Bioinformatics: Demultiplexing, Consensus Sequences and Identification

Author: Charles Xu, Ph.D.

Introduction

After carefully designing an experiment, meticulous sample collection and sequencing, what is in some ways the most challenging step is bioinformatics analysis. Assuming you now have tens of thousands, if not millions, of DNA sequences (composed of A’s, T’s, C’s, and Gs), understanding how to approach the bioinformatics analysis is the crucial final phase.

What is Bioinformatics?

The term "bioinformatics" is a portmanteau of "biology" and "informatics." It refers to the broad field that uses information technology, specifically computers, to analyze data derived from biological sources. Given the vast amounts of data generated in modern biological research, it has become impossible to interpret this information without the aid of computational tools. This is particularly true in high-throughput genetic sequence analysis, where bioinformatics plays an essential role. In the context of eDNA studies, bioinformatics can be broken down into three general steps: 1) demultiplexing, 2) creating consensus sequences, and 3) taxonomic identification.

Tools of Bioinformatics

Before going deeper into each step, it is important to note that prior knowledge of command-line computing is almost essential. While some software is available in more user-friendly formats, such as web platforms (e.g., DNA Subway or Galaxy) or R packages, most tools require command-line proficiency. If you are unfamiliar with or need a refresher on command-line computing, this resource provides a helpful introduction: [Learn Metabarcoding]

For those new to bioinformatics, we recommend reading through this page first to understand the general steps of bioinformatic data processing. Further down the page, you will find a list of bioinformatics platforms and software packages, including tools for processing data at each step of the bioinformatics workflow, as well as pipelines that offer “all-in-one” data processing solutions. To skip the basics, you may go directly to the section titled “Bioinformatics Software Options and Video Tutorials,” which also includes several video tutorials for reference.

General Steps of eDNA Bioinformatics Analysis
Demultiplexing

Demultiplexing refers to the process of ensuring that each DNA sequence obtained from a sequencing platform is correctly matched with its corresponding sample label (Petit-Marty et al., 2023). Unless sequencing is conducted at extremely high depths where each sample occupies an entire flow cell, samples will typically undergo multiplexing. This involves artificially ligating or attaching a unique short DNA sequence (e.g., an index, barcode, or sequence tag) to the end of all nucleotide strands within a given sample. This allows DNA from different samples to be combined in a single sequencing run.

Before pooling, it is essential to normalize DNA concentrations across all samples based on the required number of sequences per sample. Despite the mixing, the unique tags ensure that the sequences can later be traced back to their original samples. Depending on the setup, this tagging process may be performed by you before submitting the samples, or the sequencing center may handle it as part of their services. If the center performs the multiplexing, they will typically also handle the demultiplexing. However, if you multiplexed your samples, be prepared to manage the demultiplexing process as well.

Creating Consensus Sequences

Consensus sequences are composite DNA sequences created from multiple individual sequences of the same region that have been aligned, typically resulting in what are known as Operational Taxonomic Units (OTUs). OTUs serve as summaries of groups of similar sequences, and various approaches exist for assigning them (Deiner et al., 2017; Liu et al., 2020). Consensus sequences are generated because they provide a consistent, representative sequence for each OTU, simplifying subsequent analyses. It is important to note that repeatedly analyzing very similar or identical sequences from the same sample will not yield new information and will only consume unnecessary computational resources.

Before generating consensus sequences, demultiplexed sequences must first be trimmed to remove several elements:

  • Sequencing Adaptors: These artificial sequences are used to attach samples to the sequencing platform.
  • Primer Sequences: Although these primers were chosen specifically for your project, they do not represent natural genetic variation within your samples due to the nature of PCR amplification. Therefore, they should be removed.
  • Low-Quality Nucleotides: Low-quality nucleotides, often found at the beginning or end of sequences, should be trimmed to produce higher-quality consensus sequences.

Once the sequences are trimmed, paired-end sequences—those originating from the same DNA fragment but from opposite strands (forward 5' → 3' and reverse 3' → 5')—can be merged using a minimum overlap sequence threshold. These merged sequences are then aligned to form consensus sequences, with ambiguity codes from the International Union of Pure and Applied Chemistry (IUPAC) (e.g., W = A or T, K = G or T, N = A, C, G, or T) used at positions of genetic variation. The similarity threshold for aligning consensus sequences typically falls around 97-98%, though this depends on the study’s objectives.

If your goal is to determine species presence from eDNA samples, it is important that each consensus sequence approximates individual species or higher taxonomic levels, such as genus or family. In this case, a consensus sequence for a particular taxon should be consistent across multiple samples, reliably defining each OTU.

However, if the focus is on population genetic diversity, a lower similarity threshold may be used to capture intraspecific variation. An alternative approach is the use of Amplicon Sequence Variants (ASVs), which are real biological sequences inferred by modeling amplification and sequencing errors. ASVs offer fine-resolution, distinguishing sequences by even a single nucleotide difference (Callahan et al., 2017). Studies have demonstrated that ASVs can be more sensitive in biodiversity surveys, particularly for detecting rare taxa (Porter & Hajibabaei, 2020).

