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GPTomics

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   Bio Free Energy Calculations · gptomics bundle

   Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda scheduling, soft-core potentials, MBAR/BAR analysis, cycle-closure validation, and protocol-appropriate enhanced sampling. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.
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   Bio Clinical Databases Variant Prioritization · gptomics bundle

   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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   Bio Ecological Genomics Conservation Genetics · gptomics bundle

   Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairway Plot 2 / dadi / fastsimcoal2 / PSMC), F-statistics, runs of homozygosity binned by length class to date inbreeding, genetic-load decomposition (Bertorelle 2022 realized vs masked), the modern 100/1000 Ne rule (Frankham 2014), Ne/Nc 2-6 orders of magnitude in marine fish (Hauser & Carvalho 2008), tree-sequence forward simulations (SLiM 4 + pyslim + tskit), and the Sukumaran-Knowles caveat against MSC methods for management-unit definition. Use when estimating Ne by time horizon, detecting inbreeding via F_ROH, decomposing genetic load, justifying conservation thresholds, distinguishing ESU/MU/DPS, configuring NeEstimator V2, or correcting LDNe physical linkage.
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   Bio Epidemiological Genomics Amr Surveillance · gptomics bundle

   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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   Bio Imaging Mass Cytometry Data Preprocessing · gptomics bundle

   Load and preprocess imaging mass cytometry (IMC) and MIBI data from raw MCD/TXT through hot-pixel removal, spillover compensation, and variance-stabilizing transformation, covering readimc/steinbock ingestion, NNLS spillover compensation (CATALYST), IMC-Denoise, and the IMC arcsinh-cofactor question. Use when starting analysis from raw MCD files, building per-channel TIFF stacks, compensating channel spillover, choosing an arcsinh cofactor, or preparing single-cell intensities for phenotyping.
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   Bio Immunoinformatics Mhc Class Ii Prediction · gptomics bundle

   Predict peptide-MHC class II (HLA-DR/DQ/DP) binding and presentation for CD4 T-cell epitopes with NetMHCIIpan-4.3 and MixMHC2pred-2.0. Covers why class II is far less reliable than class I (open binding groove, 9-mer register ambiguity, sparse noisy training data, DR>DP>DQ accuracy asymmetry), the DQ/DP heterodimer alpha/beta pairing trap, and the looser 1%/5% %Rank thresholds. Use when predicting CD4 epitopes for vaccine help, mapping class II neoantigens, or scoring long peptides against DR/DQ/DP. For CD8/class I see mhc-binding-prediction.
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   Bio Methylation Based Detection · gptomics bundle

   Detects cancer and infers tissue-of-origin from cfDNA methylation by choosing conversion chemistry (bisulfite vs EM-seq vs TAPS vs cfMeDIP), calling read-level methylation haplotypes rather than averaged beta values, and deconvolving a hematopoietic-dominated cfDNA mixture against a methylation atlas via NNLS/quadratic programming. Encodes the GRAIL/CCGA thesis that thousands of tissue-specific markers make methylation outperform sparse mutations for multi-cancer early detection (MCED) and localization, and that single concordantly-methylated fragments give ppm-level sensitivity. Uses MethylDackel for extraction (mbias-then-extract), MEDIPS/QSEA for enrichment data, scipy.optimize.nnls for deconvolution. Use when building an MCED or methylation-MRD assay, picking a conversion chemistry for low-input plasma, or deconvolving tissue-of-origin from cfDNA.
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   Bio Long Read Sequencing Nanopore Methylation · gptomics bundle

   Calls DNA base modifications (5mC, 5hmC, 6mA, 4mC) directly from Oxford Nanopore and PacBio HiFi long reads encoded as MM/ML SAM tags, piles them into per-site bedMethyl with modkit (or pb-CpG-tools for PacBio), and produces phased allele-specific methylation. Covers why methylation is a basecalling decision that cannot be recovered later, the MM/ML tag-drop failure that silently zeroes methylation through alignment, the MM ? vs . no-call semantics, 5mC/5hmC resolution vs bisulfite, modkit's 10th-percentile auto-threshold, and the haplotagged ASM workflow. Use when calling 5mC/5hmC/6mA from a modBAM, generating bedMethyl, preserving methylation tags through alignment, doing allele-specific or differential methylation, or QC-ing a modification BAM.
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   Bio Multi Omics Mixomics Analysis · gptomics bundle

