gptomics/bioskills
GitHubbio-workflows-microbiome-pipeline
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-microbiome-diversity-analysis
Alpha and beta diversity analysis for microbiome data. Calculate within-sample richness, evenness, and between-sample dissimilarity with phyloseq and vegan. Use when comparing community composition across samples or testing for group differences in microbiome structure.
bio-longread-alignment
Align long reads using minimap2 for Oxford Nanopore and PacBio data. Supports various presets for different read types and applications. Use when aligning ONT or PacBio reads to a reference genome for variant calling, SV detection, or coverage analysis.
bio-metagenomics-kraken
Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database. Use when performing initial taxonomic classification of shotgun metagenomic reads before abundance estimation with Bracken.
bio-rna-quantification-count-matrix-qc
Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE analysis.
bio-genome-assembly-contamination-detection
Detect contamination and assess genome quality using CheckM, CheckM2, GTDB-Tk, and GUNC for metagenome-assembled genomes and isolate assemblies. Use when checking assemblies for contamination.
bio-chip-seq-super-enhancers
Identifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools. Use when studying cell identity genes, cancer-associated regulatory elements, or master transcription factor binding regions that cluster into large enhancer domains.
bio-pathway-go-enrichment
Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and customizable statistical thresholds.
bio-clinical-databases-variant-prioritization
Filter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.
bio-data-visualization-circos-plots
Create circular genome visualizations with Circos and pyCircos. Display multi-track data including ideograms, genes, variants, CNVs, and interaction arcs. Use when creating circular genome visualizations.
bio-pdb-geometric-analysis
Perform geometric calculations on protein structures using Biopython Bio.PDB. Use when measuring distances, angles, and dihedrals, superimposing structures, calculating RMSD, or computing solvent accessible surface area (SASA).
bio-proteomics-dia-analysis
Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling.
bio-workflow-management-cwl-workflows
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-proteomics-proteomics-qc
Quality control and assessment for proteomics data. Use when evaluating proteomics data quality before downstream analysis. Covers sample metrics, missing value patterns, replicate correlation, batch effects, and intensity distributions.
bio-workflows-imc-pipeline
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-tcr-pipeline
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-spatial-transcriptomics-spatial-neighbors
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-metabolomics-xcms-preprocessing
XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.
bio-data-visualization-color-palettes
Select and apply colorblind-friendly palettes for scientific figures using viridis, RColorBrewer, and custom color schemes. Use when selecting colorblind-friendly palettes for figures.
bio-atac-seq-footprinting
Detect transcription factor binding sites through footprinting analysis in ATAC-seq data using TOBIAS. Use when identifying TF occupancy patterns within accessible regions, as TF binding protects DNA from Tn5 cutting.
bio-spatial-transcriptomics-spatial-communication
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-metabolomics-statistical-analysis
Statistical analysis for metabolomics data. Covers univariate testing, multivariate methods (PCA, PLS-DA), and biomarker discovery. Use when identifying differentially abundant metabolites or building classification models.
bio-workflows-genome-assembly-pipeline
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-single-cell-multimodal-integration
Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq, Multiome, or other multi-modal single-cell data.
bio-workflows-metagenomics-pipeline
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-variant-calling-joint-calling
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-rna-quantification-alignment-free-quant
Quantify transcript expression using pseudo-alignment with Salmon or kallisto. Use when quantifying transcripts with Salmon or kallisto.
bio-imaging-mass-cytometry-phenotyping
Cell type assignment from marker expression in IMC data. Covers manual gating, clustering, and automated classification approaches. Use when assigning cell types to segmented IMC cells based on protein marker expression or when phenotyping cells in multiplexed imaging data.
bio-workflow-management-snakemake-workflows
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-pathway-enrichment-visualization
Visualize enrichment results using enrichplot package functions. Use when creating publication-quality figures from clusterProfiler results. Covers dotplot, barplot, cnetplot, emapplot, gseaplot2, ridgeplot, and treeplot.
bio-single-cell-batch-integration
Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets.
bio-imaging-mass-cytometry-cell-segmentation
Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.
bio-data-visualization-multipanel-figures
Combine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, and gridExtra with shared legends and annotations. Use when combining multiple plots into publication figures.
bio-atac-seq-atac-peak-calling
Call accessible chromatin regions from ATAC-seq data using MACS3 with ATAC-specific parameters. Use when identifying open chromatin regions from aligned ATAC-seq BAM files, different from ChIP-seq peak calling.
