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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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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Related Experiment Video

Updated: Jun 28, 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

Published on: September 18, 2021

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SIEVE: One-stop differential expression, variability, and skewness analyses using RNA-Seq data.

Hongxiang Li, Tsung Fei Khang

    Biorxiv : the Preprint Server for Biology
    |April 22, 2024
    PubMed
    Summary
    This summary is machine-generated.

    A new method, SIEVE, analyzes RNA-Seq data for differential expression, variability, and skewness. This approach offers a more comprehensive understanding of complex diseases like Alzheimer's by revealing broader biological changes beyond just gene expression levels.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Systems Biology

    Background:

    • RNA-Seq data analysis is often limited to detecting differentially expressed genes, failing to capture the full complexity of gene expression changes.
    • Discrete models for RNA-Seq counts struggle to fully characterize the mean, variance, and skewness of gene expression distributions.
    • A unified framework is essential for comprehensive RNA-Seq analysis in systems biology.

    Approach:

    • Introduces SIEVE, a statistical methodology providing a unified framework for RNA-Seq data analysis.
    • Employs a compositional data analysis framework to transform discrete RNA-Seq counts into a continuous form.
    • Utilizes a skew-normal distribution to model the transformed gene expression data.

    Key Points:

    • SIEVE demonstrates superior control over false discovery rate and Type II error compared to existing differential expression methods.
    • Analysis of Alzheimer's disease RNA-Seq data reveals gene sets with significant differences in mean, standard deviation, and skewness can predict disease state.
    • Beyond differential expression, SIEVE uncovers broader biological aspects associated with complex diseases by analyzing differential variability and skewness.

    Conclusions:

    • SIEVE provides a novel perspective for understanding intricate gene expression changes in complex diseases.
    • The methodology enhances the potential of RNA-Seq data analysis for systems biology applications.
    • Enables a more holistic view of biological alterations in disease states by considering multiple facets of gene expression distribution.