   Builds supervised and unsupervised multivariate integration across bulk omics blocks with mixOmics - sPLS for sparse pairwise correlation, DIABLO (block.splsda) for a multi-block discriminant signature, rCCA for regularized canonical correlation, and MINT for multi-study integration. Covers why these projection methods maximize covariance or correlation and not truth, why DIABLO's design matrix is the central correlation-versus-discrimination decision, why cross-validation must wrap keepX selection or the reported error is leaked, why balanced error rate is required under class imbalance, and why DIABLO needs matched samples while MINT handles multiple cohorts. Use when finding a cross-omic discriminant signature for a known outcome, selecting correlated features between two omics, tuning keepX, or integrating one omic across studies. For unsupervised factors see mofa-integration; for the method decision see integration-design; for cross-validation theory see machine-learning/model-validation.
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    Bio Pathway Enrichment Visualization · gptomics bundle

    Turns an enrichResult or gseaResult from clusterProfiler/enrichplot into a figure that collapses or shows gene-set redundancy, using dotplot, barplot, cnetplot, emapplot, treeplot, ridgeplot, gseaplot2, and upsetplot. Covers why a default top-20 GO dotplot is one biological theme drawn twenty times (the DAG/nesting guarantees redundant overlapping terms), so the figure is a modeling choice between SHOWING redundancy (pairwise_termsim -> emapplot/treeplot) and DELETING it (simplify/REVIGO); why cnetplot/emapplot/treeplot need pairwise_termsim first; why enrichplot ships no barplot for gseaResult (a bar cannot carry a signed NES); why GeneRatio is not fold enrichment; and why showCategory silently truncates. Use when plotting ORA or GSEA results, collapsing redundant GO terms visually, encoding a dotplot, or building a publication enrichment figure. Statistics come from go-enrichment and gsea; generic ggplot -> data-visualization/ggplot2-fundamentals.
    1 install
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    Bio Restriction Golden Gate Assembly · gptomics bundle

    Design and validate Type IIS scarless DNA assembly (Golden Gate, MoClo) using Biopython Bio.Restriction. Screens parts for internal BsaI/BsmBI/BbsI/SapI sites (domestication), previews the fusion overhangs a digest exposes, and validates a fusion-overhang set for distinctness and fidelity. Use when designing a Golden Gate or MoClo assembly, domesticating a part by removing internal Type IIS sites, or choosing and checking fusion overhangs for one-pot assembly.
    1 install
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    Bio Rna Quantification Featurecounts Counting · gptomics bundle

    Count reads per gene from aligned BAM files using Subread featureCounts. Use when turning STAR/HISAT2 BAMs into a gene-level count matrix for DESeq2/edgeR, deciding library strandedness, handling paired-end fragment counting, choosing how to treat multi-mapping and multi-overlapping reads, or diagnosing a low assignment rate from the summary file.
    1 install
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    Bio Sequence Properties · gptomics bundle

    Calculate nucleotide and protein sequence properties (GC content, GC skew, molecular weight, melting temperature, isoelectric point, instability, hydropathy) with Biopython. Use when analyzing sequence composition, computing primer Tm, estimating DNA or protein mass, or profiling protein biophysical properties.
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    Bio Spatial Transcriptomics Spatial Neighbors · gptomics bundle

    Build the spatial neighbor graph that every downstream spatial statistic (Moran's I, neighborhood enrichment, co-occurrence, spatial domains) inherits, using Squidpy. Use when choosing the graph type (kNN vs Delaunay vs fixed-radius vs Visium hex grid) and understanding why it silently changes every downstream result; handling variable cell density (kNN fixes neighbor COUNT, fixed-radius fixes physical DISTANCE -- each distorts the other); getting coordinate units right (pixels vs microns; Visium array coords are not distance); pruning Delaunay long edges across tissue gaps; running the graph sensitivity analysis almost nobody runs; and knowing when planar section neighbors misrepresent a 3D tissue.
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    Bio Structural Biology Binding Site Detection · gptomics bundle

