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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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Nonsense-mediated mRNA Decay

The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
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Improving Translational Accuracy

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...
RNA Editing02:23

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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...

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Related Experiment Video

Updated: May 30, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Bias detection and correction in RNA-Sequencing data.

Wei Zheng1, Lisa M Chung, Hongyu Zhao

  • 1Biostatistics Resource, Keck Laboratory, Yale University, 300 George Street, New Haven, Connecticut, 06510, USA.

BMC Bioinformatics
|July 21, 2011
PubMed
Summary
This summary is machine-generated.

This study reveals biases in RNA-Seq gene expression estimates like RPKM due to nucleotide composition. A new generalized additive model corrects these biases for more accurate transcriptome analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Transcriptomics

Background:

  • High-throughput sequencing, particularly RNA-Seq, offers advanced transcriptome analysis.
  • RNA-Seq provides higher resolution and identifies novel transcripts compared to microarrays.
  • Base-level read counts in RNA-Seq can exhibit biases influenced by nucleotide composition, impacting gene expression estimates.

Purpose of the Study:

  • To investigate the impact of base-level read count biases on gene-level expression estimates from RNA-Seq data.
  • To develop and validate a method for correcting multiple sources of bias in RNA-Seq gene expression quantification.

Main Methods:

  • Analysis of five diverse RNA-Seq datasets with varying preprocessing.
  • Examination of gene-level biases related to gene length, GC content, and dinucleotide frequencies.
  • Development of a generalized additive model for simultaneous bias correction.

Main Results:

  • Commonly used RNA-Seq expression metrics (e.g., RPKM) are shown to be biased by gene length, GC content, and dinucleotide frequencies.
  • The proposed generalized additive model effectively corrects multiple bias sources.
  • The new method demonstrates superior bias reduction in gene-level expression estimates compared to existing base-level correction techniques.

Conclusions:

  • The developed method accurately identifies and corrects biases in RNA-Seq gene-level expression measures.
  • This approach yields more reliable gene expression estimates from RNA-Seq data.
  • The method is beneficial for meta-analyses involving RNA-Seq data from diverse platforms and protocols.