脱氧核糖核苷酸(DNA)分子是科研孔检大量遗传信息的稳定载体,为下一代信息处理技术提供了理想的利用存储介质。处理 DNA 信息的纳米技术代表了生物学和计算机技术的跨学科整合,已经成为单独处理电子信息的测辅存储处理技术的有吸引力的替代品。DNA技术的进行具体应用可以分为三个部分:存储、计算和自组装。信息DNA 信息处理的科研孔检质量取决于 DNA 读取的准确性。纳米孔检测使研究人员能够准确地对核苷酸进行测序,利用因此被广泛用于读取 DNA。纳米在本文中,我们介绍了纳米孔检测的原理和发展历史,并对涉及纳米孔检测和基于纳米孔的存储的 DNA 信息处理的最新进展和具体应用进行了系统回顾。我们还讨论了人工智能在纳米孔检测和 DNA 信息处理方面的潜力。该工作不仅为未来纳米孔检测的发展提供了新的途径,也为构建更先进的DNA信息处理技术奠定了基础。
分子信息的解码对于 DNA 信息处理至关重要,DNA 存储就是一个典型的例子。在 DNA 存储中,数字信息可以按照特定的算法编码成 DNA 序列,然后通过核苷酸合成技术存储在 DNA 链中,并通过 DNA 测序的方法读出。DNA 存储的质量取决于 DNA 合成和测序[ 10、11 ]。大规模的 DNA 可以通过快速低成本的固相合成法合成 [ 12 , 13 ]。传统的 DNA 测序方法,例如 Sanger 测序 [ 14 ]] 和 Illumina 测序,成本高昂,可能导致大规模 DNA 存储失败。因此,需要一种高效的DNA测序方法来支持DNA信息处理商业应用的扩展。
本综述旨在涵盖纳米孔检测技术的最新进展及其在 DNA 信息处理中的应用。为此,我们对以下内容进行了系统的研究:(1)纳米孔检测技术的原理和发展历程;(2) 纳米孔检测的进展;(3) 两种利用纳米孔检测的DNA存储方式;(4)人工智能(AI)在纳米孔数据处理和DNA信息技术中的应用。我们设想这篇评论文章将推动 DNA 信息处理领域纳米孔检测的发展。
纳米孔可用于同时检测有关单链/双链 DNA 在蛋白质结合或无机化学修饰过程中发生的多种变化或损伤的信息[ 81、82 ]。
3.1. 检测 DNA 损伤
未修复的 DNA 损伤的积累可能导致细胞过早衰老、癌症和一些神经退行性疾病[ 83、84 ]。目前,纳米孔被用作 DNA 损伤检测的生物传感器。2019 年,Ma 等人。提出了一种纳米孔检测方法来识别 DNA 上的顺铂损伤 [ 85 ](图 1a)。顺铂与N 7结合嘌呤原子在 DNA 上形成损伤,抑制 DNA 在癌细胞中的正常复制和转录。马等。应用 MspA 准确检测顺铂诱导的 DNA 损伤,证明纳米孔测序技术可以在小于 10 ng 的输入测序文库中识别顺铂损伤。此外,通过观察 DNA 通过纳米孔的易位速度,可以区分多个 DNA 损伤。2022 年,张等人。通过观察酶促减速的动力学,实现了对 O6-羧甲基鸟嘌呤 (O6-CMG)、O6-甲基鸟嘌呤 (O6-MG) 和无碱 (AP) 位点的直接鉴定 [ 86 ](图 1b). 他们观察到 phi29 DNA 聚合酶的渐进运动受到 DNA 损伤(如 O6-CMG)的阻碍,并记录了酶促停滞,表明运动酶和 DNA 损伤之间相互作用产生的动力学信息可用于识别多个 DNA 损伤。
图 1. 基于纳米孔的分子检测系统示意图。( a ) 检测 DNA 上的顺铂损伤。经 ACS Sensors Fubo Ma 许可转载;由美国化学学会出版,2021 年。( b ) 使用 MspA 纳米孔(蓝色)和 phi29 DNAP(黄色)在纳米孔测序过程中的停滞动力学读数。经 Jinyue Zhang 许可转载,Nano Letters;由美国化学学会出版,2022 年。( c ) 嵌合 DNA(灰色)-FANA(青色)与无碱基间隔区(红色)的测序。转载自 [ 90 ]。( d ) 使用 NIPSS 的 POC 棘轮运动。转载自颜双红,Nano Letters;美国化学学会,2021 年。(e) 通过PNRSS检测无机化学分子。转载自 [ 95 ]。