    Detects putative ligand-binding pockets and druggable cavities de novo on an apo protein structure with fpocket, P2Rank, CASTp, and DoGSiteScorer, ranking them by druggability/ligandability score. Use when detecting cavities on an apo structure with no bound ligand; choosing geometric pocket enumeration (fpocket alpha-spheres, CASTp) vs ML ligandability scoring (P2Rank, DoGSiteScorer); recognizing that a geometric cavity is a hypothesis, not automatically a functional or druggable site (may be a crystal-additive or non-functional cleft); knowing druggability scores were trained on holo sets and under-detect apo, shallow, and cryptic pockets; detecting cryptic or transient pockets over an MD or conformational ensemble (mdpocket); and detecting on a predicted model whose pocket-lining rotamers are the least reliable atoms. Keywords binding site, pocket, cavity, druggability, ligandability, fpocket, P2Rank, CASTp, DoGSiteScorer, apo, cryptic pocket, alpha sphere, mdpocket.
    1 install
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    Bio Structural Biology Structure Modification · gptomics bundle

    Modifies protein structures in place with Biopython Bio.PDB - transforms coordinates, strips waters/heteroatoms, overloads the B-factor column, renumbers, and builds entities. Use when applying a rotation matrix and needing to know whether it is row-convention (Entity.transform, Superimposer) or column-convention (REMARK 350 / _pdbx_struct_oper_list assembly operators) so geometry is not silently mirrored; when overloading B-factors with pLDDT/conservation for coloring and needing to preserve the destroyed originals; when stripping solvent by HETFLAG (r.id[0]) rather than residue name so catalytic metals and cofactors survive; and when building or copying entities through StructureBuilder/Select without breaking SMCRA parent-child links or the (hetflag, resseq, icode) id tuple. Keywords transform, rotation matrix, occupancy, assembly operators.
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    Bio Tcr Bcr Analysis Repertoire Visualization · gptomics bundle

    Draws TCR/BCR repertoire figures - V-J chord/circos, CDR3 spectratype, clonal-space stratification, clonal tracking across timepoints, rarefaction/extrapolation curves, overlap heatmaps, and clonotype-similarity networks - and encodes how to read them. Use when choosing between a raw Shannon bar and a rarefaction curve for a diversity comparison; deciding a depth-robust overlap metric (Morisita-Horn) vs a set metric (Jaccard) for a heatmap; setting the distance threshold that defines a clonotype-similarity network; interpreting a Gaussian vs skewed spectratype as polyclonal vs clonally expanded; or laying out clonal-space and clone-tracking plots. Covers VDJtools PlotFancyVJUsage/RarefactionPlot, R circlize and iNEXT, and matplotlib/seaborn recipes.
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    Bio Differential Splicing · gptomics bundle

    Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
    1 install
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    Bio Molecular Standardization · gptomics bundle

    Standardizes molecular structures using the ChEMBL structure pipeline for normalization and parent selection plus RDKit rdMolStandardize for explicit custom steps such as tautomer canonicalization, salt/solvent stripping, charge handling, stereochemistry handling, mixture selection, and isotope normalization. Explicitly compares ChEMBL, canSARchem, RDKit, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.
    1 install
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    Bio Clinical Biostatistics Cdisc Data · gptomics bundle

    Reads, validates, and prepares CDISC SDTM and ADaM clinical trial data for analysis. Covers SDTM domain joins (DM, AE, EX, VS, LB, DS), ADaM architecture (ADSL, BDS, OCCDS, ADTTE) with traceability, treatment-emergent AE conventions, baseline derivation, SUPPQUAL/NSV handling, Define-XML 2.1, and Pinnacle 21 / CORE validation. Use when working with clinical trial datasets in CDISC SDTM/ADaM format, preparing analysis-ready data, or validating for regulatory submission.
    1 install
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    Bio Clinical Biostatistics Logistic Regression · gptomics bundle

    Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.
    1 install
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    Bio Clinical Databases Tumor Mutational Burden · gptomics bundle

    Calculates tumor mutational burden from WES/WGS/panel data with Friends of Cancer Research harmonization equations, per-assay calibration (FDA 10/Mb = 7.8 TSO500 = 8.4 OncomineTML), synonymous/indel/germline filtering, hypermutator tiering, blood TMB, and integration with HLA-LOH and neoantigen quality (Luksza 2017 fitness). Use when assessing ICI eligibility under tumor-specific cutoffs (McGrail 2021), comparing tissue vs bTMB, or auditing TMB-H reporting against ESMO 2024 and FDA pembrolizumab pan-tumor 2020.
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    Bio Comparative Genomics Gene Family Evolution · gptomics bundle

    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.
    1 install
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    Bio Data Visualization Matplotlib Fundamentals · gptomics bundle

    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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    Bio Experimental Design Randomization Blocking · gptomics bundle

    Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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    Bio Flow Cytometry Compensation Transformation · gptomics bundle

    Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.
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    Bio Multi Omics Data Harmonization · gptomics bundle

    Harmonizes already-normalized per-omic matrices onto a common footing before joint integration - assembling a MultiAssayExperiment, choosing the per-omic variance-stabilizing transform, deciding per-view versus per-feature scaling, picking a cross-omic batch strategy, and triaging missing data (feature, value, or whole sample; MAR versus MNAR). Covers why a shared-latent integrator is blind to what an omic is so scaling silently decides which block dominates, why batch confounded with biology is irrecoverable and should be modeled as a covariate not scrubbed, and why stacking blocks and running one ComBat erases cross-omic signal. Use when preparing two or more omics for MOFA2, mixOmics, or SNF, deciding a transform or scaling, correcting batch across modalities, or handling missing omics per sample. For deep per-omic normalization see differential-expression, methylation-analysis, proteomics, metabolomics; for the method decision see integration-design; for fusion see mofa-integration, mixomics-analysis.
    1 install
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    Bio Multi Omics Integration Design · gptomics bundle

    Chooses a bulk multi-omics integration strategy before any tool runs by mapping the biological question (subtype discovery, shared axis of variation, predictive signature, pairwise correlation) to a method class, naming the sample correspondence (paired-vertical, horizontal, mosaic, diagonal), enforcing the n<<p discipline that makes a held-out cohort the endpoint instead of in-cohort cross-validation, and running the per-view variance-imbalance diagnostic. Covers the early/mixed/intermediate/late taxonomy, why vertical and horizontal integration are different problems, and why a shared factor dominated by one omic is not integration. Use when deciding which integration method fits a question, whether data is paired or mosaic, supervised or unsupervised, or how to validate an integrated result. For unsupervised factors see mofa-integration; for supervised signatures see mixomics-analysis; for stratification see similarity-network; for single-cell see single-cell/multimodal-integration.
    1 install
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    Bio Multi Omics Similarity Network · gptomics bundle

    Stratifies patients into multi-omics subtypes by building one patient-by-patient similarity network per omic, fusing them with SNF's cross-network diffusion, and spectral-clustering the fused graph - then defending the clusters with stability, survival separation, and replication. Covers why spectral clustering always returns the requested cluster count so a subtype is a claim not a discovery, why the eigengap is a graph property not a biological truth, why fusion is not automatically better than the best single omic, why SNF needs complete data while NEMO handles mosaic cohorts, and the SNFtool API gotchas (dist2 returns squared distance, affinityMatrix width is sigma, spectralClustering K is the cluster count). Use when discovering patient subtypes from multiple omics, choosing a cluster number, validating subtypes, or handling partial multi-omic data. For feature-space factors see mofa-integration; for supervised signatures see mixomics-analysis; for survival see clinical-biostatistics/survival-analysis.
    1 install