debar, a sequence-by-sequence denoiser for COI-5P DNA barcode data

Abstract

DNA barcoding and metabarcoding are now widely used to advance species discovery and biodiversity assessments. High-throughput sequencing (HTS) has expanded the volume and scope of these analyses, but elevated error rates introduce noise into sequence records that can inflate estimates of biodiversity. Denoising —the separation of biological signal from instrument (technical) noise—of barcode and metabarcode data currently employs abundance-based methods which do not capitalize on the highly conserved structure of the cytochrome c oxidase subunit I (COI) region employed as the animal barcode. This manuscript introduces debar, an R package that utilizes a profile hidden Markov model to denoise indel errors in COI sequences introduced by instrument error. In silico studies demonstrated that debar recognized 95% of artificially introduced indels in COI sequences. When applied to real-world data, debar reduced indel errors in circular consensus sequences obtained with the Sequel platform by 75%, and those generated on the Ion Torrent S5 by 94%. The false correction rate was less than 0.1%, indicating that debar is receptive to the majority of true COI variation in the animal kingdom. In conclusion, the debar package improves DNA barcode and metabarcode workflows by aiding the generation of more accurate sequences aiding the characterization of species diversity.

Profile hidden Markov model sequence analysis can help remove putative pseudogenes from DNA barcoding and metabarcoding datasets

“…Our pseudogene removal approach was most effective on datasets of the full length COI barcode sequence region but is less effective for shorter sequences (~ 300 bp). Now that newer sequencing technologies such as LoopSeq, compatible with Illumina sequencing platforms but currently only available for RNA genes, or HiFi circular consensus sequencing (PacBio), it may one day be possible for COI metabarcoding to target the full length of the barcoding region to facilitate more efficient nuMT detection [39,[66][67][68]. It would also be helpful if DNA barcode studies reported and deposited full length verified pseudogenes into public databases when possible.…”

“…For example, COI marker analysis need not be limited to operational taxonomic units (OTUs), but may also include the use of exact sequence variant (ESV) analysis for improved taxonomic resolution and permit intraspecific phylogeographic analyses [34][35][36][37]. Bioinformatic tools to remove sequence artefacts and noise specifically from COI datasets have also become available [38][39][40]. COI nuMTs have been discussed in the literature largely with regards to COI barcoding efforts [18,19,41] and only recently have tools appropriate for screening nuMTs from large batches of COI sequences become available [42].…”

Profile hidden Markov model sequence analysis can help remove putative pseudogenes from DNA barcoding and metabarcoding datasets

“…Our pseudogenes removal approach was most effective on datasets of the full length COI barcode sequence region but is less effective for shorter sequences (∼ 300 bp). This is especially relevant now that newer sequencing technologies such as LoopSeq (compatible with Illumina sequencing platforms, but currently only available for RNA genes) or HiFi circular consensus sequencing (PacBio) could one day be used for COI metabarcoding targeting the full length of the barcoding region facilitating pseudogene detection [12, 77–79]. It would also be helpful if COI barcode studies reported and deposited full length verified pseudogenes into public databases when possible.…”