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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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NanoTrans: an integrated computational framework for comprehensive transcriptome analysis with nanopore direct RNA

Ludong Yang¹, Xinxin Zhang¹, Fan Wang²

¹State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, China.

Journal of Genetics and Genomics = Yi Chuan Xue Bao

|July 14, 2024

Summary

NanoTrans offers a comprehensive computational framework for Nanopore direct RNA sequencing (DRS) data analysis. This tool streamlines gene expression profiling, including isoform analysis, poly(A) tail estimation, and RNA modification detection.

Keywords:

DRS Direct RNA sequencing Long reads Nanopore Transcriptome

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Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Nanopore direct RNA sequencing (DRS) offers native RNA analysis with full-length information.
DRS provides rich qualitative and quantitative gene expression data.
Existing computational tools may not cover all major DRS applications comprehensively.

Purpose of the Study:

To present NanoTrans, an integrated computational framework for Nanopore DRS data.
To provide a one-stop solution for diverse DRS-based applications.
To demonstrate the utility and effectiveness of NanoTrans across various biological contexts.

Main Methods:

Developed an integrated computational framework named NanoTrans.
Implemented modules for isoform clustering and quantification.
Included functionalities for poly(A) tail length estimation, RNA modification profiling, and fusion gene detection.
Designed with a workflow-orientated modular structure, batch processing, and comprehensive reporting.

Main Results:

NanoTrans successfully covers major Nanopore DRS application scopes.
The framework demonstrates streamlined analysis for isoform quantification, poly(A) tail length, RNA modifications, and fusion genes.
Applied NanoTrans to diverse datasets (yeast, Arabidopsis, human cell lines), confirming its effectiveness.

Conclusions:

NanoTrans provides a versatile and efficient one-stop solution for Nanopore direct RNA sequencing data analysis.
Its modular design and comprehensive features facilitate a wide range of applications.
NanoTrans is effective across various biological samples and research settings.