Browse Skills — Page 45

bio-phylo-divergence-dating

GPTomics/bioSkills

Estimate divergence times using molecular clock models with BEAST2, MCMCTree, and TreePL. Use when dating speciation events, calibrating phylogenies with fossils, choosing between strict and relaxed clock models, or estimating evolutionary rates across lineages.

bio-phylo-modern-tree-inference

GPTomics/bioSkills

Build maximum likelihood phylogenetic trees using IQ-TREE2 and RAxML-NG with expert model selection, branch support assessment, and topology testing. Use when inferring publication-quality ML trees, selecting substitution models, interpreting bootstrap and concordance factor support, or running partitioned phylogenomic analyses.

bio-phylo-species-trees

GPTomics/bioSkills

Estimate species trees using coalescent methods including ASTRAL-III, wASTRAL, ASTRAL-Pro, SVDQuartets, and BPP. Use when multi-locus data shows gene tree discordance from incomplete lineage sorting, when in the anomaly zone where concatenation is misleading, or when computing concordance factors to assess topological support.

bio-phylo-tree-io

GPTomics/bioSkills

Read, write, and convert phylogenetic tree files using Biopython Bio.Phylo. Use when parsing Newick, Nexus, PhyloXML, or NeXML tree formats, converting between formats, or handling multiple trees.

bio-phylo-tree-manipulation

GPTomics/bioSkills

Modify phylogenetic tree structure using Biopython Bio.Phylo. Use when rooting trees with outgroups, midpoint, or MAD methods, pruning taxa, collapsing clades, ladderizing branches, or extracting subtrees. Includes rooting method decision guidance.

bio-phylo-tree-visualization

GPTomics/bioSkills

Draw and export phylogenetic trees using Biopython Bio.Phylo with matplotlib and modern alternatives. Use when creating tree figures, customizing colors and labels, exporting to image formats, or choosing between Bio.Phylo, ggtree, ETE4, and iTOL for publication.

bio-pileup-generation

GPTomics/bioSkills

Generate pileup data for variant calling using samtools mpileup and pysam. Use when preparing data for variant calling, analyzing per-position read data, or calculating allele frequencies.

bio-population-genetics-association-testing

GPTomics/bioSkills

Genome-wide association studies (GWAS) with PLINK. Perform case-control and quantitative trait association testing using logistic/linear regression with covariates, generate Manhattan and QQ plots for result visualization. Use when running GWAS or association tests.

bio-population-genetics-linkage-disequilibrium

GPTomics/bioSkills

Calculate linkage disequilibrium statistics (r², D'), perform LD pruning for population structure analysis, identify haplotype blocks, and visualize LD patterns using PLINK, scikit-allel, and LDBlockShow. Use when calculating LD or pruning variants.

bio-population-genetics-plink-basics

GPTomics/bioSkills

PLINK file formats, format conversion, and quality control filtering for population genetics. Convert between VCF, BED/BIM/FAM, and PED/MAP formats, apply MAF, genotyping rate, and HWE filters using PLINK 1.9 and 2.0. Use when working with PLINK format files or running QC.

bio-population-genetics-population-structure

GPTomics/bioSkills

Analyze population structure using PCA and admixture analysis with PLINK and ADMIXTURE. Identify population clusters, assess ancestry proportions, visualize genetic structure, and choose optimal K for admixture models. Use when analyzing population stratification with PCA or admixture.

bio-population-genetics-scikit-allel-analysis

GPTomics/bioSkills

Python population genetics with scikit-allel. Read VCF files, compute allele frequencies, calculate diversity statistics, perform PCA, and run selection scans using GenotypeArray and HaplotypeArray data structures. Use when analyzing population genetics in Python.

bio-population-genetics-selection-statistics

GPTomics/bioSkills

Detect signatures of natural selection using Fst, Tajima's D, iHS, XP-EHH, and other selection statistics. Calculate population differentiation, test for departures from neutrality, and identify selective sweeps with scikit-allel and vcftools. Use when computing selection signatures like Fst or Tajima's D.

bio-primer-design-primer-basics

GPTomics/bioSkills

Design PCR primers for a target sequence using primer3-py. Specify target regions, product size, melting temperature, and other constraints. Returns ranked primer pairs with quality metrics. Use when designing standard PCR primers.

bio-primer-design-primer-validation

GPTomics/bioSkills

Validate PCR primers for specificity, dimers, hairpins, and secondary structures using primer3-py thermodynamic calculations. Check self-complementarity, heterodimer formation, and 3' stability. Use when validating primer specificity and properties.

