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Sequoia – a revolutionary epitranscriptomic tool
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Sequoia – a revolutionary epitranscriptomic tool

bioxone July 9, 2021July 9, 2021

Saakshi Bangera, DY Patil School of Biotechnology and Bioinformatics

Chemical modifications of nucleotide bases of RNA are important regulators of RNA metabolism. Detecting such modifications may revolutionize transcriptomics, but is equally challenging to develop. RNA profiling concerning the transcriptome is quite difficult due to the lack of effective technology. A solid detection and analysis technique is required to dig the field of epitranscriptomics deeper. However, single-molecule, long-read sequencing techniques facilitate commercial RNA sequencing. For instance, MinION measures the fluctuations in the current as an RNA molecule passes through a nanopore. Through this technique, a modification at a single base level can also be detected. Existing software used for analyzing nanopore sequencing data provides a summarized statistic of the test sample. But these tools do not give detailed information. 

Sequoia is a tool for visualizing signals generated by Oxford Nanopore (ONT), called Sequoia. Sequoia allows users to interactively study the current signals generated by nanopore sequencing. The main aim of Sequoia is to find differentiating features between modified and unmodified bases. The tool clusters signals generated by ONT by using base-calling algorithms. Along with providing detailed analysis, Sequoia also provides an overview. The software used by Sequoia consists of a Python-based component of processing and visualizing data. This software can also be run on a web browser. The tool allows the qualitative detection and discrimination of m6A and m5C modifications from non-modified bases. For this study, two RNAs with m6A and m5A were compared with the signals generated by their unmodified nucleotides. Significant differences in the signal lengths of an m5C and unmodified C were reported. 

Long-read RNA sequencing is a potential tool to study cellular interaction and transcriptomic function. As the use of long-read sequencing increases, the need to understand the data generated also increases. Nanopore sequencers generate output in the form of Fast5 files, which follow an HDF5 organization. A Fast5 file contains a list of electric current values of each 5-mer. Therefore, each 5-mer possesses a list of signal values from each read. 

Since time series data visualization is the need of the hour. Existing tools that allow time-series data visualization do not allow the segregation of data into groups. TimeSearcher is an example of such a tool. Sequoia can visualize, compare, as well as segregate the time-series data. Using Sequoia, users may load sequencing data, generate plots, compare identical signal patterns, and cluster signals with similar patterns. Sequoia also allows users to visualize the computed statistics in formats like: 

  • box plots – to depict uniformity 
  • Line graphs – to superimpose signals
  • t-SNE plots – to compare signal patterns 

Future prospects for Sequoia

Sequoia can also be used to sequence DNA as well as analyze the modifications in it. Sequoia can be successfully used to compare signals of any k-mers across direct RNA and datasets of DNA sequencing. Nanopore-based datasets that are publicly available can be analyzed on a large scale. This enables a decent understanding of RNA and DNA functional characteristics. The accuracy of existing tools can also be improved using Sequoia. This is done by distinguishing modifications within RNA and DNA, by comparing them with regular bases. A better range of time-series elements can be included in Sequoia, thus making it more versatile. This range can be integrated into machine learning models, eventually allowing Sequoia to predict the location of the modifications.

Also read: Tocilizumab and Sarilumab receives WHO Approval for critically-ill Patients

Source: 

Koonchanok, R., Daulatabad, S.V., Mir, Q. et al. Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets. BMC Genomics 22, 513 (2021). https://doi.org/10.1186/s12864-021-07791-z 

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About the Author: Saakshi is currently pursuing MSc in Biotechnology from DY Patil School of Biotechnology and Bioinformatics. She believes that she doesn’t have a specific area of interest yet. She wishes to explore toxicology and food biotechnology. She’s quite passionate about Biotechnology and aims to grab every opportunity she comes across.

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Tagged Bioinformatics chemical modifications Deep Learning DNA DNA sequencing nanopore sequencing RNA RNA sequencing Sequoia transcriptomics

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