coil: an R package for cytochrome C oxidase I (COI) DNA barcode data cleaning, translation, and error evaluation

Nugent, Cameron M.; Elliott, Tyler A.; Ratnasingham, Sujeevan; Adamowicz, Sarah J.

doi:10.1101/2019.12.12.865014

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…NUMTdumper provides a method to screen for NuMTs based on read counts while acknowledging the trade-offs between removing all possible NuMTs while erroneously removing genuine reads. An R package called ‘coil’ has also recently been developed that will place COI barcode and metabarcode sequences in frame using profile HMM analysis [58]. MetaWorks aims to extend the COI metabarcode toolkit that provides a harmonized environment where data from other organismal markers in multi-marker, multi-trophic studies can also be analyzed.…”

Section: Resultsmentioning

confidence: 99%

MetaWorks: A flexible, scalable bioinformatic pipeline for high-throughput multi-marker biodiversity assessments

Porter

Hajibabaei

2020

Preprint

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Background: Multi-marker metabarcoding is increasingly being used to generate biodiversity information across different domains of life from microbes to fungi to animals such as in ecological and environmental studies. Current popular bioinformatic pipelines support microbial and fungal marker analysis, while ad hoc methods are used to process animal metabarcode markers from the same study. The purpose of this paper is to introduce MetaWorks, a "meta"barcode pipeline that does "the works" and supports the bioinformatic processing of various metabarcoding markers including rRNA and their spacers as well as protein coding loci. Results: MetaWorks provides a Conda environment to quickly gather most of the programs and dependencies for the pipeline. MetaWorks is automated using Snakemake to ensure reproducibility and scalability. We have supplemented existing RDP-trained classifiers for SSU (prokaryotes), ITS (fungi), and LSU (fungi) with trained classifiers for COI (eukaryotes), rbcL (diatoms or eukaryotes), SSU (diatoms or eukaryotes), and 12S (fish). MetaWorks can process rRNA genes, but it can also properly handle ITS spacers by trimming flanking conserved rRNA gene regions, as well as handle protein coding genes by removing obvious pseudogenes. Conclusions: As far as we are aware, MetaWorks is the first flexible multi-marker metabarcode pipeline that can accommodate rRNA genes, spacer, and protein coding markers in the same pipeline. This is ideal for large-scale, multi-marker studies to provide a harmonized processing environment, pipeline, and taxonomic assignment approach. Updates to MetaWorks will be made as needed to reflect advances in the underlying programs, reference databases, or hidden Markov model (HMM) profiles for pseudogene filtering. Future developments will include support for additional metabarcode markers, RDP trained reference databases, and HMM profiles for pseudogene filtering.

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Section: Resultsmentioning

confidence: 99%

MetaWorks: A flexible, scalable bioinformatic pipeline for high-throughput multi-marker biodiversity assessments

Porter

Hajibabaei

2020

Preprint

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“…The dataset was obtained on 22 November 2022 from the BOLD Systems website [4] after searching for "Tetrigidae" in the public data portal and downloading the combined TSV file of sequences and specimen information on all public records. The subsequent curation of the dataset was performed in R [27] using the "janitor" [28] and "coil" [12,13] packages.…”

Section: Methodsmentioning

confidence: 99%

“…The coil package detected 96 sequences (4% of the dataset) that likely contain indels causing shifts in the reading frame and none that contain stop codons. Those sequences were generally shorter, so it is possible that some were wrongly labeled, as coil has an error rate of up to 25% for short sequences [13]. Most of the non-indel sequences are close to the expected length, meaning that BOLD's own error-seeking system is adequate and the low amount of indels detected with coil could be due to differences in the algorithms used.…”

Section: Strengths and Shortcomings Of The Bold Databasementioning

confidence: 99%

“…Errors can arise from two sources: errors in the sequences, which mainly include sequencing the wrong loci, i.e., pseudogenes or numts [10], and errors that are the result of misidentifications [11]. BOLD has its own system for detecting technical errors to which all the uploaded sequences are subjected [4], but there are also independent solutions, such as the R package "coil" that detects insertions and deletions (indels) and internal stop codons [12,13]. A good example of a taxon where the second problem is common is the pygmy hopper in the family Tetrigidae [11].…”

Section: Introductionmentioning

confidence: 99%

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DNA Barcoding of Pygmy Hoppers—The First Comprehensive Overview of the BOLD Systems’ Data Shows Promise for Species Identification

