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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Gene Duplication and Divergence02:37

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are characterized.
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...

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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

Differential expression--the next generation and beyond.

Paul L Auer1, Sanvesh Srivastava, R W Doerge

  • 1Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Briefings in Functional Genomics
|January 3, 2012
PubMed
Summary
This summary is machine-generated.

RNA-sequencing (RNA-seq) is a powerful tool for transcriptome analysis, but lacks standardized methods for experimental design, data processing, and statistical analysis. This review highlights key challenges in RNA-seq experiments, focusing on design, analysis, and dimensionality reduction.

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Related Experiment Videos

Last Updated: May 26, 2026

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

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Maintaining Biological Cultures and Measuring Gene Expression in Aphis nerii: A Non-model System for Plant-insect Interactions
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Maintaining Biological Cultures and Measuring Gene Expression in Aphis nerii: A Non-model System for Plant-insect Interactions

Published on: August 31, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • RNA-sequencing (RNA-seq) has become the preferred method for transcriptome mapping and quantification.
  • The increasing complexity of RNA-seq experiments outpaces current standardized analytical practices.
  • Laboratories face challenges in computational and analytical capacities due to RNA-seq advancements.

Purpose of the Study:

  • To review the primary challenges encountered in RNA-sequencing experiments.
  • To identify areas lacking standardized protocols in RNA-seq data analysis.
  • To provide an overview of critical issues in experimental design, data processing, and statistical analysis.

Main Methods:

  • Literature review of current RNA-sequencing methodologies.
  • Identification of common challenges across diverse RNA-seq experimental designs.
  • Analysis of existing gaps in standardized protocols for data processing and statistical analysis.

Main Results:

  • RNA-sequencing offers advanced capabilities but lacks universal standards for experimental design.
  • Significant challenges persist in data processing, normalization, and dimensionality reduction.
  • Standardized statistical analysis methods for RNA-seq data are not yet established.

Conclusions:

  • Addressing the lack of standardization in RNA-seq experimental design is crucial.
  • Development of robust methods for RNA-seq data processing and statistical analysis is needed.
  • Efficient dimensionality reduction techniques are essential for interpreting complex transcriptomic data.