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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: Feb 19, 2026

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

MicroRNA Sequencing Data Analysis Toolkits.

¹Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California in San Francisco, 600 16th Street Mission Bay/Genentech Hall, Room N212, San Francisco, CA, 94143, USA. wwu65@yahoo.com.

Methods in Molecular Biology (Clifton, N.J.)

|November 1, 2017

Summary

This summary is machine-generated.

MicroRNA sequencing (miRNA-seq) enables genome-wide discovery of miRNA expression. This guide provides essential tools and strategies for computationally analyzing miRNA-seq data, addressing its complexity.

Keywords:

Bioinformatics Data analysis Massively parallel DNA sequencing microRNA microRNAseq

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression.
miRNAs are crucial in cellular processes like development, differentiation, and cancer.
Differential miRNA expression analysis is vital for understanding disease.

Purpose of the Study:

To provide guidelines and tools for microRNA sequencing (miRNA-seq) data analysis.
To address the computational challenges associated with miRNA-seq analysis.
To facilitate genome-wide discovery of miRNA expression profiles.

Main Methods:

Review of current miRNA-seq analysis pipelines.
Identification of available bioinformatics tools for miRNA-seq data.
Guidelines for efficient and accurate miRNA expression profiling.

Main Results:

Established protocols for analyzing miRNA-seq data.
Highlighted key software and algorithms for miRNA identification and quantification.
Addressed computational bottlenecks in miRNA-seq analysis.

Conclusions:

miRNA-seq is a powerful technique for comprehensive miRNA expression profiling.
Accessible tools and clear guidelines can overcome the computational hurdles of miRNA-seq analysis.
Standardized analysis is crucial for advancing miRNA research in various biological contexts.