RNA-SeqEZPZ: a point-and-click pipeline for comprehensive transcriptomics analysis with interactive visualizations
Bibliographic record
Abstract
BACKGROUND: RNA sequencing (RNA-seq) analysis has become a routine task in numerous genomic research labs, driven by the reduced cost of bulk RNA sequencing experiments. These studies generate billions of reads that require easy-to-run, comprehensive, and reproducible analysis. However, many labs rely on in-house scripts, which can be challenging for bench scientists to use and hinder standardization and reproducibility. While existing RNA-seq pipelines attempt to address these challenges, they often lack a complete end-to-end user interface. FINDINGS: To bridge this gap, we developed RNA-SeqEZPZ, an automated pipeline with a user-friendly point-and-click interface, enabling rigorous and reproducible RNA-seq analysis without requiring programming or bioinformatics expertise. For advanced users, the pipeline can also be executed from the command line, allowing customization of steps to suit specific applications. The innovation of this pipeline lies in the combination of 3 key features: (i) all software is packaged within a Singularity container, eliminating installation issues; (ii) it offers a graphical, point-and-click interface from raw FASTQ files through differential expression and pathway analysis; and (iii) it includes a Nextflow implementation, enabling scalability and portability for seamless execution across various platforms, including job submission in the cloud and cluster computing. Additionally, RNA-SeqEZPZ generates a comprehensive statistical report and offers an option for batch adjustment to minimize effects of noise due to technical variation across replicates. Reports can also be reviewed by a bioinformatician to ensure the overall quality of the analysis. CONCLUSIONS: RNA-SeqEZPZ is a robust, accessible, and scalable solution for comprehensive RNA-seq analysis, enabling researchers to focus on biological insights rather than computational challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".