GPTomics

@gptomics source repo

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Bio Genome Annotation Prokaryotic Annotation · gptomics bundle
Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotating a newly assembled prokaryotic genome, choosing an annotation tool, re-annotating a collection for pangenomics, or preparing annotations for NCBI/DDBJ submission.

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Bio Genome Engineering Off Target Prediction · gptomics bundle
Nominates and assesses CRISPR off-target sites genome-wide. Enumerates candidate sites by mismatch and bulge tolerance with Cas-OFFinder/CRISPRitz, ranks them with the published CFD score (SpCas9-only, relative ranker) or MIT/CRISTA/energy models, runs variant-aware screening against gnomAD/individual genomes (CRISPRme), and frames the empirical genome-wide discovery assays (GUIDE-seq, CIRCLE-seq, CHANGE-seq, DISCOVER-seq, Digenome-seq) and high-fidelity nuclease choice (HiFi Cas9, Sniper-Cas9, eSpCas9, SpCas9-HF1). Use when assessing guide RNA specificity, choosing among candidate guides, screening a therapeutic guide against population variation, or planning empirical off-target validation. Distinguishes predicted vs detected vs validated. On-target activity scoring and deaminase (Cas-independent) base/prime-editor off-targets are separate skills.

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Bio Imaging Mass Cytometry Cell Segmentation · gptomics bundle
Segment single cells from multiplexed IMC/MIBI tissue images using Mesmer/DeepCell, Cellpose, or ilastik+CellProfiler, covering whole-cell vs nuclear segmentation, the summed-membrane-channel decision, nuclear-expansion bias, lateral spillover, resolution-floor parameters, and downstream-proxy evaluation. Use when delineating cells after preprocessing, choosing a segmentation model, building a cell mask for quantification, diagnosing impossible double-positive populations, or troubleshooting over/under-segmentation.

1 install
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Bio Immunoinformatics Immunogenicity Scoring · gptomics bundle
Rank and prioritize neoantigen/epitope candidates by likely T-cell response using NeoFox feature annotation, PRIME2.0, BigMHC-IM, the Łuksza/Balachandran fitness model (agretopicity + foreignness), and pVACtools tiering. Encodes the field's hard truths that immunogenicity is the least-solved layer (dedicated scores ~AUROC 0.6-0.7, modest PPV), that scores are valid only for RANKING within one patient (never absolute go/no-go or cross-patient), that DAI has anchor-inflation and WT-denominator traps, and that stacking weak correlated scores into one number is a red flag. Use when ordering a candidate list for a vaccine. Binding lives in mhc-binding-prediction; calling in neoantigen-prediction.

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Bio Immunoinformatics Mhc Binding Prediction · gptomics bundle
Predict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes. Covers the binding-affinity (BA) vs eluted-ligand (EL/presentation) distinction, why %Rank beats raw nM for cross-allele work, the MS abundance bias that misranks low-expression neoantigens, allele-coverage inequity, and length bias. Use when scanning a protein or peptide set for class I epitopes, scoring neoantigen candidates, or choosing a binding predictor. For CD4/HLA class II see mhc-class-ii-prediction.

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Bio Long Read Sequencing Long Read Alignment · gptomics bundle
Aligns Oxford Nanopore and PacBio long reads (and assemblies) to a reference with minimap2 using the error-rate-matched preset (map-ont, lr:hq, map-hifi, map-pb, splice/splice:hq, asm5/10/20, ava), producing a sorted/indexed BAM for variant, SV, methylation, or isoform analysis. Covers why the preset rewrites the scoring/chaining model, why SV calling rides on supplementary not secondary alignments, carrying MM/ML methylation tags through with -y, the multi-part-index MAPQ trap, and when to swap in Winnowmap/VACmap/lra/pbmm2. Use when mapping ONT or PacBio reads, choosing a minimap2 preset by platform/chemistry, preparing input for Clair3/medaka/Sniffles/modkit, aligning into repeats/centromeres, or spliced-aligning cDNA/Iso-Seq.

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Bio Long Read Sequencing Structural Variants · gptomics bundle
Detects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM, and assembly-based callers, joint-genotypes cohorts via the Sniffles2 .snf workflow, and benchmarks with Truvari against GIAB. Covers why an SV call is a representation artifact (the tandem-repeat BED, aligner, and Truvari params set precision/recall as much as the caller), the cuteSV per-platform parameter trap, soft-clipped supplementary alignments as the SV substrate, and the somatic/mosaic boundary to Severus/nanomonsv. Use when calling germline or somatic SVs from ONT/HiFi reads, joint-genotyping a cohort, choosing or tuning an SV caller, or benchmarking SV calls.

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Bio Methylation Array Preprocessing · gptomics bundle
Turns raw Illumina Infinium methylation BeadChip IDATs (450K, EPIC, EPICv2) into a defensible beta/M matrix with sesame (openSesame/SigDF) or minfi (RGChannelSet -> MethylSet -> GenomicRatioSet). Covers Type I vs Type II probe chemistry and why raw Type II beta is compressed, the signal-to-beta math (beta = M/(M+U+100)) and M-value logit, detection-p / pOOBAH masking including the out-of-band deletion-artifact catch, dye-bias correction, and the normalization decision (noob, funnorm, quantile, SWAN, BMIQ, dasen, sesame QCDPB). Use when reading IDATs, choosing a normalization for a 450K/EPIC/EPICv2 cohort, deciding beta vs M, masking failed probes, or producing the corrected matrix before testing. For probe/sample filtering, EPICv2 replicate collapse, and sample-identity QC see array-qc-filtering; for native long-read 5mC see long-read-sequencing/nanopore-methylation (a different platform).

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Bio Methylation Calling · gptomics bundle
Extracts per-cytosine methylation calls from aligned bisulfite/EM-seq reads with bismark_methylation_extractor (Bismark BAM) or the aligner-agnostic MethylDackel/BISCUIT (bwa-meth BAM), producing the beta value M/(M+U) as a coverage file, bedGraph, or genome-wide cytosine report across CpG/CHG/CHH context. Covers conversion-rate QC as the first gate, the 5mC vs 5hmC summed caveat, variant-aware calling so a C/T SNP does not masquerade as unmethylation, paired-end --no_overlap double-counting, symmetric CpG dyad collapse, and the 0-based vs 1-based coordinate trap. Use when extracting methylation levels from a bisulfite/EM-seq alignment, choosing an extractor for a non-Bismark BAM, QC-ing conversion efficiency, or producing coverage/cytosine-report input for testing. For long-read MM/ML modification calling see long-read-sequencing/nanopore-methylation; for the upstream BAM see bismark-alignment; for per-CpG statistics see differential-cpg-testing.

