Biology subjects

Adamowicz, S. J.

Publications and source records attributed to Adamowicz, S. J..

9 recordsLinked to original sources

bioRxiv2024

Building a Collaborative DNA Barcode Library for the Diptera of Churchill, Canada: A Resource for Northern Research

Churchill, Manitoba, Canada is a diverse subarctic region that has been the focus of DNA barcoding efforts and research for decades. Despite this effort, there are still taxa in this region that are underrepresented in public databases, particularly hyper diverse insect groups. We present a collaborative large-scale DNA barcode reference library for Diptera based on molecular data and expert taxonomic identification, and we explore habitat occupancy, biogeographic patterns, and molecular evolution. The reference library contains 16786 specimens sampled around Churchill from 2005 to 2011. Selected specimens were identified by morphological means, sequenced for the animal barcode marker, and sorted into 2225 Molecular Operational Taxonomic Units (using Barcode Index Numbers: BINs) representing 68 families and 1216 named species. Eighty-five species shared a BIN with another species, 143 species were split across several BINs, and 990 species were assigned to a unique BIN, including 129 BINs new to the Barcode of Life Data System. The intraspecific and nearest neighbour distances varied across families, but most species can be easily distinguished from their nearest neighbours. By combining molecular data with taxonomic expertise, the barcode library provides species-level information for 12608 specimens, supplying future researchers with detailed taxonomic information and the opportunity to perform barcode-based specimen identifications for large-scale studies through sequence matching. Comparison of the Diptera composition of Churchill to other subarctic regions reflected likely postglacial colonization northwards as well as a Beringian and circumpolar component. This study also provides an initial investigation of molecular evolution in the best-represented species and finds variability in the rate of molecular evolution, opening intriguing avenues for further investigation. This study provides a publicly available, detailed reference library for use in future research, as well as new insights into the current diversity and taxonomic composition of Diptera present among the diverse habitats of Churchill.

molecular biology↗

bioRxiv2023

CanFlyet: Habitat Zone and Diet Trait Dataset for Diptera Species of Canada and Greenland

BackgroundTrue flies (Diptera) are an ecologically important group that play a role in agriculture, public health and ecosystem functioning. As researchers continue to investigate this order, it is beneficial to link the growing occurrence data to biological traits. However, large-scale ecological trait data are not readily available for fly species. While some databases and datasets include fly data, many ecologically relevant traits for taxa of interest are not included. In this study we create a dataset containing ecological traits (habitat and diet) for fly species of Canada and Greenland having occurrence records on the Barcode of Life Data Systems (BOLD). We present a dataset containing trait information for 981 Diptera species. New InformationDiptera were chosen for the dataset based on the occurrence records available for Diptera species from Canada and Greenland on the Barcode of Life Data Systems (BOLD). Trait data were then compiled based on literature searches conducted from April 2021 - April 2024 and assigned at the lowest taxonomic level possible. Three biological traits were included: habitat, larval diet, and adult diet. The dataset contains traits for 981 species across 380 genera, 34 subfamilies, and 61 families. This dataset allows for assignment of traits to occurrence data for Diptera species and can be used for further research into the ecology, evolution, and conservation of this order.

ecology↗

bioRxiv2023

Novel Pipeline for Large-Scale Comparative Population Genetics

As scientists continue to ask complex questions about biodiversity and deal with increasingly large amounts of data, there is a demand for new methods and computational developments to perform scientific analyses. Analytical pipelines and modules can provide a way to meet these demands and ensure reproducibility in scientific methods and analyses. The goal of this study was to create efficient, reproducible, reusable programming modules that are publicly available for future research. These modules were used to determine population genetic structure measures and compare these measures across species with different biological traits. The functionality of the modules is shown through a case study on Diptera (true fly) species from Canada and Greenland. We leveraged high-throughput DNA sequencing data from Northern areas, as it is a valuable resource and provides new opportunities to study the Arctic. Data were pulled from public databases (Barcode of Life Data System and Global Biodiversity Information Facility), as well as taxon-specific literature. The pipeline we developed in R includes fifteen modules, including modules to prepare and filter the data, calculate population genetic structure measures (e.g., FST), and run a multiple regression. These modules can be easily adapted and applied to a diverse set of animal groups, geographic regions, and biological traits. Best practices were followed for pipeline development, and the modules were designed and tested to work for datasets of different sizes by providing multiple different analyses and filtering options. Biological results were also obtained for Diptera species. Habitat and larval diet were both significantly related to population genetic structure. Evidence of isolation by distance and a relationship between population genetic structure and both latitude and longitude were also found. Overall, this study has created efficient, reusable bioinformatics modules, and provided insight into the factors affecting population genetic structure in Northern fly communities.

bioinformatics↗

bioRxiv2022

Reconstruction of Small Subunit Ribosomal RNA from High-Throughput Sequencing Data: A Comparative Study of Metagenomics and Total RNA Sequencing

The small subunit (SSU) ribosomal RNA (rRNA) is the most commonly used marker for the identification of microbial taxa, but its full-length reconstruction from high-throughput sequencing (HTS) data remains challenging, especially for complex and diverse environmental samples. Metagenomics and total RNA sequencing (total RNA-Seq) are target-PCR-free HTS methods that are used to characterize microbial communities and simultaneously reconstruct SSU rRNA sequences. However, more testing is required to determine and improve their effectiveness. In this study, we processed metagenomics and total RNA-Seq data retrieved from a commercially available mock microbial community using 112 combinations of commonly used data-processing tools, determined SSU rRNA reconstruction completeness of both sequencing methods for each species in the mock community, and analyzed the impact of data-processing tools on SSU rRNA and genome completeness. Total RNA-Seq allowed for the complete or near-complete reconstruction of all mock community SSU rRNA sequences and outperformed metagenomics. SSU rRNA completeness of metagenomics strongly correlated with the genome size of mock community species. The impact of data-processing tools was overall low, although certain tools resulted in significantly lower SSU rRNA completeness. These results are promising for the high-throughput reconstruction of novel full-length SSU rRNA sequences and could advance the simultaneous application of multiple -omics approaches in routine environmental assessments to allow for more holistic assessments of ecosystems.

