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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 13, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Facilitating pathway and network based analysis of RNA-Seq data with pathlinkR.

Travis M Blimkie1, Andy An1, Robert E W Hancock1

  • 1REW Hancock Laboratory, Center for Microbial Diseases and Immunity Research, Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada.

Plos Computational Biology
|September 16, 2024
PubMed
Summary

The pathlinkR R package simplifies transcriptomic analysis for human RNA-Seq data. It integrates pathway enrichment and network analysis, generating figures for efficient biological interpretation.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcriptomic analysis of human RNA-Seq data generates large datasets of differentially expressed genes.
  • Interpreting these gene lists to extract biological meaning and identify key pathways can be complex and time-consuming.
  • Existing tools may lack integrated approaches for both pathway enrichment and network analysis.

Purpose of the Study:

  • To introduce pathlinkR, an R package designed to streamline and simplify the analysis and interpretation of differentially expressed genes from human RNA-Seq data.
  • To provide an integrated workflow for pathway enrichment and network-based analyses.
  • To facilitate the generation of publication-quality figures for summarizing transcriptomic analysis results.

Main Methods:

  • Development of the pathlinkR R package, available on Bioconductor.
  • Integration of pathway enrichment algorithms.
  • Implementation of network analysis functionalities.
  • Creation of functions for generating high-quality visualizations.

Main Results:

  • pathlinkR offers a unified approach to analyze and interpret transcriptomic data.
  • The package facilitates the identification of significantly enriched pathways.
  • Network visualization tools aid in understanding gene interactions and biological context.
  • Publication-ready figures enhance the communication of findings.

Conclusions:

  • pathlinkR enhances the efficiency of transcriptomic data analysis.
  • The package empowers researchers to extract deeper biological insights from RNA-Seq data.
  • pathlinkR serves as a valuable tool for the bioinformatics and genomics research community.