3 installs
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Bio Multi Omics Mofa Integration · gptomics bundle
Discovers shared and view-specific latent factors across bulk multi-omics blocks (RNA-seq, proteomics, methylation) on a common sample axis with MOFA2's unsupervised Bayesian group factor model, then attributes per-view variance explained and interprets signed factor weights. Covers why a factor is an unsupervised axis of variance and not a pathway, why a factor that correlates with batch is a batch factor, why the per-view variance-explained table is the primary read-out rather than p-values, why raw counts in a Gaussian view make factor 1 the library-size factor, and why MOFA2 handles missing omics-per-sample natively. Use when integrating two or more bulk omics to find joint axes of variation, choosing factor count, labeling factors against metadata, or running enrichment on factor weights. For supervised discriminant integration see mixomics-analysis; for the method decision see integration-design; for single-cell see single-cell/multimodal-integration; for enrichment see pathway-analysis/gsea.

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Bio Population Genetics Selection Statistics · gptomics bundle
Scans genomes for natural selection with SFS tests (Tajima's D, Fay & Wu H, Zeng E, SweepFinder2 CLR), haplotype tests (iHS, nSL, XP-EHH, Rsb, H12), and differentiation (FST, PBS) using scikit-allel, selscan, and SweepFinder2. No single statistic separates selection from demography at one locus, so the deliverable is empirical genome-wide outliers plus multiple orthogonal signals, not an absolute cutoff. iHS detects incomplete sweeps and collapses to zero at fixation while XP-EHH catches fixed sweeps; iHS/nSL standardize within derived-allele-frequency bins but XP-EHH gets a genome-wide z-score; derived-allele tests need substitution-model polarization; background selection mimics FST and CLR. Use when computing selection statistics like FST, Tajima's D, iHS, or XP-EHH, or scanning for selective sweeps. For phasing inputs see phasing-imputation/haplotype-phasing; for dN/dS see comparative-genomics/positive-selection.

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Bio Restriction Mapping · gptomics bundle
Build restriction maps showing enzyme cut positions and inter-site distances along DNA using Biopython Bio.Restriction. Produces text or graphical maps for linear and circular molecules, orders sites from single and double digests, and overlays GenBank features. Use when creating a restriction map of a sequence, ordering cut sites along a plasmid, or relating sites to annotated features.

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Bio Reverse Complement · gptomics bundle
Generate reverse complements and complements of DNA/RNA sequences using Biopython, including IUPAC ambiguity codes, gapped alignments, and minus-strand features. Use when working with the opposite strand, building reverse primers, normalizing strand orientation before alignment, or extracting a coding sequence from a minus-strand feature.

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Bio Structural Biology Alphafold Predictions · gptomics bundle
Retrieves and interprets AlphaFold Protein Structure Database (AFDB) models by UniProt accession, reading pLDDT and PAE confidence correctly. Use when treating pLDDT as PER-RESIDUE confidence (not global accuracy) and recognizing a long low-pLDDT stretch as an intrinsically disordered region rather than a modeling error; reading PAE to segment confident domains and judge inter-domain/relative-position confidence that high mean pLDDT cannot certify; recognizing a static AFDB model carries NO ligands, ions, cofactors, PTMs, quaternary assembly, or alternative conformations (pLDDT sits in the B-factor column with opposite polarity to thermal motion); and deciding an AFDB entry vs re-running prediction. Keywords AlphaFold DB, pLDDT, PAE, B-factor column, intrinsic disorder, UniProt, Foldseek.

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Bio Structural Biology Structure Preparation · gptomics bundle
Prepares a deposited or predicted structure for docking, molecular dynamics, or electrostatics by adding hydrogens, assigning protonation and tautomer states, and filling missing atoms and short loops with PDBFixer, reduce, PROPKA, and PDB2PQR. Use when adding hydrogens an X-ray model never resolved; assigning His HID/HIE/HIP tautomers, Asn/Gln/His 180-degree flips, and Cys/Lys/Asp/Glu pKa-shifted protonation at a stated pH and microenvironment rather than trusting standard pKa 7; filling missing side-chain atoms and modeling short missing loops as disorder hypotheses; making a receptor docking- or MD-ready and recording what was built; preparing a predicted model after trimming low-pLDDT regions; and writing a PQR for Poisson-Boltzmann electrostatics. Keywords PDBFixer, reduce, PROPKA, PDB2PQR, protonation, tautomer, missing atoms, hydrogens, pKa, docking prep, MD prep.

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Bio Systems Biology Metabolic Reconstruction · gptomics bundle
Builds draft genome-scale metabolic models from an annotated genome using CarveMe (top-down carving of a BiGG universal model) or gapseq (bottom-up pathway-evidence reconstruction), then loads and sanity-checks the draft in COBRApy. Use when creating a model for an organism without one, choosing between CarveMe and gapseq, gap-filling to a target medium, understanding why a draft that grows is still only a hypothesis, handling BiGG-vs-ModelSEED namespace mismatch, or preparing a draft for curation and community modeling.

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Bio Structural Biology Structure Validation · gptomics bundle
Judges whether a macromolecular model (or a region of it) is reliable enough to build on, using resolution, R-free, B-factors, MolProbity geometry, and predicted-model confidence with Bio.PDB. Use when deciding if a structure or a specific region is trustworthy before docking/mechanism/measurement; reading resolution, R-work vs R-free and the R-free-minus-R-work overfitting gap; sanity-checking per-residue and mean B-factors; flagging clashscore, Ramachandran and rotamer outliers and cis non-proline peptides; validating a PREDICTED (AlphaFold/ESMFold) model via pLDDT bands and PAE before docking or molecular replacement; and interpreting cryo-EM global-vs-local resolution (FSC 0.143 half-map vs 0.5 map-model) or an NMR ensemble spread. Keywords validation, resolution, R-free, B-factor, MolProbity, clashscore, Ramachandran, rotamer, pLDDT, PAE, wwPDB, cryo-EM local resolution.

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Bio Systems Biology Context Specific Models · gptomics bundle
Builds tissue-, cell-type-, and condition-specific metabolic models by integrating transcriptomic or proteomic data into a generic genome-scale model, using extraction algorithms (GIMME, iMAT, INIT/tINIT, MADE, E-Flux, CORDA, FASTCORE) via troppo and corda in Python or the COBRA Toolbox/RAVEN in MATLAB. Use when pruning a generic model to a context, choosing an extraction method and expression threshold, mapping expression through GPR rules to reactions, deciding whether an objective is required (GIMME vs iMAT), avoiding the growth-objective trap for non-proliferating tissue, or judging how much of a context-specific model is real signal versus an artifact of the threshold and method.

