预测能力、教授对自主性的支持以及对感知成就的学习参与度
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Seonghye Yoon;Seyoung Kim;Minjeng Kang
  • 通讯作者:
    Minjeng Kang
Utilizing Case-based Reasoning for Consumer Choice Prediction based on the Similarity of Compared Alternative Sets
基于比较替代集的相似性,利用基于案例的推理进行消费者选择预测
Research in Acute Lung Injury and Pulmonary Fibrosis Surfactant replacement therapy reduces acute lung injury and collapse induration-related lung remodeling in the bleomycin model
急性肺损伤和肺纤维化研究表面活性剂替代疗法可减少博来霉素模型中的急性肺损伤和塌陷硬结相关的肺重塑
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Daniel J. Fesenmeier;S. Park;Seyoung Kim;You
  • 通讯作者:
    You

Seyoung Kim的其他文献

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{{ truncateString('Seyoung Kim', 18)}}的其他基金

CAREER: Dissecting the Mechanisms of Genetic Control of Biological Systems via High-Dimensional Sparse Graphical Models
职业:通过高维稀疏图形模型剖析生物系统遗传控制机制
  • 批准号:
    1149885
  • 财政年份:
    2012
  • 资助金额:
    $ 70.17万
  • 项目类别:
    Continuing Grant

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