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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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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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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: Apr 25, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Detecting and correcting systematic variation in large-scale RNA sequencing data.

Sheng Li1, Paweł P Łabaj2, Paul Zumbo1

  • 11] Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA. [2] The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, USA. [3].

Nature Biotechnology
|August 25, 2014
PubMed
Summary
This summary is machine-generated.

Standardized RNA samples reveal reproducibility issues in large-scale RNA sequencing (RNA-seq) studies. Site-specific effects can be identified and removed using normalization methods that combine gene data across sites, improving DEG detection.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • High-throughput RNA sequencing (RNA-seq) is crucial for transcriptome analysis.
  • Standardized best practices for large-scale RNA-seq studies, especially with multi-platform or multi-site data, are not fully established.

Purpose of the Study:

  • To identify sources of error in large-scale RNA-seq studies.
  • To assess the impact of these errors on detecting differentially expressed genes (DEGs).
  • To evaluate the effectiveness of various normalization methods in mitigating biases.

Main Methods:

  • Utilized standardized RNA samples with internal controls.
  • Analyzed variations in guanine-cytosine content, gene coverage, sequencing error rate, and insert size.
  • Assessed the performance of multiple normalization methods (cqn, EDASeq, RUV2, sva, PEER).

Main Results:

  • Decreased reproducibility was observed across different sequencing sites.
  • Normalization method efficacy varied based on sample complexity and data quality.
  • Site-specific biases were identified as a significant challenge.

Conclusions:

  • Normalization methods integrating data across sites are essential for robust RNA-seq analysis.
  • These methods can effectively identify and remove site-specific effects.
  • Implementing such strategies substantially improves the reliability of large-scale RNA-seq studies and DEG detection.