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dc.contributor.authorAntich, Adrià
dc.contributor.authorPalacín, Creu
dc.contributor.authorTuron, Xavier
dc.date.accessioned2022-03-21T12:42:36Z
dc.date.available2022-03-21T12:42:36Z
dc.date.issued2022-01-19
dc.description.abstractDNA metabarcoding is broadly used in biodiversity studies encompassing a wide range of organisms. Erroneous amplicons, generated during amplification and sequencing procedures, constitute one of the major sources of concern for the interpretation of metabarcoding results. Several denoising programs have been implemented to detect and eliminate these errors. However, almost all denoising software currently available has been designed to process non-coding ribosomal sequences, most notably prokaryotic 16S rDNA. The growing number of metabarcoding studies using coding markers such as COI or RuBisCO demands a re-assessment and calibration of denoising algorithms. Here we present DnoisE, the first denoising program designed to detect erroneous reads and merge them with the correct ones using information from the natural variability (entropy) associated to each codon position in coding barcodes. We have developed an open-source software using a modified version of the UNOISE algorithm. DnoisE implements different merging procedures as options, and can incorporate codon entropy information either retrieved from the data or supplied by the user. In addition, the algorithm of DnoisE is parallelizable, greatly reducing runtimes on computer clusters. Our program also allows different input file formats, so it can be readily incorporated into existing metabarcoding pipelines.en_US
dc.identifier.citationAntich A, Palacín C, Turon X, Wangensteen OS. DnoisE: distance denoising by entropy. An open-source parallelizable alternative for denoising sequence datasets. PeerJ. 2022;10:e12758en_US
dc.identifier.cristinIDFRIDAID 1986445
dc.identifier.doi10.7717/peerj.12758
dc.identifier.issn2167-8359
dc.identifier.urihttps://hdl.handle.net/10037/24471
dc.language.isoengen_US
dc.publisherPeerJen_US
dc.relation.journalPeerJ
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titleDnoisE: distance denoising by entropy. An open-source parallelizable alternative for denoising sequence datasetsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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