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Related Concept Videos

RNA-seq03:21

RNA-seq

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 microarray-based...
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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.
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Related Experiment Video

Updated: Jun 3, 2026

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

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Published on: September 16, 2019

Improving RNA-Seq expression estimates by correcting for fragment bias.

Adam Roberts1, Cole Trapnell, Julie Donaghey

  • 1Department of Computer Science, 387 Soda Hall, UC Berkeley, Berkeley, CA 94720, USA.

Genome Biology
|March 18, 2011
PubMed
Summary

RNA sequencing (RNA-Seq) library preparation causes uneven cDNA fragment distribution, biasing expression estimates. Our likelihood-based method corrects this bias, improving accuracy and replicability across different sequencing methods.

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Last Updated: Jun 3, 2026

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • RNA sequencing (RNA-Seq) is a powerful tool for gene expression analysis.
  • Library preparation introduces biases, leading to non-uniform cDNA fragment distribution across transcripts.
  • Accurate gene expression quantification is crucial for biological insights.

Purpose of the Study:

  • To develop and validate a method for correcting biases in RNA-Seq expression estimates.
  • To improve the accuracy and reproducibility of RNA-Seq data analysis.

Main Methods:

  • A likelihood-based statistical approach was developed to model and correct for non-uniform fragment distribution.
  • Expression estimates were compared against quantitative real-time PCR (qRT-PCR) data for validation.
  • Replicability was assessed across different sequencing libraries and technologies.

Main Results:

  • The developed method significantly improved the accuracy of gene expression estimates.
  • Corrected estimates showed higher correlation with independent qRT-PCR measurements.
  • Bias correction enhanced the consistency and replicability of RNA-Seq results.

Conclusions:

  • Accounting for library preparation biases is essential for reliable RNA-Seq expression quantification.
  • The proposed likelihood-based method offers a robust solution for bias correction.
  • This approach improves the overall quality and trustworthiness of RNA-Seq data.