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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 microarray-based...

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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

Optimizing a massive parallel sequencing workflow for quantitative miRNA expression analysis.

Francesca Cordero¹, Marco Beccuti, Maddalena Arigoni

¹Department of Computer Sciences, University di Torino, Torino, Italy.

|February 25, 2012

Summary

This summary is machine-generated.

Massive Parallel Sequencing (MPS) analysis for microRNAs (miRNAs) requires optimized workflows. This study identifies the best tools for accurate miRNA quantification and differential expression analysis, improving data interpretation.

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Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

Published on: November 18, 2014

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Massive Parallel Sequencing (MPS) offers advancements over microarrays for RNA analysis.
Computational tools for MPS data are numerous but their comparative performance is underexplored.
Efficient data processing is crucial for analyzing large MPS datasets.

Purpose of the Study:

To benchmark computational tools for Massive Parallel Sequencing (MPS) data analysis.
To evaluate the performance of different tools in microRNA (miRNA) detection and quantification.
To establish an optimized analytical workflow for miRNA digital analysis.

Main Methods:

Assembled a benchmark MPS miRNA dataset using public spike-in data and healthy donor samples.
Evaluated six miRNA-specific aligners for read count estimation and detection sensitivity.
Assessed five tools for differential expression analysis accuracy.

Main Results:

Whole genome referencing underestimates duplicate miRNA read counts.
SHRiMP and MicroRazerS aligners demonstrated the highest sensitivity for miRNA detection.
DESeq and baySeq tools showed excellent specificity and sensitivity for differential expression analysis.

Conclusions:

Defined a clear and optimized analytical workflow for miRNA digital quantitative analysis.
Highlights the importance of tool selection for accurate miRNA profiling.
Provides a foundation for reliable miRNA expression studies using MPS data.