共同转录折叠的可视化。

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Anda Ramona Tănasie, Peter Kerpedjiev, Stefan Hammer, Stefan Badelt
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引用次数: 1

摘要

动机:在二级结构水平上理解RNA折叠可以提供有关分子功能的重要见解。我们有兴趣了解二级结构在转录过程中是如何动态变化的,以及特定的二级结构是在转录过程中已经形成还是在转录之后才形成。虽然存在不同的方法来模拟共转录折叠,但目前的可视化策略是滞后的。新的,更合适的方法是必要的,以帮助探索从共转录折叠模拟生成的数据。结果:我们提出了DrForna,一个交互式可视化应用程序,用于查看共转录RNA折叠模拟的时间过程。具体来说,用户可以沿着时间轴滚动,查看在任何特定时间点出现的结构的总体。可用性和实现:DrForna是一个JavaScript项目,可以在Github上获得https://github.com/ViennaRNA/drforna,部署在https://viennarna.github.io/drforna。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

DrForna: visualization of cotranscriptional folding.

DrForna: visualization of cotranscriptional folding.

Motivation: Understanding RNA folding at the level of secondary structures can give important insights concerning the function of a molecule. We are interested to learn how secondary structures change dynamically during transcription, as well as whether particular secondary structures form already during or only after transcription. While different approaches exist to simulate cotranscriptional folding, the current strategies for visualization are lagging behind. New, more suitable approaches are necessary to help with exploring the generated data from cotranscriptional folding simulations.

Results: We present DrForna, an interactive visualization app for viewing the time course of a cotranscriptional RNA folding simulation. Specifically, users can scroll along the time axis and see the population of structures that are present at any particular time point.

Availability and implementation: DrForna is a JavaScript project available on Github at https://github.com/ViennaRNA/drforna and deployed at https://viennarna.github.io/drforna.

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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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