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RACE - Rapid Amplification of cDNA Ends

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...

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Updated: Jul 6, 2025

Aptamer-Based Target Detection Facilitated by a 3-Stage G-Quadruplex Isothermal Exponential Amplification Reaction

Aptamer-Based Target Detection Facilitated by a 3-Stage G-Quadruplex Isothermal Exponential Amplification Reaction

Published on: October 6, 2022

使用RaptGen发现生成性亚胺体.

Natsuki Iwano¹, Tatsuo Adachi², Kazuteru Aoki²

¹Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan.

Nature computational science

|January 4, 2024

概括

此摘要是机器生成的。

研究人员开发了RaptGen,这是一种用于设计核酸体的新计算工具. 这种方法使用人工智能来生成新型的阿巴特默序列,改进了传统的实验限制.

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Primer-Free Aptamer Selection Using A Random DNA Library

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相关实验视频

Last Updated: Jul 6, 2025

Aptamer-Based Target Detection Facilitated by a 3-Stage G-Quadruplex Isothermal Exponential Amplification Reaction

Aptamer-Based Target Detection Facilitated by a 3-Stage G-Quadruplex Isothermal Exponential Amplification Reaction

Published on: October 6, 2022

Primer-Free Aptamer Selection Using A Random DNA Library

Primer-Free Aptamer Selection Using A Random DNA Library

Published on: July 26, 2010

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

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科学领域:

生物技术是生物技术.
计算生物学计算生物学
分子生物学分子生物学

背景情况:

核酸体是通过指数式丰富 (SELEX) 的连接体的系统演化生成的关键分子工具.
当前的SELEX方法通常受到实验测序数据的数量和范围的限制.
需要使用计算方法来增强体体的发现和设计.

研究的目的:

介绍RaptGen,一个新的变异自编码器模型用于in silico aptamer生成.
证明RaptGen能够有效地表示和嵌入aptamer序列图案的能力.
验证RaptGen在生成新型体 (包括截断序列) 的实用性,以及其在活动引导发现中的适用性.

主要方法:

开发RaptGen,一个变化自编码器,包含一个隐藏的Markov模型解码器.
嵌入模拟和实验序列数据到一个低维的潜空间.
贝叶斯优化用于活动引导的阿巴特默生成的应用.

主要成果:

RaptGen成功地将序列数据嵌入到基于图案信息的潜在空间中.
该模型从潜伏空间中生成了新型的aptamer,甚至是那些不在高通量测序数据中的人.
拉普丁 (RaptGen) 证明了产生截断的阿普坦体的能力,并促进了活动引导的阿普坦体发现.

结论:

RaptGen提供了一种强大的生成方法,用于在体中发现体.
隐藏空间表示是识别和设计功能性体的有价值策略.
拉普特基因方法在分子进化和体设计方面推进了计算策略.