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Method Article

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

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DOI:

10.3791/68323

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July 11th, 2025

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1Ateneo Biodiversity Research Laboratory, Department of Biology, School of Science and Engineering, Higher Education Cluster, Ateneo de Manila University, 2Ateneo Institute of Sustainability, Ateneo de Manila University, 3Ateneo School of Medicine and Public Health, Higher Education Cluster, Ateneo de Manila University

In This Article

Summary

The protocol presented here aims to guide the users on the computer-based processing of cytochrome c oxidase subunit I (COI) gene sequences generated from beetles, such that the species clustering hypothesis called molecular operational taxonomic units (MOTUs) can be generated from DNA.

Abstract

Biodiversity decline is transpiring at rates unimaginable, yet biodiversity monitoring is hampered by a plethora of factors, including the incomplete inventory of baseline diversity and the dwindling population of skilled taxonomists. Bridging this impediment in species inventory of "dark taxa", or hyperdiverse groups often understudied in taxonomy, is particularly crucial in the tropics facing insurmountable environmental pressures. While morphology-based alpha-taxonomy remains the gold standard in species identification, DNA-based methods have been showing tremendous potential in accelerating species delineation and discovery. Here, we describe a concoction pipeline for generating molecular operational taxonomic units (MOTUs) involving six molecular species delimitation approaches implemented on two highly inconspicuous beetle genera, namely Byrrhinus Motschulsky, 1858 of family Limnichidae and Anacaena Thomson, 1859 of family Hydrophilidae, from the Philippines. As this protocol focuses on processing DNA barcodes and given that these genetic data can be obtained either by sequencing or by downloading from online databases, the starting point for the protocol described here are the DNA sequences. Given the COI barcodes, the pipeline utilizes a combination of three threshold-based molecular species delimitation approaches, namely TaxonDNA, K2P, and ASAP, which use sequence alignment as input data. Additionally, the pipeline proceeds with three coalescent-based approaches, namely PTP-ML, PTP-BI, and mPTP, which require a tree file as input data. Some approaches entail the use of web servers, while others utilize local software. With the selection of algorithmic methods presented, MOTUs can be identified, whether by consensus or by majority. Remarkably, this pipeline delineated almost conspicuous beetle species from the Philippines, even those documented from the same or adjacent localities.

Introduction

With the unprecedented rates of global species decline across varied taxa1,2,3,4, the impetus to create a comprehensive biodiversity inventory, which includes previously undiscovered and undescribed species, becomes a race against time2,5. For one, biodiversity conservation only makes sense in light of proper taxonomic knowledge6,7. Thus, the identification of biodiversity at the species level is necessary, given that closely related yet different species may have unique fundamental niches8.

In beetle systematics, alpha-taxonomy or traditional taxonomy is the gold standard in species identification similar to most, if not all, animal groups9. This approach relies on morphological characters to classify organisms at higher taxonomy and to provide identification at the species-level10,11. In many instances, family12 or even genus-level identification13 can be done using external morphological features alone, such as body form and specialized adaptations. Meanwhile, species-level identification in beetles is usually done via the comparison of the structural details of the aedeagus or the male genitalia14,15,16.

Despite the wide acceptance of this morphology-based alpha-taxonomy, this approach to species discovery is hampered by numerous 'taxonomic impediments'6,17,18, especially among invertebrates19. In beetle systematics, species identification and/or discovery using alpha-taxonomy faces the challenges of having a limited number of skilled taxonomists20,21, of brief and barely informative earlier descriptions22, and of logistical problems related to access to literature and type specimens23.

This is even exacerbated when the taxon in question is cryptic or highly inconspicuous. Cryptic species refers to the set of species of the same genus, or even different genera, which cannot be easily delineated by comparative morphology, given subtle interspecific phenetic differences24. Some cryptic taxa, regarded as 'dark taxa'25,26,27, additionally suffer from being hyperdiverse yet heavily understudied. Unfortunately, this is not uncommon in riparian and aquatic beetles8,28. Failure to address species delineation among cryptic dark taxa threatens biodiversity as a one-size-fits-all conservation measure is given to organisms of potentially different niches and ecological requirements8,29.

