Raw 16S rRNA fastq data files for herbivore microbiome study
Bibliographic record
Abstract
This repository contains the RAW sequencing data for the herbivorous reef fish microbiome study. Trimmed reads (with primers removed) were deposited at the European Nucleotide Archive, study accession number PRJEB28397 (ERP110594). Raw fastq data files are named using the root format RunQ_GnSpe000_G, where Q is the run number (1, 2, or 3), GnSpe is the host genus and species, 00 is a unique host ID number, and G is the gut segment (F = foregut; M = midgut; H = hind). So the file name Run1_SpVir11_M_S147_L001_R2_001.fastq corresponds to: the R2 reads; midgut sample; Sparisoma viride; individual 11; Run01. Raw fastq files are deposited here by Run. To process please use the scripts in the Pipeline for 16S rRNA processing using DADA2 directory. DNA Extraction & Sequencing Methods For all samples, we homogenized material from each gut segment (fore, mid, hind) separately in 50mL conical tubes for 2 minutes on a Vortex Genie 2. We collected 200 mg (wet weight) of homogenate for DNA extraction following the Human Microbiome Project Core Microbiome Sampling Protocol A (v12.0, HMP Protocol # 07-001) for stool samples. Prior to extraction, we heat treated each sample, first at 65℃ f or 10 minutes, followed by 95℃ for 10 minutes. We then used the PowerSoil® DNA Isolation Kit (MoBio) following the manufacturer's protocol to extract community DNA from each sample. Extracted DNA was sequenced on an Illumina MiSeq by Integrated Microbiome Resource at the Centre for Comparative Genomics and Evolutionary Bioinformatics (Dalhousie University). We targeted the V4-V5 hypervariable region using 515F (5′-GTGYCAGCMGCCGCGGTA) and 926R (5′-CCGYCAATTYMTTTRAGT). We collected 53 individual fish encompassing seven species and three genera. Two species—Sparisoma chrysopterum and Scarus vetula—were only represented by 1 and 2 individuals, respectively. Though we chose to omit these samples from the final analysis, these samples were sequenced and analyzed along with the rest of the samples and made the data available for analysis. We generate sequence data for all 159 sample—three gut segments (fore, mid, and hind) from 53 individuals. Sequencing was conducted across three runs. In the first run (Run01), 144 samples were sequenced and, due to lower than average yield, were re-sequenced (Run02). The remaining 15 samples (5 individuals) were sequenced on a separate run (Run03).
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.299 | 0.257 |
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".