GSEA and over-representation analysis (ORA) for RNA-seq and proteomics. Wraps Enrichr for ORA against MSigDB, KEGG, GO, and 200+ databases; runs preranked GSEA on ranked DE gene lists. Outputs enrichment tables and running-score plots. Use after DESeq2 or edgeR for pathway-level interpretation.
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Cell type annotation strategy for scRNA-seq: name clusters manually from canonical markers, transfer labels from a reference (CellTypist, popV), or do both and validate one against the other. Covers matching tissue and developmental stage, marker evidence, annotation confidence, doublets, unresolved clusters. Use when planning or troubleshooting how to annotate cells.
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Guide to interpreting BUSCO completeness statuses: why Duplicated BUSCOs count as complete, parsing output files, computing/comparing completeness across proteomes/genomes, common counting mistakes. Use when running BUSCO QC, comparing assemblies, or reporting completeness. See also: prokka-genome-annotation for annotation workflows feeding BUSCO.
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Detect somatic CNVs from WES/WGS/targeted BAMs (CNVkit v0.9.x). Bin coverage in target/antitarget regions, normalize vs reference, segment with CBS/HMM, call amps/dels, scatter/diagram plots, purity/ploidy, VCF/SEG export. CLI plus Python API (cnvlib). Use GATK CNV for deep WGS with population controls; use CNVkit for targeted/exome where antitarget bins matter.
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Cancer genomics (TCGA et al.) via cBioPortal REST API. Retrieve somatic mutations, CNAs, expression, clinical data (survival/stage/treatment) across thousands of studies. Use for TMB, oncoprints, survival analysis. For population frequencies use gnomad-database; for drug-gene interactions use opentargets-database.
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Query NCBI ClinVar via E-utilities for variant clinical significance, pathogenicity, disease associations. Search by gene/rsID/condition/review status; returns ClinSig, submitter data, conditions, HGVS. For GWAS use gwas-database; for variant consequence prediction use Ensembl VEP.
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Harmony batch correction for scRNA-seq and other omics. Removes batch effects from PCA embeddings while preserving biology. Run after PCA, before UMAP. Scales to millions of cells. Python (harmonypy, scanpy) and R (Seurat).
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Consensus cell type annotation: runs 10+ algorithms (KNN-Harmony/BBKNN/Scanorama/scVI, CellTypist, ONCLASS, Random Forest, SCANVI, SVM, XGBoost) on a labeled reference and transfers labels via majority voting. Outputs per-method labels, consensus, agreement score. Use when single-method annotation is insufficient or you need ensemble uncertainty for novel states.
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Compute the bacterial pan-genome from Prokka/Bakta GFF3 annotations with Roary's CD-HIT + BLAST + MCL clustering pipeline. Builds gene presence/absence matrices, core/soft-core/shell/cloud partitions, multi-FASTA core gene alignments (with `-e`), and a pan-genome reference. Use Panaroo for higher-accuracy pan-genomes from highly fragmented assemblies, PIRATE for paralog-aware clustering, or PPanGGOLiN for graph-based partitioning.
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Query CELLxGENE Census (61M+ cells). Search by cell type/tissue/disease/organism; get AnnData, stream out-of-core, train PyTorch models. For your own data use scanpy; for annotated data use anndata.
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mdtraj molecular dynamics trajectory analysis (Python). Reads DCD/XTC/TRR/NetCDF/H5/PDB topologies and trajectories; computes RMSD vs time, radius of gyration, per-residue RMSF, residue-residue contact frequency maps, phi/psi torsions for Ramachandran plots (general + Gly/Pro), and 8-state DSSP secondary structure. Modules: trajectory I/O, geometry (distances/angles/dihedrals), structural analysis (RMSD/Rg/RMSF/SASA), contacts, hydrogen bonds, secondary structure (DSSP), NMR observables. For broader atom-selection grammar use mdanalysis-trajectory; for running MD simulations use OpenMM/GROMACS.
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Analyze MD trajectories from GROMACS, AMBER, NAMD, CHARMM, LAMMPS. Reads topology/trajectory into Universe objects; supports RMSD, RMSF, radius of gyration, contact maps, H-bonds, PCA, and custom distance/angle calculations. Use for post-simulation structural analysis; use OpenMM/GROMACS for running simulations.
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Medicinal chemistry filters for compound triage. Drug-likeness rules (Lipinski Ro5, Veber, Oprea, CNS, leadlike, REOS, Golden Triangle, Ro3), structural alerts (PAINS, NIBR, Lilly Demerits), chemical group detectors, complexity metrics, and filter composition query language. Built on RDKit/datamol. For hit-to-lead filtering, library design, ADMET pre-screening. For molecular I/O use rdkit-cheminformatics or datamol.
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Query openFDA REST API for adverse events (FAERS), labeling, product info, recalls, enforcement. Search by drug name, ingredient, MedDRA, or NDC. 1k req/day no key; 120k with free key. For trials use clinicaltrials-database-search; for structures use drugbank-database-access or chembl-database-bioactivity.
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CLI toolkit for SAM/BAM/CRAM: sort, index, convert, filter, QC alignments. Core commands: view, sort, index, flagstat, stats, depth, markdup, merge. Required between alignment and variant/peak calling. Use pysam for Python-native BAM access; deeptools for normalized coverage tracks.
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Query IUPHAR/BPS Guide to Pharmacology (GtoPdb) for receptor-ligand interactions, target/ligand metadata, families, and approved drugs. Affinities (pKi/pIC50/pKd), action (Agonist/Antagonist/etc.), species, structures (SMILES/InChI). No auth. Always resolve targets via geneSymbol/accession; most metadata lives in sub-resources (/databaseLinks, /structure, /synonyms).
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Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.
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Deep learning for drug discovery. 60+ models (GCN, GAT, AttentiveFP, MPNN, ChemBERTa, GROVER), 50+ featurizers, MoleculeNet benchmarks, HPO, transfer learning. Unified load-featurize-split-train-evaluate API. For fingerprints use rdkit-cheminformatics; for featurization-only use molfeat.
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Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.
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Diffusion-based docking that predicts protein-ligand poses without a predefined site. Use for blind docking, when traditional docking fails, or exploring multiple binding modes. Pipeline: prep protein (PDB) and ligand (SMILES/SDF), run inference, analyze confidence-ranked poses.
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Query ChEMBL (2M+ compounds, 19M+ bioactivity measurements, 13K+ targets) via the public REST/JSON API with plain `requests` — no SDK install required. Search compounds, retrieve IC50/Ki/EC50 bioactivities, find target inhibitors, run SAR, access drug mechanism/indication data.
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BRENDA Enzyme DB SOAP/REST queries: kinetic parameters (Km, Vmax, kcat, Ki), EC classes, substrate specificity, inhibitors, cofactors, organism data. 80K+ enzymes, 7M+ values. Free academic registration. For metabolic modeling use cobrapy-metabolic-modeling; metabolites use hmdb-database.
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Query FDA drug labels (DailyMed) via REST API. Search structured product labels (SPLs) by name, NDC, set ID, or RxCUI; get indications, dosage, warnings, adverse reactions, packaging. No auth. For adverse events use fda-database; for DDIs use ddinter-database.
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Parse local DrugBank XML for drug info, interactions, targets, and properties. Search by ID/name/CAS, extract DDIs with severity, map targets/enzymes/transporters, compute SMILES similarity. Primary via local XML; REST API rate-limited (3k/month dev). For live bioactivity use chembl-database-bioactivity; for compound properties use pubchem-compound-search.
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