Browse Skills — Page 46

bio-single-cell-scatac-analysis

GPTomics/bioSkills

Single-cell ATAC-seq analysis with Signac (R/Seurat) and ArchR. Process 10X Genomics scATAC data, perform QC, dimensionality reduction, clustering, peak calling, and motif activity scoring with chromVAR. Use when analyzing single-cell ATAC-seq data.

bio-single-cell-splicing

GPTomics/bioSkills

Analyzes alternative splicing at single-cell resolution. The first decision is library chemistry — 10X 3' is fundamentally limited (RT primes from poly-A, R2 falls in 3' UTR, <0.1 junction read per cell per AS event). Plate-based full-length methods (Smart-seq3, FLASH-seq, VASA-seq, STORM-seq) and single-cell long-read (MAS-Iso-seq, scISOr-Seq2) are the chemistries that give per-cell isoform structure. Tools include MARVEL (R, Smart-seq integrated), BRIE2 (Bayesian PSI with regulatory features and ELBO_gain test), scQuint (junction-cluster, plate-based; not for 10X), SpliZ (annotation-free Z-score), Psix (graph-smoothness regulated AS), and Sierra (alternative polyadenylation, often confused with AS). Use when analyzing isoform usage in scRNA-seq, identifying cell-type-specific splicing, or determining whether scRNA-seq chemistry supports splicing analysis at all.

bio-single-cell-trajectory-inference

GPTomics/bioSkills

Infer developmental trajectories and pseudotime from single-cell RNA-seq data using Monocle3, Slingshot, and scVelo for RNA velocity analysis. Use when inferring developmental trajectories or pseudotime.

bio-small-rna-seq-differential-mirna

GPTomics/bioSkills

Perform differential expression analysis of miRNAs between conditions using DESeq2 or edgeR with small RNA-specific considerations. Use when identifying miRNAs that change between treatment groups, disease states, or developmental stages.

bio-small-rna-seq-mirdeep2-analysis

GPTomics/bioSkills

Discover novel miRNAs and quantify known miRNAs using miRDeep2 de novo prediction from small RNA-seq data. Use when identifying new miRNAs or performing comprehensive miRNA profiling with discovery.

bio-small-rna-seq-mirge3-analysis

GPTomics/bioSkills

Fast miRNA quantification with isomiR detection and A-to-I editing analysis using miRge3. Use when quantifying known miRNAs quickly or analyzing isomiR variants and RNA editing.

bio-small-rna-seq-smrna-preprocessing

GPTomics/bioSkills

Preprocess small RNA sequencing data with adapter trimming and size selection optimized for miRNA, piRNA, and other small RNAs. Use when preparing small RNA-seq reads for downstream quantification or discovery analysis.

bio-small-rna-seq-target-prediction

GPTomics/bioSkills

Predict miRNA target genes using sequence-based algorithms and database lookups. Use when identifying potential mRNA targets of differentially expressed or functionally important miRNAs.

bio-spatial-transcriptomics-image-analysis

GPTomics/bioSkills

Process and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.

bio-spatial-transcriptomics-spatial-communication

GPTomics/bioSkills

Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.

bio-spatial-transcriptomics-spatial-data-io

GPTomics/bioSkills

Load spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data.

bio-spatial-transcriptomics-spatial-deconvolution

GPTomics/bioSkills

Estimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots.

bio-spatial-transcriptomics-spatial-domains

GPTomics/bioSkills

Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.

bio-spatial-transcriptomics-spatial-multiomics

GPTomics/bioSkills

Analyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.

bio-spatial-transcriptomics-spatial-neighbors

GPTomics/bioSkills

Build spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use when building spatial neighborhood graphs.

bio-spatial-transcriptomics-spatial-preprocessing

GPTomics/bioSkills

Quality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data.

bio-spatial-transcriptomics-spatial-proteomics

GPTomics/bioSkills

Analyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.

bio-spatial-transcriptomics-spatial-statistics

GPTomics/bioSkills

Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.

bio-spatial-transcriptomics-spatial-visualization

GPTomics/bioSkills

Visualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns.

bio-splice-variant-prediction

GPTomics/bioSkills

Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.