Taxonomic Identification

The final step in the bioinformatic processing of metabarcoding sequence data is taxonomic identification, or taxonomic assignment. Whether you opt for OTUs or ASVs, the ultimate goal is to determine the taxonomic identity of your sequences, allowing you to answer the core question of your study: What organisms are present in my eDNA samples?

Generally, taxonomic assignment relies on the use of a reference sequence database. If a suitable reference database is unavailable, one must be created. These databases contain curated DNA sequences with verified taxonomic identities, allowing you to compare and match them with your eDNA sequences. However, reference databases vary significantly in size and applicability (Xiong et al., 2022).

It is important to recognize that species identification will not be possible if a taxonomically verified DNA barcode for that species does not exist. Larger databases offer broader coverage, but their size can make them difficult to manage and may increase the likelihood of spurious matches. In contrast, smaller, curated databases may focus on specific taxonomic groups, habitats, or geographically localized taxa. These can improve accuracy and efficiency but may introduce bias due to the initial assumptions made in selecting the reference sequences.

Before using reference sequence databases for taxonomic assignment, ensure that portions of sequences not targeted by your primers—such as the primer-binding sites themselves—are removed, as these do not reflect natural variation. The level of taxonomic resolution you achieve can vary widely, from broad categories (e.g., kingdom-level) to highly specific identifications (e.g., species or individual organism-level). The accuracy of taxonomic assignment depends on several factors, including the size and genetic variability of the target amplicon, the representation and specificity within your reference database, and the particular software, algorithm, or approach used for taxonomic assignment.

If possible, consider supplementing pre-existing databases by generating reference sequences from the tissues of taxonomically identified organisms relevant to your study. These supplemental sequences can enhance detection accuracy, particularly when the species or populations are the same as those targeted in your eDNA or metabarcoding study. This approach helps bridge gaps in existing databases and improves the reliability of species identification in your analyses

HTS = high-throughput sequencing. Adapted from Liu et al. 2020. (Image copyright policy: https://resjournals.onlinelibrary.wiley.com/hub/journal/13652311/homepage/forauthors.html)

Managing Bioinformatic Data

While bioinformatic analysis involves numerous decision points, one of the advantages is that you can revisit previous steps, adjust parameters, and try alternative approaches, provided you have kept the intermediate files. At a minimum, you can always return to your original data, which underscores the importance of data storage and management.

Your original data must be stored securely and backed up multiple times. Even with advanced computing infrastructure, unforeseen issues can occur, making data redundancy critical. Intermediate files are useful to retain, as they reduce the need to rerun computationally intensive steps. However, these files can quickly grow in size, consuming valuable storage space. It is essential to strike a balance between storing intermediate files and managing storage efficiently based on the time it takes to regenerate them.

Additionally, FAIR data management principles—Findable, Accessible, Interoperable, and Reusable—should be prioritized. eDNA metabarcoding data often have value beyond the immediate study and may be utilized in future meta-analyses (Shea et al., 2023; Wilkinson et al., 2016). Implementing these strategies ensures that your data remain organized, accessible, and valuable for future research

Example Protocol: Bioinformatic Analysis of eDNA Data
1. Demultiplex Reads (if not already completed by sequencing core)
  1. Use index/barcode/tag sequences to allocate DNA sequences to their corresponding samples.
2. Sequence Trimming and Filtering
  1. Trim sequences to remove adaptor, index/barcode/tag, primer, and low-quality bases at the beginning and end.
  2. For paired-end sequencing, align paired reads according to a minimum overlap (typically at least 10 bp, but longer is better).
  3. Use Phred scores to filter out low-quality sequences, applying a minimum quality threshold.
  4. Remove reads that are significantly shorter than the target amplicon.
  5. Identify and remove chimeras (PCR artifacts where two different sequences are fused) using metrics such as guanine-cytosine (GC) content.
3. Create Consensus Sequences
  1. Align trimmed, paired, and filtered sequences per sample.
  2. Cluster OTUs or identify ASVs. You may also choose to work with reads directly, but this is usually not recommended.
  3. OTUs/ASVs can be filtered by abundance using a minimum frequency threshold (e.g. remove singletons or doubletons) to minimize effects of potential contamination.
4. Choose or Create Reference Sequence Database
5. Assign Taxonomy
  • Assign taxonomic identities to OTUs, ASVs, or reads. Many taxonomic assignment approaches exist, including those based on directly comparing sequence similarity, hidden Markov models, machine learning, phylogenetic placement, probabilistic placement, etc.
    1. Some approaches may require a phylogenetic tree, which may either be provided externally or built using your sequences.
Bioinformatics Software Options and Video Tutorials (citations provided where available):
Demultiplexing and Quality Control (QC)
Creating Consensus Sequences
Taxonomic Assignment
Pipelines

Note that full or partial integration of software is needed for multiple processing steps and dependencies may need to be installed separately.

Video Tutorials:
Frequently Asked Questions
What do I do if the quality of my DNA sequences is low/poor?