Given these challenges, 'integrative taxonomy'23, or the approach of coupling alpha-taxonomy with another line of evidence, has gained traction as a means to erect new species in the last two decades30,31,32. One such line of evidence used in integrative taxonomic studies on beetles33,34,35,36,37,38,39 and other insect orders40,41,42,43,44,45 is DNA sequence. In particular, the mitochondrial cytochrome c oxidase subunit I (COI) gene is being used to barcode a diverse set of animals due to its moderately conservative nature46,47,48. While its 658bp-long 5′-end (COI-5′), also known as the 'barcoding fragment' or 'Folmer fragment', has been proposed as the 'diagnostic' sequence49, the 723 bp-long COI-3′ is also being used owing to a robust set of primers for the 3′-end50,51,52.

Whereas DNA barcoding using COI was conceived for species-level identification, it is limited by an a priori sequence repository53, such as GenBank54 and BOLD55, molecular species delimitation approaches set species limits using only the provided DNA sequences with no pre-requisite repository56. There are two classes of molecular species delimitation approaches based on the input file. First, using sequence alignment as the input, threshold-based approaches use cut-off values to determine divergence between and within species48,57,58. As distance-based methods, these approaches rely on an observed maximum intraspecific distance of 3% for insects, which is being referred to as the 'barcoding gap'51,59. Some of the threshold-based approaches are SpeciesIdentifier implemented in TaxonDNA60, genetic distance via Kimura 2-parameter (K2P)61, and Assemble Species by Automatic Partitioning (ASAP)62.

Second, using the tree as the input file, coalescent-based approaches identify species boundaries and sort independent lineages by determining change in lineage branching rate63,64,66. Often relying on phylogenetic species concept67, coalescent-based approaches include Poisson tree processes (PTP)68 and their variation, multi-rate PTP69. In any case, the clusters formed by the molecular species delimitation approaches are collectively referred to as molecular operational taxonomic units (MOTUs).

Here, we present methods for a concoction pipeline for generating MOTUs involving three threshold-based approaches and three coalescent-based approaches (Figure 1). The pipeline is tested on two datasets, namely (1) COI-3′ sequences of the riparian Byrrhinus Motschulsky, 1858 beetles, which is composed of two published species35 previously erected using integrative taxonomy and additional sequences from undescribed species, and (2) COI-5′ sequences of Anacaena Thomson, 1859 beetles, which is composed of sequences from two published species70 previously erected using alpha-taxonomy and additional sequences from undescribed species.

DNA sequence analysis flowchart; alignment, threshold, coalescent methods, tree estimation, MOTU steps.
Figure 1: Flowchart of the concoction pipeline for generating MOTUs. Please click here to view a larger version of this figure.

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Protocol

The starting materials for this concoction pipeline are DNA sequences, which can either be downloaded from online repositories (e.g., BOLD, GenBank) or generated from Sanger sequencing71. Table 1 and Table 2 list the accession numbers for the datasets whose representative results are presented in this paper.

