Science & Research for Claude Code

Differential abundance testing for microbiome data using compositionally-aware methods like ALDEx2, ANCOM-BC2, and MaAsLin2. Use when identifying taxa that differ between experimental groups while accounting for the compositional nature of microbiome data.

Alpha and beta diversity analysis for microbiome data. Calculate within-sample richness, evenness, and between-sample dissimilarity with phyloseq and vegan. Use when comparing community composition across samples or testing for group differences in microbiome structure.

Predict metagenome functional content from 16S rRNA marker gene data using PICRUSt2. Infer KEGG, MetaCyc, and EC abundances from ASV tables. Use when functional profiling is needed from 16S data without shotgun metagenomics sequencing.

QIIME2 command-line workflow for 16S/ITS amplicon analysis. Alternative to DADA2/phyloseq R workflow with built-in provenance tracking. Use when preferring CLI over R, needing reproducible provenance, or working within QIIME2 ecosystem.

Taxonomic classification of ASVs using reference databases like SILVA, GTDB, or UNITE. Covers naive Bayes classifiers (DADA2, IDTAXA) and exact matching approaches. Use when assigning taxonomy to ASVs after DADA2 amplicon processing.

Calculates molecular descriptors and fingerprints using RDKit. Computes Morgan fingerprints (ECFP), MACCS keys, Lipinski properties, QED drug-likeness, TPSA, and 3D conformer descriptors. Use when featurizing molecules for machine learning or filtering by drug-likeness criteria.

Reads, writes, and converts molecular file formats (SMILES, SDF, MOL2, PDB) using RDKit and Open Babel. Handles structure parsing, canonicalization, and full standardization pipeline including sanitization, normalization, and tautomer canonicalization. Use when loading chemical libraries, converting formats, or preparing molecules for analysis.

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Preprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration analysis.

Supervised and unsupervised multi-omics integration with mixOmics. Includes sPLS for pairwise integration and DIABLO for multi-block discriminant analysis. Use when performing supervised multi-omics integration or identifying features that discriminate between groups.

Multi-Omics Factor Analysis (MOFA2) for unsupervised integration of multiple data modalities. Identifies shared and view-specific sources of variation. Use when integrating RNA-seq, proteomics, methylation, or other omics to discover latent factors driving biological variation across modalities.

Similarity Network Fusion (SNF) for patient stratification using multi-omics data. Integrates multiple data types into a unified patient similarity network. Use when performing patient stratification or integrating multi-omics data into unified similarity networks.

Meta-agent that routes bioinformatics requests to specialised sub-skills. Handles file type detection, analysis planning, report generation, and reproducibility export.

Handle paired-end FASTQ files (R1/R2) using Biopython. Use when working with Illumina paired reads, synchronizing pairs, interleaving/deinterleaving, or filtering paired data.

Visualize enrichment results using enrichplot package functions. Use when creating publication-quality figures from clusterProfiler results. Covers dotplot, barplot, cnetplot, emapplot, gseaplot2, ridgeplot, and treeplot.

Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and customizable statistical thresholds.

Gene Set Enrichment Analysis using clusterProfiler gseGO and gseKEGG. Use when analyzing ranked gene lists to find coordinated expression changes in gene sets without arbitrary significance cutoffs. Detects subtle but coordinated expression changes.

KEGG pathway and module enrichment analysis using clusterProfiler enrichKEGG and enrichMKEGG. Use when identifying metabolic and signaling pathways over-represented in a gene list. Supports 4000+ organisms via KEGG online database.

Reactome pathway enrichment using ReactomePA package. Use when analyzing gene lists against Reactome's curated peer-reviewed pathway database. Performs over-representation analysis and GSEA with visualization and pathway hierarchy exploration.

WikiPathways enrichment using clusterProfiler and rWikiPathways. Use when analyzing gene lists against community-curated open-source pathways. Performs over-representation analysis and GSEA for 30+ species.

Perform geometric calculations on protein structures using Biopython Bio.PDB. Use when measuring distances, angles, and dihedrals, superimposing structures, calculating RMSD, or computing solvent accessible surface area (SASA).

Parse and write protein structure files using Biopython Bio.PDB. Use when reading PDB, mmCIF, and MMTF files, downloading structures from RCSB PDB, or writing structures to various formats.

Modify protein structures using Biopython Bio.PDB. Use when transforming coordinates, removing atoms or residues, adding new entities, modifying B-factors and occupancies, or building structures programmatically.

Navigate protein structure hierarchy using Biopython Bio.PDB SMCRA model. Use when accessing models, chains, residues, and atoms, iterating over structure levels, or extracting sequences from PDB files.

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