Construction of a massive genetic resource by transcriptome sequencing and genetic characterization of <i>Megasyllis nipponica</i> (Annelida: Syllidae)
Bibliographic record
Abstract
Understanding the processes and consequences of the morphological diversity of organisms is one of the major goals of evolutionary biology. Studies on the evolution of developmental mechanisms of morphologies, or evo-devo, have been extensively conducted in many taxa and have revealed many interesting phenomena at the molecular level. However, many other taxa exhibiting intriguing morphological diversity remain unexplored in the field of evo-devo. Although the annelid family Syllidae shows spectacular diversity in morphological development associated with reproduction, its evo-devo study, especially on molecular development, has progressed slowly. In this study, we focused on Megasyllis nipponica as a new model species for evo-devo in syllids and performed transcriptome sequencing to develop a massive genetic resource, which will be useful for future molecular studies. From the transcriptome data, we identified candidate genes that are likely involved in morphogenesis, including genes involved in hormone regulation, sex determination and appendage development. Furthermore, a computational analysis of the transcriptome sequence data indicated the occurrence of DNA methylation in coding regions of the M. nipponica genome. In addition, flow cytometry analysis showed that the genome size of M. nipponica was approximately 524 megabases. These results facilitate the study of morphogenesis in molecular terms and contribute to our understanding of the morphological diversity in syllids.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".