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

Porter, Teresita M.; Hajibabaei, Mehrdad

doi:10.1101/2020.07.14.202960

Cited by 10 publications

(8 citation statements)

References 74 publications

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“…Illumina MiSeq paired-end reads were processed using the MetaWorks-1.0.0 pipeline available from https://github.com/terrimporter/MetaWorks 58 . MetaWorks is an automated Snakemake 59 bioinformatic pipeline that runs in a conda 60 environment.…”

Section: Methodsmentioning

confidence: 99%

Multi-marker DNA metabarcoding detects suites of environmental gradients from an urban harbour

Robinson

Porter

McGee

et al. 2022

Sci Rep

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There is increasing need for biodiversity monitoring, especially in places where potential anthropogenic disturbance may significantly impact ecosystem health. We employed a combination of traditional morphological and bulk macroinvertebrate metabarcoding analyses to benthic samples collected from Toronto Harbour (Ontario, Canada) to compare taxonomic and functional diversity of macroinvertebrates and their responses to environmental gradients. At the species rank, sites assessed using COI metabarcoding showed more variation than sites assessed using morphological methods. Depending on the assessment method, we detected gradients in magnesium (morphological taxa), ammonia (morphological taxa, COI sequence variants), pH (18S sequence variants) as well as gradients in contaminants such as metals (COI & 18S sequence variants) and organochlorines (COI sequence variants). Observed responses to contaminants such as aromatic hydrocarbons and metals align with known patchy distributions in harbour sediments. We determined that the morphological approach may limit the detection of macroinvertebrate responses to lake environmental conditions due to the effort needed to obtain fine level taxonomic assignments necessary to investigate responses. DNA metabarcoding, however, need not be limited to macroinvertebrates, can be automated, and taxonomic assignments are associated with a certain level of accuracy from sequence variants to named taxonomic groups. The capacity to detect change using a scalable approach such as metabarcoding is critical for addressing challenges associated with biodiversity monitoring and ecological investigations.

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Section: Methodsmentioning

confidence: 99%

Multi-marker DNA metabarcoding detects suites of environmental gradients from an urban harbour

Robinson

Porter

McGee

et al. 2022

Sci Rep

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“…Raw sequences from two previously published datasets were obtained from the Sequence Read Archive (PRJNA450340 and PRJNA418115) (3,19). All bioinformatic processing of the raw reads was prepared using the MetaWorks v1.8.0 pipeline (available online at: https://githib.com/terrimporter/MetaWorks) (20). The default settings for merging reads were used except for the parameter controlling the minimum fraction of matching bases, which was increased from 0.90 to 0.95.…”

Section: Methodsmentioning

confidence: 99%

Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta-Diversity in Medically Relevant 16S Amplicon Sequencing Data

Rudar

Golding

Kremer

et al. 2022

Preprint

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Canonical distance and dissimilarity measures can fail to capture important relationships in high-throughput sequencing datasets since these measurements are unable to represent feature interactions. By learning a dissimilarity using decision tree ensembles, we can avoid this important pitfall. We used 16S rRNA data from the lumen and mucosa of the distal and proximal human colon and the stool of patients suffering from immune-mediated inflammatory diseases and compared how well the Jaccard and Aitchison metrics preserve the pairwise relationships between samples to dissimilarities learned using Random Forests, Extremely Randomized Trees, and LANDMark. We found that dissimilarities learned by unsupervised LANDMark models were better at capturing differences between communities in each set dataset. For example, differences in the microbial communities of colon's distal lumen and mucosa were better reflected using LANDMark dissimilarity (p ≤ 0.001, R2 = 0.476) than using the Jaccard distance (p ≤ 0.001, R2 = 0.313) or Random Forest dissimilarity (p ≤ 0.001, R2 = 0.237). In addition, applying Uniform Manifold Approximation and Projection to dissimilarity matrices and transforming the result using principal components analysis created two-dimensional projections that captured the main axes of variation while also preserving the pairwise distances between samples (eg: ρ = 0.8804, p ≤ 0.001 for the distal colon dissimilarities). Finally, supervised LANDMark models tend to outperform both Random Forest and Extremely Randomized Tree classifiers. Models employing multivariate splits can improve the analysis of complex high-throughput sequencing datasets. The improvements observed in this work likely result from the ability of these models to reduce noise from uninformative features. In an unsupervised setting, LANDMark models can preserve pairwise relationships between samples. When used in a supervised manner, these methods tend to learn a decision boundary that is more reflective of the biological variation within the dataset.

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“…Illumina MiSeq paired-end reads for both COI and rbcL were processed using the MetaWorks-1.3.1 pipeline 85,86 available from https://github.com/terrimporter/MetaWorks. MetaWorks is an automated Snakemake 87 bioinformatic pipeline that runs in a conda 88 environment.…”

Section: Bioinformatic Processingmentioning

confidence: 99%

Multi-marker metabarcoding resolves subtle variations in freshwater condition: Bioindicators, ecological traits, and trophic interactions

Robinson

Porter²,

Maitland³

et al. 2021

Preprint

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Freshwater systems are experiencing rapid biodiversity losses resulting from high rates of habitat degradation. Ecological condition is typically determined through identifying either macroinvertebrate or diatom bioindicator assemblages and comparing them to their known tolerance to stressors. These comparisons are typically conducted at family or genus levels depending on the availability of taxonomic keys and expertise for focal groups. The objective of this study was to test whether a more taxonomically comprehensive assessment of communities in benthic samples can provide a different perspective of ecological conditions. DNA metabarcoding was used to identify macroinvertebrates and diatoms from kick-net samples collected from sites with different habitat status. Sites with ‘good’ condition were associated with higher beta diversity as well as slightly higher directed connectance and modularity indicating higher resilience compared with ‘fair’ condition sites. Indicator value and correlation analyses used DNA metabarcoding data to detect 29 site condition indicator species consistent with known bioindicators and expected relative tolerances. DNA metabarcoding and trophic network analysis also recovered 11 keystone taxa. This study demonstrates the importance of taxonomic breadth across trophic levels for generating biotic data to study ecosystem status, with the potential to scale-up ecological assessments of freshwater condition, trophic stability, and resilience.

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

Cited by 10 publications

References 74 publications

Multi-marker DNA metabarcoding detects suites of environmental gradients from an urban harbour

Multi-marker DNA metabarcoding detects suites of environmental gradients from an urban harbour

Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta-Diversity in Medically Relevant 16S Amplicon Sequencing Data

Multi-marker metabarcoding resolves subtle variations in freshwater condition: Bioindicators, ecological traits, and trophic interactions

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