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Updated: Apr 14, 2026

Pyrosequencing: A Simple Method for Accurate Genotyping

Pyrosequencing: A Simple Method for Accurate Genotyping

Published on: January 8, 2008

FlowClus: efficiently filtering and denoising pyrosequenced amplicons.

John M Gaspar¹, W Kelley Thomas²

¹Department of Molecular Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH, USA. jsh58@unh.edu.

BMC Bioinformatics

|April 18, 2015

Summary

This summary is machine-generated.

FlowClus offers efficient denoising for amplicon metagenomics, reducing sequencing errors while preserving data integrity. This tool provides control and adaptability for analyzing diverse datasets, improving accuracy in microbial community studies.

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Last Updated: Apr 14, 2026

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Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

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Published on: December 22, 2014

Area of Science:

Bioinformatics
Metagenomics
Computational Biology

Background:

Sequencing errors and PCR artifacts impact amplicon-based metagenomic studies.
Existing denoising algorithms may alter real community data inconsistently and face limitations with dataset size and sequencing technology.
There is a need for robust methods to accurately filter and denoise sequence data.

Purpose of the Study:

To introduce FlowClus, a novel algorithm for efficient read filtering and denoising in amplicon metagenomics.
To provide a denoising approach that offers user control and adaptability for real-world datasets.
To evaluate FlowClus's performance against existing methods in terms of error reduction and data retention.

Main Methods:

FlowClus employs a systematic approach for read filtering and denoising.
The algorithm provides feedback for parameter adjustment tailored to specific datasets.
It supports analysis of longer reads from technologies like 454 and Ion Torrent.

Main Results:

FlowClus achieved lower error rates and retained significantly more sequence information compared to other denoising algorithms on mock community data.
The tool efficiently processed a large dataset (2.2 million reads) in under twelve hours, with options for further speed enhancement.
FlowClus demonstrated adaptability for denoising real datasets by allowing parameter adjustments.

Conclusions:

FlowClus mitigates potential deleterious effects of previous denoising pipelines on raw metagenomic data.
The tool enables researchers to maintain control over filtering and denoising, leading to more reliable conclusions.
Its efficiency allows for standardized re-analysis of multiple large datasets, and it is freely available on GitHub.