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Related Experiment Video

Updated: Jun 15, 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

Recount: expectation maximization based error correction tool for next generation sequencing data.

Edward Wijaya1, Martin C Frith, Yutaka Suzuki

  • 1AIST, Computational Biology Research Center, 2-42 Aomi, Koutou-Ku, Tokyo 135-0064, Japan. e-wijaya@aist.go.jp

Genome Informatics. International Conference on Genome Informatics
|February 25, 2010
PubMed
Summary
This summary is machine-generated.

Next-generation sequencing (NGS) errors create inaccurate gene counts. RECOUNT software corrects these errors, improving biological interpretation of NGS data with high efficiency and low memory usage.

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Last Updated: Jun 15, 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

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
09:30

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) technologies generate vast amounts of data but suffer from sequencing errors.
  • These errors introduce false reads and reduce accurate read counts, potentially biasing downstream analyses.
  • Existing error correction methods for SAGE data lack scalability for NGS datasets.

Purpose of the Study:

  • To introduce RECOUNT, an efficient Expectation Maximization algorithm for correcting tag counts in NGS data.
  • To compare the performance of RECOUNT against FREC, a previously developed program for NGS error correction.
  • To evaluate the impact of tag count correction on gene expression analysis using real data.

Main Methods:

  • Implementation of an Expectation Maximization algorithm for tag count correction.
  • Comparison of RECOUNT with FREC using reference genomes and simulated datasets.
  • Application of tag count correction to real gene expression data.

Main Results:

  • RECOUNT demonstrates comparable or superior performance to FREC in tag count correction.
  • RECOUNT utilizes significantly less memory than FREC (5GB vs. 75GB).
  • Tag count correction increases mappable tags and significantly impacts the biological interpretation of gene expression data.

Conclusions:

  • RECOUNT provides an efficient and scalable solution for NGS tag count correction.
  • Tag count correction is crucial for accurate gene expression analysis and biological discovery.
  • RECOUNT is an open-source tool available for the research community.