Conserved Sequence Identification Within Large Genomic Datasets Using ‘ <scp> <i>Unikseq2</i> </scp> ’: Application in Environmental <scp>DNA</scp> Assay Development
Bibliographic record
Abstract
Identification of conserved genomic sequences and their utilisation as anchor points for clade detection and/or characterisation is a mainstay in ecological studies. For environmental DNA (eDNA) assays, effective processing of large genomic datasets is crucial for reliable species detection in biodiversity monitoring. While considerable focus has been on developing robust species-targeted assays, eDNA assays with broader taxonomic coverage (e.g., detecting any species within a taxonomic group such as fish), can significantly streamline environmental monitoring, especially when detecting individual species' DNA proves challenging. Designing such assays requires identifying conserved regions representing the target taxonomic group, a chiefly manual task that is often labor-intensive and error-prone, particularly when working with large sequence datasets. To address these challenges, we present unikseq2, an enhanced, alignment-free, k-mer-based tool for identifying unique and conserved sequences. It introduces a new functionality to identify sequence conservation among target species, enabling more informed marker selection for applications such as universal primer design. This automates sequence selection in large-scale mitochondrial genome datasets eliminating the need for manual inspection of computationally costly multiple sequence alignments. Herein, we demonstrate unikseq2's capabilities by developing and validating eDNA assays for various taxa, including Osteichthyes (bony fishes), the Salmonidae family (salmon and trout), Myotis bats and Cervus deer. Unikseq2-based eDNA assays allow for accurate detection across multiple taxonomic levels, from genus to class, enhancing the flexibility, scalability and reliability of eDNA tools in environmental monitoring. By leveraging genomic data from public repositories, unikseq2 supports efficient, reproducible assay design, making it an invaluable tool for a wide range of ecological and biodiversity research applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".