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A powerful denoising tool for correcting sequencing errors in Illumina MiSeq 16S rRNA gene amplicon sequencing data. IPED includes a machine learning method which predicts potentially erroneous positions in sequencing reads based on a combination of quality metrics. Subsequently, this information is used to group those error-containing reads with correct reads, resulting in error-free consensus reads. This is achieved by masking potentially erroneous positions during this clustering step. IPED obtains a better performance on mock datasets compared with the available alternatives Pre-cluster and UNOISE, and on average can correct double the amount of errors compared to both algorithms. Reducing the error rate had a positive effect on the clustering of reads in operational taxonomic units, with an almost perfect correspondence between the number of clusters and the theoretical number of species present in the mock communities.

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IPED classification

IPED specifications

Software type:
Restrictions to use:
Biological technology:
Programming languages:
Java, Perl
Computer skills:
Mothur, WEKA
Command line interface
Input data:
IPED require a special quality file as an input, this can only be generated via our adapted version of make.contigs (included with IPED executable).
Operating system:
GNU General Public License version 2.0

IPED distribution


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IPED support


  • Pieter Monsieurs <>


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Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), Mol, Belgium; Department of Bioscience Engineering, Vrije Universiteit Brussel, Brussels, Belgium; VIB Center for the Biology of Disease, VIB, Leuven, Belgium; Department of Microbiology and Immunology, REGA institute, KU Leuven, Belgium

Funding source(s)

This work is funded by an SCK-CEN PhD Grant.

Link to literature

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