bio-primer-design-qpcr-primers

GPTomics/bioSkills

Design qPCR primers and TaqMan/molecular beacon probes using primer3-py. Configure probe Tm, primer-probe spacing, and hydrolysis probe constraints for real-time PCR assays. Use when designing qPCR primers and probes.

bio-proteomics-data-import

GPTomics/bioSkills

Load and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt. Use when starting a proteomics analysis with raw or processed MS data. Handles contaminant filtering and missing value assessment.

bio-proteomics-dia-analysis

GPTomics/bioSkills

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-proteomics-peptide-identification

GPTomics/bioSkills

Peptide-spectrum matching and protein identification from MS/MS data. Use when identifying peptides from tandem mass spectra. Covers database searching, spectral library matching, and FDR estimation using target-decoy approaches.

bio-proteomics-protein-inference

GPTomics/bioSkills

Protein grouping and inference from peptide identifications. Use when resolving protein ambiguity from shared peptides. Handles protein groups and protein-level FDR control using parsimony and probabilistic approaches.

bio-proteomics-proteomics-qc

GPTomics/bioSkills

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-proteomics-ptm-analysis

GPTomics/bioSkills

Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Covers site localization, motif analysis, and quantitative PTM analysis. Use when analyzing phosphoproteomic data or other modification-enriched samples.

bio-proteomics-quantification

GPTomics/bioSkills

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-proteomics-spectral-libraries

GPTomics/bioSkills

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-reaction-enumeration

GPTomics/bioSkills

Enumerates chemical libraries through reaction SMARTS transformations using RDKit. Generates virtual compound libraries from building blocks using defined chemical reactions with product validation. Use when creating combinatorial libraries or enumerating products from synthetic routes.

bio-read-alignment-bowtie2-alignment

GPTomics/bioSkills

Align short reads using Bowtie2 with local or end-to-end modes. Supports gapped alignment. Use when aligning ChIP-seq, ATAC-seq, or when flexible alignment modes are needed.

bio-read-alignment-bwa-alignment

GPTomics/bioSkills

Align DNA short reads to reference genomes using bwa-mem2, the faster successor to BWA-MEM. Use when aligning DNA short reads to a reference genome.

bio-read-alignment-hisat2-alignment

GPTomics/bioSkills

Align RNA-seq reads with HISAT2, a memory-efficient splice-aware aligner. Use when STAR's memory requirements are too high or for general RNA-seq alignment.

bio-read-alignment-star-alignment

GPTomics/bioSkills

Align RNA-seq reads with STAR (Spliced Transcripts Alignment to a Reference). Supports two-pass mode for novel splice junction discovery. Use when aligning RNA-seq data requiring splice-aware alignment.

bio-read-qc-adapter-trimming

GPTomics/bioSkills

Remove sequencing adapters from FASTQ files using Cutadapt and Trimmomatic. Supports single-end and paired-end reads, Illumina TruSeq, Nextera, and custom adapter sequences. Use when FastQC shows adapter contamination or before alignment of short reads.

bio-read-qc-contamination-screening

GPTomics/bioSkills

Detect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination.

bio-read-qc-fastp-workflow

GPTomics/bioSkills

All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.

bio-read-qc-quality-filtering

GPTomics/bioSkills

Filter reads by quality scores, length, and N content using Trimmomatic and fastp. Apply sliding window trimming, remove low-quality bases from read ends, and discard reads below thresholds. Use when reads have poor quality tails or require minimum quality for downstream analysis.

bio-read-qc-quality-reports

GPTomics/bioSkills

Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.

bio-read-qc-umi-processing

GPTomics/bioSkills

Extract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep includes UMIs and accurate molecule counting is needed, such as in single-cell RNA-seq, low-input RNA-seq, or targeted sequencing to distinguish PCR from biological duplicates.

bio-read-sequences

GPTomics/bioSkills

Read biological sequence files (FASTA, FASTQ, GenBank, EMBL, ABI, SFF) using Biopython Bio.SeqIO. Use when parsing sequence files, iterating multi-sequence files, random access to large files, or high-performance parsing.

bio-reference-operations

GPTomics/bioSkills

Generate consensus sequences and manage reference files using samtools. Use when creating consensus from alignments, indexing references, or creating sequence dictionaries.

bio-reporting-automated-qc-reports

GPTomics/bioSkills

Generates standardized quality control reports by aggregating metrics from FastQC, alignment, and other tools using MultiQC. Use when summarizing QC metrics across samples, creating shareable quality reports, or building automated QC pipelines.