1. Preparatory step for threshold-based approaches: Sequence alignment

  1. Load sequences in MEGA X72.
    NOTE: MEGA X can be downloaded from https://www.megasoftware.net/
    1. Click on Align to open a dropdown menu. Click on Edit/Build Alignment and select Create a new alignment. Click on OK to confirm this selection. Select DNA as the datatype for alignment.
    2. Mouse over the Edit tab, and select Insert Sequence from File. Navigate to the directory with the sequences and select them to be loaded into MEGA.
  2. Align sequences by clicking on Alignment, and then Align by ClustalW73. Continue with default settings and click on OK.
  3. Manually edit the sequences by trimming both ends and by cleaning any insertions or deletions.
    1. To delete insertions, click on the inserted bases and/or positions, and press Delete on the keyboard.
    2. To correct deletions, click on the position supposed to have been deleted as denoted by "-". Delete the "-" and type the intended base.
    3. Find the earliest position where all sequences have a character. Click on the blank box on the row header of the position to the left of the determined position, and drag until the excess positions for all sequences until the starting end are selected. Press Delete to trim.
    4. Repeat step 1.3.3 by looking for the last position where all sequences have a character. Click on the blank box on the header row of the position to the right of the determined position, drag until the excess positions for all sequences until the terminal end are selected. Press Delete.
      NOTE: Figure 2A shows a good alignment of DNA sequences. The final dataset is a matrix of sequences of equal length; thereby, sequence length is not a factor that affects the outcomes of the study. The reference sequences serve as a basis for the final length of the alignment matrix.
  4. Translate the sequence to check for stop codons, given that COI is a protein-coding sequence.
    1. Select all sequences, then click on the Translated Protein Sequences tab.
    2. When prompted, verify the genetic code as Invertebrate Mitochondrial. If the genetic code is different, click No, and a menu will appear allowing one to tick the box for Invertebrate Mitochondrial genetic code.
    3. If stop codons, as denoted by asterisks in the alignment body, are present for an entire column, click on DNA Sequences and delete the first position for all sequences.
    4. Repeat the previous step if stop codons are still present in any sequence.
    5. If stop codons are still present, restore the previously deleted positions. Select all sequences, then mouse over the Data tab and click on Reverse.
    6. Repeat step 1.4.1. If stop codons are still present, repeat steps 1.4.3 and 1.4.4.
      NOTE: Figure 2B shows a good alignment of codons.
  5. Click on DNA Sequences, and save the sequence alignment to .mas/x. Export the sequence into other pertinent file types, such as .meg file and .fasta file.
    NOTE: Certain molecular species delimitation approaches work better without the outgroup. Thus, a possible consideration is generating a copy of the alignment file without the outgroup.

Genetic sequence alignment, MAFFT software, DNA to protein analysis, comparative genomics.
Figure 2: Sequence alignment in MEGA X. (A) Manually cleaned final DNA sequence alignment and (B) translated protein sequence alignment of the Byrrhinus dataset showing no insertions, deletions, and gaps. Please click here to view a larger version of this figure.

2. Delimitation by Taxon DNA module in Species Identifier 1.8

NOTE: Species Identifier can be downloaded from https://taxondna.sourceforge.net/

  1. Open TaxonDNA. Click on Import, then click on FASTA. Upload the alignment in .fasta format.
  2. Click on Modules, and click on Cluster. Set the threshold to 3%. Then, check on Generate information about individual cluster (Figure 3).
  3. Click on Make clusters now!. Save the results by taking a screenshot.

DNA sequence clustering interface for taxonomy analysis; shows sequence clustering results and statistics.
Figure 3: TaxonDNA in Species Identifier 1.8 window showing the 3% user-identified threshold for delimitation. Please click here to view a larger version of this figure.

3. Delimitation by Kimura 2-parameter (K2P) in MEGA X

  1. In MEGA, open the .meg file by clicking on File, then Open A File/Session. Click on Distance, then click on Compute Pairwise Distances. Confirm the .meg file for delimitation.
  2. Mouse over the yellow box beside Model/Method, then click the arrow that appears on the right of the box.
  3. Select Kimura 2-parameter model from the dropdown menu. Click on Ok to run the program. Open the data output window (Figure 4).
  4. Click on File, then Export/Print Distances.
  5. Open the dropdown menu beside Output Format and select XL: Microsoft Excel workbook. For Decimal Places, choose 4.
  6. Save the resulting spreadsheet in Microsoft Excel. Use Conditional Formatting to highlight those with a distance of less than 0.03 (or 3%).
    ​NOTE: Unlike the other approaches discussed in this article, K2P does not immediately provide the clustering because it is dependent on the user-identified threshold. This pipeline is working on the assumption of the threshold being 3%, which means that any pair of sequences whose genetic distance is less than 3% belongs to the same molecular cluster.

Phylogenetic distance matrix, DNA comparison, table, species genetic differences.
Figure 4: Resulting window in MEGA X showing the outcomes of Kimura-2-parameter. Please click here to view a larger version of this figure.

4. Delimitation by Assemble Species by Automatic Partitioning (ASAP)

  1. Go to the ASAP web server: https://bioinfo.mnhn.fr/abi/public/asap/asapweb.html
  2. Upload the alignment by clicking on the orange box labeled Choose a file… and selecting the .fasta file. Alternatively, drag the .fasta file to the orange box. Scroll down, and click on Go.
  3. To download the clustering, click on list for the row with the lowest ASAP-score and highest p-val rank (Figure 5).
    ​NOTE: The result output is a .txt file, which presents subsets of the molecular clusters.

