A unified platform for RNA-seq analysis in non-model species
Bibliographic record
Abstract
Abstract The increasing application of RNA-seq to study non-model organisms demands easy-to-use and efficient bioinformatics tools to help researchers quickly uncover biological and functional insights from large datasets. Here, we present a unified software suite for processing, analyzing, and interpreting RNA-seq data from any eukaryotic species. This suite consists of a) EcoOmicsDB (www.ecoomicsdb.ca), a database for ortholog mapping and cross-species comparison; b) EcoOmicsAnalyst (www.ecoomicsanalyst.ca), a platform for raw data processing and annotation; and c) ExpressAnalyst (www.expressanalyst.ca), a platform for statistical and functional analysis. The utilities of this suite are demonstrated through case studies of RNA-seq data from multiple non-model species with or without reference transcriptomes. By coupling ultra-fast read mapping algorithms with high-resolution ortholog databases through a user-friendly web interface, the tool suite enables researchers to obtain global expression profiles and gene-level insights from raw RNA-seq reads within 24 hours.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".