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Bio Tcr Bcr Analysis Specificity Annotation · gptomics bundle
Maps TCR/BCR receptor sequences toward candidate antigen specificity and clusters repertoires by shared-specificity signal, while enforcing that a database match or a cluster label is a HYPOTHESIS, not a specificity call. Use when deciding among database annotation (VDJdb/McPAS/IEDB+TCRMatch, requiring V-gene and HLA concordance plus a confidence score) versus sequence clustering (tcrdist3 meta-clonotypes, GLIPH2, GIANA, clusTCR, which find enrichment not per-receptor labels) versus generation-probability nulls (OLGA Pgen, IGoR, SONIA Ppost) for testing public/convergent/shared claims; and when guarding against overclaiming specificity, base-rate false positives from bare CDR3 matches, unpaired beta-only annotation, ML predictor failure on unseen epitopes, and ignored MHC restriction. TCR-focused with a BCR/antibody note (SHM, conformational epitopes, IGHV3-53/3-66 public clonotypes). Keywords CDR3, pMHC, HLA restriction, cross-reactivity, meta-clonotype, Pgen, public clonotype, convergent recombination.

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Bio Temporal Genomics Periodicity Detection · gptomics bundle
Discovers a periodic signal of UNKNOWN period in time-series omics data and puts a defensible significance on it, especially when sampling is IRREGULAR (dropped timepoints, pooled harvests) so FFT/Welch/JTK are invalid. Estimates the dominant period with Lomb-Scargle / generalized Lomb-Scargle (scipy, astropy), corroborates with autocorrelation, resolves transient/time-varying periodicity with the wavelet CWT (pywt), and screens genome-wide with false-alarm probabilities under BH FDR. Use when finding an oscillation whose period is not known a priori, analyzing cell-cycle or ultradian rhythms, or handling unevenly sampled time courses. Not for testing a KNOWN 24-hour rhythm (see temporal-genomics/circadian-rhythms).

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Bio Variant Calling Clinical Interpretation · gptomics bundle
Classify variant clinical significance with the ACMG/AMP germline framework and its 2018-2025 ClinGen refinements (graded PVS1 decision tree, PM2 downgraded to Supporting, PP5/BP6 retired, calibrated PP3/BP4, Bayesian points), the AMP/ASCO/CAP somatic tiers and ClinGen oncogenicity system, ClinVar star-rating and gnomAD grpmax filtering-AF interpretation. Use when deciding germline-vs-somatic framework, applying current (not flat-2015) ACMG points, checking for a gene-specific VCEP specification, judging whether a ClinVar assertion or gnomAD frequency is usable evidence, calibrating a pathogenicity predictor, evaluating PVS1 on the MANE Select transcript, or building a VUS reanalysis loop. Not for functional annotation itself (see variant-calling/variant-annotation).

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Bio Workflow Management Snakemake Workflows · gptomics bundle
Authors reproducible bioinformatics pipelines with Snakemake - rules wired by output-file pattern, wildcards and expand() for sample fan-out, checkpoints for runtime-unknown outputs, resource/retry escalation, and conda/container software deployment on HPC and cloud. Use when deciding rule-based (Snakemake) vs channel/dataflow (Nextflow) authoring; wiring rules by OUTPUT-file pattern rather than imperative order; using wildcards + expand() for sample fan-out and constraining them to stop silent mis-routing; adding checkpoints when the set of outputs is unknown until a step runs (dynamic DAG); diagnosing why a job reran (or did not) under the mtime-plus-provenance trigger set; escalating memory on retry for OOM-killed jobs; and porting a Snakemake 7 `--cluster`/remote-provider command to the Snakemake 8+ executor-plugin and storage-plugin model (snakemake-executor-plugin-slurm) with `--software-deployment-method`.

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Bio Clinical Biostatistics Categorical Tests · gptomics bundle
Tests associations between categorical variables in clinical data using chi-square, Fisher's exact, Boschloo, Cochran-Mantel-Haenszel, and modern McNemar variants with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen). Use when analyzing categorical outcomes, paired binary endpoints, or testing treatment-outcome independence in confirmatory or exploratory clinical trials.

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Bio Clinical Biostatistics Subgroup Analysis · gptomics bundle
Performs subgroup and heterogeneous treatment effect (HTE) analyses for clinical trials. Covers Mantel-Haenszel pooling, Breslow-Day, interaction tests in regression, RERI for additive interaction, modern data-adaptive HTE methods (STEPP, SIDES, causal forests, X/R-learners), Bayesian shrinkage (Dixon-Simon, MAP, EXNEX), graphical multiplicity (Bretz-Maurer), and credibility frameworks (Sun BMJ, EMA 2019). Use when analyzing treatment effects across patient subgroups for regulatory submissions or precision-medicine claims.

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Bio Clinical Biostatistics Survival Analysis · gptomics bundle
Performs time-to-event analysis for clinical trials including Cox proportional hazards regression with PH diagnostics, restricted mean survival time (RMST) under non-PH, competing risks via Fine-Gray vs cause-specific Cox, weighted log-rank and MaxCombo for non-proportional hazards, recurrent events (Andersen-Gill, PWP, WLW), and interval-censored data. Use when analyzing time-to-event endpoints (OS, PFS, DOR, TTR, TTNT) in oncology or other clinical trials.

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Bio Differential Expression Batch Correction · gptomics bundle
Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.

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Bio Differential Expression De Visualization · gptomics bundle
Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.

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Bio Ecological Genomics Biodiversity Metrics · gptomics bundle
Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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Bio Ecological Genomics Species Delimitation · gptomics bundle
Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.

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Bio Epidemiological Genomics Pathogen Typing · gptomics bundle
Assigns isolate identity at the right resolution for the question -- ANI/Mash species triage, 7-locus MLST historical comparability, cgMLST/wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR/SeqSero2 Salmonella, SerotypeFinder E. coli, Kaptive Klebsiella, SeroBA pneumococcus, spa+SCCmec S. aureus), and lineage callers (TB-Profiler/Mykrobe barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas/database versions, assigning MTBC lineage with the Napier 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.

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Bio Gene Regulatory Networks Scenic Regulons · gptomics bundle
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.

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Bio Comparative Genomics Ortholog Inference · gptomics bundle
Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.

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Bio Comparative Genomics Pangenome Analysis · gptomics bundle
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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Bio Comparative Genomics Positive Selection · gptomics bundle
Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.

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Bio Copy Number Allele Specific Copy Number · gptomics bundle
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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Bio Copy Number Germline Cnv Interpretation · gptomics bundle
Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.

