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Related Concept Videos

RNA-seq03:21

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...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Experimental RNAi02:15

Experimental RNAi

RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
Since the...

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Related Experiment Video

Updated: Jun 1, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Published on: July 29, 2022

USADAE: a deep learning approach to disentangle hidden covariates in RNA-seq data.

Xu Chen1, Luoyuan Guo1, Yaosheng Chen1

  • 1State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, No. 135, Xingang West Road, Haizhou District, Guangzhou, Guangdong 510275, China.

Briefings in Bioinformatics
|May 31, 2026
PubMed
Summary

We developed a new method, UnSupervised Adversarial Deconfounding AutoEncoder (USADAE), to accurately separate biological signals from hidden technical factors in RNA-seq data. This approach improves the reliability of downstream analyses like differential expression and eQTL studies.

Keywords:
RNA-seqadversarial learningautoencoderconfounder disentanglement

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • RNA-sequencing (RNA-seq) data analysis is challenged by hidden technical variations (confounders) that obscure true biological signals.
  • Existing methods like SVA, PEER, and RUVSeq often fail due to their reliance on linear assumptions, inadequate for complex nonlinear confounding patterns.
  • Current deep learning methods primarily address known batch effects, not the disentanglement of unknown confounders.

Purpose of the Study:

  • To develop a novel computational framework for robustly distinguishing biological signals from unmeasured confounders in RNA-seq data.
  • To create a method capable of handling complex nonlinear interactions between biological and technical variables.
  • To enhance the accuracy of downstream analyses, including differential gene expression and expression quantitative trait loci (eQTL) studies.

Main Methods:

  • Developed the UnSupervised Adversarial Deconfounding AutoEncoder (USADAE), an autoencoder framework utilizing adversarial learning.
  • Implemented adversarial disentanglement to encode RNA-seq data into separate biological and confounder latent spaces.
  • Validated the method through comprehensive simulations and real-world data applications in cancer genomics and eQTL studies.

Main Results:

  • USADAE significantly outperformed existing methods in simulations for covariate extraction while preserving biological signals.
  • Demonstrated the model's robustness and effectiveness across diverse real-world RNA-seq datasets.
  • Successfully enabled accurate downstream differential expression and eQTL analyses after confounder correction.

Conclusions:

  • USADAE offers a powerful new approach for addressing nonlinear confounding in RNA-seq data analysis.
  • The method effectively disentangles hidden technical variability, leading to more reliable biological insights.
  • USADAE shows significant promise for improving the analysis of various genomic datasets, including cancer and eQTL studies.