Code for Environmental transcriptomics under heat stress: Can environmental RNA reveal changes in gene expression of aquatic organisms?
Bibliographic record
Abstract
Overview of the pipeline used to analyze RNA-seq data from environmental RNA and Daphnia pulex tissue samples for the publication “Environmental transcriptomics under heat stress: Can environmental RNA reveal changes in gene expression of aquatic organisms?” by Hechler, Yates, Chain and Cristescu. Raw FASTQ files underwent initial sequencing quality inspection using FastQC (Andrews, 2010). Low quality sequences and adapters were removed using Trimmomatic (Bolger et al., 2014). All statistical analyses were conducted using R (R Core Team, 2021, R version 4.1.2). With default parameters, STAR (Dobin et al., 2013) was used to map all eRNA and oRNA reads that passed quality control and trimming. FeatureCounts (Liao et al., 2014) was used on the BAM file output from STAR to quantify gene counts. Differential expression analysis was conducted with DESeq2 (Love et al., 2014). GO enrichment analysis was conducted using topGO (Alexa & Rahnenfuhrer, 2022). We used the SqueezeMeta metatranscriptomics pipeline for the eRNA community-wide analysis (Tamames & Puente-Sánchez, 2019) and performed differential gene expression analyses as described above for Daphnia, after we mapped, using Kallisto (Bray et al., 2016), eRNA reads to the reference genomes and transcriptomes of species known a priori to persist in the communities and highly abundant species identified by SqueezeMeta. Lastly, we used SimKa to estimate k-mer similarity between eRNA samples (Benoit et al., 2016).
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.226 | 0.183 |
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".