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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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Updated: Sep 13, 2025

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Protocol for mapping 2'-O-methylation using nanopore direct RNA-seq data with NanoNm.

Yanqiang Li¹, Jiayi Chen², Yunxia Wang¹

¹Basic and Translational Research Division, Department of Cardiology, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.

|August 2, 2025

Summary

This study introduces a bioinformatics protocol to identify 2'-O-methylation (Nm) in RNA using nanopore direct RNA sequencing. The method enables mapping of this crucial RNA modification in yeast and human cells.

Keywords:

Bioinformatics Genomics RNA-seq

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Last Updated: Sep 13, 2025

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Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

2 -O-methylation (Nm) is a vital post-transcriptional modification found in ribosomal RNA (rRNA) and messenger RNA (mRNA).
Accurate identification of Nm is essential for understanding RNA function and regulation.
Existing methods for detecting RNA modifications can be limited in scope or throughput.

Purpose of the Study:

To develop and present an integrated bioinformatics protocol for identifying 2 -O-methylation (Nm) using nanopore direct RNA sequencing (RNA-seq) data.
To provide a comprehensive guide for the software installation, data collection, and machine learning model training required for Nm detection.
To demonstrate the application of the protocol for mapping Nm in both rRNA and mRNA within yeast and human cellular contexts.

Main Methods:

Utilizing nanopore direct RNA sequencing for native RNA molecule analysis.
Developing a bioinformatics pipeline for processing RNA-seq data.
Implementing machine learning models trained on sequencing data to identify 2 -O-methylation sites.
Applying the protocol to Saccharomyces cerevisiae (yeast) and Homo sapiens (human) cell samples.

Main Results:

Successful establishment of a bioinformatics protocol for 2 -O-methylation identification.
Demonstrated capability to map Nm modifications in both rRNA and mRNA.
Validation of the protocol's effectiveness in different model organisms (yeast and human).

Conclusions:

The presented integrated bioinformatics protocol offers a robust method for identifying 2 -O-methylation using nanopore direct RNA sequencing.
This protocol facilitates the study of Nm's role in gene expression and regulation across different species.
The findings provide a valuable tool for researchers investigating RNA modifications and their functional implications.