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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 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
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Related Experiment Video

Updated: Jun 19, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

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

Published on: September 18, 2021

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Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy.

Nicolas Carels1

  • 1Laboratory of Biological System Modeling, Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro 21040-900, RJ, Brazil.

Biology
|July 26, 2024
PubMed
Summary

This study identifies optimal RNA-sequencing (RNA-seq) workflows for preserving biological data. Combining TPM, RLE, and TMM normalization with log2 fold change best preserves biological facts in RNA-seq analysis.

Keywords:
5-year OSPPI networkRPKMbenchmarkingcancerentropymedian normalization

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Last Updated: Jun 19, 2025

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • RNA-sequencing (RNA-seq) data processing lacks standardized workflows, hindering reliable biological interpretation.
  • Existing methods vary in their ability to accurately capture biological signals from RNA-seq data.

Purpose of the Study:

  • To evaluate and identify the most effective RNA-seq workflow for preserving biological facts.
  • To assess the utility of Shannon entropy as a metric for optimizing RNA-seq analysis pipelines.

Main Methods:

  • Shannon entropy was employed to measure the biological status of RNA-seq data.
  • Multiple RNA-seq workflow approaches, including DESeq2 and edgeR, were analyzed.
  • Nine normalization methods combined with log2 fold change were tested on TCGA RNA-seq datasets from 515 patients across 12 cancer types.

Main Results:

  • TPM, RLE, and TMM normalization methods, when used with a log2 fold change threshold of ≥1 for differential gene expression, demonstrated superior performance.
  • These optimal combinations effectively preserved biological information within the RNA-seq datasets.

Conclusions:

  • Shannon entropy provides an objective metric for assessing and optimizing RNA-seq workflows.
  • The identified workflow (TPM, RLE, or TMM normalization with log2 fold change ≥1) enhances the accuracy of RNA-seq data analysis and mRNA sequencing technologies.