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Bio Data Visualization Volcano And Ma Plots · gptomics bundle
Build volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.

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Bio Gene Regulatory Networks Multiomics Grn · gptomics bundle
Build enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR, DIRECT-NET, TRIPOD, and scMEGA. Covers the accessibility-defines-enhancers principle, peak-to-gene linking and its cell-composition confound, the paired-vs-unpaired decision, and TF-region-gene eRegulon triplets. Use when analyzing 10x multiome or paired/unpaired scRNA+scATAC to infer cis-regulatory GRNs. For RNA-only regulons see scenic-regulons; for in silico TF perturbation see perturbation-simulation.

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Bio Genome Annotation Functional Annotation · gptomics bundle
Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

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Bio Genome Assembly Contamination Detection · gptomics bundle
Detects and removes contamination in genome assemblies via two disjoint workflows - foreign-sequence screening of a single-organism (eukaryote/isolate) assembly with NCBI FCS-GX (GenBank-submission-mandatory), FCS-adaptor, and BlobToolKit blob plots; and MAG/bin quality assessment with CheckM2 plus GUNC (chimerism) plus GTDB-Tk taxonomy, judged against MIMAG. Covers why CheckM2 alone is blind to disjoint-marker chimeras, the FCS-GX RAM wall, organelle/NUMT triage, strain heterogeneity, and the HGT-vs-contamination (tardigrade) trap. Use when screening an assembly for foreign contamination before GenBank submission, assessing MAG completeness/contamination/chimerism, deciding which contigs to remove, or distinguishing real HGT from contaminant contigs.

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Bio Genome Engineering Prime Editing Design · gptomics bundle
Designs pegRNAs and nicking guides for prime editing (PE) -- choosing the nick/strand, tuning the primer-binding site (PBS) and reverse-transcription template (RTT) as a per-locus panel, selecting the PE system (PE2/PE3/PE3b/PE4/PE5/PEmax/PE7), adding MMR-evading and PAM-disrupting silent edits, appending epegRNA 3' motifs (tevopreQ1/mpknot), and ranking with PRIDICT/DeepPrime. Covers twinPE/PASTE for large insertions and the prime-vs-base-editing decision. Use when designing a scarless point mutation, small insertion/deletion, or any of the 12 base conversions without a double-strand break, when efficiency is low and MMR inhibition or pegRNA stabilization is needed, or when routing a large insertion to an integrase method. Generic guide scoring and base editing are separate skills.

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Bio Imaging Mass Cytometry Spatial Analysis · gptomics bundle
Analyze spatial cell-cell interactions, neighborhoods, and niches in IMC/MIBI data with squidpy and imcRtools, covering neighborhood-enrichment permutation nulls, the abundance-vs-density confound, inhomogeneous Ripley's K, cellular-neighborhood discovery, graph-construction (contact vs proximity), and edge effects. Use when testing whether cell types co-locate, choosing a spatial null, building a neighbor graph, discovering tissue niches, or deciding whether a spatial pattern is real or a density/segmentation artifact.

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Bio Immunoinformatics Neoantigen Prediction · gptomics bundle
Identify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers. Encodes the field's hard truth that binding prediction is the easy, near-solved part and single-digit-percent PPV lives downstream — so it centers clonality/CCF, HLA LOH (the silent invalidator), expression, proximal-variant phasing, agretopicity/foreignness quality, and the predicted->presented->immunogenic validation tiers. Use when nominating vaccine targets, ranking neoantigens, or building a tumor-to-candidate pipeline. Binding details in mhc-binding-prediction; ranking in immunogenicity-scoring.

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Bio Machine Learning Prediction Explanation · gptomics bundle
Explains ML predictions on omics data with SHAP, LIME, and permutation importance, handling the correlated-feature trap, the conditional-vs-interventional Shapley choice, and the attribution-is-not-causation boundary. Use when interpreting an omics classifier, debugging shortcut/batch learning, or deciding whether an attribution ranking can be trusted as biology. For validated feature selection see machine-learning/biomarker-discovery; explanations are not a selection method.

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Bio Rna Quantification Alignment Free Quant · gptomics bundle
Quantify transcript expression from FASTQ with Salmon (selective alignment) or kallisto (pseudoalignment), bypassing genome mapping. Use when quantifying RNA-seq without alignment, deciding whether a decoy-aware index is required, detecting and verifying library strandedness, enabling GC and sequence bias correction, or choosing whether to generate inferential replicates (bootstraps/Gibbs) for transcript-level downstream testing.

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Bio Spatial Transcriptomics Spatial Data Io · gptomics bundle
Loads spatial transcriptomics data from Visium, Visium HD, Xenium, MERFISH/MERSCOPE, CosMx, Slide-seq/Curio, and Stereo-seq into AnnData or SpatialData using spatialdata-io and Squidpy. Use when deciding which platform class is in hand (imaging/in-situ vs sequencing/capture), which reader matches the platform (spatialdata_io.xenium/merscope/cosmx vs squidpy.read.visium/vizgen/nanostring), whether to work from the per-transcript molecule table (the re-segmentable source of truth) or the segmentation-derived per-cell matrix (quality-filtered, inherits all segmentation error), whether a molecule table even exists (spot platforms have none), and how to keep coordinate frames and units (pixel vs micron) registered to histology.

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Bio Spatial Transcriptomics Spatial Domains · gptomics bundle
Identify spatially coherent tissue domains (regions like cortical layers, tumor vs stroma) in Visium, Visium HD, Xenium, MERFISH, Slide-seq, and Stereo-seq data with Squidpy, BANKSY, BayesSpace, STAGATE, and GraphST. Use when distinguishing a domain (a region with many cell types) from a cell type (one cell's identity) and a niche (local cell-type composition); choosing a domain method by tissue geometry (laminar/continuous vs high-resolution imaging vs non-contiguous); tuning the spatial-weight knob (BANKSY lambda, BayesSpace smoothing, SpaGCN histology weight, GNN graph radius) to avoid over-smoothing into blobs or under-smoothing into salt-and-pepper; choosing the number of domains k as a biological decision with k+-1 sensitivity; and reading the Yuan 2024 benchmark with the DLPFC continuous-laminar caveat.

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Bio Structural Biology Structure Navigation · gptomics bundle
Navigate the Bio.PDB SMCRA hierarchy (Structure-Model-Chain-Residue-Atom) safely, surfacing the heterogeneity it hides by default. Use when deciding how to handle altloc/DisorderedAtom conformers before a distance or RMSD, indexing residues insertion-code-safe with the full (hetflag, resseq, icode) tuple, choosing the ATOM/observed vs SEQRES/canonical vs UniProt sequence, selecting the right Model for an NMR ensemble, filtering waters/hetero/metals correctly, and reconciling auth vs label numbering. Keywords SMCRA, altloc, DisorderedAtom, insertion code, SEQRES, PPBuilder, auth_seq_id.

