Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

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...

Improving Translational Accuracy

Improving Translational Accuracy

RNA-seq

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...

Genome Copying Errors

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their survival. Therefore, the copying errors are checked and repaired at three levels.

Mismatch Repair

Mismatch Repair

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Self-Gated Late Gadolinium Enhancement at 7T to Image Rats with Reperfused Acute Myocardial Infarction.

Korean journal of radiology·2018

Same author

A major natural genetic variation associated with root system architecture and plasticity improves waterlogging tolerance and yield in soybean.

Plant, cell & environment·2018

Same author

Genomic differences between nasal Staphylococcus aureus from hog slaughterhouse workers and their communities.

PloS one·2018

Same author

Three zinc iodide complexes based on phosphane ligands: syntheses, structures, optical properties and TD-DFT calculations.

Acta crystallographica. Section C, Structural chemistry·2018

Same author

Super Nonlinear Electrodeposition-Diffusion-Controlled Thin-Film Selector.

ACS applied materials & interfaces·2018

Same author

Surface Oxidation of AuNi Heterodimers to Achieve High Activities toward Hydrogen/Oxygen Evolution and Oxygen Reduction Reactions.

Small (Weinheim an der Bergstrasse, Germany)·2018

Search research articles

Related Experiment Video

Updated: Mar 31, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads.

Li Song¹, Liliana Florea²

¹Department of Computer Science, Johns Hopkins University, Baltimore, 21218 USA.

|October 27, 2015

Summary

Rcorrector corrects sequencing errors in RNA-seq reads using a k-mer based method. This tool offers high accuracy and efficiency, making transcriptomic analysis more reliable.

Keywords:

Error correction Next-generation sequencing RNA-seq k-mers

More Related Videos

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

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

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

Published on: September 16, 2019

Related Experiment Videos

Last Updated: Mar 31, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

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

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

Published on: September 16, 2019

Area of Science:

Bioinformatics
Genomics
Transcriptomics

Background:

Next-generation sequencing (RNA-seq) is crucial for transcriptomic analysis.
Sequencing errors in RNA-seq reads hinder bioinformatics analyses like alignment and assembly.
Existing error correction methods for whole-genome sequencing (WGS) are not suitable for RNA-seq due to expression variation and splicing.

Purpose of the Study:

To develop an effective method for correcting random sequencing errors in RNA-seq reads.
To address the limitations of existing error correction techniques for transcriptomic data.

Main Methods:

Developed Rcorrector, a k-mer based error correction method for RNA-seq reads.
Utilized a De Bruijn graph to represent trusted k-mers from input reads.
Implemented a local threshold computation for k-mer validation at each read position, differing from WGS methods.

Main Results:

Rcorrector demonstrates high accuracy, comparable or superior to existing methods like SEECER.
The method is time and memory efficient, requiring only 5 GB of memory for 100 million reads.
Rcorrector can be run on standard desktop or server hardware.

Conclusions:

Rcorrector provides an accurate and efficient solution for RNA-seq read error correction.
The software's low resource requirements make it accessible for widespread use in transcriptomic studies.
Rcorrector is freely available, facilitating its adoption in the research community.