bio-reporting-figure-export

GPTomics/bioSkills

Exports publication-ready figures in various formats with proper resolution, sizing, and typography. Use when preparing figures for journal submission, creating vector graphics for presentations, or ensuring consistent figure styling across analyses.

bio-reporting-jupyter-reports

GPTomics/bioSkills

Creates reproducible Jupyter notebooks for bioinformatics analysis with parameterization using papermill. Use when generating automated analysis reports, running notebook-based pipelines, or creating shareable computational notebooks.

bio-reporting-quarto-reports

GPTomics/bioSkills

Build reproducible scientific documents, presentations, and websites with Quarto supporting R, Python, Julia, and Observable JS. Use when creating reproducible reports with Quarto.

bio-reporting-rmarkdown-reports

GPTomics/bioSkills

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-restriction-enzyme-selection

GPTomics/bioSkills

Select restriction enzymes by criteria using Biopython Bio.Restriction. Find enzymes that cut once, don't cut, produce specific overhangs, are commercially available, or have compatible ends for cloning. Use when selecting restriction enzymes for cloning or analysis.

bio-restriction-mapping

GPTomics/bioSkills

Create restriction maps showing enzyme cut positions on DNA sequences using Biopython Bio.Restriction. Visualize cut sites, calculate distances between sites, and generate text or graphical maps. Use when creating or analyzing restriction maps.

bio-restriction-sites

GPTomics/bioSkills

Find restriction enzyme cut sites in DNA sequences using Biopython Bio.Restriction. Search with single enzymes, batches of enzymes, or commercially available enzyme sets. Returns cut positions for linear or circular DNA. Use when finding restriction enzyme cut sites in sequences.

bio-reverse-complement

GPTomics/bioSkills

Generate reverse complements and complements of DNA/RNA sequences using Biopython. Use when working with opposite strands, primer design, or converting between template and coding strands.

bio-ribo-seq-orf-detection

GPTomics/bioSkills

Detect and quantify translated ORFs from Ribo-seq data including uORFs and novel ORFs using RiboCode and ORFquant. Use when identifying translated regions beyond annotated coding sequences or quantifying ORF-level translation.

bio-ribo-seq-riboseq-preprocessing

GPTomics/bioSkills

Preprocess ribosome profiling data including adapter trimming, size selection, rRNA removal, and alignment. Use when preparing Ribo-seq reads for downstream analysis of translation.

bio-ribo-seq-ribosome-periodicity

GPTomics/bioSkills

Validate Ribo-seq data quality by checking 3-nucleotide periodicity and calculating P-site offsets. Use when assessing library quality or determining read offsets for downstream analysis.

bio-ribo-seq-ribosome-stalling

GPTomics/bioSkills

Detect ribosome pausing and stalling sites from Ribo-seq data at codon resolution. Use when studying translational regulation, identifying pause sites, or analyzing codon-specific translation dynamics.

bio-ribo-seq-translation-efficiency

GPTomics/bioSkills

Calculate translation efficiency (TE) as the ratio of ribosome occupancy to mRNA abundance. Use when comparing translational regulation between conditions or identifying genes with altered translation independent of transcription.

bio-rna-quantification-alignment-free-quant

GPTomics/bioSkills

Quantify transcript expression using pseudo-alignment with Salmon or kallisto. Use when quantifying transcripts with Salmon or kallisto.

bio-rna-quantification-count-matrix-qc

GPTomics/bioSkills

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-rna-quantification-featurecounts-counting

GPTomics/bioSkills

Count reads per gene from aligned BAM files using Subread featureCounts. Use when processing BAM files from STAR/HISAT2 to generate gene-level counts for DESeq2/edgeR.

bio-rna-quantification-tximport-workflow

GPTomics/bioSkills

Import transcript-level quantifications from Salmon/kallisto into R for gene-level analysis with DESeq2/edgeR using tximport or tximeta. Use when importing transcript counts into R for DESeq2/edgeR.

bio-rna-structure-ncrna-search

GPTomics/bioSkills

Searches for non-coding RNA homologs and classifies RNA families using Infernal covariance model searches against the Rfam database. Identifies structured RNAs by sequence and secondary structure conservation. Use when querying sequences against Rfam, building custom covariance models for novel RNA families, or classifying non-coding transcripts by family.