bio-workflows-chipseq-pipeline
End-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3, and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation.
bio-data-visualization-specialized-omics-plots
Reusable plotting functions for common omics visualizations. Custom ggplot2/matplotlib implementations of volcano, MA, PCA, enrichment dotplots, boxplots, and survival curves. Use when creating volcano, MA, or enrichment plots.
bio-clinical-databases-dbsnp-queries
Query dbSNP for rsID lookups, variant annotations, and cross-references to other databases. Use when mapping between rsIDs and genomic coordinates or retrieving basic variant information.
bio-workflows-scrnaseq-pipeline
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-expression-to-pathways
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-epitranscriptomics-merip-preprocessing
Align and QC MeRIP-seq IP and input samples for m6A analysis. Use when preparing MeRIP-seq data for peak calling or differential methylation analysis.
bio-methylation-bismark-alignment
Bisulfite sequencing read alignment using Bismark with bowtie2/hisat2. Handles genome preparation and produces BAM files with methylation information. Use when aligning WGBS, RRBS, or other bisulfite-converted sequencing reads to a reference genome.
bio-proteomics-quantification
Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.
bio-expression-matrix-metadata-joins
Merge sample metadata with count matrices and add gene annotations. Use when preparing data for differential expression analysis or visualization.
bio-workflows-clip-pipeline
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-proteomics-spectral-libraries
Build, manage, and search spectral libraries for proteomics. Use when creating or working with spectral libraries for DIA analysis. Covers DDA-based library generation, predicted libraries (Prosit, DeepLC), and library formats.
bio-long-read-sequencing-clair3-variants
Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.
bio-genome-intervals-gtf-gff-handling
Parse, query, and convert GTF and GFF3 annotation files. Extract gene, transcript, and exon coordinates using gffread, gtfparse, and gffutils. Use when extracting specific features from gene annotations or converting between annotation formats.
bio-data-visualization-genome-tracks
Create genome browser-style visualizations showing multiple data tracks (coverage, peaks, genes) using pyGenomeTracks, Gviz, and IGV. Use when visualizing genomic data at specific loci with multiple aligned tracks.
bio-reporting-rmarkdown-reports
Create reproducible bioinformatics analysis reports with R Markdown including code, results, and visualizations in HTML, PDF, or Word format. Use when generating analysis reports with RMarkdown.
bio-clip-seq-clip-peak-calling
Call protein-RNA binding site peaks from CLIP-seq data using CLIPper, PureCLIP, or Piranha. Use when identifying RBP binding sites from aligned CLIP reads.
bio-paired-end-fastq
Handle paired-end FASTQ files (R1/R2) using Biopython. Use when working with Illumina paired reads, synchronizing pairs, interleaving/deinterleaving, or filtering paired data.
bio-pathway-reactome
Reactome pathway enrichment using ReactomePA package. Use when analyzing gene lists against Reactome's curated peer-reviewed pathway database. Performs over-representation analysis and GSEA with visualization and pathway hierarchy exploration.
bio-format-conversion
Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when changing file formats or preparing data for different tools.
bio-de-deseq2-basics
Perform differential expression analysis using DESeq2 in R/Bioconductor. Use for analyzing RNA-seq count data, creating DESeqDataSet objects, running the DESeq workflow, and extracting results with log fold change shrinkage. Use when performing DE analysis with DESeq2.
bio-vcf-statistics
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-bedgraph-handling
Create, manipulate, and convert bedGraph files for genome browser visualization. Covers bedGraph format, conversion to/from bigWig, normalization, and signal processing. Use when handling coverage and signal tracks from ChIP-seq, ATAC-seq, or RNA-seq.
bio-alignment-pairwise
Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.
bio-copy-number-gatk-cnv
Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.
bio-pdb-structure-io
Parse and write protein structure files using Biopython Bio.PDB. Use when reading PDB, mmCIF, and MMTF files, downloading structures from RCSB PDB, or writing structures to various formats.
bio-alignment-filtering
Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.
bio-entrez-link
Find cross-references between NCBI databases using Biopython Bio.Entrez. Use when navigating from genes to proteins, sequences to publications, finding related records, or discovering database relationships.
bio-longread-qc
Quality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read characteristics. Use when assessing ONT or PacBio run quality or filtering reads before assembly or alignment.