Bioinformatics

torchdrug is a PyTorch-native toolkit for molecular and protein machine learning. Use the torchdrug skill to choose tasks, datasets, and modular models for graph neural networks, protein modeling, knowledge graph reasoning, molecular generation, and retrosynthesis. It is best for custom model development and reproducible configs, not just canned demos.

The rdkit skill helps with precise cheminformatics workflows: parsing SMILES, SDF, MOL, PDB, and InChI; calculating descriptors; generating fingerprints; running substructure search; handling reactions; and building 2D/3D coordinates. Use this rdkit guide for advanced control, custom sanitization, and rdkit for Data Analysis workflows.

dnanexus-integration is a practical skill for DNAnexus cloud genomics work. Use it to build apps and applets, manage uploads and downloads, run workflows, and automate pipelines with dxpy. The dnanexus-integration guide helps Backend Development tasks involving FASTQ, BAM, and VCF files, plus platform-specific configuration and job execution.

diffdock is a docking skill for predicting protein-ligand binding poses from PDB structures or protein sequences plus ligands in SMILES, SDF, or MOL2. Use the diffdock skill for structure-based drug design, virtual screening, and confidence-scored pose analysis. It is not for binding affinity prediction.

scvi-tools is a Python framework for probabilistic single-cell analysis. Use this scvi-tools skill for batch correction, latent embeddings, differential expression with uncertainty, transfer learning, and multimodal integration. It is a strong fit for single-cell RNA-seq, ATAC, CITE-seq, multiome, and spatial workflows, especially for advanced Machine Learning use cases.

scvelo is a Python skill for RNA velocity analysis in single-cell RNA-seq data. Use it to estimate cell state transitions from unspliced and spliced mRNA, infer trajectory direction, compute latent time, and identify driver genes. It is especially useful for scvelo for Data Analysis when you need directionality beyond standard clustering or pseudotime.

scikit-survival skill for survival analysis and time-to-event modeling in Python. Use this guide for censored data, Cox models, random survival forests, gradient boosting, Survival SVMs, and survival metrics like concordance index and Brier score.

scanpy skill for single-cell RNA-seq data analysis in Python. Use it for QC, normalization, PCA, UMAP/t-SNE, clustering, marker gene discovery, trajectory analysis, and publication-quality plots. Best for exploratory scRNA-seq workflows built around AnnData, with clear scanpy usage and install guidance.

pyopenms is a Python-based mass spectrometry skill for proteomics and metabolomics workflows. Use it to install pyopenms, load and inspect mzML and related files, process spectra, detect features, identify peptides and proteins, and build reproducible LC-MS/MS data analysis pipelines.

pydeseq2 is a Python DESeq2 skill for bulk RNA-seq differential gene expression analysis. Use it to compare conditions, fit single- or multi-factor designs, apply Wald tests and FDR correction, and generate volcano or MA plots in pandas and AnnData workflows.

neuropixels-analysis skill for Neuropixels neural recording analysis. Load SpikeGLX, Open Ephys, or NWB data, preprocess, correct motion, run spike sorting, compute quality metrics, and curate units for downstream data analysis. Best for users who need a practical neuropixels-analysis guide from raw files to publication-ready results.

molfeat is a molecular featurization skill for ML and Data Analysis. It helps convert SMILES or RDKit molecules into fingerprints, descriptors, and pretrained embeddings for QSAR, virtual screening, similarity search, and chemical space analysis. Use this molfeat guide to pick practical representations and build reusable featurization pipelines.

latchbio-integration is the skill for building and deploying bioinformatics workflows on Latch. Use it to package Python pipelines with @workflow and @task decorators, manage LatchFile and LatchDir data, and adapt Nextflow or Snakemake workflows for serverless execution.

imaging-data-commons helps you query and download public cancer imaging data from NCI Imaging Data Commons with idc-index. Use it for imaging-data-commons usage across CT, MR, PET, and pathology datasets, including metadata search, browser preview, licensing checks, and AI training or data analysis workflows. No authentication required.

Analyze and engineer protein glycosylation with the glycoengineering skill. Identify N-glycosylation sequons, estimate O-glycosylation hotspots, and support antibody optimization, vaccine design, and glycoengineering for Data Analysis workflows with practical decision guidance.

gget is a bioinformatics skill for fast, unified access to 20+ genomic databases and analysis tools from CLI or Python. Use it for gene info, BLAST-related lookups, AlphaFold structures, expression data, disease associations, and enrichment-style analysis. It suits quick exploration and gget for Data Analysis workflows.

geniml is a skill for genomic interval machine learning on BED files, scATAC-seq outputs, and chromatin accessibility data. Use it for Region2Vec, BEDspace, scEmbed, consensus peaks, and other region-level ML workflows. It is a good fit when you need embeddings, clustering, or preprocessing guidance for genomic regions.

etetoolkit is a phylogenetic tree toolkit for ETE workflows. Use the etetoolkit skill to parse, edit, compare, root, prune, and visualize trees in Newick, NHX, PhyloXML, or NeXML. It supports phylogenomics, orthology/paralogy analysis, NCBI taxonomy, and publication-style PDF or SVG output.

depmap helps analyze the Cancer Dependency Map for cancer cell line gene dependency scores, drug sensitivity, and gene effect profiles. Use it to identify cancer-specific vulnerabilities, synthetic lethal interactions, and validate oncology drug targets with a reproducible depmap guide for Data Analysis.

The deeptools skill helps with NGS analysis workflows in deepTools: BAM to bigWig conversion, QC, sample comparison, and heatmaps or profile plots for ChIP-seq, RNA-seq, ATAC-seq, and related assays. Use it as a practical deeptools guide when you need reproducible command-line analysis and visualization.

cellxgene-census skill for querying the CELLxGENE Census programmatically. Use it to explore expression data, metadata, embeddings, and cross-dataset patterns across tissues, diseases, and cell types. Best for population-scale single-cell analysis and reference atlas comparisons; for your own data, use scanpy or scvi-tools.

bioservices is a Python skill for querying 40+ bioinformatics services through one interface. Use it for cross-database workflows, ID mapping, pathway and compound lookups, and backend development tasks that need reliable API-based retrieval across UniProt, KEGG, ChEMBL, Reactome, and more.

adaptyv helps you use the Adaptyv Bio Foundry API and Python SDK to install, submit protein sequences, and retrieve assay results. Use this adaptyv skill for API Development, auth setup, request shaping, and practical guidance for binding, screening, thermostability, expression, and fluorescence workflows.