Detecting and characterizing fusions and tandem duplications in acute myeloid leukemia transcriptome assemblies using Barnacle
Bibliographic record
Abstract
Chimeric transcripts are RNA molecules that cannot be explained by linear models of alternative splicing and can arise from events at either the DNA or the RNA level.Three types of chimeras, fusions, partial tandem duplications (PTDs), and internal tandem duplications (ITDs), are important in the detection, prognosis, and treatment of many human cancers.Here we report Barnacle, a high-throughput analysis tool that detects and characterizes fusions, PTDs, and ITDs in de novo assembled RNA-seq data.We characterized Barnacle's sensitivity and specificity with simulated data, and compared Barnacle's fusion detection performance with that of TopHat-Fusion.We ran Barnacle on two deeply-sequenced acute myeloid leukemia (AML) RNA-seq datasets.Among the events that Barnacle predicted in these libraries are three known to be important in AML: fusions between PML and RARA, PTDs in MLL, and ITDs in FLT3.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".