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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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Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Updated: Aug 12, 2025

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

scRNA-seq data analysis method to improve analysis performance.

Junru Lu¹, Yuqi Sheng¹, Weiheng Qian¹

¹State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, China.

IET Nanobiotechnology

|February 2, 2023

Summary

This summary is machine-generated.

Single-cell RNA sequencing (scRNA-seq) enables cell-level transcriptome analysis. This review covers scRNA-seq protocols, data processing pipelines, and evaluation methods for analysis tools.

Keywords:

RNA bioinformatics biomedical engineering genomics

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Last Updated: Aug 12, 2025

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Published on: September 16, 2019

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Single-cell RNA sequencing (scRNA-seq) technology allows for the analysis of individual cell transcriptomes.
A growing number of analysis tools have been developed specifically for scRNA-seq data.

Purpose of the Study:

To review commonly used scRNA-seq protocols.
To introduce the upstream processing pipeline for scRNA-seq data analysis.
To present methods for evaluating scRNA-seq analysis tools.

Main Methods:

Discussion of current scRNA-seq protocols.
Overview of the scRNA-seq data processing workflow, including reads mapping, expression quantification, quality control, normalization, imputation, and batch effect removal.
Presentation of evaluation methods for clustering and differential expression analysis.

Main Results:

Commonly used scRNA-seq protocols are discussed.
The upstream processing pipeline for scRNA-seq data is introduced, covering key steps and popular tools.
Methods for evaluating analysis tools in cellular and genetic dimensions are presented.

Conclusions:

This review provides a comprehensive overview of scRNA-seq protocols and data analysis workflows.
It highlights essential steps and tools for processing and analyzing scRNA-seq data.
The review also covers methods for assessing the performance of these analysis tools.