bio-rna-structure-secondary-structure-prediction

GPTomics/bioSkills

Predicts RNA secondary structures using minimum free energy folding and partition function analysis with ViennaRNA (RNAfold, RNAalifold, RNAcofold). Computes base-pair probabilities, centroid structures, and consensus structures from alignments. Use when predicting RNA folding, evaluating structural stability, or comparing structures across homologs.

bio-rna-structure-structure-probing

GPTomics/bioSkills

Analyzes experimental RNA structure probing data from SHAPE-MaP and DMS-MaPseq experiments using ShapeMapper2. Converts mutation rates to per-nucleotide reactivity profiles that constrain structure prediction. Use when processing SHAPE-MaP or DMS-MaPseq sequencing data to obtain experimental RNA structure information.

bio-rnaseq-qc

GPTomics/bioSkills

RNA-seq specific quality control including rRNA contamination detection, strandedness verification, gene body coverage, and transcript integrity metrics. Use when validating RNA-seq libraries before differential expression analysis.

bio-sam-bam-basics

GPTomics/bioSkills

View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.

bio-sashimi-plots

GPTomics/bioSkills

Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/SUPPA2/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.

bio-seq-objects

GPTomics/bioSkills

Create and manipulate Seq, MutableSeq, and SeqRecord objects using Biopython. Use when creating sequences from strings, modifying sequence data in-place, or building annotated sequence records.

bio-sequence-properties

GPTomics/bioSkills

Calculate sequence properties like GC content, molecular weight, isoelectric point, and GC skew using Biopython. Use when analyzing sequence composition, computing physical properties, or comparing sequences.

bio-sequence-similarity

GPTomics/bioSkills

Find homologous sequences using iterative BLAST (PSI-BLAST), profile HMMs (HMMER), and reciprocal best hit analysis. Use when identifying orthologs, distant homologs, or protein family members where standard BLAST is not sensitive enough.

bio-sequence-slicing

GPTomics/bioSkills

Slice, extract, and concatenate biological sequences using Biopython. Use when extracting subsequences, joining sequences, or manipulating sequence regions by position.

bio-sequence-statistics

GPTomics/bioSkills

Calculate sequence statistics (N50, length distribution, GC content, summary reports) using Biopython. Use when analyzing sequence datasets, generating QC reports, or comparing assemblies.

bio-similarity-searching

GPTomics/bioSkills

Performs molecular similarity searches using Tanimoto coefficient on fingerprints via RDKit. Finds structurally similar compounds using ECFP or MACCS keys and clusters molecules by structural similarity using Butina clustering. Use when finding analogs of a query compound or clustering chemical libraries.

bio-single-cell-batch-integration

GPTomics/bioSkills

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-single-cell-cell-annotation

GPTomics/bioSkills

Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets.

bio-single-cell-cell-communication

GPTomics/bioSkills

Infer cell-cell communication networks from scRNA-seq data using CellChat, NicheNet, and LIANA for ligand-receptor interaction analysis. Use when inferring ligand-receptor interactions between cell types.

bio-single-cell-clustering

GPTomics/bioSkills

Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when performing dimensionality reduction and clustering on single-cell data.

bio-single-cell-data-io

GPTomics/bioSkills

Read, write, and create single-cell data objects using Seurat (R) and Scanpy (Python). Use for loading 10X Genomics data, importing/exporting h5ad and RDS files, creating Seurat objects and AnnData objects, and converting between formats. Use when loading, saving, or converting single-cell data formats.

bio-single-cell-lineage-tracing

GPTomics/bioSkills

Reconstruct cell lineage trees from CRISPR barcode tracing or mitochondrial mutations. Use when studying clonal dynamics, cell fate decisions, or developmental trajectories.

bio-single-cell-markers-annotation

GPTomics/bioSkills

Find marker genes and annotate cell types in single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for differential expression between clusters, identifying cluster-specific markers, scoring gene sets, and assigning cell type labels. Use when finding marker genes and annotating clusters.

bio-single-cell-metabolite-communication

GPTomics/bioSkills

Analyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when studying metabolic crosstalk between cell populations or metabolite-receptor interactions.

bio-single-cell-multimodal-integration

GPTomics/bioSkills

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-single-cell-perturb-seq

GPTomics/bioSkills

Analyze Perturb-seq and CROP-seq CRISPR screening data integrated with scRNA-seq. Use when identifying gene function through pooled genetic perturbations in single cells.

bio-single-cell-preprocessing

GPTomics/bioSkills

Quality control, filtering, and normalization for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for calculating QC metrics, filtering cells and genes, normalizing counts, identifying highly variable genes, and scaling data. Use when filtering, normalizing, and selecting features in single-cell data.

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.