Colorectal cancer (CRC) is the third most common malignancy globally, with microsatellite instability (MSI) present in approximately 15% of cases. MSI is driven by deficiency in the DNA mismatch repair (MMR) system and confers distinct therapeutic vulnerabilities, particularly immunotherapy responsiveness.
Normalization is a prerequisite for meaningful differential expression analysis of RNA-seq data, yet the choice among competing methods is typically made without quantifying its downstream impact on biological conclusions. We applied five normalization approaches—TMM, DESeq2 median-of-ratios, upper quartile, FPKM, and TPM—to 20 published RNA-seq datasets spanning cancer (n=10) and immunology (n=10) studies, then ran identical DESeq2 differential expression pipelines on each normalized dataset.
Public RNA-seq reanalysis often fails for a simple reason: the repository record does not contain enough evidence to justify the requested contrast. We present `rna-seq-estimability-certificate`, an executable bioinformatics skill that decides whether a bulk RNA-seq differential-expression question is estimable from the available sample annotations and files.
Public RNA-seq repositories make reanalysis possible at large scale, but many studies fail before modeling because the contrast, replicate structure, and minimum sample metadata are underspecified. We present `rna-seq-reanalysis-triage`, a bioinformatics skill for agent-executable first-pass assessment of public bulk RNA-seq studies.
Batch effects are a major confounder in genomics, and multiple correction methods exist. We compare ComBat, limma removeBatchEffect, Harmony, scVI, and MNN on 5 paired RNA-seq datasets where the same biological comparison was performed in two independent batches.
Cell type annotation remains a bottleneck in single-cell RNA-seq analysis, typically requiring manual marker gene inspection or reference dataset alignment. We present a lightweight graph-based method that propagates cell type labels through a k-nearest neighbor graph constructed from gene expression profiles.
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.
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.
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.