bio-splicing-pipeline

GPTomics/bioSkills

End-to-end alternative splicing analysis from FASTQ to differential splicing results for short-read bulk RNA-seq. Aligns with STAR 2-pass cohort-style, performs junction QC (RSeQC, MaxEntScan, SpliceAI), runs rMATS-turbo and leafcutter for concordant differential analysis, optionally MAJIQ V3 for complex events / heterogeneous cohorts, isoform-switching with NMD/ORF/domain consequences (IsoformSwitchAnalyzeR v2 + DRIMSeq+DEXSeq+stageR DTU), and sashimi visualizations. Use when performing comprehensive splicing analysis from raw bulk RNA-seq data; for variant-driven splice prediction see splice-variant-prediction; for rare-disease single-patient outlier detection see outlier-splicing-detection; for full-isoform PacBio/ONT analysis see long-read-splicing.

bio-splicing-qc

GPTomics/bioSkills

Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.

bio-splicing-quantification

GPTomics/bioSkills

Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normalization. Use when measuring splice-site usage or isoform inclusion ratios from short-read RNA-seq.

bio-sra-data

GPTomics/bioSkills

Download sequencing data from NCBI SRA using the SRA toolkit. Use when downloading FASTQ files from SRA accessions, prefetching large datasets, or validating SRA downloads.

bio-structural-biology-alphafold-predictions

GPTomics/bioSkills

Access and analyze AlphaFold protein structure predictions. Use when predicted structures are needed for proteins without experimental structures, or for confidence scores (pLDDT).

bio-structural-biology-modern-structure-prediction

GPTomics/bioSkills

Predict protein structures using modern ML models including AlphaFold3, ESMFold, Chai-1, and Boltz-1. Use when predicting structures for novel proteins, protein complexes, or when comparing predictions across multiple methods.

bio-substructure-search

GPTomics/bioSkills

Searches molecular libraries for substructure matches using SMARTS patterns with RDKit. Filters compounds by pharmacophore features, functional groups, or scaffold matches with atom mapping. Use when finding compounds containing specific chemical moieties or filtering libraries by structural features.

bio-systems-biology-context-specific-models

GPTomics/bioSkills

Build tissue and condition-specific metabolic models using GIMME, iMAT, and INIT algorithms with expression data constraints. Create models that reflect cell-type specific metabolism. Use when building tissue-specific metabolic models or integrating transcriptomics with FBA.

bio-systems-biology-flux-balance-analysis

GPTomics/bioSkills

Perform flux balance analysis (FBA) and flux variability analysis (FVA) on genome-scale metabolic models using COBRApy. Predict growth rates, metabolic fluxes, and optimal resource utilization. Use when predicting metabolic phenotypes or optimizing flux distributions.

bio-systems-biology-gene-essentiality

GPTomics/bioSkills

Perform in silico gene knockout analysis and synthetic lethality screens using COBRApy single and double deletions. Predict essential genes and identify synthetic lethal pairs for drug target discovery. Use when identifying essential genes or finding synthetic lethal drug targets.

bio-systems-biology-metabolic-reconstruction

GPTomics/bioSkills

Build genome-scale metabolic models from genome sequences using CarveMe and gapseq for automated reconstruction. Generate draft models ready for curation and analysis. Use when creating metabolic models for organisms without existing models.

bio-systems-biology-model-curation

GPTomics/bioSkills

Validate, gap-fill, and curate genome-scale metabolic models using memote for quality scores and COBRApy for manual curation. Ensure models meet SBML standards and produce biologically meaningful predictions. Use when improving draft models or preparing models for publication.

bio-tcr-bcr-analysis-immcantation-analysis

GPTomics/bioSkills

Analyze BCR repertoires for somatic hypermutation, clonal lineages, and B cell phylogenetics using the Immcantation framework. Use when studying B cell affinity maturation, germinal center dynamics, or antibody evolution.

bio-tcr-bcr-analysis-mixcr-analysis

GPTomics/bioSkills

Perform V(D)J alignment and clonotype assembly from TCR-seq or BCR-seq data using MiXCR. Use when processing raw immune repertoire sequencing data to identify clonotypes and their frequencies.

bio-tcr-bcr-analysis-repertoire-visualization

GPTomics/bioSkills

Create publication-quality visualizations of immune repertoire data including circos plots, clone tracking, diversity plots, and network graphs. Use when generating figures for repertoire comparisons, clonal dynamics, or V(D)J gene usage.

bio-tcr-bcr-analysis-scirpy-analysis

GPTomics/bioSkills

Analyze single-cell TCR and BCR data integrated with gene expression using scirpy. Use when working with 10x Genomics VDJ data alongside scRNA-seq or when integrating immune receptor information with cell state analysis.

