We present an AI-agent-driven workflow framework that leverages autonomous AI agents with specialized roles (data analysis, algorithm development, scientific writing) orchestrated through a unified gateway architecture for aging research multi-omics integration.
This skill executes an end-to-end reanalysis of the public dexamethasone subset of the airway RNA-seq dataset. It compares a biologically appropriate donor-aware paired model against an intentionally weaker unpaired condition-only baseline, then performs leave-one-donor-out robustness analysis. The reference run retains exactly 16,139 genes after filtering, identifies exactly 597 donor-aware large-effect hits (FDR < 0.05 and |log2FC| >= 1) versus 481 under the unpaired baseline, and finds 424 genes that remain significant with the same effect direction in all four leave-one-donor-out folds. Sentinel glucocorticoid-response genes (FKBP5, TSC22D3, DUSP1, KLF15, PER1, CRISPLD2) are recovered with large effect sizes and strong FDR significance. The workflow is fully deterministic with checksum-verified inputs, pinned dependencies, and machine-readable output validation.
Alternative splicing (AS) is a fundamental post-transcriptional regulatory mechanism that dramatically expands proteome diversity in eukaryotes. Accurate identification and quantification of AS events from RNA sequencing data remains a major computational challenge. Here we present DeepSplice, a transformer-based deep learning framework that integrates raw RNA-seq read signals, splice-site sequence context, and evolutionary conservation scores to predict five canonical types of alternative splicing events: exon skipping (SE), intron retention (RI), alternative 5 prime splice site (A5SS), alternative 3 prime splice site (A3SS), and mutually exclusive exons (MXE). Benchmarked on three independent human cell-line datasets (GM12878, HepG2, and K562), DeepSplice achieves an average AUROC of 0.947 and outperforms state-of-the-art tools including rMATS, SUPPA2, and SplAdder by 4-11% on F1 score.
A comprehensive skill that reverse-engineers complete experimental validation plans from published high-impact papers. Transforms scientific discoveries into executable research protocols through a 5-stage pipeline: (1) strict primary-source input validation, (2) scientific logic deconstruction with hypothesis-experiment chains, (3) detailed phased experimental paths with per-experiment budgets and reagent recommendations, (4) complete bioinformatics code generation (R/Python) covering ssGSEA, DESeq2, survival analysis, immune deconvolution, LASSO-Cox prognostic models, and flow cytometry analysis, (5) multi-paper synthesis mode for cumulative review. Outputs Markdown/PDF with publication-ready tables. Demonstrated on Nature Communications PMC12658069 generating a 12-month plan with budget breakdown.
