Bioinformatics

This skill provides comprehensive documentation for integrating with the LatchBio platform for bioinformatics workflows. It covers workflow creation using Python decorators, cloud data management with LatchFile/LatchDir, resource configuration for CPU/GPU tasks, and pre-built verified workflows like AlphaFold and DESeq2. No code is executed - it serves as a reference guide.

This skill provides comprehensive documentation for integrating with DNAnexus, a cloud platform for biomedical data analysis and genomics. It covers app/applet development, file upload/download operations, job execution, workflow orchestration, and the dxpy Python SDK for building genomics pipelines with FASTQ, BAM, and VCF files.

Provides comprehensive documentation for integrating with Benchling, a cloud platform for life sciences R&D. Covers Python SDK usage, REST API endpoints, authentication methods, registry management, inventory operations, electronic lab notebooks, and workflow automation.

This skill enables direct access to UniProt, the world's leading protein sequence database. It allows searching for proteins by name, gene, or organism, retrieving sequences in FASTA format, and mapping identifiers between UniProt and other biological databases like PDB, Ensembl, and RefSeq.

This skill enables querying the STRING database for protein-protein interaction networks, functional enrichment analysis (GO, KEGG, Pfam), and homology comparisons. It covers 59 million proteins across 5000+ species and supports workflows like network visualization, pathway analysis, and hub protein discovery.

This skill enables querying the Reactome database for biological pathway analysis. It supports pathway enrichment analysis on gene/protein lists, expression data analysis, gene-to-pathway mapping, and visualization of results in the Reactome Pathway Browser for systems biology research.

This skill enables access to the RCSB Protein Data Bank, the worldwide repository for 3D structural data of biological macromolecules. It supports searching for protein and nucleic acid structures by text, sequence, or structural similarity, downloading coordinate files in various formats, and retrieving metadata for structural biology and drug discovery workflows.

This skill enables querying the Open Targets Platform for drug target discovery, allowing users to search for gene targets, disease associations, known drugs, and evidence data. It provides Python helper functions to access tractability assessments, safety liabilities, clinical precedence, and genetic evidence for therapeutic target prioritization.

This skill provides comprehensive documentation for accessing the NIH Metabolomics Workbench REST API, enabling queries for metabolite structures, study metadata, RefMet nomenclature standardization, mass spectrometry m/z searches, and gene/protein associations across 4,200+ metabolomics studies.

This skill enables querying the KEGG (Kyoto Encyclopedia of Genes and Genomes) bioinformatics database through REST API calls. It supports pathway analysis, gene-pathway mapping, metabolic pathway exploration, drug interaction checking, and ID conversion between KEGG and external databases like UniProt and PubChem.

This skill provides comprehensive guidance for accessing the Human Metabolome Database (HMDB), a freely available resource containing detailed information about 220,000+ human metabolites. It covers web-based searches, spectral matching, data downloads, and common research workflows for metabolomics, biomarker discovery, and metabolite identification.

This skill provides guidance for querying the NHGRI-EBI GWAS Catalog, a comprehensive database of genome-wide association studies. It includes documentation on searching for SNP-trait associations, retrieving study metadata, accessing summary statistics, and integrating with Python for genetic epidemiology research and polygenic risk score development.

This skill enables access to the NCBI Gene Expression Omnibus (GEO), a public repository containing over 264,000 gene expression studies. It provides tools to search for datasets, download microarray and RNA-seq data, parse SOFT/matrix files, and perform expression analysis including differential expression and meta-analysis across studies.

This skill enables querying the NCBI Gene database for gene annotation and functional analysis. It provides Python scripts to search genes by symbol or ID, retrieve detailed gene metadata including RefSeqs, GO annotations, chromosomal locations, and phenotypes, and perform efficient batch lookups with built-in rate limiting.

This skill enables querying the Ensembl genome database, a comprehensive resource for vertebrate genomic data maintained by EMBL-EBI. It supports gene information retrieval, DNA/protein sequence fetching, variant effect prediction using VEP, ortholog discovery, and coordinate mapping between genome assemblies for over 250 species.

