Environmental DNA Metabarcoding- Full Pipeline Collection v1
Bibliographic record
Abstract
This is a collection of protocols used to analyze environmental DNA (eDNA) of seawater samples. As part of the Hakai Institute Ocean Observing Program, biomolecular samples have been collected weekly from 0 m to near bottom (260 m), to genetically characterize plankton communities in the Northern Salish Sea since 2015. These protocols are developed to work across all domains of life, from viruses to prokaryotes to eukaryotes, allowing for both amplicon sequencing and shotgun sequencing. This pipeline includes the following steps: 1. Seawater filtration: Our standard protocol for filtering seawater on sterivex filters. Alternatives may be used. 2. DNA extraction from sterivex filters: Here we offer two alternatives: The robust Phenol Chloroform extraction protocol is used in our long-term monitoring program. A kit alternative requires less handling of toxic chemicals, and are used for stand-alone projects. 3. DNA metabarcoding, library prep, and sequencing: With the options of analyzing different marker genes: 18S rRNA nuclear gene: Diversity of Eukaryotes. (Balzano et al 2015) COI mitochondrial gene: Diversity of marine invertebrates. (Leray et al 2013) 12S rRNA mitochondrial gene: Diversity of fish. (Miya et al 2015) 4. Bioinformatic analyses Examples bioinformatic analysis for the different genes can be found on our GitHub page: https://github.com/hakaigenomics More information to be found under each protocol:
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.070 | 0.090 |
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".