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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 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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Related Experiment Video

Updated: Apr 11, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Towards next generation CHO cell biology: Bioinformatics methods for RNA-Seq-based expression profiling.

Craig Monger1,2, Paul S Kelly2, Clair Gallagher2

  • 1The National Institute for Bioprocessing Research and Training, Fosters Avenue, Blackrock, Dublin, Ireland.

Biotechnology Journal
|June 11, 2015
PubMed
Summary

Next-generation sequencing (NGS) and RNA-Seq offer insights into Chinese hamster ovary (CHO) cell biology. Developing robust bioinformatics pipelines is critical for analyzing complex RNA-Seq data and advancing CHO cell research.

Keywords:
BioinformaticsChinese hamster ovaryNext generation sequencingRNA-SeqTranscriptomics

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Area of Science:

  • Biotechnology
  • Genomics
  • Bioinformatics

Background:

  • High-throughput, cost-effective next-generation sequencing (NGS) has led to genome sequencing of Cricetulus griseus and Chinese hamster ovary (CHO) cell lines.
  • RNA-Seq, using NGS to study the transcriptome, is crucial for understanding CHO cell biology, from transcription start sites to small noncoding RNAs.

Purpose of the Study:

  • To address the challenge of analyzing large RNA-Seq datasets in CHO cell biology.
  • To present critical bioinformatics approaches for RNA-Seq data analysis to fully exploit its potential.

Main Methods:

  • Quality control, pre-processing, alignment, and de novo transcriptome assembly for RNA-Seq data.
  • Algorithms for mRNA and microRNA (miRNA) expression analysis.
  • Methods for detecting alternative splicing from RNA-Seq data.

Main Results:

  • Outlined bioinformatics approaches for key stages of RNA-Seq expression profiling.
  • Presented algorithms and methods for comprehensive transcriptomic analysis.

Conclusions:

  • Robust data analysis pipelines are essential for advancing CHO cell biology research using RNA-Seq.
  • A combination of isoform deconvolution and raw count methods is recommended for accurate transcript expression profiling in CHO cells, given the current stage of Cricetulus griseus genome assembly and annotation.