Bioinformatics

This skill provides comprehensive documentation for integrating with Latch, a Python framework for bioinformatics workflows. It covers workflow creation with @workflow/@task decorators, cloud data management with LatchFile/LatchDir, resource configuration for CPU/GPU tasks, and access to verified workflows like AlphaFold and DESeq2.

DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.

Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO/KEGG enrichment, interaction discovery, 5000+ species, for systems biology.

This skill enables querying the Reactome biological pathway database to perform pathway enrichment analysis, map genes to pathways, and explore molecular interactions. It provides a Python helper script and REST API documentation for systems biology research.

This skill provides comprehensive guidance for accessing the RCSB Protein Data Bank programmatically. It enables searching for protein and nucleic acid 3D structures by text, sequence, or structural similarity, downloading coordinate files in various formats, and retrieving structural metadata for applications in drug discovery and structural biology.

Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.

This skill provides comprehensive guidance for using the NIH Metabolomics Workbench REST API, a repository of over 4,200 metabolomics studies. It enables querying metabolite structures by various identifiers, accessing study metadata and experimental results, standardizing nomenclature with RefMet, performing mass spectrometry m/z searches, and retrieving gene/protein-metabolite associations.

Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP/REST work or KEGG-specific control.

Access Human Metabolome Database (220K+ metabolites). Search by name/ID/structure, retrieve chemical properties, biomarker data, NMR/MS spectra, pathways, for metabolomics and identification.

Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores.

Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.

Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.

Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.

Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. Supports multiple formats.

This skill enables access to the DrugBank pharmaceutical database containing ~9,591 drug entries with comprehensive drug information including chemical properties, drug-drug interactions, protein targets, metabolic pathways, and ADMET predictions. It supports drug discovery research, polypharmacy safety analysis, drug repurposing studies, and pharmacology research using Python with RDKit for chemical analysis.

Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication.

This skill provides comprehensive guidance for accessing NCBI's ClinVar database, which contains clinical interpretations of human genetic variants. It covers searching via the E-utilities API, interpreting clinical significance classifications (pathogenic, benign, VUS), downloading bulk data from FTP, and processing variants in XML, VCF, and tab-delimited formats for genomic medicine applications.

Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.

Query ChEMBL's bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.

Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information for biochemical research and metabolic pathway analysis.

Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.

This skill provides detailed documentation and best practices for analyzing single-cell omics data using scvi-tools. It covers probabilistic modeling for scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities including batch correction, differential expression, and cell type annotation.

Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.

This skill enables complete single-cell RNA-seq analysis using the scanpy Python toolkit. It guides users through quality control, normalization, dimensionality reduction (PCA/UMAP/t-SNE), Leiden clustering, marker gene identification, and cell type annotation, with included scripts and templates for automated workflows.

Pysam is a comprehensive reference skill for bioinformatics developers working with genomic data. It provides documentation and code examples for reading, manipulating, and writing sequencing alignment files (SAM/BAM/CRAM), genetic variant files (VCF/BCF), and sequence files (FASTA/FASTQ) using the pysam Python library.

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.