Kasalo¹,

Skejo

Husemann

2023

Diversity

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The COI gene is widely used as a DNA barcode in animals that can assist in the identification of species. One of the widely used aggregators of DNA barcodes is the Barcode of Life Data System (BOLD Systems), which contains around 2500 sequences of Tetrigidae, an understudied orthopteran family with unresolved taxonomy and species that are difficult to identify. In this paper, we provide a summary of the metadata provided with the COI sequences and present a phylogenetic analysis of photographically vouchered sequences using Maximum Likelihood and Bayesian analysis. We found that (1) the subfamily Tetriginae is disproportionately represented in the dataset, (2) most of the records are not identified beyond the family level, (3) most regions, except for Costa Rica, are undersampled, (4) most of the sequences do not have photographic vouchers, and (5) the taxonomic backbone of BOLD is out of date. The phylogenetic analysis showed that the clusters of COI barcodes mostly correspond to species, but some clusters remain ambiguous. The deeper nodes in the phylogenetic trees are not well-supported, indicating that this gene has a very weak phylogenetic signal beyond the specific level.

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“…The Barcode of Life Data System (BOLD) [59] was used as the source for mitochondrial sequence data as it contains thousands of published cytochrome c oxidase subunit I (COI) partial gene sequence records (16,676 sequences for over 2000 Squamata species as of July 16 th , 2021). COI barcode sequence data [60] for Squamata were obtained from a published dataset [61], originally downloaded into R on March 12 th , 2020. Data were filtered for records that have been identified to the species level, as this information was necessary for trait matching purposes.…”

Section: Trait Datamentioning

confidence: 99%

A real data-driven simulation strategy to select an imputation method for mixed-type trait data

May

Feng

Adamowicz

2022

Preprint

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Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Imputation offers an alternative to removing cases with missing values from datasets. Imputation techniques that incorporate phylogenetic information into their estimations have demonstrated improved accuracy over standard techniques. However, previous studies of phylogenetic imputation tools are largely limited to simulations of numerical trait data, with categorical data not evaluated. It also remains to be explored whether the type of genetic data used affects imputation accuracy. We conducted a real data-based simulation study to compare the performance of imputation methods using a mixed-type trait dataset (lizards and amphisbaenians; order: Squamata). Selected methods included mean/mode imputation, k-nearest neighbour, random forests, and multivariate imputation by chained equations (MICE). Known values were removed from a complete-case dataset to simulate different missingness scenarios: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Each method (with and without phylogenetic information derived from mitochondrial and nuclear gene trees) was used to impute the removed values. The performances of the methods were evaluated for each trait and in each missingness scenario. A random forest method supplemented with a nuclear-derived phylogeny performed best overall, and this method was used to impute missing values in the original squamate dataset. Data with imputed values better reflected the characteristics and distributions of the original data compared to the complete-case data. However, phylogeny did not always improve performance for every trait and in every missingness scenario, and caution should be taken when imputing trait data, particularly in cases of extreme bias. Ultimately, these results support the use of a real data-based simulation procedure to select a suitable imputation strategy for a given mixed-type trait dataset. Moreover, they highlight the potential biases that complete-case usage may introduce into analyses.Author summaryThe issue of missing data is problematic in trait datasets as observations for rare or threatened species are often missing disproportionately. When only complete cases are used in an analysis, derived results may be biased. Imputation is an alternative to complete-case analysis and entails filling in the missing values using known observations. It has been demonstrated that including phylogenetic information in the imputation process improves accuracy of predicted values. However, most previous evaluations of imputation methods for trait datasets are limited to numerical, simulated data, with categorical traits not considered. Using a reptile dataset comprised of both numerical and categorical trait data, we employed a real data-based simulation strategy to select an optimal imputation method for the dataset. We evaluated the performance of four different imputation methods across different missingness scenarios (e.g. missing completely at random, values missing disproportionately for smaller species. Results indicate that imputed data better reflected the original dataset characteristics compared to complete-case data; however, the optimal imputation strategy for a given scenario was contingent on missingness scenario and trait type. As imputation performance varies depending on the properties of a given dataset, a real data-based simulation strategy can be used to provide guidance on best imputation practices.

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coil: an R package for cytochrome C oxidase I (COI) DNA barcode data cleaning, translation, and error evaluation

Cited by 5 publications

References 33 publications

MetaWorks: A flexible, scalable bioinformatic pipeline for high-throughput multi-marker biodiversity assessments

MetaWorks: A flexible, scalable bioinformatic pipeline for high-throughput multi-marker biodiversity assessments

DNA Barcoding of Pygmy Hoppers—The First Comprehensive Overview of the BOLD Systems’ Data Shows Promise for Species Identification

A real data-driven simulation strategy to select an imputation method for mixed-type trait data

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