bio-tcr-bcr-analysis-vdjtools-analysis

GPTomics/bioSkills

Calculate immune repertoire diversity metrics, compare samples, and track clonal dynamics using VDJtools. Use when analyzing repertoire diversity, finding shared clonotypes, or comparing immune profiles between conditions.

bio-temporal-genomics-circadian-rhythms

GPTomics/bioSkills

Detects circadian and ultradian rhythms in time-series omics data using CosinorPy cosinor models, MetaCycle (JTK_CYCLE, ARSER), and RAIN non-parametric tests. Fits cosine models to estimate phase and amplitude, tests rhythmicity significance at pre-specified periods. Use when testing for 24-hour or other known-period oscillations in circadian, feeding-fasting, or light-dark cycle experiments. Not for unknown-period discovery (see temporal-genomics/periodicity-detection).

bio-temporal-genomics-periodicity-detection

GPTomics/bioSkills

Discovers periodic signals of unknown period in time-series omics data using Lomb-Scargle periodograms (scipy), autocorrelation, and wavelet time-frequency decomposition (pywt). Identifies dominant frequencies, handles irregularly sampled data, and detects transient periodicity. Use when searching for periodic patterns of unknown period length, analyzing cell cycle oscillations, or processing unevenly spaced time-series. Not for testing known 24-hour rhythms (see temporal-genomics/circadian-rhythms).

bio-temporal-genomics-temporal-clustering

GPTomics/bioSkills

Clusters genes by temporal expression profile shape using Mfuzz soft clustering, TCseq, and DEGreport degPatterns. Groups co-regulated genes into shared trajectory patterns via fuzzy c-means or hierarchical approaches. Use when categorizing temporally dynamic genes into response groups or identifying co-expression modules across time points. Requires temporally variable genes identified first (see differential-expression/timeseries-de).

bio-temporal-genomics-temporal-grn

GPTomics/bioSkills

Infers dynamic gene regulatory networks from bulk time-series expression data using Granger causality (statsmodels), dynGENIE3 (Extra-Trees on ODE-derived expression derivatives), and dynamic Bayesian networks (bnlearn). Identifies time-delayed regulatory relationships and tracks network rewiring across conditions. Use when inferring causal regulatory relationships from bulk temporal expression data or detecting TF influence propagation over time. Not for static co-expression networks (see gene-regulatory-networks/coexpression-networks).

bio-temporal-genomics-trajectory-modeling

GPTomics/bioSkills

Models continuous temporal trajectories from bulk or time-resolved omics data using generalized additive models (mgcv), spline regression, and changepoint detection (segmented, ruptures). Fits smooth gene expression curves and tests trajectory differences between conditions. Use when fitting non-linear temporal models to bulk time-series data or comparing developmental trajectories across conditions. Not for single-cell pseudotime (see single-cell/trajectory-inference).

bio-transcription-translation

GPTomics/bioSkills

Transcribe DNA to RNA and translate to protein using Biopython. Use when converting between DNA, RNA, and protein sequences, finding ORFs, or using alternative codon tables.

bio-tumor-fraction-estimation

GPTomics/bioSkills

Estimates circulating tumor DNA fraction from shallow whole-genome sequencing using ichorCNA. Detects copy number alterations via HMM segmentation and calculates ctDNA percentage. Requires 0.1-1x sWGS coverage. Use when quantifying tumor burden from liquid biopsy or monitoring treatment response.

bio-uniprot-access

GPTomics/bioSkills

Access UniProt protein database for sequences, annotations, and functional information. Use when retrieving protein data, GO terms, domain annotations, or protein-protein interactions.

bio-variant-annotation

GPTomics/bioSkills

Comprehensive variant annotation using bcftools annotate/csq, VEP, SnpEff, and ANNOVAR. Add database annotations, predict functional consequences, and assess clinical significance with MANE transcript selection and pathogenicity scoring. Use when annotating variants with functional and clinical information.

bio-variant-calling

GPTomics/bioSkills

Call SNPs and indels from aligned reads using bcftools mpileup and call. Use when detecting variants from BAM files or generating VCF from alignments.

bio-variant-calling-clinical-interpretation

GPTomics/bioSkills

Clinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants.

bio-variant-calling-deepvariant

GPTomics/bioSkills

Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller or when highest germline calling accuracy is required.

bio-variant-calling-filtering-best-practices

GPTomics/bioSkills

Comprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices.

