RNA-Seq employing a novel rRNA depletion strategy reveals a rich repertoire of snoRNAs in <i>Euglena gracilis</i> including box C/D and Ψ-guide RNAs targeting the modification of rRNA extremities
Bibliographic record
Abstract
Previous mRNA transcriptome studies of Euglena gracilis have shown that this organism possesses a large and diverse complement of protein coding genes; however, the study of non-coding RNA classes has been limited. The natural extensive fragmentation of the E. gracilis large subunit ribosomal RNA presents additional barriers to the identification of non-coding RNAs as size-selected small RNA libraries will be dominated by rRNA sequences. In this study we have developed a strategy to significantly reduce rRNA amplification prior to RNA-Seq analysis thereby producing a ncRNA library allowing for the identification of many new E. gracilis small RNAs. Library analysis reveals 113 unique new small nucleolar (sno) RNAs and a large collection of snoRNA isoforms, as well as the first significant collection of nuclear tRNAs in this organism. A 3' end AGAUGN consensus motif and conserved structural features can now be defined for E. gracilis pseudouridine guide RNAs. snoRNAs of both classes were identified that target modification of the 3' extremities of rRNAs utilizing predicted base-pairing interactions with internally transcribed spacers (ITS), providing insight into the timing of steps in rRNA maturation. Cumulatively, this represents the most comprehensive analysis of small ncRNAs in Euglena gracilis to date.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".