Statistical analysis results; dataset table, histogram, ranked distances graph; data distribution analysis.
Figure 5: Resulting window in ASAP showing the possible delimitations. The first outcome (blue box) is accepted because of the lowest ASAP-score (red box) and highest P-val rank (yellow box). Please click here to view a larger version of this figure.

5. Preparatory step for coalescent-based approaches: Tree estimation

  1. Open the .meg file by clicking on Data, then Open A File/Session. Test the best-fit substitution model.
    1. Click on Models, then find Best DNA/Protein Models (ML). Confirm the .meg file for analysis.
    2. Click on Ok on the Analysis Preferences menu (Figure 6A). Take note of the best model using the lowest BIC and AICc value (Figure 6B).
      NOTE: This is usually the first in the list of models in the resulting window. Refer to the complete names of abbreviated model names at the bottom part of the resulting window.
  2. Generate the tree.
    1. Click on Phylogeny and then Construct/Test Maximum Likelihood Tree.
    2. Use the best-fit nucleotide substitution model. Do this by clicking on the yellow box beside Model/Method. Select the model from the dropdown menu.
    3. If the best fit nucleotide substitution model has '+G' or '+I' or both, click on the box beside Rates among Sites and select the parameters (G, I, G + I) from the dropdown menu (Figure 6C).
    4. Under Phylogeny Test, click on the box beside Test of Phylogeny and select Bootstrap method from the dropdown menu. Click on the box beside "No. of Bootstrap Replications" and type 1000 (Figure 6C).Click on Ok to run the analysis.
  3. Once a window opens containing the resulting tree (Figure 6D), save the tree session as .mts/x. Save the output as .nwk file. Save also the tree as .png picture file.

Phylogenetic analysis method settings, likelihood table, and tree diagram; maximum likelihood, bootstrap.
Figure 6: Tree estimation in MEGA X as a preparatory step for coalescent-based approaches. (A) Setting to determine the best-fit nucleotide substitution model. (B) For the Byrrhinus dataset, the best-fit model is GTR + I due to the lowest BIC and AICc values (red box). (C) In running the maximum likelihood tree, GTR (yellow box) + I (blue box) were set as parameters. (D) The resulting raw ML tree to be used in the subsequent analyses. Please click here to view a larger version of this figure.

6. Delimitation by Poisson Tree Processes (PTP)

NOTE: The steps below will yield the outcomes of PTP-ML and PTP-BI.

  1. Go to the PTP web server: https://species.h-its.org/ptp/
  2. Upload a newick file by clicking on Choose file. Under My tree is, select Rooted (Figure 7A).
  3. Input the parameters for the analysis. Under No. MCMC generations, input 100000. Under Thinning, input 100. Under Burn-in, input 0.1. Under Seed, input 123.
  4. Input the outgroup by typing the name of the tips in the box under Outgroup taxa names (if any). Instructions for formatting input are to the right of the input box.
  5. Input email address in the corresponding field, and click Submit. In the PTP species delimitation results window, click on here to open the result window.
  6. Under the Maximum likelihood solution, download the results of PTP-ML by clicking on Download delimitation results (Figure 7B).
  7. Under the Bayesian-supported solution, download the results of PTP-BI by clicking on Download delimitation results.
    ​NOTE: The results would indicate groups of sequences in partitions. These will be labeled as numbered species followed by a support value, wherein 1.000 is ideal.

bPTP species delimitation results, phylogenetic tree diagram, MCMC settings, maximum likelihood analysis.
Figure 7: Molecular species delimitation by Poisson tree processes. (A) Windows showing the input parameters. (B) Resulting window in PTP web server, with link containing delimitation results (red box). Please click here to view a larger version of this figure.