4. 基于纳米孔测序技术的 DNA 存储
虽然携带数字信息的核苷酸链很脆弱,在保存过程中可能会受到外界环境因素的干扰和破坏,如紫外线、极端温度变化、细菌、病毒等生物污染等。然而,DNA具有超高信息密度和长寿命等特性,有望成为下一代信息处理系统的新型数据存储介质。我们有理由相信,随着DNA保存技术和保存设备的进步[ 96 , 97 , 98 , 99】,DNA存储的容量、持续时间和存储质量将得到极大提升。目前,DNA纳米孔可用于鉴定DNA/RNA的序列和DNA/RNA的分子或化学修饰,这些都可以被视为位点,从而为DNA存储系统提供更多选择。作为数字数据存储载体的DNA有两类:(1)人工合成的DNA碱基序列;(2) DNA 纳米结构/修饰。
4.1. 基于合成 DNA 序列的 DNA 存储
如图2a所示,DNA存储的整个过程通常分为六个步骤[ 100 ](图2a):(1)将数字信息编码成DNA序列;(2)设计和合成DNA序列;(3) 体内或体外保存DNA;(4) 特定DNA序列的随机存取;(5) 特定DNA序列的读取;(6) 将 DNA 序列解码并恢复为数字信息。目前,纳米孔测序技术广泛应用于步骤4和5。
图 2. 基于合成核苷酸序列的 DNA 存储系统的工作流程。( a ) 与传统硬盘存储相对应的 DNA 中数字数据存储的主要步骤。转载自 Yaya Hao,SMALL STRUCTURES;由 John Wiley and Sons 出版,2020 年。( b ) 使用 ONT 纳米孔作为工具对通过随机访问获得的长双链 DNA 链进行测序的 DNA 数据存储工作流程概述。转载自 [ 101 ]。( c ) 使用人工酵母染色体的体内 DNA 存储系统的工作流程。转载自 [ 22 ]。
纳米孔测序技术可准确识别 DNA 修饰或纳米结构,为 DNA 存储提供了新的解决方案。高度可编程的 DNA 纳米结构提供了多种用于存储数字数据的地址位点[ 102、103 ]。2018 年,Chen 和 Kong 等人。提出了一种将 DNA 发夹视为位点的 DNA 存储方案 [ 104 ](图 3一种)。在他们的工作中,不同长度的 DNA 发夹被视为数字位,用于开发高分辨率固态纳米孔测序方法。使用内径约为 5 nm 的石英纳米孔可以清楚地区分长度为 8 bp 和 16 bp 的 DNA 发夹。因此,8-bp和16-bp的发夹分别被指定为bit-0和bit-1,用于将56个发夹连接到7228-bp长的寡核苷酸上,从而形成56-bit的存储片段。使用类似的想法,Bell 和 Keyser [ 105]根据DNA折纸原理设计了一个DNA纳米结构库,其中每个成员都有一个唯一的条形码,条形码上的每一位都由一个DNA哑铃发夹的存在与否来表示。他们最终证实,可以通过固态纳米孔测序以 94% 的准确率识别 3 位条形码。
图 3. 基于 DNA 纳米结构的分子存储系统。( a ) 使用纳米孔测量 DNA 载体的示意图,其中位“1”和“0”分别代表 16 bp 和 8 bp 的 DNA 发夹结构。经 Kaikai Chen 许可转载,Nano Letters;美国化学学会,2019 年。( b ) 生物聚合物序列图解,其中“0”代表单体分子,“1”代表其甲基化形式。转载自 [ 106 ]。
纳米孔的替代 DNA 存储系统正在用于识别生物聚合物序列。2020 年,Cao 等人。使用专门定制的生物聚合物序列作为位信息存储载体[ 106 ](图3b). 生物聚合物序列是生物杂化大分子,包含两种不同大小的单体(正丙基磷酸酯和 [2,2-二炔基]-丙基磷酸酯)和天然核苷酸,其中单体分别映射为 bit-0 和 bit-1。该研究使用气溶素毒素的生物工程纳米孔,以单碱基分辨率成功实现了定制生物聚合物的位点识别。此外,还应用深度学习实现了高达4位数字序列的高精度编解码。这个独特的系统为开发新的 DNA 存储系统提供了灵感。
