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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
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What is Gene Expression?01:36

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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The Uncertainty Principle04:08

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Cell Specific Gene Expression01:58

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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Updated: Jan 25, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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aFold - using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data.

Wentao Yang1, Philip Rosenstiel2, Hinrich Schulenburg3,4

  • 1Evolutionary Ecology and Genetics, Zoological Institute, CAU Kiel, Am Botanischen Garten 9, 24118, Kiel, Germany. wyang@zoologie.uni-kiel.de.

BMC Genomics
|May 12, 2019
PubMed
Summary
This summary is machine-generated.

A new RNA-Seq analysis tool, aFold, improves differential gene expression identification. It uses novel normalization and polynomial algorithms to handle noisy, real-life data, outperforming existing methods with asymmetrical gene distributions and outliers.

Keywords:
ABSSeqDESeq2Differential expression analysisNormalizationRNASeqTranscriptomicsVoombaySeqedgeR

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Area of Science:

  • Transcriptomics
  • Bioinformatics
  • Computational Biology

Background:

  • RNA-Seq analysis requires robust data normalization and differential expression identification.
  • Existing tools often fail due to unmet assumptions like symmetrical gene distribution and limited outliers.
  • Arbitrary cut-offs for gene selection hinder downstream analysis.

Purpose of the Study:

  • Introduce a novel tool for differential gene expression analysis in noisy, real-life RNA-Seq data.
  • Address limitations of current methods, particularly with asymmetrical gene distributions and outliers.
  • Provide a reliable method for identifying significant differential expression.

Main Methods:

  • A new normalization procedure (qtotal) standardizes read counts based on overall distribution.
  • A polynomial algorithm (aFold) models read count uncertainty across treatments and genes.
  • Extensive benchmarking on simulated and real-life datasets (ABRF, SEQC, MAQC-II).

Main Results:

  • The aFold tool enhances reliable identification of differentially expressed genes, especially with asymmetrical distributions.
  • It effectively handles datasets with outliers, common in real-world biological data.
  • Inferred fold change values are comparable across experiments, aiding downstream applications like clustering and visualization.

Conclusions:

  • A new transcriptomics analysis tool offers improved normalization and differential expression analysis.
  • The tool reliably identifies significant differential expression across diverse data distributions.
  • aFold surpasses alternative methods when dealing with asymmetrical gene regulation and data outliers.