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Bio Ctdna Mutation Detection · gptomics bundle
Detects somatic mutations in circulating tumor DNA, treating low-VAF detection as a signal-versus-noise problem set by error suppression and molecules sampled, not by the choice of caller. Distinguishes de novo CALLING (scanning a panel for unknown variants, bounded by per-locus error and multiple testing) from tumor-informed DETECTION (tracking a pre-specified variant set, where panel integration reaches single-ppm). Covers VarDict and Mutect2 for de novo calling, UMI-aware callers, and a pysam-based known-variant VAF tracker, with matched-WBC subtraction as the mandatory defense against clonal hematopoiesis (the dominant false positive). Use when calling or tracking tumor mutations from plasma cfDNA, setting a VAF threshold, or deciding whether a low-VAF call is tumor versus CHIP.

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Bio Long Read Sequencing Haplotype Phasing · gptomics bundle
Phases small variants, SVs, and methylation from Oxford Nanopore and PacBio long reads (read-backed/physical phasing) with WhatsHap, LongPhase, or HiPhase, and haplotags the BAM (HP/PS tags) for allele-resolved downstream analysis. Covers why phase blocks break at het-sparse gaps (read length x heterozygosity), why phasing the VCF is useless until the BAM is haplotagged, the GT-pipe/PS and read HP/PS tag spec, reporting block N50 with switch error, the diploid-assumption/CNV/haploid-region traps, trio phasing as the gold standard, and the boundary to statistical panel phasing. Use when phasing long-read variants, haplotagging reads for allele-specific methylation/expression or phased SVs, choosing WhatsHap vs LongPhase vs HiPhase, trio phasing, or assessing phasing quality.

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Bio Methylation Bismark Alignment · gptomics bundle
Aligns bisulfite-converted (WGBS, RRBS, PBAT) and enzymatic (EM-seq) short reads to an in-silico C->T/G->A-converted reference with Bismark (Bowtie2 or HISAT2), preparing the genome index, choosing the directional vs non-directional vs PBAT strand flag, deduplicating WGBS/EM-seq (never RRBS), and bounding bisulfite conversion efficiency with unmethylated lambda and methylated pUC19 spike-ins. Covers why the library protocol (not the aligner) decides whether calls are meaningful, why incomplete conversion masquerades as methylation, the 3-letter reduced-complexity mapping bias (50-70% efficiency is normal), and M-bias end-clipping. Use when aligning bisulfite or EM-seq reads, preparing a bisulfite genome, choosing the strand flag, or diagnosing low mapping efficiency. For methylation extraction see methylation-calling; for long-read MM/ML modification calling see long-read-sequencing/nanopore-methylation.

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Bio Phasing Imputation Genotype Imputation · gptomics bundle
Imputes untyped genotypes against a phased reference panel with Beagle, Minimac4, or IMPUTE5 (array data) or from genotype likelihoods with GLIMPSE2, QUILT2, or STITCH (low-coverage WGS), producing per-variant dosages (DS) with a self-estimated quality (Beagle DR2, Minimac R2, IMPUTE INFO). Covers why the honest output is a dosage posterior not a hard call, why GWAS regresses on DS, why the quality metric is an ESTIMATE of r2 from posterior spread (not validation against truth), the DS/GP/HDS fields, the phasing prerequisite, chunking, chrX ploidy, the Michigan/TOPMed servers (the only access to HRC/TOPMed), and low-coverage WGS as the modern array replacement. Use when increasing variant density for GWAS, harmonizing arrays, inferring untyped variants, or imputing low-coverage sequence. Phase first with haplotype-phasing; prepare the panel with reference-panels; filter with imputation-qc; the GWAS test is population-genetics/association-testing; end-to-end orchestration is workflows/gwas-pipeline.

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Bio Restriction Fragment Analysis · gptomics bundle
Predict restriction digest fragment sizes and gel patterns using Biopython Bio.Restriction. Computes fragment lengths and sequences for single and double digests on linear or circular DNA, and interprets them against an agarose gel. Use when predicting the fragments from a digest, planning a diagnostic digest to verify a clone, or matching observed gel bands to an expected pattern.

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Bio Restriction Sites · gptomics bundle
Find restriction enzyme cut sites in DNA sequences using Biopython Bio.Restriction. Searches single enzymes, batches, or commercial enzyme sets and returns cut positions for linear or circular DNA. Use when locating where one or more restriction enzymes cut a sequence, screening a sequence for the presence or absence of a site, or counting how often an enzyme cuts.

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Bio Sequence Slicing · gptomics bundle
Slice, extract, and concatenate biological sequences and annotated records using Biopython. Use when extracting subsequences by position, splicing exons into a transcript, joining sequences, or carrying a sub-region of an annotated record (with quality scores and features) into a new record.

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Bio Spatial Transcriptomics Image Analysis · gptomics bundle
Segments cells/nuclei and extracts image features from imaging spatial transcriptomics (Xenium, MERFISH/MERSCOPE, CosMx) and H&E/IF tissue images using Cellpose, StarDist, Baysor, and Squidpy. Use when choosing a segmentation strategy (DAPI nucleus + expansion vs membrane-stain whole-cell vs transcript-aware Baysor/proseg vs segmentation-free SSAM) given the available stain; judging whether transcript spillover is fabricating false co-expression and short-range cell-cell signal; and deciding whether the derived cell-by-gene matrix is trustworthy before downstream typing, DE, or ligand-receptor analysis.

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Bio Tcr Bcr Analysis Immcantation Analysis · gptomics bundle
Reconstructs B-cell clonal families, quantifies somatic hypermutation and selection, and builds antibody lineage trees with the Immcantation R suite (alakazam, shazam, scoper, dowser, tigger) on AIRR-format BCR data. Use when deriving the clonal-clustering threshold from the distToNearest bimodal valley (never a hardcoded 0.15); choosing hierarchicalClones vs spectralClones (vj vs novj) for SHM-diverged repertoires; personalizing the germline with TIGGER before mutation counting; reconstructing D-masked germlines with createGermlines; measuring R/S mutation frequency by CDR and FWR region; testing antigen-driven selection with BASELINe; comparing Hill-number diversity at equal sampling depth; and inferring IgPhyML lineage trees for affinity maturation, class-switch, and ancestral-antibody analysis.

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Bio Workflow Management Nextflow Pipelines · gptomics bundle
Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.