genomics↗

bioRxiv2022

Metagenomics vs. total RNA sequencing: most accurate data-processing tools, microbial identification accuracy, and implications for freshwater assessments

Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine freshwater assessments. However, these targeted-PCR- free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 768 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for targeted-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine freshwater assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into freshwater assessments.

genomics↗

bioRxiv2022

A real data-based simulation procedure to select an imputation strategy for mixed-type trait data

Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a framework for selecting a suitable imputation method is advantageous. We invoked a real data-driven simulation strategy to select an imputation method for a given mixed-type (categorical, count, continuous) target dataset. Candidate methods included mean/mode imputation, k-nearest neighbour, random forests, and multivariate imputation by chained equations (MICE). Using a trait dataset of squamates (lizards and amphisbaenians; order: Squamata) as a target dataset, a complete-case dataset consisting of species with nearly complete information was formed for the imputation method selection. Missing data were induced by removing values from this dataset under different missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). For each method, combinations with and without phylogenetic information from single gene (nuclear and mitochondrial) or multigene trees were used to impute the missing values for five numerical and two categorical traits. The performances of the methods were evaluated under each missing mechanism by determining the mean squared error and proportion falsely classified rates for numerical and categorical traits, respectively. A random forest method supplemented with a nuclear-derived phylogeny resulted in the lowest error rates for the majority of traits, and this method was used to impute missing values in the original dataset. Data with imputed values better reflected the characteristics and distributions of the original data compared to complete-case data. However, caution should be taken when imputing trait data as phylogeny did not always improve performance for every trait and in every scenario. Ultimately, these results support the use of a real data-driven simulation strategy for selecting a suitable imputation method for a given mixed-type trait dataset. Author summaryThe issue of missing data is problematic in trait datasets as the missingness pattern may not be entirely random. Whether data are missing may depend on other known observations in the dataset, or on the value of the missing data points themselves. 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 information provided by other trait values present in the dataset. Including phylogenetic information in the imputation process can improve the accuracy of imputed values, though results are dependent on the amount and pattern of missingness. Most previous evaluations of imputation methods for trait datasets are limited to numerical simulated data, with categorical traits not considered. Given a particular dataset, we propose the use of a real data-driven simulation strategy to select an imputation method. We evaluated the accuracies of four different imputation methods, with and without phylogeny information, and under different simulated missingness patterns using an example reptile trait dataset. Results indicated that data imputed using the best-performing method better reflected the original dataset characteristics compared to complete-case data. As imputation performance varies depending on the properties of a given dataset, a real data-driven simulation strategy can be used to provide guidance on best imputation practices.

bioinformatics↗

bioRxiv2021

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

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.

bioinformatics↗

bioRxiv2020

Alignment-free identification of COI DNA barcode data with the Python package Alfie

Characterization of biodiversity from environmental DNA samples and bulk metabarcoding data is hampered by off-target sequences that can confound conclusions about a taxonomic group of interest. Existing methods for isolation of target sequences rely on alignment to existing reference barcodes, but this can bias results against novel genetic variants. Effectively parsing targeted DNA barcode data from off-target noise improves the quality of biodiversity estimates and biological conclusions by limiting subsequent analyses to a relevant subset of available data. Here, we present Alfie, a Python package for the alignment-free classification of cytochrome c oxidase subunit I (COI) DNA barcode sequences to taxonomic kingdoms. The package determines k-mer frequencies of DNA sequences, and the frequencies serve as input for a neural network classifier that was trained and tested using ~58,000 publicly available COI sequences. The classifier was designed and optimized through a series of tests that allowed for the optimal set of DNA k-mer features and optimal machine learning algorithm to be selected. The neural network classifier rapidly assigns COI sequences to kingdoms with greater than 99% accuracy and is shown to generalize effectively and make accurate predictions about data from previously unseen taxonomic classes. The package contains an application programming interface that allows the Alfie packages functionality to be extended to different DNA sequence classification tasks to suit a users need, including classification of different genes and barcodes, and classification to different taxonomic levels. Alfie is free and publicly available through GitHub (https://github.com/CNuge/alfie) and the Python package index (https://pypi.org/project/alfie/).

bioinformatics↗

bioRxiv2019

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

Biological conclusions based on DNA barcoding and metabarcoding analyses can be strongly influenced by the methods utilized for data generation and curation, leading to varying levels of success in the separation of biological variation from experimental error. The five-prime region of cytochrome c oxidase subunit I (COI-5P) is the most common barcode gene for animals, with conserved structure and function that allows for biologically informed error identification. Here, we present coil (https://CRAN.R-project.org/package=coil), an R package for the pre-processing and error assessment of COI-5P animal barcode and metabarcode sequence data. The package contains functions for placement of barcodes into a common reading frame, accurate translation of sequences to amino acids, and highlighting insertion and deletion errors. The analysis of 10,000 barcode sequences of varying quality demonstrated how coil can place barcode sequences in reading frame and distinguish sequences containing indel errors from error-free sequences with greater than 97.5% accuracy. Package limitations were tested through the analysis of COI-5P sequences from the plant and fungal kingdoms as well as the analysis of potential contaminants: nuclear mitochondrial pseudogenes and Wolbachia COI-5P sequences. Results demonstrated that coil is a strong technical error identification method but is not reliable for detecting all biological contaminants.

bioinformatics↗