FindBacksplice: a Tool for Locating Circular RNA Backsplice Coordinates
Bibliographic record
Abstract
Abstract Circular RNAs (circRNAs) are generated through back-splicing, a process where a backsplice junction (BSJ) is formed, based on the circRNAs’ unique sequence. BSJs are highly conserved and can be mapped to chromosomal coordinates. Current platforms determine these coordinates from bulk RNA sequencing data. We aimed to develop a tool capable of determining backsplice coordinates based on BSJ sequences for any species and genome version. Motivation circRNAs are emerging as an important regulator of cellular differentiation and other biologically important processes. Common tools for circRNA analyses require a circular RNA’s backsplice coordinates. These can be accessed from public databases. However, the coordinates are specific to a version of a species’ genome, and are unavailable for many model organisms. Results We have developed a Python-based, command line tool, FindBacksplice, which produces backsplice coordinates for any available genome, based on a circRNA’s BSJ sequence. Implemented in Python, this script is integrated with BLAST for use in existing pipelines. We were able to find valid locations of backsplices for known human BSJs in the rat genome and produce backsplice coordinates for use in existing pipelines. Availability and implementation FindBacksplice is available at github.com/m-kraljevic/findbacksplice
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.040 |
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".