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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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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Quantitative transcriptomic and epigenomic data analysis: a primer.

Louis Coussement¹, Wim Van Criekinge¹, Tim De Meyer¹

¹Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, 9000, Belgium.

Bioinformatics Advances

|April 8, 2024

Summary

This summary is machine-generated.

This study introduces a generic workflow for analyzing quantitative transcriptomic and epigenomic data, addressing the shift in molecular biology research towards omics data analysis. It aims to provide conceptual insight for researchers with basic omics expertise.

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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
Bioinformatics
Genomics

Background:

Microarray and next-generation sequencing technologies enable comprehensive transcriptomic and epigenomic analysis.
Advancements allow single-cell resolution, shifting research bottlenecks to data analysis.
Existing literature often assumes expert knowledge and focuses on data-type specific methods.

Purpose of the Study:

To provide conceptual insight into genome-wide quantitative transcriptomic and epigenomic data analysis.
To describe a generic workflow applicable across different omics data types.
To enable readers with basic omics expertise to understand general strategies and pitfalls.

Main Methods:

Description of a generic workflow for omics data analysis.
Explanation of underlying principles and assumptions.
Introduction of data-analytical solutions for specific data types.

Main Results:

A generalized framework for omics data analysis is presented.
The need for tailored solutions based on data type is highlighted.
Conceptual and statistical understanding of omics data analysis is enhanced.

Conclusions:

The generic workflow facilitates understanding of complex omics data analysis.
It bridges the gap between basic omics expertise and specialized literature.
Researchers can better navigate challenges in transcriptomic and epigenomic data analysis.