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Bio Causal Genomics Colocalization Analysis · gptomics bundle
Test whether two or more traits share a causal variant at a locus using Bayesian colocalization (coloc.abf, coloc.susie, HyPrColoc, moloc, eCAVIAR, SMR/HEIDI, PWCoCo, SharePro). Use when integrating GWAS with eQTL/sQTL/pQTL/mQTL, distinguishing shared causal variants from LD-driven coincidence, handling allelic heterogeneity, choosing between single-causal vs multi-causal methods, picking PP.H4 thresholds, running sensitivity over p12, or harmonising summary statistics for colocalization.

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Bio Causal Genomics Mendelian Randomization · gptomics bundle
Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.

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Bio Causal Genomics Proteome Mr Drug Target · gptomics bundle
Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.

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Bio Pharmacophore Modeling · gptomics bundle
Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al. 2023), Pharmer / Pharmit for search, and PharmacoForge for protein-pocket-conditioned pharmacophore generation (Flynn et al. 2025), covering ligand-based pharmacophores from active-set alignment and receptor-based pharmacophores from binding-pocket geometry. Explicitly handles feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.

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Bio Clinical Biostatistics Adaptive Designs · gptomics bundle
Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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Bio Workflows Metabolic Modeling Pipeline · gptomics bundle
Orchestrates genome-scale metabolic modeling from a protein FASTA to flux predictions, chaining CarveMe/gapseq reconstruction, memote QC, gap-filling, media-constrained FBA/FVA, gene essentiality, and context-specific models. Use when committing the reconstruction tool (which locks the identifier NAMESPACE forever - BiGG vs ModelSEED vs KEGG, no automatic translation), setting the medium BEFORE FBA (the exchange bounds ARE the medium; essentiality and gap-fill are computed relative to it), curating iteratively (stoichiometric-consistency first, then mass/charge, then directionality, then GPR) with energy-generating-cycle removal, and reading a MEMOTE score as well-formedness NOT correctness. Hands mechanism to the systems-biology component skills; not a re-teach of any single step.

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Bio Isoform Switching · gptomics bundle
Analyzes differential transcript usage (DTU) and isoform switches with functional consequence prediction (NMD via 50nt rule, ORF disruption, protein domain loss/gain, signal peptide changes, IDR alterations, coding-potential shifts). Tools include IsoformSwitchAnalyzeR v2 (auto-selects satuRn for >5 reps else DEXSeq), the manual DRIMSeq -> DEXSeq/satuRn -> stageR DTU pipeline, and fishpond/swish for inferential-uncertainty-aware DTE. Distinguishes DTU from DGE and DTE; integrates external annotators (CPC2, Pfam, SignalP, IUPred2A or DeepTMHMM). Use when investigating how splicing differences alter protein function or trigger NMD-mediated degradation.

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Bio Atac Seq Allele Specific Accessibility · gptomics bundle
Detect allele-specific chromatin accessibility from ATAC-seq using WASP, GATK ASEReadCounter, or RASQUAL. Use when mapping cis-regulatory genetic variants from heterozygous SNPs, separating cis from trans regulation, building chromatin QTL (caQTL) maps, validating GWAS variant function with allelic imbalance, or detecting reference allele mapping bias before downstream analysis.

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Bio Molecular Descriptors · gptomics bundle
Calculates molecular fingerprints (ECFP/Morgan, FCFP, MACCS, RDKit, AtomPair, TopologicalTorsion, Avalon, MAP4, MHFP6) and physicochemical descriptors (Lipinski, QED, TPSA, Crippen LogP, 3D shape) with explicit choice tables, bit vs count semantics, and partial-charge model selection. Use when featurizing molecules for similarity, QSAR, virtual screening, or ML, or selecting the correct fingerprint for a chemotype-aware task.

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Bio Clinical Biostatistics Bayesian Trials · gptomics bundle
Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.

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Bio Clinical Biostatistics Effect Measures · gptomics bundle
Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.

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Bio Clinical Biostatistics Trial Reporting · gptomics bundle
Prepares statistical reports for clinical trials following CONSORT 2025, SPIRIT 2025, ICH E9(R1) estimands, and FDA 2023 covariate adjustment guidance. Covers Table 1 generation, analysis populations (ITT/FAS/PP/Safety), the 5 ICH E9(R1) intercurrent-event strategies, MMRM under MAR (mmrm), reference-based MI (rbmi J2R/CR/CIR), Permutt tipping-point sensitivity, and Rubin's-rules vs frequentist variance debate. Use when preparing regulatory submissions, defining estimands, or implementing missing-data sensitivity analyses.

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Bio Clinical Databases Acmg Classification · gptomics bundle
Applies ACMG/AMP 2015 framework with ClinGen SVI specifications, Tavtigian 2018/2020 Bayesian point system, Abou Tayoun 2018 PVS1 decision tree, Pejaver 2022 and Bergquist 2025 calibrated PP3/BP4 thresholds for REVEL/BayesDel/AlphaMissense, Brnich 2020 PS3/BS3 OddsPath, Walker 2023 SpliceAI splicing framework, and AMP/ASCO/CAP 2017 tumor tiers. Use when classifying germline variants P / LP / VUS / LB / B, applying VCEP-specific CSpec rules, computing Whiffin BS1, or assigning cancer Tier I-IV per Li 2017.

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Bio Data Visualization Forest Funnel Plots · gptomics bundle
Build forest plots (HR, OR, RR, beta-coefficient summaries with CIs) and funnel plots (meta-analysis publication-bias diagnostics) using forestplot, metafor, ggforest, and MendelianRandomization with proper axis-scaling, summary-diamond placement, subgroup nesting, and Egger / trim-and-fill asymmetry tests. Use when summarizing effects across subgroups, trials, or instruments — meta-analysis, Mendelian randomization, subgroup HRs.

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Bio Ecological Genomics Edna Metabarcoding · gptomics bundle
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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Bio Ecological Genomics Landscape Genomics · gptomics bundle
Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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Bio Epidemiological Genomics Phylodynamics · gptomics bundle
Estimates time-scaled phylogenies, molecular-clock rates, effective reproduction number R_e, and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict/relaxed clocks; coalescent, Bayesian-Skyline, Skygrid, Birth-Death-Skyline, and sampled-ancestor priors; structured coalescent via MASCOT). Covers root-to-tip clock QC via TempEst, date-randomisation tests, recombination masking via Gubbins/ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction, multi-chain convergence diagnostics, and reconciling phylodynamic R_e with case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline models, diagnosing temporal-signal failure, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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Bio Epitranscriptomics Merip Preprocessing · gptomics bundle
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.

