Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets.

BACKGROUND: Direct-sequencing technologies, such as Oxford Nanopore's, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. RESULT: Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. CONCLUSIONS: Sequoia's interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia .

Sequoia: A Framework for Visual Analysis of RNA Modifications from Direct RNA Sequencing Data.

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 .