sncRNAP: Prediction and profiling of full sncRNA repertoires from sRNAseq data
Bibliographic record
Abstract
Abstract Motivation Non-coding RNAs (ncRNAs), which include long non-coding RNAs (lncRNAs) and small non-coding RNAs (sncRNAs), have been shown to play essential roles in various biological processes. Over the past few years, a group of sncRNA identification tools have been developed but none has shown the capacity to fully profile and accurately identify those that are differentially expressed in control vs treated samples. Therefore, a tool that fully profiles and identifies differentially expressed sncRNAs in group comparisons is required. Results We developed sncRNAP, a Nextflow pipeline for the profiling and identification of differentially abundant sncRNAs from sRNAseq datasets. sncRNAP primary use case is the comparison of multiple small RNA-seq datasets belonging to two conditions such as the comparison of treatment (T) and control (C) cohorts. sncRNAP can be used to analyze human, mouse, and rat datasets. The pipeline carries out all the steps required to assess raw sequencing data, performs differential gene expression (DE) analysis, profiles sncRNAs in each sample, and outputs TXT, PDF, CSV, and interactive HTML files for the quality score and the top identified sncRNA candidates. We verified sncRNAP on publicly available sRNAseq datasets in chronic hepatitis-infected liver tissue and pancreatic ductal adenocarcinoma (PDAC) datasets. Our results support the identification of Val[C/A]AC in hepatitis patients and miR135b in PDAC as potential disease biomarkers. Furthermore, we applied sncRNAP on mouse samples from control and Opa1 mouse mutants and identified AspGTC, ValAAC, SerTGA, and AspGTC as the top DE tsRNAs. In addition, sncRNAP identified mmu-miR-136-5p, mmu-miR-10b-5p, mmu-miR-351-5p, and mmu-miR-6390 as the top DE miRNA candidates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".