7. Delimitation by Multi-rate Poisson Tree Processes (mPTP)

  1. Go to the mPTP web server: https://mptp.h-its.org/#/tree
  2. Upload the newick file by dragging it onto the gray rectangle or clicking on the rectangle. Once the data loads on the same page, click on Proceed to outgroup selection (Figure 8A).
  3. On the Outgroup specification page, select the outgroup by clicking on the box beside the taxa labels of outgroup specimens. Click on the Crop Outgroup option at the bottom of the sequence. Click on Model selection.
  4. Click on MPTP, then click on Visualization Options. On the Visualization options page, accept the default settings.
    NOTE: Alternatively, the total image size may be adjusted by inputting a value (in px) under SVG width. Font and spacing of text can be adjusted by inputting desired sizes (in px) under SVG font size, Space between tips, Left Margin, Right Margin, Top Margin, and Bottom margin.
  5. Click on Contact details. Input the email address for future reference of results (Optional).
  6. Click on Overview. Make sure to review the number of taxa input and the outgroups selected.
  7. Click on Submit. Right-click the files under Downloadable Files (Figure 8B), and select Save as… to save the results.
    NOTE: The output text file will indicate the total number of delimited species at the beginning of the results. It will then present which sequences fall under each of the clusters. These clusters and corresponding sequences are arranged similarly to the generated tree in the .svg file. The mPTP SVG file will display the clustering of the sequences. Clusters are presented by groups of sequences connected by red branches along the tree. Each isolated set of red branches indicates a singular MOTU.

Phylogenetic analysis interface for tree upload, outgroup selection, and file parsing in research setup.
Figure 8: Molecular species delimitation by multi-rate Poisson tree processes. (A) Windows showing the mPTP-read information regarding the uploaded tree. (B) Resulting window in mPTP web server, with link containing delimitation results (red box). Please click here to view a larger version of this figure.

8. MOTU generation

  1. Open the tree using a photo editor program or using PowerPoint.
  2. Create a bar to represent the results of each molecular species delimitation approach (see Figure 9 and Figure 10 for samples). Ensure that any single bar aligns with the sequences it includes in its molecular cluster.
  3. Determine the MOTU by one of these two approaches74
    1. If all the approaches generate identical results for a given molecular cluster, erect the molecular cluster as a MOTU by consensus.
    2. If most approaches generate identical results for a given molecular cluster, erect the molecular cluster as a MOTU by majority.

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Results

This article implemented a molecular species delimitation concoction pipeline, which includes TaxonDNA, K2P, ASAP, two variations of PTP, and mPTP, to aquatic and riparian beetles. The outcome of each of the six approaches is referred to as 'molecular clusters', while the summary is referred to as 'molecular operational taxonomic units' or MOTUs75.

The concoction pipeline was tested on two datasets. The first dataset (Table 1, Figur...

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Discussion

While DNA sequences, especially COI49, may be compared against reference sequences22,53,78 in online public repositories54,55,79,80 for a bioidentification system46, such an approach is limited by the available deposited sequences. On the contrary, the us...

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Disclosures

We have nothing to disclose.

Acknowledgements

We thank the Bureau of Fisheries and Aquatic Resources (BFAR) and the concerned local government units for granting us prior informed consent to perform biodiversity sampling. We thank Dr Hendrik Freitag for guidance on taxonomic work. We also thank the reviewers and editors for their helpful, constructive comments. Lastly, we express our utmost gratitude to the University Research Council Big Project Grant (URC 2022-09) and the Open Access Publication Grant of the Ateneo de Manila University, the SC Johnson Student Research and Development Fund for Environmental Leadership of the Ateneo Research Institute of Science and Engineering, and the LinnéSys: Systematics Research Fund of the Linnean Society of London and the Systematics Association for funding this study.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
ASAP Spart explorerhttps://bioinfo.mnhn.fr/abi/public/asap/asapweb.html
ComputerAny processing unit
MEGA X MEGA Softwarehttps://www.megasoftware.net/
mPTP web serverhttps://mptp.h-its.org/#/tree
PTP web serverHeidelberg Institute for Theoretical Studies (HITS)https://species.h-its.org/ptp/
Species Identifier  https://taxondna.sourceforge.net/

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MOTU PipelineDNA BarcodingSpecies DelimitationRiparian BeetlesCOI SequencesSequence AlignmentMaximum Likelihood TreeKimura 2 Parameter
A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
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