I'd like to share a tool I've developed for the community: RAPTOR (RNA-seq Analysis Pipeline Testing & Optimization Resource).

THE PROBLEM:

• We often choose RNA-seq pipelines based on habit, not data
• Thresholds like |logFC| > 1 and padj < 0.05 are arbitrary
• Different datasets may benefit from different pipelines

THE SOLUTION: RAPTOR benchmarks multiple pipelines on YOUR data and provides ML-powered recommendations.

NEW IN v2.1.1 - Adaptive Threshold Optimizer:

• MAD-based logFC optimization
• pi0 estimation for FDR control
• Multiple p-value adjustment methods
• Data-driven threshold selection

LINKS:

GitHub: https://github.com/AyehBlk/RAPTOR

LOOKING FOR COLLABORATORS!

RAPTOR is 100% open-source and I'm actively seeking collaborators.

For Bioinformaticians:

• Test RAPTOR on your datasets and share feedback
• Validate results against your current analysis methods
• Co-author future benchmark publications

For Developers:

• Python: Core features, performance optimization
• R: Statistical methods, Bioconductor integration

For Everyone (No Coding Required!):

• Report bugs and issues
• Improve documentation and tutorials
• Share RAPTOR with your network

Check issues labeled "good first issue" on GitHub!

WHAT YOU GET:

• Recognition: All contributors listed in README
• Co-authorship: Significant contributions lead to publication credit
• Mentorship: I'll guide first-time contributors

HOW TO GET INVOLVED:

1. Star the repo: https://github.com/AyehBlk/RAPTOR
2. Report issues or contribute code
3. Spread the word to colleagues

Questions or interested in collaborating? Comment below or open an issue on GitHub!