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Proofreading

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Related Experiment Video

Updated: May 18, 2026

Pyrosequencing: A Simple Method for Accurate Genotyping

Pyrosequencing: A Simple Method for Accurate Genotyping

Published on: January 8, 2008

Implications of pyrosequencing error correction for biological data interpretation.

Matthew G Bakker¹, Zheng J Tu, James M Bradeen

¹Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, USA. matt.g.bakker@gmail.com

|September 7, 2012

Summary

This summary is machine-generated.

Different DNA sequence processing methods significantly impact biological interpretations. AmpliconNoise de-noising alters operational taxonomic unit (OTU) richness and diversity, affecting downstream ecological analyses and comparisons.

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Pyrosequencing: A Simple Method for Accurate Genotyping

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Published on: January 8, 2008

Pyrosequencing for Microbial Identification and Characterization

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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

Area of Science:

Bioinformatics
Computational Biology
Ecology

Background:

Second-generation DNA sequencing generates vast amounts of data.
Processing methods for this data can influence biological interpretations.
Uncertainty exists regarding the impact of different processing pipelines.

Purpose of the Study:

To compare the output of two distinct DNA sequence processing pipelines.
To investigate the impact of de-noising (AmpliconNoise) versus standard quality filtering on data interpretation.
To assess how processing choices affect operational taxonomic unit (OTU) richness, diversity, and phylogenetic diversity.

Main Methods:

Comparison of AmpliconNoise (de-noising) with a standard pipeline (quality filtering and preclustering).
Analysis of read removal, OTU generation, and richness/diversity metrics.
Evaluation of taxon-based and phylogenetic diversity estimates.

Main Results:

AmpliconNoise removed more reads but resulted in more OTUs unique to the standard pipeline.
Total OTU richness decreased with AmpliconNoise, while per-sample richness, diversity, and evenness increased.
Phylogenetic diversity decreased with de-noising, altering sample diversity rankings.

Conclusions:

Processing pipeline choice significantly impacts biological interpretations from DNA sequencing data.
De-noising can alter ecological diversity metrics and phylogenetic relationships.
Cautions are needed for data archiving and comparing studies with different processing methods.