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Bio Gene Regulatory Networks Grn Inference · gptomics bundle
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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Bio Genome Engineering Base Editing Design · gptomics bundle
Designs cytosine (CBE, C-to-T) and adenine (ABE, A-to-G) base-editor guides by positioning the target base at the activity-peak of the editing window (protospacer positions ~5-7, PAM-distal numbering), minimizing bystander edits for product purity, reading dinucleotide context (APOBEC1 TC favored / GC disfavored), and selecting the editor variant (BE4max, ABEmax, ABE8e, YE1/SECURE, TadCBE, CGBE, SpG/SpRY-BE). Covers knockout by premature stop (CRISPR-STOP/iSTOP) and splice-site disruption, the three off-target classes (Cas-dependent, Cas-independent DNA, RNA), outcome prediction (BE-Hive/DeepBE), and the base-vs-prime-vs-HDR decision. Use when installing a transition mutation without a double-strand break, knocking out a gene without indels, or choosing CBE vs ABE. Generic guide scoring, prime editing, and HDR donors are separate skills.

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Bio Genome Engineering Hdr Template Design · gptomics bundle
Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed) mutation so the edited allele is not re-cut. Frames the HDR-vs-NHEJ-vs-MMEJ pathway competition, the MMEJ (PITCh) and homology-independent (HITI/HMEJ) alternatives for post-mitotic cells, ssODN strand/asymmetry choice, phosphorothioate end-protection, and ranked HDR enhancers. Use when designing a donor for a point mutation, epitope/fluorophore tag, allele replacement, or knock-in, or when HDR efficiency is low. Guide design and base/prime editing are separate skills.

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Bio Imaging Mass Cytometry Quality Metrics · gptomics bundle
Quality control for IMC/MIBI data across pixel, channel, image, slide, and batch levels, covering Poisson-count SNR (cell-level Gaussian-mixture and empty-channel comparison), spillover-matrix QC (the three physical sources), drift and the missing EQ-bead analog, acquisition artifacts, and sample-of-origin batch effects. Use when deciding whether to keep or drop a channel, ROI, or slide, distinguishing a dim antibody from a failed one, reading a spillover matrix, or diagnosing batch-driven clustering before analysis.

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Bio Flow Cytometry Clustering Phenotyping · gptomics bundle
Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization. Covers the type-vs-state marker distinction (cluster on lineage, test state within clusters), over-provision-then-metacluster, the Weber-Robinson benchmark, seed dependence and metacluster stability, why embeddings are for looking not measuring, and median-heatmap annotation/merging. Use when discovering populations without predefined gates, choosing a clustering algorithm, selecting the number of metaclusters, or annotating clusters into cell types.

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Bio Genome Annotation Annotation Transfer · gptomics bundle
Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-vs-de-novo validation. Use when annotating a new assembly of a species with an existing reference, harmonizing coordinates across builds, or mapping annotations across related species.

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Bio Genome Intervals Overlap Significance · gptomics bundle
Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.

1 install
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Bio Genome Intervals Proximity Operations · gptomics bundle
Performs proximity operations on genomic intervals with bedtools (closest, window, flank, slop) and pybedtools - nearest-feature queries with signed/strand-aware distance, fixed-radius window searches, strand-aware promoter construction, and interval extension. Covers the closest -d/-D a/b/ref/-t/-k/-io/-iu/-id flags, the -D ref strand sign-flip, silent chromosome-end clipping in slop/flank, -t all tie double-counting, and the critical distinction between a geometry answer (nearest TSS) and a biology answer (which gene an element regulates). Use when assigning peaks or variants to genes, defining promoters from a gene model, building distance-to-TSS distributions, finding features within a window, or extending intervals - and when deciding whether nearest-gene is a fair prior (GWAS locus) or a trap (distal enhancer).

1 install
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Bio Immunoinformatics Tcr Epitope Binding · gptomics bundle
Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

3 installs
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Bio Longitudinal Monitoring · gptomics bundle
Tracks ctDNA across serial liquid-biopsy timepoints for molecular residual disease (MRD) and treatment-response monitoring, treating MRD as a binary integrated detection call across the patient's full variant set (with a defined LoD95 and per-sample specificity) rather than a per-timepoint VAF threshold, and handling undetectable samples as left-censored at the per-sample limit of detection rather than true zeros. Covers tumor-informed bespoke vs tumor-naive design, landmark vs surveillance sampling, molecular-response definitions and their non-standardization, censoring-aware clearance kinetics, and the multiple-testing structure of repeated surveillance. Use when monitoring ctDNA during therapy, calling molecular relapse before imaging, or estimating clearance half-life from serial samples.

1 install
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Bio Long Read Sequencing Medaka Polishing · gptomics bundle
Polishes Oxford Nanopore draft assemblies to higher consensus accuracy with medaka, a basecaller-model-specific neural consensus net, produces haploid variant calls (VCF) for microbial, mitochondrial, or viral samples, and generates amplicon/viral consensus sequences. Covers the model-matching footgun that silently degrades output, why Racon-first is obsolete and medaka runs directly on Flye output as a single pass, why HiFi must never be fed to medaka, the v1->v2 subcommand renames, and the precise medaka_variant deprecation. Use when polishing an ONT-only assembly, generating an amplicon/viral consensus, calling a haploid ONT consensus, or deciding whether medaka, dorado polish, or Clair3 is the right tool.

1 install
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Bio Metagenomics Visualization · gptomics bundle
Turns a shotgun profiler table (MetaPhlAn relative abundance, Bracken counts, HUMAnN function tables) into honest figures and defensible community statistics with phyloseq, vegan, microViz, and Python. Covers why an ordination/bar/diversity number is a modeling choice that can manufacture a result, the MetaPhlAn-percent-vs-Bracken-counts fork that decides everything, CLR/Aitchison vs Bray-Curtis, Hill numbers and why shotgun richness is a database readout, pairing PERMANOVA with betadisper, and the multi-tool differential-abundance consensus. Use when plotting taxonomic/functional profiles, computing alpha/beta diversity, running ordination/PERMANOVA, or testing differential abundance. For amplicon/QIIME2 stats see the microbiome category; for compositional theory see abundance-estimation.

1 install
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Bio Restriction Enzyme Selection · gptomics bundle
Select restriction enzymes for cloning or diagnostics using Biopython Bio.Restriction. Finds enzymes by cut frequency, overhang type, recognition-site length, commercial availability, compatible ends, and methylation sensitivity, and identifies isoschizomers and compatible pairs. Use when choosing which enzymes to use to linearize a vector, drop in an insert, set up a diagnostic digest, or pick a methylation-insensitive enzyme.