碱基调用是在测序过程中推断 DNA 片段中核苷酸顺序的过程 [ 112 ]。由于纳米孔测序会产生电流信号,因此碱基检出需要计算机算法来处理序列数据。迄今为止,包括 ONT 团队在内的许多研究人员已经设计了多种基于深度学习模型的软件程序来进行碱基识别。这些软件程序可以按两种类型的输入数据分类:分段事件和原始电流信号。
深度学习在快速分析纳米孔读数方面具有巨大潜力。2020 年,Nivala 等人。提出了一种在射频识别标签和快速响应代码等传统方法不适用的情况下使用 DNA 或其他分子标记物理对象的方法 [ 120 ](图4c)。他们开发了 Porcupine 系统,这是一种最终用户分子标记系统,能够使用便携式纳米孔设备在几秒钟内读取基于 DNA 的标记。它的数字位由不同 DNA 链的存在或不存在表示,称为分子位 (molbits),由 CNN 直接从原始纳米孔信号中分类。这种方法避免了使用 DNA 序列进行碱基调用的需要,从而大大减少了时间要求和复杂性。
综上所述,深度学习可以应用于快速提高DNA测序的碱基识别准确率(同一性从68%提高到90.57%),极大地扩展了纳米孔可检测的分子类型范围,为高分子测序提供了可能。加快 DNA 读取速度,所有这些都是 DNA 存储和纳米孔技术大规模商业化的关键因素。
深度学习还可以用于通过计算机模拟扩展 DNA 存储系统的容量,这为指导研究提供了有益的建议。能够使用大规模的 DNA 存储系统相当于能够设计复杂的 DNA 引物序列,这在大多数研究环境中是负担不起的。因此,DNA杂交过程的精确控制和预测对于大规模DNA存储系统的设计至关重要。2021 年,David Buterez 率先提出了用于 DNA 杂交预测的机器学习技术的综合研究 [ 121 ](图4d)。作为基准,他在计算机上对多个机器学习模型进行了性能评估-生成的杂交数据集包含超过 250 万个 DNA 序列对。接下来,他使用 CNN、RNN 和 RoBERTa 模型评估了这个数据集,发现深度学习模型提供了更准确的 DNA 杂交预测,并且与基线模型相比,运行时间减少了一到两个数量级。
6.结论
DNA信息处理技术利用DNA分子作为数据存储介质和数据计算单元,具有存储大数据的潜力。然而,这些技术的发展一直受到传统 DNA 测序成本高、速度慢、准确性相对较低的阻碍。纳米孔检测是一种新的单分子检测技术,通过分析分子通过纳米孔时产生的离子电流信号,实现分子鉴定。与其他方法相比,快速纳米孔检测具有免标记、低成本、方便等优点,满足了DNA信息处理的要求。目前,纳米孔检测广泛应用于DNA信息处理任务,如生物分子检测、DNA存储等。然而,
在 DNA 存储系统中,数字信息可以存储在 DNA 序列、包含修饰 DNA 的序列或其他生物分子中。纳米孔技术可以优化DNA信息的读取过程,为DNA存储领域提供高速、准确的碱基调用。然而,DNA 纳米孔测序在两个方面仍然受到限制。首先,虽然纳米孔测序的碱基识别准确率有了很大提高,目前在 90-95% 之间,但仍低于下一代测序的 99% 准确率 [ 122 ]]. 其次,纳米孔检测的通量相对较低。纳米孔检测依赖于分子通过纳米孔的微电流,同时检测多个纳米孔会因电信号叠加而导致信号失真。目前单个纳米孔的读取速度约为 10 毫秒/碱基,这将需要大约 20 年的时间才能以 10 倍覆盖深度对人类基因组进行测序 [ 123 ]。因此,更高的测序精度和检测通量可以促进大规模DNA存储的更广泛商业应用。
人工智能在纳米孔技术中的应用有望克服上述障碍。在生物分子检测中,深度学习模型中模式识别的强大功能可以同时检测多种分子。此外,AI 提供高精度序列预测,从而提高 DNA 存储的碱基调用速度和准确性。最后,人工智能还可以有效地预测蛋白质的分子折叠,这有助于修饰生物纳米孔的结构,可能为提高检测通量提供新途径。借助更先进的纳米技术和人工智能,我们预计DNA纳米孔将继续在DNA信息处理领域提供新的应用。
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