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Count reads per gene from aligned BAM files using Subread featureCounts. Use when processing BAM files from STAR/HISAT2 to generate gene-level counts for DESeq2/edgeR.
Detect transcription factor binding sites through footprinting analysis in ATAC-seq data using TOBIAS. Use when identifying TF occupancy patterns within accessible regions, as TF binding protects DNA from Tn5 cutting.
Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database. Use when performing initial taxonomic classification of shotgun metagenomic reads before abundance estimation with Bracken.
Ingest, QC, and map reads with reproducible outputs. Use for raw read processing and coverage stats.
Detect contamination and assess genome quality using CheckM, CheckM2, GTDB-Tk, and GUNC for metagenome-assembled genomes and isolate assemblies. Use when checking assemblies for contamination.
Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when performing dimensionality reduction and cl...
Cluster proteins into orthogroups and derive pangenome matrices.
Visualize copy number profiles, segments, and compare across samples. Create publication-quality plots of CNV data from CNVkit, GATK, or other callers. Use when creating genome-wide CNV plots, sample heatmaps, or chromosome-level visualizations.
Statistical methods for calling hits in CRISPR screens. Covers MAGeCK, BAGEL2, drugZ, and custom approaches for identifying essential and resistance genes. Use when identifying significant genes from screen count data after QC passes.
---name: rna-velocity-agent description: AI-powered RNA velocity analysis for predicting cellular state transitions, differentiation trajectories, and dynamic gene regulation from single-cell RNA sequencing data. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compa...
---name: scfoundation-model-agent description: Unified agent for leveraging single-cell foundation models (scGPT, scBERT, Geneformer, scFoundation) for cross-species annotation, perturbation prediction, and gene network inference. license: MIT metadata: author: AI Group version: "1.0.0" created: ...
---name: tumor-clonal-evolution-agent description: AI-powered analysis of tumor clonal architecture, subclonal dynamics, and evolutionary trajectories from multi-region sequencing and longitudinal liquid biopsy data. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" c...
---name: molecular-glue-discovery-agent description: AI-powered molecular glue discovery for targeted protein degradation, enabling neo-substrate recruitment and undruggable target degradation through E3 ligase interface modulation. license: MIT metadata: author: AI Group version: "1.0.0" created...
---name: antibody-design-agent description: An advanced agent for de novo antibody design and optimization using state-of-the-art protein language models (MAGE, RFdiffusion). license: MIT metadata: author: VUMC / UW Baker Lab version: "1.0.0" compatibility:
---name: cancer-metabolism-agent description: AI-powered analysis of cancer metabolic reprogramming including Warburg effect, glutamine addiction, lipid metabolism, and metabolic vulnerabilities for therapeutic targeting. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-...
---name: cellfree-rna-agent description: AI-powered cell-free RNA analysis from liquid biopsy for cancer detection, tissue-of-origin identification, and non-invasive transcriptomic profiling. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:
---name: cryoem-ai-drug-design-agent description: AI-powered integration of cryo-EM structural data with generative AI and molecular dynamics for structure-based drug design targeting flexible proteins and membrane complexes. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026...
---name: chic-ml-framework-agent description: Machine learning framework for inferring high-risk clonal hematopoiesis from complete blood count data without sequencing, reducing the number needed to sequence for CHIP screening. license: MIT metadata: author: AI Group version: "1.0.0" created: "20...
---name: deep-visual-proteomics-agent description: AI-driven integration of cellular imaging, laser microdissection, and ultra-sensitive mass spectrometry for spatially-resolved single-cell proteomics. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:
Validates jump metrics against physiological norms. Use when analyzing CMJ or drop jump results, discussing RSI values, jump heights, ground contact times, or when metrics seem unusual.
Combine supplied sources while keeping evidence, disagreement, and uncertainty visible.
Evaluate how well a supplied argument is supported and what would strengthen it.
Organize supplied papers into themes, methods, findings, and research gaps.
Translate a hypothesis into an interpretable test with metrics and guardrails.
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