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An error-corrector for pyrosequenced amplicon reads. Acacia reduces the number and complexity of alignments. Rather than performing all-against-all alignments in a cluster, each read in the cluster is aligned to a dynamically updated cluster consensus; the alignment algorithm is made more efficient using heuristics that only consider homopolymer over and under-calls. Acacia uses a quicker but less sensitive statistical approach to distinguish between error and genuine sequence differences. Acacia is an alternative to AmpliconNoise and Denoiser that maintains sensitivity without compromising genuine signal in the data.

DADA / Divisive Amplicon Denoising Algorithm

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Corrects amplicon errors without constructing Operational Taxonomic Units (OTUs). DADA2 is an R package that implements a quality-aware model of Illumina amplicon errors. This application is reference-free, and applicable to any genetic locus. It also implements the full amplicon workflow: filtering, dereplication, chimera identification, and merging paired-end reads. It enhances the study of microbial communities by allowing researchers to accurately reconstruct amplicon-sequenced communities at the highest resolution.


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.


Uses a systematic approach to filter and denoise reads efficiently. When denoising real datasets, FlowClus provides feedback about the process that can be used as the basis to adjust the parameters of the algorithm to suit the particular dataset. When used to analyze a mock community dataset, FlowClus produced a lower error rate compared to other denoising algorithms, while retaining significantly more sequence information. Among its other attributes, FlowClus can analyze longer reads being generated from all stages of 454 sequencing technology, as well as from Ion Torrent.

NoDe / Noise Detector

An error correction algorithm trains to identify positions in 454 sequencing reads that are likely to have an error, and subsequently clusters those error-prone reads with correct reads resulting in error-free representative read. A benchmarking study with other denoising algorithms shows that NoDe can detect up to 75% more errors in a large scale mock community dataset, and this with a low computational cost compared to the second best algorithm considered in this study.

MUGAN / Multi-GPU accelerated AmpliconNoise

Allows users to denoise next generation sequencing (NGS) pyrosequenced reads. MUGAN provides a platform dedicated to the removal of errors by exploiting data-level parallelism. The software merges multiple graphics processing units (GPUs), central processing units (CPUs) to the AmpliconNoise software. It aims to perform a faster denoising of information as well as to provide an improved visualization of error-correction and diversity-estimation results.


Uses algorithms to de-noise functional gene pyrosequences and performs ecological analysis on de-noised sequence data. FunFrame is an R-based data-analysis pipeline which provides users a unified set of tools, adapted from disparate sources and designed for different applications, that can be used to examine a particular protein coding gene of interest. FunFrame reduced spurious diversity while retaining more sequences than a commonly used de-noising method that discards sequences with frameshift errors.


Denoises homopolymers from pyrosequence technologies. PyroClean has been designed for protein-coding gene regions that are evolutionarily conserved with regard to amino acid composition and nucleotide insertion and deletion events, as these provide three useful properties for denoising: (i) some amino-acid residues are highly conserved, (ii) nucleotide variation is biased toward the third base positions of codons, and (iii) indels are virtually nonexistent. The program involves five steps for the removal of non-target sequences and generation of an alignment, facilitating a sixth step for the manual removal of remaining insertion-deletion error, chimeric sequences and presumed numts (nuclear mitochondrial DNA - copies of cytoplasmic mitochondrial DNA sequences that have been transferred into the nuclear genome).