Python agent skills, page 70

Process many sequence files in batch (count, merge, split, convert, summarize) with memory-safe streaming and on-disk indexing using Biopython, pysam, or pyfastx.

GPTomics/bioSkills

Analyze codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences.

GPTomics/bioSkills

Read, write, and index compressed sequence files (gzip, bzip2, xz, BGZF) with Biopython and bgzip/samtools.

GPTomics/bioSkills

Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al.

GPTomics/bioSkills

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding.

GPTomics/bioSkills

Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence…

GPTomics/bioSkills

Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces.

GPTomics/bioSkills

Extracts cfDNA fragmentomics features (DELFI genome-wide short/long ratios, WPS nucleosome positioning, Griffin GC-corrected accessibility profiles, end-motifs/MDS, OCF) for cancer detection and…

GPTomics/bioSkills

Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA.

GPTomics/bioSkills

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.

GPTomics/bioSkills

Simulate transcription factor perturbation effects on cell state in silico with CellOracle and Dynamo, and predict transcriptional responses to genetic perturbations with GEARS, scGen, and CPA.

GPTomics/bioSkills

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.

GPTomics/bioSkills

Handles BED-format genomic intervals (BED3 through BED12, narrowPeak/broadPeak) and the coordinate-system substrate the whole interval category rests on, with bedtools (CLI) and…

GPTomics/bioSkills

Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig…

GPTomics/bioSkills

Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with…

GPTomics/bioSkills

Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk.

GPTomics/bioSkills

Predict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes.

GPTomics/bioSkills

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…

GPTomics/bioSkills

Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering.

GPTomics/bioSkills

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…

GPTomics/bioSkills

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…

GPTomics/bioSkills

Read, write, and convert phylogenetic tree files with Biopython Bio.Phylo, and choose an annotation-preserving parser (treeio, DendroPy) when metadata matters.

GPTomics/bioSkills

Edit phylogenetic tree structure with Biopython Bio.Phylo, and treat rooting as a separate statistical inference rather than a display choice.

GPTomics/bioSkills

Draw and export phylogenetic trees with Bio.Phylo plus matplotlib, and route rich figures to ggtree, ETE4, or iTOL.

GPTomics/bioSkills

Computes linkage disequilibrium (r2, D', composite Rogers-Huff r2), prunes correlated variants, clumps GWAS summary statistics to lead SNPs, and defines haplotype blocks with PLINK 1.9/2.0 and…

GPTomics/bioSkills

In-memory Python population genetics with scikit-allel - GenotypeArray/HaplotypeArray/AlleleCountsArray, diversity (pi, theta, Tajima's D), SFS, FST (Weir-Cockerham, Hudson, Patterson), f3/D…

GPTomics/bioSkills

Loads mass-spectrometry data into Python/R and strips the search engine's bookkeeping before any number is trusted -- removes decoys (REV/Reverse), contaminants (CON/Potential contaminant)…

GPTomics/bioSkills

Groups proteins from peptide identifications and controls protein-level FDR, framing inference as a chosen explanation (parsimony or a probability model) of underdetermined peptide evidence rather…

GPTomics/bioSkills

Builds and manages DIA spectral libraries as peptide query parameters (precursor m/z, a few fragment m/z plus relative intensities, normalized RT, optional CCS), covering experimental DDA…

GPTomics/bioSkills

Builds reproducible Quarto reports, presentations, and websites across R, Python, and Julia, with correct engine selection, cache-vs-freeze semantics, native cross-references, parameters, and…

GPTomics/bioSkills

Calculate nucleotide and protein sequence properties (GC content, GC skew, molecular weight, melting temperature, isoelectric point, instability, hydropathy) with Biopython.

GPTomics/bioSkills

Slice, extract, and concatenate biological sequences and annotated records using Biopython.

GPTomics/bioSkills

Automated reference-based cell type annotation for single-cell RNA-seq using CellTypist, SingleR, Azimuth, scANVI, and scmap to transfer labels from a reference.

GPTomics/bioSkills

Dimensionality reduction and graph-based clustering for single-cell RNA-seq with Scanpy (Python) and Seurat (R).

GPTomics/bioSkills

Read, write, create, and convert single-cell objects across AnnData (Python), Seurat (R), and SingleCellExperiment (R).

GPTomics/bioSkills

Detect and remove doublets (two or more cells in one droplet) from single-cell RNA-seq using scDblFinder (R), Scrublet (Python), and DoubletFinder (R).

GPTomics/bioSkills

Detect cluster marker genes and assign manual cell type labels in single-cell RNA-seq using Scanpy (Python) and Seurat (R).

GPTomics/bioSkills

Quality control, ambient-RNA handling, normalization, and feature selection for single-cell RNA-seq using Scanpy (Python) and Seurat (R).

GPTomics/bioSkills

Analyze single-cell ATAC-seq with Signac/ArchR (R) and SnapATAC2 (Python alternative).

GPTomics/bioSkills

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.

GPTomics/bioSkills

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…

GPTomics/bioSkills

Maps protein-protein and protein-ligand interfaces with Bio.PDB, computing contact residues and buried surface area (BSA).

GPTomics/bioSkills

Builds and simulates multi-species metabolic community models from member genome-scale models, using MICOM for abundance-weighted steady-state community FBA and cooperative tradeoff, SMETANA for…

GPTomics/bioSkills

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…

GPTomics/bioSkills

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…

GPTomics/bioSkills

Performs in-silico single and double gene deletions, condition-dependent essentiality, and synthetic-lethality screens on genome-scale metabolic models with COBRApy, evaluating gene-protein-reaction…

GPTomics/bioSkills

Validates, gap-fills, and standardizes genome-scale metabolic models using memote for consistency and annotation scoring and COBRApy for manual curation, including mass/charge balance…

GPTomics/bioSkills

Computes metabolic-engineering strain designs on genome-scale models with StrainDesign (OptKnock, RobustKnock, minimal cut sets, OptCouple) and cameo (heuristic knockout and FSEOF…

GPTomics/bioSkills