Symposium review: Mining metagenomic and metatranscriptomic data for clues about microbial metabolic functions in ruminants
Bibliographic record
Abstract
Metagenomics and metatranscriptomics can capture the whole genome and transcriptome repertoire of microorganisms through sequencing total DNA/RNA from various environmental samples, providing both taxonomic and functional information with high resolution. The unique and complex rumen microbial ecosystem is receiving great research attention because the rumen microbiota coevolves with the host and equips ruminants with the ability to convert cellulosic plant materials to high-protein products for human consumption. To date, hundreds to thousands of microbial phylotypes have been identified in the rumen using culture-independent molecular-based approaches, and genomic information of rumen microorganisms is rapidly accumulating through the single genome sequencing. However, functional characteristics of the rumen microbiome have not been well described because there are numerous uncultivable microorganisms in the rumen. The advent of metagenomics and metatranscriptomics along with advanced bioinformatics methods can help us better understand mechanisms of the rumen fermentation, which is vital for improving nutrient utilization and animal productivity. Therefore, in this review, we summarize a general workflow to conduct rumen metagenomics and metatranscriptomics and discuss how the data can be interpreted to be useful information. Moreover, we review recent literatures studying associations between the rumen microbiome and host phenotypes (e.g., feed efficiency and methane emissions) using these approaches, aiming to provide a useful guide to include studying the rumen microbiome as one of the research objectives using these 2 approaches.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".