Next-Generation DNA-Based Approaches for Comprehensive Assessment of Marine Communities
Bibliographic record
Abstract
Abstract Biodiversity analysis is key to ecological biomonitoring in any environmental assessment program. Conventional taxonomic methods lack the speed, sensitivity, accuracy and robustness required for executing comprehensive biodiversity analysis especially in remote marine environments. Recent advances in molecular biology and bioinformatics have made DNA sequences a major source of information for biodiversity analysis. Taxonomically validated reference libraries of standardized species-specific sequences—DNA barcodes—are being assembled as part of large-scale initiatives such as International Barcode of Life Consortium. Next-Generation Sequencing (NGS) technologies provide the capacity to analyze environmental DNA from various types of samples and read biodiversity across all domains of life from bacteria to higher eukaryotes directly from environmental samples. This pilot project assessed the utility of using DNA barcoding as well as NGS-based environmental barcoding tools for biodiversity analysis. Specimens collected during a recent scientific expedition in the Beaufort Sea were used. Individual specimens were DNA barcoded using a single-specimen Sanger sequencing approach and environmental DNA from ice cores and water samples were subjected to bulk NGS analysis in a Roche 454 platform. DNA barcoding revealed 60 distinct genetic lineages (putative species) of metazoa, many of which matched sequences in the Barcode of Life Data Systems (www.boldsystems.org). The lack of reference sequences for marine taxa currently limits the usability of this approach in a wider context. NGS-based patterns of ecological diversity were able to robustly separate communities of microbial and non-microbial species in ice-core and water samples. Additionally, more detailed taxonomic assignments allowed teasing apart specific groups of organisms in these samples directly from NGS sequences. Overall, a combination of DNA barcode analysis and NGS tools demonstrated the potential of DNA-based approaches for comprehensive biomonitoring in remote marine environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".