Improving biological nanopores for precision nucleic acid sequencing using a computational microscope

使用计算显微镜改进生物纳米孔以进行精确核酸测序

基本信息

项目摘要

This project aims to develop a tool that can considerably increase the precision of nucleic acid sequencing by enabling rational engineering of biological nanopores for sequencing applications. Although the methodology of nanopore sequencing has undergone major improvement with regard to transporting DNA and RNA molecules through the nanopore, sample preparation, base- calling algorithms, etc., relatively little has been published on improving the raw accuracy of nucleotide detection, which is the most commonly quoted deficiency of the nanopore sequencing method. This project will address this deficiency by developing a computational technology that will greatly simplify the design of custom nanopores for RNA and DNA sequencing, potentially leading to orders-of-magnitude improvement in row read accuracy. The key innovation of the project exploits recent methodological advances that have made plausible de novo prediction of nanopore current levels from simulations alone. To transform this methodological breakthrough into an accurate nanopore design tool, this project will examine and improve the simulation methodology guided by a set of experiments designed specifically to provide the information needed to improve the model. The practical utility of the method will be demonstrated by designing custom pores to detect biologically significant RNA modifications. The resulting computational method will be made available to the research community in the form of self-contained and well- documented software. This project will be realized by an interdisciplinary team that combines expertise in biological (UMass) nanopore experiment with theoretical and computation modeling (UIUC).The two PIs involved each have over 15 years of experience with research on nanopore technology, which includes synthesis and characterization of biological nanopores (Chen) and microscopic simulations of DNA and ion transport through biological nanopores (Aksimentiev).
该项目旨在开发一种工具,可以显著提高核酸的精确度 通过对用于测序应用的生物纳米孔进行合理的工程来进行测序。 尽管纳米孔测序的方法已经有了很大的改进, 关于通过纳米孔传输DNA和RNA分子,样品制备,碱基- 调用算法等,在提高原始精度方面发表的文章相对较少 核苷酸检测,这是纳米孔测序中最常被引用的缺陷 方法。该项目将通过开发一种计算技术来解决这一缺陷 将极大地简化用于RNA和DNA测序的定制纳米孔的设计,潜在地 导致行读取精度的数量级改进。的关键创新之处 该项目利用了最近的方法学进步,使对 仅来自模拟的纳米孔电流水平。改变这一方法论上的突破 成为一个精确的纳米孔设计工具,这个项目将检验和改进模拟 方法论由一系列专门为提供信息而设计的实验指导 需要改进模型。通过设计验证了该方法的实用性。 自定义毛孔以检测具有生物学意义的RNA修改。由此产生的计算量 将以自给自足和完善的形式向研究界提供方法 有文档记录的软件。该项目将由一个跨学科团队来实现,该团队将结合 精通生物纳米孔实验及理论和计算建模 (UIUC)。涉及的两个PI各自具有超过15年的纳米孔研究经验 技术,包括生物纳米孔的合成和表征(Chen)和 DNA和离子通过生物纳米孔传输的微观模拟(Aksimentiev)。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Multiple rereads of single proteins at single-amino acid resolution using nanopores.
  • DOI:
    10.1126/science.abl4381
  • 发表时间:
    2021-12-17
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Brinkerhoff H;Kang ASW;Liu J;Aksimentiev A;Dekker C
  • 通讯作者:
    Dekker C
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Aleksei Aksimentiev其他文献

Aleksei Aksimentiev的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Aleksei Aksimentiev', 18)}}的其他基金

Asymmetric Single-Chain MspA nanopores for electroosmotic stretching and sequencing proteins
用于电渗拉伸和蛋白质测序的不对称单链 MspA 纳米孔
  • 批准号:
    10646810
  • 财政年份:
    2023
  • 资助金额:
    $ 7.93万
  • 项目类别:
Improving biological nanopores for precision nucleic acid sequencing using a computational microscope
使用计算显微镜改进生物纳米孔以进行精确核酸测序
  • 批准号:
    10214806
  • 财政年份:
    2021
  • 资助金额:
    $ 7.93万
  • 项目类别:
Improving biological nanopores for precision nucleic acid sequencing using a computational microscope
使用计算显微镜改进生物纳米孔以进行精确核酸测序
  • 批准号:
    10414906
  • 财政年份:
    2021
  • 资助金额:
    $ 7.93万
  • 项目类别:
Multi-resolution Approaches to Modeling the 3D Structure, Delivery, and Replication of Viral Genomes
病毒基因组 3D 结构、传递和复制建模的多分辨率方法
  • 批准号:
    10626860
  • 财政年份:
    2020
  • 资助金额:
    $ 7.93万
  • 项目类别:
Multi-resolution Approaches to Modeling the 3D Structure, Delivery, and Replication of Viral Genomes
病毒基因组 3D 结构、传递和复制建模的多分辨率方法
  • 批准号:
    10201674
  • 财政年份:
    2020
  • 资助金额:
    $ 7.93万
  • 项目类别:
Multi-resolution Approaches to Modeling the 3D Structure, Delivery, and Replication of Viral Genomes
病毒基因组 3D 结构、传递和复制建模的多分辨率方法
  • 批准号:
    10414908
  • 财政年份:
    2020
  • 资助金额:
    $ 7.93万
  • 项目类别:
Plasmonic nanopores for trapping, controlled motion and sequencing of DNA
用于 DNA 捕获、控制运动和测序的等离激元纳米孔
  • 批准号:
    9128456
  • 财政年份:
    2013
  • 资助金额:
    $ 7.93万
  • 项目类别:
Plasmonic nanopores for trapping, controlled motion and sequencing of DNA
用于 DNA 捕获、受控运动和测序的等离激元纳米孔
  • 批准号:
    8728989
  • 财政年份:
    2013
  • 资助金额:
    $ 7.93万
  • 项目类别:
Plasmonic nanopores for trapping, controlled motion and sequencing of DNA
用于 DNA 捕获、控制运动和测序的等离激元纳米孔
  • 批准号:
    8572877
  • 财政年份:
    2013
  • 资助金额:
    $ 7.93万
  • 项目类别:
DEVELOPING NANOPORES AS NANOSENSORS
开发纳米孔作为纳米传感器
  • 批准号:
    8172031
  • 财政年份:
    2010
  • 资助金额:
    $ 7.93万
  • 项目类别:

相似海外基金

DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 7.93万
  • 项目类别:
    Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 7.93万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 7.93万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 7.93万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 7.93万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 7.93万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 7.93万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 7.93万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 7.93万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 7.93万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了