RiboSubstrates: a web application addressing the cleavage specificities of ribozymes in designated genomes
Bibliographic record
Abstract
BACKGROUND: RNA-dependent gene silencing is becoming a routine tool used in laboratories worldwide. One of the important remaining hurdles in the selection of the target sequence, if not the most important one, is the designing of tools that have minimal off-target effects (i.e. cleaves only the desired sequence). Increasingly, in the current dawn of the post-genomic era, there is a heavy reliance on tools that are suitable for high-throughput functional genomics, consequently more and more bioinformatic software is becoming available. However, to date none have been designed to satisfy the ever-increasing need for the accurate selection of targets for a specific silencing reagent. RESULTS: In order to overcome this hurdle we have developed RiboSubstrates http://www.riboclub.org/ribosubstrates. This integrated bioinformatic software permits the searching of a cDNA database for all potential substrates for a given ribozyme. This includes the mRNAs that perfectly match the specific requirements of a given ribozyme, as well those including Wobble base pairs and mismatches. The results generated allow rapid selection of sequences suitable as targets for RNA degradation. The current web-based RiboSubstrates version permits the identification of potential gene targets for both SOFA-HDV ribozymes and for hammerhead ribozymes. Moreover, a minimal template for the search of siRNAs is also available. This flexible and reliable tool is easily adaptable for use with any RNA tool (i.e. other ribozymes, deoxyribozymes and antisense), and may use the information present in any cDNA bank. CONCLUSION: RiboSubstrates should become an essential step for all, even including "non-RNA biologists", who endeavor to develop a gene-inactivation system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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".