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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: Apr 25, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Data integration and reproducibility for high-throughput transcriptomics.

Michael Mooney¹, Shannon McWeeney¹

¹Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.

International Review of Neurobiology

|August 31, 2014

Summary

This summary is machine-generated.

High-throughput transcriptomics generates vast gene expression data. This study provides best practice guidelines for integrating this data and ensuring reproducibility for future research.

Keywords:

Cross-platform Data integration High-throughput Microarrays Next-generation sequencing Reproducibility Transcriptomics

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Area of Science:

Genomics
Bioinformatics

Background:

High-throughput transcriptomics technologies enable comprehensive transcriptome interrogation.
Public repositories now house large volumes of gene expression data, facilitating secondary analyses.
The increasing data availability necessitates standardized approaches for integration and analysis.

Purpose of the Study:

To provide guidelines for best practices in transcriptomics data integration.
To address considerations for ensuring reproducibility in transcriptomics studies.
To discuss challenges and strategies for multi-omic and cross-species transcriptomics comparisons.

Main Methods:

Review of current transcriptomics data integration methodologies.
Analysis of factors influencing reproducibility in gene expression studies.
Exploration of computational approaches for multi-omic and cross-species data analysis.

Main Results:

Established best practices for transcriptomics data integration.
Highlighted key considerations for achieving reproducible research in transcriptomics.
Outlined approaches for comparative transcriptomics and multi-omic integration.

Conclusions:

Standardized data integration and reproducibility are crucial for leveraging large-scale transcriptomics data.
Adherence to best practices will enhance the reliability and utility of public gene expression datasets.
Further development in multi-omic and cross-species analysis methods is warranted.