This skill provides comprehensive guidance for accessing the European Nucleotide Archive (ENA), a public repository for nucleotide sequence data. It documents REST APIs for searching samples, studies, and assemblies, retrieving FASTQ files and genome sequences, querying taxonomic information, and bulk downloading datasets via FTP or Aspera for genomics research pipelines.

This skill provides programmatic access to COSMIC (Catalogue of Somatic Mutations in Cancer), the world's largest cancer mutation database. It allows downloading mutation data, Cancer Gene Census lists, mutational signatures, gene fusions, and drug resistance information for cancer research and bioinformatics workflows.

A comprehensive guide for accessing NCBI's ClinVar database to query clinical significance of human genetic variants. Provides documentation on searching variants by gene/position, interpreting pathogenicity classifications (ACMG/AMP guidelines), downloading bulk data via FTP, and annotating VCF files with clinical significance.

This skill enables access to the AlphaFold Protein Structure Database, providing code examples and documentation for retrieving AI-predicted 3D protein structures. It supports querying by UniProt ID, downloading coordinate files (PDB/mmCIF), analyzing confidence metrics (pLDDT, PAE), and bulk data access via Google Cloud for structural biology and drug discovery workflows.

A comprehensive documentation skill for scvi-tools, a Python framework for probabilistic deep generative models in single-cell genomics. It provides guidance on models for RNA-seq, ATAC-seq, multimodal data integration, spatial transcriptomics, and specialized modalities like methylation and cytometry analysis.

A comprehensive reference skill for scikit-bio, a Python bioinformatics library. Provides documentation and code examples for biological sequence manipulation, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination analysis (PCoA, CCA), statistical tests (PERMANOVA, ANOSIM), and microbiome data processing with FASTA/BIOM file format support.

This skill provides a comprehensive toolkit for analyzing single-cell RNA-seq data using the scanpy library. It enables quality control, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, marker gene identification, cell type annotation, and publication-quality visualizations.

PyTDC (Therapeutics Data Commons) provides AI-ready datasets and benchmarks for drug discovery and development. It offers curated datasets spanning ADME, toxicity, drug-target interactions, and molecular generation with standardized evaluation metrics and meaningful data splits for therapeutic machine learning applications.

Pysam is a documentation skill for genomic data analysis using Python. It provides comprehensive reference guides for reading and writing sequencing alignment files (SAM/BAM/CRAM), genetic variant files (VCF/BCF), and sequence files (FASTA/FASTQ), along with practical code examples for bioinformatics workflows like coverage analysis and variant filtering.

PyDESeq2 is a bioinformatics skill for analyzing bulk RNA-seq count data to identify differentially expressed genes. It provides a complete workflow from data loading through statistical testing (Wald tests with FDR correction), including support for single-factor and multi-factor experimental designs, optional LFC shrinkage, and visualization with volcano/MA plots.

PathML is a full-featured computational pathology toolkit for analyzing whole-slide pathology images. It supports 160+ slide formats, provides preprocessing pipelines for H&E stain normalization and tissue detection, includes pre-trained models for nucleus segmentation, enables graph-based spatial analysis, and supports multiparametric imaging platforms like CODEX and Vectra for spatial proteomics workflows.

gget is a bioinformatics tool that provides quick access to over 20 genomic databases through both command-line and Python interfaces. It enables gene searches, BLAST/BLAT sequence analysis, AlphaFold structure predictions, single-cell expression queries, enrichment analysis, and disease/drug associations - all through a unified, consistent interface.

FlowIO is a documentation skill that teaches Claude how to help users work with Flow Cytometry Standard (FCS) files. It provides guidance on parsing FCS metadata, extracting event data as NumPy arrays, creating new FCS files, and handling multi-dataset files for scientific flow cytometry data processing.

A comprehensive phylogenetic tree analysis toolkit built on ETE (Environment for Tree Exploration). It enables tree manipulation (loading, pruning, rooting), evolutionary analysis (detecting duplications and speciations, ortholog/paralog identification), NCBI taxonomy integration, and publication-quality visualization in PDF/SVG/PNG formats. Ideal for phylogenomics research and clustering analysis.

DiffDock is a molecular docking skill for computational drug discovery. It helps predict how small molecule ligands bind to protein targets using diffusion-based deep learning, providing binding pose predictions and confidence scores for structure-based drug design workflows.