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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: Dec 20, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Data-based RNA-seq simulations by binomial thinning.

¹Department of Mathematics and Statistics, American University, Massachusetts Ave NW, Washington, DC, 20016, USA. dgerard@american.edu.

BMC Bioinformatics

|May 26, 2020

Summary

This summary is machine-generated.

Simulating RNA-seq data from theoretical models can skew results. This study introduces realistic simulation techniques for RNA sequencing (RNA-seq) data, improving method assessment in non-ideal scenarios.

Keywords:

Confounders Differential expression Factor analysis RNA-seq Scaling factors Simulation

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Last Updated: Dec 20, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Numerous RNA sequencing (RNA-seq) analysis methods exist for bulk and single-cell data.
Current method assessment relies on simulated data, often from theoretical models.
Real-world RNA-seq data frequently violate theoretical model assumptions, potentially leading to inaccurate performance evaluations.

Purpose of the Study:

To develop and present novel simulation techniques for generating more realistic RNA sequencing datasets.
To enable robust assessment of RNA-seq analysis methods under non-ideal, model-violating conditions.
To provide tools for comparing methods on data that better reflects real-world complexities.

Main Methods:

Developed methods to introduce biologically relevant signals into existing real RNA-seq datasets.
Ensured simulated data retain realistic attributes, including deviations from theoretical models.
Applied simulation techniques to both single-cell and bulk RNA sequencing data.

Main Results:

The proposed simulation method generates more realistic RNA-seq datasets compared to traditional theoretical models.
The realistic simulation approach can alter conclusions drawn from differential expression analysis studies.
Demonstrated the utility of the approach by comparing factor analysis techniques on RNA-seq data.

Conclusions:

Simulation methods based on theoretical models can significantly impact study outcomes.
Introduced advanced, realistic simulation techniques for RNA sequencing data analysis.
The developed tools are available as the seqgendiff R package.