bio-variant-calling-joint-calling

GPTomics/bioSkills

Joint genotype calling across multiple samples using GATK CombineGVCFs and GenotypeGVCFs. Essential for cohort studies, population genetics, and leveraging VQSR. Use when performing joint genotyping across multiple samples.

bio-variant-calling-structural-variant-calling

GPTomics/bioSkills

Call structural variants (SVs) from sequencing data using Manta, Delly, GRIDSS, and LUMPY. Detects deletions, insertions, inversions, duplications, and translocations too large for standard SNV callers. Use when detecting structural variants from short-read or long-read data and building consensus callsets.

bio-variant-normalization

GPTomics/bioSkills

Normalize indel representation, decompose MNPs, and split multiallelic variants using bcftools norm. Use when comparing variants from different callers, preparing VCF for database annotation, or merging VCFs from multiple sources.

bio-vcf-basics

GPTomics/bioSkills

View, query, and understand VCF/BCF variant files using bcftools and cyvcf2. Use when inspecting variants, extracting specific fields, or understanding VCF format structure.

bio-vcf-manipulation

GPTomics/bioSkills

Merge, concatenate, sort, intersect, and subset VCF files using bcftools. Use when combining variant files, comparing call sets, or restructuring VCF data.

bio-vcf-statistics

GPTomics/bioSkills

Generate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.

bio-virtual-screening

GPTomics/bioSkills

Performs structure-based virtual screening using AutoDock Vina 1.2 for molecular docking. Prepares receptor PDBQT files, generates ligand conformers, defines binding site boxes, and ranks compounds by predicted binding affinity. Use when screening chemical libraries against a protein structure to find potential binders.

bio-workflow-management-cwl-workflows

GPTomics/bioSkills

Create portable, standards-based bioinformatics pipelines with Common Workflow Language (CWL). Use when building workflows that need maximum portability across execution platforms, sharing pipelines with collaborators using different systems, or contributing to community workflow registries.

bio-workflow-management-nextflow-pipelines

GPTomics/bioSkills

Create scalable, containerized bioinformatics pipelines with Nextflow DSL2 supporting Docker, Singularity, and cloud execution. Use when building portable pipelines with container support, running workflows on cloud platforms (AWS, Google Cloud), or leveraging nf-core community pipelines.

bio-workflow-management-snakemake-workflows

GPTomics/bioSkills

Build reproducible bioinformatics pipelines with Snakemake using rules, wildcards, and automatic dependency resolution. Use when creating Python-based workflows, automating multi-step analyses with make-like dependency tracking, or running pipelines on HPC clusters with SLURM.

bio-workflow-management-wdl-workflows

GPTomics/bioSkills

Create portable bioinformatics pipelines with Workflow Description Language (WDL) using Cromwell or miniwdl execution engines. Use when running GATK best practices pipelines, working with Terra/AnVIL platforms, or building workflows for cloud execution on Google Cloud or AWS.

bio-workflows-atacseq-pipeline

GPTomics/bioSkills

End-to-end ATAC-seq workflow from FASTQ files to differential accessibility and TF footprinting. Covers alignment, peak calling with MACS3, QC metrics, and optional TOBIAS footprinting. Use when running end-to-end ATAC-seq analysis from FASTQ to differential accessibility.

bio-workflows-biomarker-pipeline

GPTomics/bioSkills

End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, classifier training with nested CV, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.

bio-workflows-causal-genomics-pipeline

GPTomics/bioSkills

End-to-end causal inference pipeline from GWAS summary statistics through Mendelian randomization, colocalization, fine-mapping, and mediation analysis. Use when performing post-GWAS causal inference to identify causal exposures, shared causal variants, and mediating mechanisms.

bio-workflows-chipseq-pipeline

GPTomics/bioSkills

End-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3 (or HOMER), and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation.

bio-workflows-clinical-trial-pipeline

GPTomics/bioSkills

End-to-end clinical trial analysis workflow from CDISC data loading through statistical testing to regulatory-compliant reporting. Covers data preparation, logistic regression, categorical tests, subgroup analysis, and Table 1 generation. Use when performing a complete analysis of clinical trial data.

bio-workflows-clip-pipeline

GPTomics/bioSkills

End-to-end CLIP-seq analysis from FASTQ to binding sites and motif enrichment. Use when analyzing protein-RNA interactions from CLIP-based methods.

