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 4 publications

(2 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

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Hajibabaei

2020

Preprint

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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

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

Porter

Hajibabaei

2021

Preprint

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BackgroundPseudogenes are non-functional copies of protein coding genes that typically follow a different molecular evolutionary path as compared to functional genes. The inclusion of pseudogene sequences in DNA barcoding and metabarcoding analysis can lead to misleading results. None of the most widely used bioinformatic pipelines used to process marker gene (metabarcode) high throughput sequencing data specifically accounts for the presence of pseudogenes in protein-coding marker genes. The purpose of this study is to develop a method to screen for obvious pseudogenes in large COI metabarcode datasets. We do this by: 1) describing gene and pseudogene characteristics from a simulated DNA barcode dataset, 2) show the impact of two different pseudogene removal methods on mock metabarcode datasets with simulated pseudogenes, and 3) incorporate a pseudogene filtering step in a bioinformatic pipeline that can be used to process Illumina paired-end COI metabarcode sequences. Open reading frame length and sequence bit scores from hidden Markov model (HMM) profile were used to detect pseudogenes.ResultsOur simulations showed that it was more difficult to identify pseudogenes from shorter amplicon sequences such as those typically used in metabarcoding (∼300 bp) compared with full length DNA barcodes that are used in construction of barcode libraries (∼ 650 bp). It was also more difficult to identify pseudogenes in datasets where there is a high percentage of pseudogene sequences. We show that existing bioinformatic pipelines used to process metabarcode sequences already remove some apparent pseudogenes, especially in the rare sequence removal step, but the addition of a pseudogene filtering step can remove more.ConclusionsThe combination of open reading frame length and hidden Markov model profile analysis can be used to effectively screen out obvious pseudogenes from large datasets. There is more to learn from COI pseudogenes such as their frequency in DNA barcode and metabarcoding studies, their taxonomic distribution, and evolution. Thus, we encourage the submission of verified COI pseudogenes to public databases to facilitate future studies.

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

Cited by 4 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

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

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

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