IPED: a highly efficient denoising tool for Illumina MiSeq Paired-end 16S rRNAgene amplicon sequencing data.

Mohamed Mysara,Natalie Leys,Pieter Monsieurs,Jeroen Raes

doi:10.1186/s12859-016-1061-2

Abstract

BackgroundThe development of high-throughput sequencing technologies has revolutionized the field of microbial ecology via the sequencing of phylogenetic marker genes (e.g. 16S rRNA gene amplicon sequencing). Denoising, the removal of sequencing errors, is an important step in preprocessing amplicon sequencing data. The increasing popularity of the Illumina MiSeq platform for these applications requires the development of appropriate denoising methods.ResultsThe newly proposed denoising algorithm 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. Compared to the second best algorithm available, IPED detects double the amount of errors. 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.ConclusionOur algorithm IPED is a powerful denoising tool for correcting sequencing errors in Illumina MiSeq 16S rRNA gene amplicon sequencing data. Apart from significantly reducing the error rate of the sequencing reads, it has also a beneficial effect on their clustering into operational taxonomic units. IPED is freely available at http://science.sckcen.be/en/Institutes/EHS/MCB/MIC/Bioinformatics/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1061-2) contains supplementary material, which is available to authorized users.

Highlights

The development of high-throughput sequencing technologies has revolutionized the field of microbial ecology via the sequencing of phylogenetic marker genes (e.g. 16S rRNA gene amplicon sequencing)
In this work we propose the Illumina Paired-End Denoiser (IPED) algorithm, an error correction algorithm developed for denoising Illumina MiSeq 16S rRNA gene amplicon sequencing data
Once the setup and training of IPED has been finalized, the algorithm was tested on a wide range of datasets against Pre-cluster and UNOISE, and this at the level of error rate, computational cost and the accuracy of the operational taxonomic units (OTU) clustering

Summary

Introduction

The development of high-throughput sequencing technologies has revolutionized the field of microbial ecology via the sequencing of phylogenetic marker genes (e.g. 16S rRNA gene amplicon sequencing). The development of high-throughput sequencing technologies has revolutionized the field of microbial ecology by offering a cost-efficient method to assess microbial diversity at an unseen depth. Initial ecological applications mainly relied on the usage of the 454 pyrosequencing platforms, resulting in an impressive repository of bioinformatics analysis tools for processing this kind of data, as used for example in 16S rRNA gene amplicon sequencing data. Due to the recent advances in other high-throughput sequencing technologies regarding throughput and read length, and the announcement of Roche to shut down its 454 services by 2016, sequencing platforms provided for example by Pacific Biosciences and Illumina gain importance for assessing microbial diversity using amplicon sequencing. Illumina sequencing data do not suffer from indel errors to the same extent, but rather from nucleotide substitutions (miscalling), mainly originating from two effects: 1) high

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