bio-workflows-cnv-pipeline

GPTomics/bioSkills

End-to-end copy number variant detection workflow from BAM files. Covers CNVkit analysis for exome/targeted sequencing with visualization and annotation. Use when detecting copy number alterations from sequencing data.

bio-workflows-crispr-editing-pipeline

GPTomics/bioSkills

End-to-end CRISPR experiment design from target selection to delivery-ready constructs. Covers guide RNA design, off-target assessment, and specialized editing strategies including knockouts, base editing, and HDR knockins. Use when designing complete CRISPR editing experiments for gene knockout, correction, or tagging.

bio-workflows-crispr-screen-pipeline

GPTomics/bioSkills

End-to-end CRISPR screen analysis from FASTQ to hit genes. Orchestrates guide counting, QC, statistical analysis with MAGeCK, and hit calling with multiple methods. Use when analyzing pooled CRISPR screens from count data to hit calling.

bio-workflows-cytometry-pipeline

GPTomics/bioSkills

End-to-end flow cytometry workflow from FCS files to differential analysis. Orchestrates compensation, transformation, gating/clustering, and statistical testing with CATALYST/diffcyt. Use when processing flow or mass cytometry data end-to-end.

bio-workflows-edna-pipeline

GPTomics/bioSkills

End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal, denoising with OBITools3 or DADA2, contamination filtering, taxonomy assignment, Hill number diversity, and constrained ordination. Use when processing environmental DNA samples for biodiversity assessment or ecological surveys.

bio-workflows-expression-to-pathways

GPTomics/bioSkills

Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.

bio-workflows-fastq-to-variants

GPTomics/bioSkills

End-to-end DNA sequencing workflow from FASTQ files to variant calls. Covers QC, alignment with BWA, BAM processing, and variant calling with bcftools or GATK HaplotypeCaller. Use when calling variants from raw sequencing reads.

bio-workflows-genome-annotation-pipeline

GPTomics/bioSkills

End-to-end genome annotation pipeline from assembled contigs to functional annotation, covering repeat masking, gene prediction, and functional assignment for both prokaryotic and eukaryotic genomes. Use when annotating a newly assembled genome from scratch.

bio-workflows-genome-assembly-pipeline

GPTomics/bioSkills

End-to-end genome assembly workflow from reads to polished assembly with QC. Supports short reads (SPAdes), long reads (Flye), and hybrid approaches. Use when assembling genomes from raw reads.

bio-workflows-grn-pipeline

GPTomics/bioSkills

End-to-end gene regulatory network inference pipeline from processed single-cell data to regulon discovery and perturbation simulation. Supports RNA-only (pySCENIC) and multiome (SCENIC+) paths. Use when building gene regulatory networks from single-cell transcriptomic or multiome data.

bio-workflows-gwas-pipeline

GPTomics/bioSkills

End-to-end GWAS workflow from VCF to association results. Covers PLINK QC, population structure correction, and association testing for case-control or quantitative traits. Use when running genome-wide association studies.

bio-workflows-hic-pipeline

GPTomics/bioSkills

End-to-end Hi-C analysis workflow from contact pairs to compartments, TADs, and loops. Covers cooler matrices, cooltools analysis, and visualization. Use when processing Hi-C data to compartments and TADs.

bio-workflows-imc-pipeline

GPTomics/bioSkills

End-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics. Use when analyzing imaging mass cytometry data end-to-end.

bio-workflows-longread-sv-pipeline

GPTomics/bioSkills

End-to-end workflow for detecting structural variants from long-read sequencing data. Covers ONT/PacBio alignment with minimap2 and SV calling with Sniffles or cuteSV. Use when detecting structural variants from long reads.

bio-workflows-merip-pipeline

GPTomics/bioSkills

End-to-end MeRIP-seq analysis from FASTQ to m6A peaks and differential methylation. Use when analyzing epitranscriptomic m6A modifications from immunoprecipitation data.

bio-workflows-metabolic-modeling-pipeline

GPTomics/bioSkills

End-to-end genome-scale metabolic modeling from genome sequence to flux predictions. Covers automated reconstruction with CarveMe, model validation with memote, FBA/FVA analysis, and gene essentiality prediction. Use when building metabolic models or predicting metabolic phenotypes from genomic data.

bio-workflows-metabolomics-pipeline

GPTomics/bioSkills

End-to-end metabolomics workflow from raw MS data to pathway analysis. Orchestrates XCMS preprocessing, annotation, normalization, statistical analysis, and pathway mapping. Use when processing LC-MS metabolomics data.

