PACeR: a bioinformatic pipeline for the analysis of chimeric RNA-seq data
Bibliographic record
Abstract
ABSTRACT MicroRNAs (miRNAs) are small non-coding RNAs that function in post-transcriptional gene regulation through imperfect base pairing with mRNA targets which results in inhibition of translation and often destabilization of bound transcripts. Sequence-based algorithms historically used to predict miRNA targets face inherent challenges in reliably reflecting in vivo interactions. Recent strategies have directly profiled miRNA-target interactions by cross-linking and ligation of miRNAs to their targets within the RNA-induced silencing complex (RISC), followed by high throughput sequencing of the chimeric RNAs. Despite the strength of these direct chimeric miRNA:target profiling approaches, standardized pipelines for analyzing the resulting chimeric RNA sequencing data are not readily available. Here we present PACeR, a robust bioinformatic p ipeline for the a nalysis of c himeric R NA sequencing data. PACeR consists of two parts, each of which are optimized for the distinctive characteristics of chimeric RNA sequencing reads: first, read processing and alignment and second, peak calling and motif analysis. We apply PACeR to chimeric RNA sequencing data generated in our lab as well as a published benchmark dataset. PACeR has minimal computational power requirements and contains extensive annotation to broaden accessibility for processing chimeric RNA sequencing data and enable insights to be gained about the targets of small non-coding RNAs in regulating diverse biological systems.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".