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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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Explicit Scale Simulation For Analysis Of Rna-sequencing Count Data With Aldex2.

Home
Explicit Scale Simulation For Analysis Of Rna-sequencing Count Data With Aldex2.

Related Experiment Video

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2.

Gregory B Gloor¹, Michelle Pistner Nixon², Justin D Silverman^3,4,5

¹Department of Biochemistry, University of Western Ontario, London ON, N6A 5C1, Canada.

NAR Genomics and Bioinformatics

|August 21, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Scale models improve transcriptomic analysis by accounting for biological system size, reducing errors in high-throughput sequencing (HTS) data. This enhances accuracy and reproducibility in differential abundance analyses.

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Published on: September 18, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput sequencing (HTS) studies face technical variations affecting sequencing depth.
Traditional normalization methods in HTS analysis make assumptions about biological system scale, potentially leading to errors.
Errors in normalization assumptions can increase false positive and false negative rates in differential abundance analyses.

Purpose of the Study:

To introduce and demonstrate the application of scale models in transcriptomic analysis.
To show how scale models mitigate errors caused by normalization assumptions in HTS data.
To enhance transparency and reproducibility in transcriptomic data analysis.

Main Methods:

Integration of scale models into the ALDEx2 R package.

Application of scale models to transcriptomic case studies, including metatranscriptomics.

Utilizing known housekeeping genes to build scale models for complex datasets.

Main Results:

Scale models reduce false positive and false negative rates compared to traditional normalizations in transcriptomic data.
Scale models enhance the transparency and reproducibility of HTS data analyses.
Scale models effectively address the disconnect between practical and statistical significance, replacing dual cutoff approaches.

Conclusions:

Scale models offer a more robust approach to analyzing HTS data, particularly in transcriptomics.
Incorporating scale into analysis mitigates technical variation and improves biological interpretation.
This work provides practical guidance for applying scale models to transcriptomic datasets for more reliable results.