bio-workflows-metagenomics-pipeline

GPTomics/bioSkills

End-to-end metagenomics workflow from FASTQ to taxonomic and functional profiles. Covers Kraken2 classification, Bracken abundance estimation, and HUMAnN functional profiling. Use when profiling metagenomic samples.

bio-workflows-methylation-pipeline

GPTomics/bioSkills

End-to-end bisulfite sequencing workflow from FASTQ to differentially methylated regions. Covers Bismark alignment, methylation calling, and DMR detection with methylKit. Use when analyzing bisulfite sequencing data.

bio-workflows-microbiome-pipeline

GPTomics/bioSkills

End-to-end 16S amplicon workflow from FASTQ reads to differential abundance. Orchestrates DADA2 ASV inference, taxonomy assignment, diversity analysis, and compositional testing with ALDEx2. Use when processing 16S/ITS amplicon data.

bio-workflows-multi-omics-pipeline

GPTomics/bioSkills

End-to-end multi-omics integration workflow. Orchestrates data harmonization, MOFA/mixOmics integration, factor interpretation, and downstream analysis across transcriptomics, proteomics, metabolomics, and other modalities. Use when integrating multiple omics datasets.

bio-workflows-multiome-pipeline

GPTomics/bioSkills

End-to-end multiome workflow for joint scRNA-seq + scATAC-seq analysis. Covers data loading, separate modality processing, and WNN integration with Seurat/Signac. Use when analyzing joint scRNA+scATAC data.

bio-workflows-neoantigen-pipeline

GPTomics/bioSkills

End-to-end neoantigen discovery from somatic variants to ranked vaccine candidates. Integrates HLA typing, MHC binding prediction, pVACtools neoantigen calling, and immunogenicity scoring. Use when identifying tumor neoantigens for personalized vaccine design or checkpoint biomarkers.

bio-workflows-outbreak-pipeline

GPTomics/bioSkills

End-to-end outbreak investigation from pathogen isolates to transmission networks. Orchestrates MLST typing, AMR surveillance, phylodynamic dating, and transmission inference with TransPhylo. Use when investigating disease outbreaks or tracking pathogen transmission chains.

bio-workflows-proteomics-pipeline

GPTomics/bioSkills

End-to-end proteomics workflow from MaxQuant output to differential protein abundance. Orchestrates data import, normalization, imputation, and statistical testing with limma (default) or MSstats for complex feature-level designs. Use when processing mass spectrometry proteomics.

bio-workflows-riboseq-pipeline

GPTomics/bioSkills

End-to-end Ribo-seq analysis from FASTQ to translation efficiency and ORF detection. Use when analyzing ribosome profiling data to study translation.

bio-workflows-rnaseq-to-de

GPTomics/bioSkills

End-to-end RNA-seq workflow from FASTQ files to differential expression results. Covers QC, quantification (Salmon or STAR+featureCounts), and DESeq2 analysis with visualization. Use when running RNA-seq from FASTQ to DE results.

bio-workflows-scrnaseq-pipeline

GPTomics/bioSkills

End-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types. Covers QC, normalization, clustering, marker detection, and cell type annotation. Use when analyzing single-cell RNA-seq data.

bio-workflows-smrna-pipeline

GPTomics/bioSkills

End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression. Use when analyzing miRNA, piRNA, or other small RNA sequencing data.

bio-workflows-somatic-variant-pipeline

GPTomics/bioSkills

End-to-end somatic variant calling from tumor-normal paired samples using Mutect2 or Strelka2. Covers preprocessing, variant calling, filtering, and annotation for cancer genomics. Use when calling somatic mutations from tumor-normal pairs.

bio-workflows-spatial-pipeline

GPTomics/bioSkills

End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data.

bio-workflows-tcr-pipeline

GPTomics/bioSkills

End-to-end TCR/BCR repertoire analysis from FASTQ to clonotype diversity metrics. Use when analyzing immune repertoire sequencing data from bulk or single-cell experiments.

bio-workflows-timecourse-pipeline

GPTomics/bioSkills

End-to-end time-course analysis from expression matrix to temporal patterns and enrichment. Covers temporal DE, Mfuzz soft clustering, optional rhythm detection, GAM trajectory fitting, and per-cluster pathway enrichment. Use when analyzing bulk time-series expression experiments from any omics platform.