Development of a Ribosome Profiling Protocol to Study Translation in the yeast <i>Kluyveromyces marxianus</i>
Bibliographic record
Abstract
Abstract Kluyveromyces marxianus is an interesting and important yeast because of particular traits like thermotolerance and rapid growth, and applications in food and industrial biotechnology. Knowing how K. marxianus responds and adapts to changing environments is important to achieve a full understanding of the its biology and to develop bioprocesses. For this, a full suite of omics tools to measure and compare global patterns of gene expression and protein synthesis is needed. Whereas transcriptome analysis by RNA-Seq quantifies mRNA abundance, ribosome profiling allows codon-resolution of translation on a genome-wide scale by deep sequencing of ribosome locations on mRNAs and is emerging as a valuable tool to study translation control of gene expression. We report here the development of a ribosome profiling method for K. marxianus and we make the procedure available as a step by step protocol. To aid in the analysis and sharing of ribosome profiling data, we also added the K. marxianus genome as well as transcriptome and ribosome profiling data to the publicly accessible GWIPS-viz and Trips-Viz browsers. Users are able to upload custom ribosome profiling and RNA-Seq data to both browsers, therefore allowing easy analysis and sharing of data. As many studies only focus on the use of RNA-Seq to study K. marxianus in different environments, the availability of ribosome profiling is a powerful addition to the K. marxianus toolbox. Graphical abstract Development of a Ribosome Profiling protocol to study gene expression in the thermotolerant yeast Kluyveromyces marxianus .
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".