<scp>eS</scp>nail: A transcriptome‐based molecular resource of the central nervous system for terrestrial gastropods
Bibliographic record
Abstract
To expand on emerging terrestrial gastropod molecular resources, we have undertaken transcriptome-based sequencing of the central nervous system (CNS) from six ecologically invasive terrestrial gastropods. Focusing on snail species Cochlicella acuta and Helix aspersa and reticulated slugs Deroceras invadens, Deroceras reticulatum, Lehmannia nyctelia and Milax gagates, we obtained a total of 367,869,636 high-quality reads and compared them with existing CNS transcript resources for the invasive Mediterranean snail, Theba pisana. In total, we obtained 419,289 unique transcripts (unigenes) from 1,410,569 assembled contigs, with blast search analysis of multiple protein databases leading to the annotation of 124,268 unigenes, of which 92,544 mapped to ncbi nonredundant protein databases. We found that these transcriptomes have representatives in most biological functions, based on comparison of gene ontology, kegg pathway and protein family contents, demonstrating a high range of transcripts responsible for regulating metabolic activities and molecular functions occurring within the CNS. To provide an accessible genetic resource, we also demonstrate the presence of 66,687 microsatellites and 304,693 single-nucleotide variants, which can be used for the design of potentially thousands of unique primers for functional screening. An online "eSnail" database with a user-friendly web interface was implemented to query all the information obtained herein (http://soft.bioinfo-minzhao.org/esnail). We demonstrate the usefulness of the database through the mining of molluscan neuropeptides. As the most comprehensive CNS transcriptome resource for terrestrial gastropods, eSnail may serve as a useful gateway for researchers to explore gastropod CNS function for multiple purposes, including for the development of biocontrol 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.035 |
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".