Koonchanok Ratanond, Daulatabad Swapna Vidhur, Reda Khairi, Janga Sarath Chandra
Department of Human-Centered Computing, School of Informatics and Computing, Indiana University Purdue University, Indianapolis, IN, USA.
Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, Indianapolis, IN, USA.
Methods Mol Biol. 2023;2624:127-138. doi: 10.1007/978-1-0716-2962-8_9.
Oxford Nanopore-based long-read direct RNA sequencing protocols are being increasingly used to study the dynamics of RNA metabolic processes due to improvements in read lengths, increased throughput, decreasing cost, ease of library preparation, and convenience. Long-read sequencing enables single-molecule-based detection of posttranscriptional changes, promising novel insights into the functional roles of RNA. However, fulfilling this potential will necessitate the development of new tools for analyzing and exploring this type of data. Although there are tools that allow users to analyze signal information, such as comparing raw signal traces to a nucleotide sequence, they don't facilitate studying each individual signal instance in each read or perform analysis of signal clusters based on signal similarity. Therefore, we present Sequoia, a visual analytics application that allows users to interactively analyze signals originating from nanopore sequencers and can readily be extended to both RNA and DNA sequencing datasets. Sequoia combines a Python-based backend with a multi-view graphical interface that allows users to ingest raw nanopore sequencing data in Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to find attributes of interest. In this tutorial, we illustrate each individual step involved in running Sequoia and in the process dissect input data characteristics. We show how to generate Nanopore sequencing-based visualizations by leveraging dimensionality reduction and parameter tuning to separate modified RNA sequences from their unmodified counterparts. Sequoia's interactive features enhance nanopore-based computational methodologies. Sequoia enables users to construct rationales and hypotheses and develop insights about the dynamic nature of RNA from the visual analysis. Sequoia is available at https://github.com/dnonatar/Sequoia .
基于牛津纳米孔的长读长直接RNA测序方案正越来越多地用于研究RNA代谢过程的动态变化，这得益于读长的改进、通量的增加、成本的降低、文库制备的简便性和便利性。长读长测序能够基于单分子检测转录后变化，有望为RNA的功能作用提供新的见解。然而，要实现这一潜力，就需要开发新的工具来分析和探索这类数据。虽然有一些工具允许用户分析信号信息，比如将原始信号轨迹与核苷酸序列进行比较，但它们不利于研究每个读段中的每个单独信号实例，也无法基于信号相似性对信号簇进行分析。因此，我们展示了红杉（Sequoia），这是一款可视化分析应用程序，它允许用户交互式地分析来自纳米孔测序仪的信号，并且可以很容易地扩展到RNA和DNA测序数据集。红杉将基于Python的后端与多视图图形界面相结合，允许用户摄取Fast5格式的原始纳米孔测序数据，根据电流相似性对序列进行聚类，并深入研究信号以找到感兴趣的属性。在本教程中，我们阐述了运行红杉所涉及的每个步骤，并在此过程中剖析输入数据的特征。我们展示了如何通过利用降维和参数调整来生成基于纳米孔测序的可视化结果，以将修饰的RNA序列与其未修饰的对应序列区分开来。红杉的交互式功能增强了基于纳米孔的计算方法。红杉使用户能够构建原理和假设，并通过可视化分析深入了解RNA的动态性质。可在https://github.com/dnonatar/Sequoia获取红杉。
