Phytoplankton ASVs: High-Resolution Marine Analysis

Updated 15 December 2025

Phytoplankton ASVs are single-nucleotide resolved sequence variants derived from rigorous amplicon sequencing and denoising workflows, enabling high-fidelity profiling of marine phytoplankton diversity.
They facilitate detailed vertical and horizontal mapping of oceanic biogeographies through compositional transformations and clustering of ASV count matrices.
Advanced methods like DADA2 and DUDE-Seq optimize error correction and integration of marker genes such as 18S rRNA and chloroplast loci, enhancing ecological and biogeographical studies.

Phytoplankton Amplicon Sequencing Variants (ASVs) provide single-nucleotide resolution of phytoplankton diversity in environmental samples, particularly through metabarcoding approaches targeting marker genes such as 18S rRNA or chloroplast loci. ASVs are inferred directly from amplicon sequencing reads after rigorous denoising, error correction, and chimera removal, resulting in high-fidelity identification of unique sequence types corresponding to individual microeukaryote or cyanobacterial taxa. They are foundational for quantitative ecological studies, vertical and horizontal community profiling, and the definition of compositional biogeographies in marine systems (Pulgrossi et al., 8 Dec 2025, Lee et al., 2015).

1. ASV Generation and Error-Correction Workflows

Phytoplankton ASVs are obtained via a multi-stage workflow encompassing sample collection, DNA extraction, target amplification, sequencing, and computational denoising:

Sample collection and DNA extraction: Seawater (2–10 L) is filtered (0.22 μm Sterivex filters) across oceanographic transects and depths (0–200 m). Lysis and DNA isolation employ phenol–chloroform or PowerWater kits.
Amplicon generation: A universal primer pair, 515FY/926R, broadly amplifies both prokaryotic (16S, including chloroplasts) and eukaryotic (18S) rRNA genes, yielding ~400 bp amplicons spanning V4–V5 regions.
Sequencing: Illumina MiSeq (2 × 250 bp) generates paired-end reads for high-throughput coverage.
Denoising and error correction:
- Amplicon reads are processed for primer removal (cutadapt) and quality filtered (DADA2: truncLen = [240, 200], maxEE = [2,2], truncQ = 2).
- DADA2 models and learns error rates, infers ASVs via denoising, merges pairs, and removes chimeras.
- An alternative or complementary denoising front-end, DUDE-Seq, models substitutions and homopolymer indels as a discrete memoryless channel, applying a two-pass context-aware correction to maximize sequence accuracy prior to ASV inference (Lee et al., 2015).
Result: A sample-by-ASV table of corrected read counts, retaining all unique, single-nucleotide-distinct phytoplankton sequence variants.

2. Mathematical and Computational Treatment of ASV Tables

ASV count matrices reflect the compositional nature of environmental sequencing data. Mathematical procedures standardize and distance-embed these data for downstream ecology:

Conversion to relative abundance: For each sample $i$ , raw counts $\mathbf{x}^{(i)} \in \mathbb{N}^d$ are converted as

$p^{(i)}_{\ell} = \frac{x^{(i)}_{\ell}}{ \sum_{j=1}^d x^{(i)}_j }, \quad \ell = 1, \dots, d.$

Compositional transformation: The centered log-ratio (clr) transform,

$\mathrm{clr}(p^{(i)}) = \bigl[ \ln( p^{(i)}_1 / g(p^{(i)}) ), \dots, \ln( p^{(i)}_d / g(p^{(i)}) ) \bigr ],$

where $g(p^{(i)})$ is the geometric mean, yields an embedding in the real vector space to enable Euclidean metric operations.

