Opportunities and challenges in applying genomics to the study of oogenesis and folliculogenesis in farm animals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ovarian oogenesis and folliculogenesis are complex and coordinated biological processes which require a series of events that induce morphological and functional changes within the follicle, leading to cell differentiation and oocyte development. In this context, the challenge of the researchers is to describe the dynamics of gene expression in the different compartments and their interactions during the follicular programme. In recent years, high-throughput arrays have become a powerful tool with which to compare the whole population of transcripts in a single experiment. Here, we review the challenges of applying genomics to this model in farm animal species. The first limitation lies in limited the availability of biological material, which makes the study of the follicle compartments (oocyte, granulosa cells and thecal cells) or early embryo much more difficult. The concept of observing all transcripts at once is very attractive but despite progress in sequencing, the genome annotation remains very incomplete in non-model species. Particularly, oogenesis and early embryo development relate to the high proportion of unknown expressed sequence tags. Then, it is important to consider post-transcriptional and translational regulation to understand the role of these genes. Ultimately, these new inferred insights will still have to be validated by functional approaches. In addition to in vitro or ex vivo functional approaches, both 'natural mutant' ewe models and RNA interference represent, at the moment, the best hope for functional genomics. Advances in our understanding of reproductive physiology should be facilitated by gene expression data exchange and translation into a better understanding of the underlying biological phenomena.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it