Investigation of the vaginal microbiota of dairy cows through genetic sequencing of short (Illumina) and long (PacBio) reads and associations with gestational status
Bibliographic record
Abstract
The vaginal microbiota has been shown to be important in local immune regulation and may play a role in reproduction and fertility. Next-generation sequencing (NGS) technologies have been used to characterize the bovine vaginal microbiota, mainly using short-read sequencing (Illumina). However, the main limitation of this technique is its inability to classify bacteria at the species level. The objective of this study was to characterize the bovine vaginal microbiota at the species level using long-read sequencing (PacBio) and to compare it with the results of short-read sequencing. In addition, the vaginal microbiota of cows that became pregnant after artificial insemination (AI) was compared with that of infertile animals. Thirteen Holstein cows had vaginal swabs collected prior to AI. DNA was extracted and subjected to Illumina and PacBio sequencing to characterize the V4 region and the entire 16S rRNA gene, respectively. PacBio sequencing yielded 366,509 reads that were assigned to 476 species from 27 phyla. However, none of the most abundant reads (>1%) could be classified at the species level. Illumina sequencing yielded more reads and consequently was able to detect a more observed species, but PacBio sequencing was able to detect more unique and rare species. The composition of the vaginal microbiota varies according to the sequencing method used, which might complicate the interpretation of results obtained in the majority of the current studies. The present study expands on the current knowledge of bovine microbiota, highlighting the need for further efforts to improve the current databanks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".