If the quality of your raw sequences is too low for successful read pairing, even with a low minimum overlap, you can attempt to process each single read individually. While this means you will lose the benefits of paired reads—such as confirmation of the overlapping sequence and longer contigs—you will still have some usable data. However, it is important to remember the computing principle: "garbage in, garbage out." Poor-quality input data will likely result in poor-quality output. If, after the initial quality check of your DNA extractions, you find that your sequences are unusable, you have two general options:

  • Retry the DNA extraction if you have retained portions of your samples. Consider adding or modifying purification steps, then resubmit the samples for sequencing.
  • Proceed with sequencing the samples as they are. Depending on the diversity within your samples and the goals of your study, you may still be able to obtain useful data despite suboptimal sample quality.

It is important to note that sequencing centers often have higher quality standards than necessary to ensure excellent data for their clients. Therefore, maintaining open and transparent communication with the sequencing center's technical support team is crucial. Their expertise can guide you through these challenges, and as a paying customer, you should take full advantage of their advice.

2. How do I decide on my quality filtration parameters?

The answer to this question depends on several factors. Ideally, you will want to set parameters that exclude unreliable sequences while maximizing the retention of samples in your dataset. For example, if your low-quality samples are relatively evenly distributed across your treatments, you may be able to exclude those samples and still conduct valid comparisons across treatments. The exact parameters will vary depending on the sequencing platform, sequencing depth, quality score distribution, and bioinformatics pipeline used

3. Which is better, OTUs or ASVs?

ASVs (Amplicon Sequence Variants) are generally recommended over OTUs (Operational Taxonomic Units) because each ASV represents a biologically meaningful sequence, and there are demonstrable benefits to ASV analysis compared to OTU analysis (Callahan et al., 2017). However, if you prefer OTUs, they are still widely accepted in the literature and often provide similar data interpretations. It may be helpful to review studies that explicitly compare the two approaches before deciding which analysis to use (Chiarello et al., 2022; García-López et al., 2021; Jeske & Gallert, 2022; Joos et al., 2020; Prodan et al., 2020).

4. Which taxonomic assignment method is best?

As with many bioinformatic questions, there is no simple answer. The best method is ultimately the one that allows you to achieve your scientific objectives within the time available. For example, if you have little or no bioinformatics experience, it may be best to start with a user-friendly pipeline that offers a graphical user interface (GUI). Additionally, choosing a well-maintained pipeline with extensive support is advantageous, as you can benefit from an active user community and expert advice. For instance, QIIME2 has a very active user forum, making it a solid choice for beginners.

Due to discrepancies between different taxonomic assignment approaches, it is also considered good practice to use a combination of software platforms to verify that they yield consistent results. This will help ensure the robustness and reliability of your analysis

5. How do I know if my reference database is adequate?

If you are interested in specific organisms, it is important to verify that their reference sequences are included in your database. One way to test the reliability of your reference database is to use mock communities—groups of organisms for which the taxonomic identities are already known. Mock communities can be generated de novo from DNA samples of taxonomically confirmed organisms or in silico using sequence data from your reference database. In addition to validating the reference database, mock communities are valuable for benchmarking other aspects of your bioinformatic workflow, such as filtration parameters and taxonomic assignment methods

Additional background reading:

Open access:

  • Coissac, Eric, Tiayyba Riaz, and Nicolas Puillandre. "Bioinformatic challenges for DNA metabarcoding of plants and animals."Molecular Ecology 21.8 (2012): 1834-1847. https://doi.org/10.1111/j.1365-294X.2012.05550.x
  • Deiner, Kristy, et al. "Environmental DNA metabarcoding: Transforming how we survey animal and plant communities." Molecular Ecology 26.21 (2017): 5872-5895. https://doi.org/10.1111/mec.14350
  • Liu, Mingxin, et al. "A practical guide to DNA metabarcoding for entomological ecologists." Ecological Entomology 45.3 (2020): 373-385. https://doi.org/10.1111/een.12831
  • Petit-Marty, Natalia, Laura Casas, and Fran Saborido-Rey. "State-of-the-art of data analyses in environmental DNA approaches towards its applicability to sustainable fisheries management." Frontiers in Marine Science 10 (2023): 1061530. https://doi.org/10.3389/fmars.2023.1061530
  • Xiong, Fan, et al. "Methodology for fish biodiversity monitoring with environmental DNA metabarcoding: The primers, databases and bioinformatic pipelines." Water Biology and Security 1.1 (2022): 100007. https://doi.org/10.1016/j.watbs.2022.100007

Not open access but very useful nonetheless:

  • Hakimzadeh, Ali, et al. "A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses."Molecular Ecology Resources 24.5 (2024): e13847. https://doi.org/10.1111/1755-0998.13847
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Expert interviews

GenBank is a user friendly reference database that allows the user to specificy different criteria

Javier Izquierdo, Ph.D.

Inhibition, user error, and contamination can lead to false negatives or positives

Susanna Theroux, Ph.D.

To validate eDNA results, compare with a different survey method

Ellen Pikitch, Ph.D.

eDNA can be used for long-term ecological monitoring to determine who, what, where, and why organisms are present

Susanna Theroux, Ph.D.