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Related Concept Videos

RNA-seq03:21

RNA-seq

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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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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
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The technique...
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Related Experiment Video

Updated: Dec 10, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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A tail-based test to detect differential expression in RNA-sequencing data.

Jiong Chen1, Xinlei Mi2, Jing Ning3

  • 1Data Science, LinkedIn, Mountain View, CA, USA.

Statistical Methods in Medical Research
|September 2, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel tail-based statistical test for RNA sequencing data. The new method enhances the power and robustness of differential gene expression analysis, particularly for biomarker discovery.

Keywords:
Correlated dataRNA sequencingdifferential expression analysisquantile regressionrobust tail-based test

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Area of Science:

  • Biostatistics
  • Genomics
  • Bioinformatics

Background:

  • RNA sequencing (RNA-seq) data are widely used in biomedical research, but exon-level data often exhibit heavy tails and correlations.
  • Traditional differential expression tests focusing on mean or median differences can lack power when variations occur in the distribution tails.
  • Identifying differentially expressed genes is crucial for biomarker discovery and understanding disease mechanisms.

Purpose of the Study:

  • To develop a novel statistical test for differential gene expression analysis of RNA-seq data.
  • To improve the power and robustness of detecting gene expression differences, especially in the tails of the distribution.
  • To provide a more effective tool for biomarker discovery using high-throughput sequencing data.

Main Methods:

  • A tail-based statistical test derived from quantile regression was proposed.
  • The method adjusts for covariates and accounts for within-sample exon dependence using a specified correlation structure.
  • Monte Carlo simulations were employed to evaluate the performance of the proposed test.

Main Results:

  • The proposed tail-based test demonstrated superior power and robustness compared to conventional mean- or single-quantile-based tests.
  • Simulations confirmed the effectiveness of the method in detecting differential gene expression across various scenarios.
  • The test successfully identified potential biomarkers in a real-world application using TCGA lung adenocarcinoma data.

Conclusions:

  • The proposed tail-based test offers a more powerful and robust approach for differential gene expression analysis in RNA-seq studies.
  • This method is particularly advantageous when differences lie in the distribution tails, improving biomarker discovery potential.
  • The application to lung adenocarcinoma data highlights the practical utility and promise of this novel statistical framework.