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RNA-seq

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Related Experiment Video

Updated: Jun 2, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Protocol dependence of sequencing-based gene expression measurements.

Tal Raz¹, Philipp Kapranov, Doron Lipson

¹Applications, Methods, and Collaborations Group, Helicos BioSciences, Cambridge, Massachusetts, United States of America.

|May 17, 2011

Summary

RNA sequencing protocols significantly impact transcriptome analysis. Ribosomal-depleted RNA offers broader coverage than polyA selection, while single-tag counting aids gene expression and short RNA detection.

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Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Published on: August 8, 2013

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

RNA sequencing (RNA Seq) offers extensive transcriptome data.
Understanding technical variations is crucial for accurate RNA Seq results.
Protocol choices influence RNA detection and data interpretation.

Purpose of the Study:

To evaluate how technical variations affect RNA Seq quality and interpretability.
To assess the impact of RNA source and protocol steps on transcript detection.
To guide researchers in selecting optimal RNA Seq analysis strategies.

Main Methods:

Analysis of multiple human RNA samples.
Assessment of RNA fragmentation, fractionation, and cDNA synthesis.
Comparison of single versus multiple tag counting methods.
Evaluation of polyA selection versus ribosomal depletion protocols.

Main Results:

PolyA selection yields precise coding transcript measurements but misses non-ribosomal RNA.
Ribosomal-depleted RNA provides cost-effective, complete transcriptome coverage.
Single-tag counting excels in gene expression and short RNA detection.
RNA fragmentation negatively impacts short RNA detection.

Conclusions:

PolyA selection offers incomplete transcriptome views.
Ribosomal depletion is recommended for comprehensive transcriptome analysis.
Single-tag counting is advantageous for specific gene expression and short RNA studies.
Researchers must weigh protocol trade-offs for optimal RNA Seq analysis.