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Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.

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Assesses the quality and completeness of a genome annotation with BUSCO (conserved single-copy ortholog recovery), OMArk (proteome completeness, consistency, and contamination), CheckM2 (prokaryotic completeness/contamination), and a gene-set sanity panel (gene count, mono-exonic fraction, protein-length distribution, mRNA:gene ratio, coding density). Covers the assembly-BUSCO-vs-proteome-BUSCO diagnostic, what BUSCO-Duplicated really means, why gene count is a vanity metric, and the QC of transferred annotations. Use when judging whether an annotation is good enough to publish or submit, diagnosing a suspect annotation, or comparing annotation completeness across pipelines.

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Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.

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Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).

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Query UniProt's REST API (post-2022 endpoint at rest.uniprot.org) for protein sequences, annotations, GO terms, cross-references, ID mappings, and proteomes. Use when fetching UniProtKB entries, navigating the JSON schema, choosing between UniProtKB/UniRef/UniParc/Proteomes resources, deciding stream vs search endpoint for batch retrieval, running ID-mapping jobs with the async pattern, handling isoform suffixes, or filtering reviewed Swiss-Prot vs auto-annotated TrEMBL. Encodes the legacy URL migration (2022), the new JSON schema layout, and bulk-pull patterns.

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Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.

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Build clustered heatmaps for expression matrices and other features-by-samples data with rigorous distance/linkage/scaling choices, robust color mapping, optimal leaf ordering, and ComplexHeatmap/pheatmap/seaborn rendering. Covers the ward.D vs ward.D2 trap, the row-vs-column scaling decision, multi-track annotations, oncoPrint, and raster rendering for large matrices. Use when visualizing expression patterns across samples or identifying co-regulated clusters.

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Detect distant homologs using profile and structure-aware methods that go beyond standard BLAST. Use when sequence identity falls into the twilight zone (<35% pairwise), when BLAST fails to find homologs that should exist, when working at metagenomic scale (DIAMOND, MMseqs2), or when structure beats sequence (Foldseek). Covers PSI-BLAST (iterative PSSM), jackhmmer (iterative HMM), HHblits/HHsearch (profile-profile), DIAMOND, MMseqs2, and Foldseek (3Di structural alphabet, van Kempen 2024).

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Designs pooled sgRNA libraries for CRISPR knockout, interference (CRISPRi), activation (CRISPRa), Cas12a multiplex, base-editor, and prime-editor screens. Covers on-target scoring (Rule Set 2, Azimuth, DeepSpCas9, CRISPRon), off-target scoring (CFD, MIT), TSS-relative positioning for CRISPRi/a (Horlbeck, Dolcetto, Calabrese), PAM-variant chemistries, control-guide composition, oligo cloning architecture, and library QC. Use when choosing a genome-wide library (GeCKOv2 vs Avana vs Brunello vs TKOv3 vs Inzolia), designing a focused or paralog-focused custom library, picking CRISPRi vs CRISPRa TSS windows, deciding control-guide proportions, or diagnosing library skew and dropout in a freshly cloned pool.

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Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.

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Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).

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Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.

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Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.

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Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.

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Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.

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Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.

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Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.

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Estimate SNP heritability and partition it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.

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Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

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Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, MMTF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.

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Designs covalent inhibitors and warheads targeting cysteine (most common, 98% of covalent drugs), lysine, serine, threonine, tyrosine, and aspartate residues, with explicit handling of warhead reactivity (acrylamide, chloroacetamide, vinyl sulfone, sulfonyl fluoride, fluorosulfate, aldehyde, boronate, nitrile), reversibility (kinact/Ki, t_residence), glutathione (GSH) stability, intrinsic reactivity assays, and covalent docking (DOCKovalent, GOLD, HCovDock). Use when designing covalent inhibitors for targeted covalent inhibition (TCI), KRAS G12C-style approaches, or rationalizing covalent SAR.

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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

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Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.

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Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.

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