Research & Science · 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

Calculate assembly and sequence statistics (N50/L50, auN, NG50/NGA50, length distribution, GC content with ambiguity handling, summary reports) using Biopython.

GPTomics/bioSkills

Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures…

GPTomics/bioSkills

Integrate multiple scRNA-seq samples or batches with Harmony, scVI/scANVI, Seurat (CCA/RPCA), fastMNN, Scanorama, or BBKNN.

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

Infers ligand-receptor cell-cell communication from scRNA-seq with a consensus-first workflow (LIANA), plus CellPhoneDB specificity tests, CellChat pathway probabilities, and NicheNet downstream…

GPTomics/bioSkills

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

GPTomics/bioSkills

Infer large-scale copy-number alterations from tumor single-cell or single-nucleus RNA-seq to separate malignant from normal cells and call subclones, using inferCNV, copyKAT, Numbat, and SCEVAN.

GPTomics/bioSkills

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

GPTomics/bioSkills

Test whether cell-type proportions or composition changed between conditions in single-cell data using Milo (miloR), scCODA, sccomp, and propeller.

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

Assign cells to their sample of origin from cell or nucleus hashing (CITE-seq HTOs, MULTI-seq lipid/cholesterol tags, CellPlex CMOs) and call cross-sample doublets using Seurat…

GPTomics/bioSkills

Reconstructs single-cell lineage trees and clonal relationships from CRISPR/Cas9 scars, static expressed barcodes (LARRY/CellTag), or somatic mtDNA mutations using Cassiopeia, Startle, and CoSpar.

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

Infers metabolite-mediated cell-cell communication from scRNA-seq by scoring enzyme-to-sensor pairs (MEBOCOST), with metabolic flux (scFEA), FBA state (Compass), and neurotransmitter (NeuronChat)…

GPTomics/bioSkills

Integrate multimodal single-cell data (CITE-seq RNA+protein, 10x Multiome RNA+ATAC, unpaired/diagonal RNA+ATAC) and choose the right joint method.

GPTomics/bioSkills

Analyze Perturb-seq / CROP-seq single-cell CRISPR screens. An agent skill from GPTomics/bioSkills.

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

Infers developmental trajectories, pseudotime, RNA velocity, and directed fate probabilities from single-cell data using PAGA, Slingshot, Monocle3, DPT, Palantir, scVelo, and CellRank 2.

GPTomics/bioSkills

Tests miRNAs for differential expression with DESeq2 or edgeR using small-RNA-aware normalization and filtering.

GPTomics/bioSkills

Discovers novel miRNAs and quantifies known miRNAs with miRDeep2 by scoring genome-mapped read stacks against the Dicer/Drosha biogenesis signature.

GPTomics/bioSkills

Quantifies known miRNAs, isomiRs, tRFs, and A-to-I editing fast with miRge3.0 by aligning collapsed reads to curated miRBase or MirGeneDB libraries.

GPTomics/bioSkills

Trims kit-specific 3' adapters, strips UMIs or 4N degenerate ends, size-selects, and collapses small RNA-seq reads (miRNA, piRNA, tRF) with cutadapt or fastp.

GPTomics/bioSkills

Predicts and prioritizes miRNA target genes with seed-based tools (miRanda, TargetScan, miRDB) and experimentally validated databases (miRTarBase, multiMiR).

GPTomics/bioSkills

Profiles non-miRNA small RNAs - tRNA-derived fragments (tRFs/tsRNAs), piRNAs, and rRNA/snoRNA-derived species - with MINTmap, unitas, SPORTS, and proTRAC.

GPTomics/bioSkills

Reconstructs single cells from sub-cellular spatial capture units (Visium HD 2um bins, Stereo-seq DNB spots, Slide-seqV2 beads) by aggregating bins UP into cells rather than deconvolving a mixture…

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

Maps cell-cell communication and ligand-receptor co-expression in spatial transcriptomics (Visium, Xenium, MERFISH, CosMx, Slide-seq) with Squidpy ligrec, COMMOT, stLearn, CellChat-spatial, and…

GPTomics/bioSkills

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.

GPTomics/bioSkills

Estimates per-spot cell type composition of spatial transcriptomics mixtures (Visium, Slide-seq, Stereo-seq) from an scRNA-seq reference with cell2location, RCTD, SPOTlight, stereoscope…

GPTomics/bioSkills

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…

GPTomics/bioSkills

Integrates spatial RNA with a second modality (protein, ATAC, or histone marks) on spatial CITE-seq, DBiT-seq, spatial-ATAC, or Visium CytAssist data.

GPTomics/bioSkills

Build the spatial neighbor graph that every downstream spatial statistic (Moran's I, neighborhood enrichment, co-occurrence, spatial domains) inherits, using Squidpy.

GPTomics/bioSkills

Quality control, filtering, and normalization for spatial transcriptomics (Visium, Visium HD, Xenium, MERFISH/MERSCOPE, CosMx, Slide-seq) with Squidpy and Scanpy.

GPTomics/bioSkills

Analyzes multiplexed antibody-imaging data (CODEX/PhenoCycler, MIBI-TOF, IMC, CyCIF, Opal/Vectra mIF) as continuous protein intensity rather than transcript counts, using scimap and squidpy.

GPTomics/bioSkills

Detects spatially variable genes, spatial autocorrelation, and cell-type colocalization for spatial transcriptomics using Squidpy with PySAL/esda for local statistics.

GPTomics/bioSkills

Plots spatial transcriptomics expression, clusters, and annotations on tissue using Squidpy and Scanpy.

GPTomics/bioSkills

Retrieves and interprets AlphaFold Protein Structure Database (AFDB) models by UniProt accession, reading pLDDT and PAE confidence correctly.

GPTomics/bioSkills

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.

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

Predicts protein and complex structures with deep-learning models (ESMFold, AlphaFold2/ColabFold, AlphaFold3, Chai-1, Boltz-1/2) and reconciles them with confidence metrics.

GPTomics/bioSkills

Reads, writes, downloads, and converts macromolecular structures with Biopython Bio.PDB.

GPTomics/bioSkills

Modifies protein structures in place with Biopython Bio.PDB - transforms coordinates, strips waters/heteroatoms, overloads the B-factor column, renumbers, and builds entities.

GPTomics/bioSkills

Navigate the Bio.PDB SMCRA hierarchy (Structure-Model-Chain-Residue-Atom) safely, surfacing the heterogeneity it hides by default.

GPTomics/bioSkills

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…

GPTomics/bioSkills