RNA-editing in Basidiomycota, revisited
Bibliographic record
Abstract
We downloaded genomic and transcriptomic read data used in this study from the NCBI database (Table 2 ). The genome and transcriptome reads were de-duplicated to remove polymerase chain reaction products using Dedupe in BBTools ( https://sourceforge.net/projects/bbmap/ ). Subsequently, the reads were quality controlled using TrimGalore ( https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ ) by filtering out the reads with bad base quality (<20) and short length (<40). We aligned filtered genomic and transcriptome reads against the corresponding genome assemblies using Bowtie v2.3.5.1 ( http://bowtie-bio.sourceforge.net ) and HISAT v2.1.0 ( http://www.ccb.jhu.edu/software/hisat/ ), respectively. The candidate RNA-editing sites were detected using JACUSA v1.3.0 [ 9 ] (call-2 --filter-flags 1024 --min-mapq1 0 --min-mapq2 0 --pileup-filter S). The variants supported with <20 total mapped reads, <5 or <10% of variant reads, and any sites with matching gDNA variants (>5% variants of mapped reads) were ignored. We used SPAdes v3.13.0 ( http://cab.spbu.ru/software/spades ) (--careful --cov-cutoff auto) to reassemble D. quercina genomic reads. The original and newly assembled genomes were aligned using NUCmer 4.0.0beta2 ( http://mummer.sourceforge.net ) to find corresponding sites. The RNA reads from the same isolates that DNA reads were generated were downloaded from MycoCosm ( https://mycocosm.jgi.doe.gov/ ) [ 10 ].
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".