Dissimilarity metrics: The Aitchison distance for compositional differences between two samples $\mathbf{p}, \mathbf{q} \in \Delta^{d-1}$ is

$d_{\mathrm{Aitchison}}(\mathbf{p}, \mathbf{q}) = \| \mathrm{clr}(\mathbf{p}) - \mathrm{clr}(\mathbf{q}) \|_2.$

3. Advanced Denoising: DUDE-Seq and Error Models

High-fidelity ASV inference requires robust correction for PCR and sequencing artifacts. DUDE-Seq employs a formal statistical model:

Error representation: Each sequence is viewed as emission from a discrete memoryless channel (DMC) characterized by confusion matrix $\Pi(x,z) = P(Z_i = z \mid X_i = x)$ , where $x$ is the true base and $z$ is the observed base.
Algorithm: Sliding-window context statistics are collected, and for each base, the minimum expected loss (typically Hamming loss) across possible corrections is computed using the channel model and empirical context frequencies. A formal two-pass procedure enables both substitution and indel (via virtual DMC) correction.
Empirical results: DUDE-Seq achieves substantial reductions in per-base error rate and outperforms alternatives like AmpliconNoise and Coral in both simulated and real datasets, with high efficiency (runtime $O(Nk)$ for total read-bases $N$ and context size $k$ ) (Lee et al., 2015).
Integration: Denoised reads are fed to ASV-caller engines (DADA2, Deblur, UNOISE) for final variant inference. A plausible implication is that improved denoising yields lower spurious singleton ASVs and enhances reproducibility.

4. Clustering and Definition of 3D Marine Bioprovinces from ASV Data

Phytoplankton ASVs enable explicit spatially resolved ecological partitioning:

Joint biological and spatial distance: ASV-based biological distances (Aitchison) and spatial distances (latitude, depth) are convexly combined:

$D_{\alpha} = (1-\alpha) h(D_{\mathrm{bio}}) + \alpha h(D_{\mathrm{spatial}})$

where $h(\cdot)$ scales by the operator norm, and $\alpha$ tunes the biology-geography tradeoff.

Agglomerative clustering: Ward’s linkage applied to $D_{\alpha}$ clusters samples into $K$ "bioclusters," representing coherent phytoplankton communities.
Extension across 3D ocean grid: Bioprovinces are mapped onto a latitude–longitude–depth grid by $k$ -nearest-neighbor voting based on environmental (temperature, salinity) similarity and spatial proximity, assigning abiotic-matched locations to the most likely cluster.

5. Ecological and Biogeographical Insights from ASVs

Fine-grained taxonomic resolution via ASVs quantifies vertical and regional partitioning of phytoplankton:

Vertical community structure: Subtropical gyre provinces exhibit depth-stratification of Prochlorococcus ecotypes (HL, LL) and eukaryotic phytoplankton (diatoms, prymnesiophytes), with deeper euphotic layers dominated by low-light Prochlorococcus and diatoms.
Regionalization: Equatorial marine provinces (Longhurst PEQD) divide into distinct surface and sub-euphotic communities, demarcated at ~100 m depth.
Boundary refinement: ASVs reveal that classical Longhurst provinces segment into finer vertical and horizontal zones, especially in oligotrophic gyres, while certain polar provinces (e.g., ANTA) remain vertically homogeneous in ASV composition.
Quantitative mapping: Concordance between traditional and ASV-derived provinces is strong in some regions (e.g., 100% assignment for ANTA to province 9), but increased ASV resolution exposes ecological boundaries not captured by legacy schemes (Pulgrossi et al., 8 Dec 2025).

6. Practical Considerations and Computational Efficiency

Critical factors for robust ASV-based phytoplankton studies include marker choice, denoising, and scalability:

Marker selection: 18S rRNA (V4, V9) and chloroplast loci (e.g., rbcL, psbA) support broad taxonomic coverage and have standardized primer sets.
Parameterization for denoising tools: Empirical estimation of confusion matrices using lab-generated mock communities increases correction accuracy; context size $k$ and quality thresholds must be tailored per chemistry (e.g., $k=5$ for MiSeq, lower for homopolymer-rich runs).
Algorithmic throughput: Both DUDE-Seq and DADA2 exhibit manageable computational demands for large environmental datasets, especially when coupled with sparse data structures for context indexing.
Pipeline integration: Sequential use of DUDE-Seq denoising and modern ASV callers yields high-fidelity variant tables with minimal overhead and optimized variant boundary resolution. A plausible implication is that these advances underpin rigorous quantitative biogeographical synthesis and environmental monitoring.

Phytoplankton ASVs, coupled with denoising advances and compositional ecological analysis, have transformed the study of marine community structure, enabling 3D ocean partitioning and refined understanding of ecological dynamics across depth, latitude, and regional gradients (Pulgrossi et al., 8 Dec 2025, Lee et al., 2015).