1 install
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Bio Structural Biology Geometric Analysis · gptomics bundle
Measures geometric properties of protein structures with Biopython Bio.PDB - interatomic distances, distance matrices, bond and dihedral angles (phi/psi/chi, Ramachandran), superposition and RMSD, center of mass, radius of gyration, and solvent accessible surface area (SASA). Use when deciding that RMSD depends on BOTH the superposition and the atom selection (a global all-atom RMSD is dominated by flexible loops and hinge motion and is NOT a cross-protein similarity metric); choosing the metric that matches the question (RMSD for same-molecule displacement, TM-score for same-fold, lDDT for superposition-free local model quality - the quantity pLDDT predicts); recognizing Superimposer needs an equal-length ordered atom-to-atom correspondence; and reporting SASA only alongside its probe radius (1.4A water, Shrake-Rupley) with a preference for relative SASA. Keywords RMSD, TM-score, lDDT, SASA, Shrake-Rupley, superposition, Kabsch, dihedral, Ramachandran, radius of gyration.

1 install
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Bio Structural Biology Interface Analysis · gptomics bundle
Maps protein-protein and protein-ligand interfaces with Bio.PDB, computing contact residues and buried surface area (BSA). Use when choosing a contact cutoff and stating its rationale (heavy-atom 4-5A vs CA-CA 8A vs a SASA-based definition); deciding a contact list is not an interface and computing buried surface area (dSASA/BSA) instead; distinguishing a genuine biological interface from a crystal-packing artifact; identifying ligand-contact or epitope residues; and computing on the biological assembly rather than the asymmetric unit. Keywords interface, buried surface area, BSA, contacts, NeighborSearch, PISA, crystal packing, epitope, binding site, ShrakeRupley.

1 install
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Bio Systems Biology Flux Balance Analysis · gptomics bundle
Performs flux balance analysis (FBA), flux variability analysis (FVA), parsimonious FBA (pFBA), loopless FBA, flux sampling, and production envelopes on genome-scale metabolic models with COBRApy, solving the biomass-maximization linear program under a defined medium. Use when predicting growth rate on a carbon source, computing flux ranges and alternative optima (FVA), setting exchange bounds and minimal media, distinguishing a real growth phenotype from an under-constrained model, sampling the flux solution space, or choosing between FBA, pFBA, loopless FBA, and sampling for a flux distribution.

1 install
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Bio Temporal Genomics Temporal Clustering · gptomics bundle
Clusters temporally variable genes by expression-profile SHAPE (not significance) using Mfuzz fuzzy c-means, TCseq, DEGreport degPatterns, and tslearn DTW/soft-DTW. Use when grouping pre-selected time-course genes into shared trajectory programs (co-expression modules), choosing between soft vs hard clustering, picking k, selecting a distance metric (Euclidean/correlation/DTW), or interpreting clusters with per-cluster enrichment. Requires temporally variable genes selected FIRST (differential-expression/timeseries-de or a variance filter); clustering is descriptive and downstream of selection, never a test of which genes are dynamic.

1 install
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Bio Variant Normalization · gptomics bundle
Left-align and trim indels to parsimonious canonical form, decompose MNPs (atomize), and split multiallelic variants with bcftools norm. Use when comparing variants across callers or cohorts, preparing a VCF for database annotation or ClinVar/dbSNP matching, merging VCFs, reconciling vt-vs-bcftools representation discordance, or resolving the VCF-left-align vs HGVS-3'-rule clash.

1 install
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Bio Workflow Management Nf Core Pipelines · gptomics bundle
Runs and configures curated nf-core community Nextflow pipelines (rnaseq, sarek, atacseq, methylseq, ampliseq, taxprofiler, fetchngs) reproducibly, pinning the pipeline revision with -r and selecting a container engine and institutional config via -profile. Use when deciding to adopt a community pipeline versus author one from scratch; picking a pipeline and pinning its -r revision; selecting -profile test/docker/singularity/conda plus an institutional config from nf-core/configs; building and validating a samplesheet CSV against the pipeline schema (nf-schema); choosing --genome/iGenomes versus custom references; configuring resources and max_memory for SLURM/AWS Batch; using -resume and -stub; and reading MultiQC outputs.

1 install
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Bio Gatk Variant Calling · gptomics bundle
Call germline SNPs and indels with GATK HaplotypeCaller and the GVCF joint-genotyping workflow. Covers the local-reassembly + PairHMM mechanism (why HC beats pileup callers on indels), the -ERC GVCF reference-confidence model and <NON_REF> allele, BQSR-vs-DRAGSTR and --dragen-mode error modeling, allele-specific (AS_) annotations, and edge cases (ploidy, Mutect2 mitochondria mode, sex chromosomes/PAR, contamination gating). Use when deciding whether to use HaplotypeCaller vs a pileup or DRAGEN caller, whether BQSR still earns its place, whether to call per-sample GVCFs for a cohort, or how to handle non-diploid, mitochondrial, sex-chromosome, or contaminated samples. Not for post-calling filtering depth (see variant-calling/filtering-best-practices) or cohort joint-genotyping scaling (see variant-calling/joint-calling).

1 install
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Bio Workflows Genome Annotation Pipeline · gptomics bundle
Orchestrates genome annotation from assembled contigs to functional annotation, forking prokaryotic (Bakta one-step, genetic-code table from GTDB-Tk) vs eukaryotic (RepeatMask -> BRAKER3 -> functional -> ncRNA), then eggNOG/InterProScan functional assignment and Infernal/tRNAscan ncRNA. Use when committing the pro-vs-eukaryotic path and the genetic-code table from taxonomy (never guessing), annotating ONLY a decontaminated QC-passed assembly (CheckM2 before prokaryotic annotation is non-negotiable), committing the evidence set (RNA-seq + protein drives BRAKER3 training), soft-masking with a curated repeat library before gene prediction, or pinning the tool + DB version for any pangenome comparison. Hands mechanism to the genome-annotation component skills; not a re-teach of any single step.

1 install
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Bio Conformer Generation · gptomics bundle
Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.

1 install
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Bio Reaction Enumeration · gptomics bundle
Enumerates virtual chemical libraries via reaction SMARTS transformations using RDKit and reaction templates, with explicit handling of atom mapping, RDChiral template extraction, product validation, RECAP/BRICS fragmentation, R-group decomposition, matched molecular pair analysis (MMPA), and Free-Wilson analysis. Use when generating combinatorial libraries from building blocks, enumerating analog series, deriving structure-activity rules, or extracting transformations from reaction data.

1 install
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Bio Similarity Searching · gptomics bundle
Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, MHFP6 LSH forest, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.

1 install

529 published skills · page 1 of 6