computational-chemistry
471
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.
GPTomics
research
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
scientific-computing
471
bio-population-genetics-linkage-disequilibrium
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.
GPTomics
research
bioinformatics
471
bio-population-genetics-population-structure
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.
GPTomics
research
scientific-computing
471
bio-comparative-genomics-ortholog-inference
Infer orthologous gene groups across species using OrthoFinder and ProteinOrtho. Identify orthologs, paralogs, and co-orthologs for comparative genomics and functional annotation transfer. Use when identifying gene orthologs across species or building orthogroups for evolutionary analysis.
GPTomics
research
scientific-computing
471
bio-population-genetics-selection-statistics
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.
GPTomics
research
scientific-computing
471
bio-population-genetics-scikit-allel-analysis
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.
GPTomics
research
bioinformatics
471
bio-clip-seq-clip-motif-analysis
Identify enriched sequence motifs at CLIP-seq binding sites for RBP binding specificity. Use when characterizing the sequence preferences of an RNA-binding protein.
GPTomics
research
bioinformatics
471
bio-multi-omics-similarity-network
Similarity Network Fusion (SNF) for patient stratification using multi-omics data. Integrates multiple data types into a unified patient similarity network. Use when performing patient stratification or integrating multi-omics data into unified similarity networks.
GPTomics
research
scientific-computing
471
bio-clinical-databases-somatic-signatures
Extract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.
GPTomics
research
bioinformatics
471
bio-clinical-databases-polygenic-risk
Calculate polygenic risk scores using PRSice-2, LDpred2, or PRS-CS from GWAS summary statistics. Use when predicting disease risk from genome-wide genetic variants.
GPTomics
research
bioinformatics
471
bio-clinical-databases-tumor-mutational-burden
Calculate tumor mutational burden from panel or WES data with proper normalization and clinical thresholds. Use when assessing immunotherapy eligibility or characterizing tumor immunogenicity.
GPTomics
research
bioinformatics
471
bio-clinical-databases-hla-typing
Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for transplant matching, neoantigen prediction, or pharmacogenomic screening.
GPTomics
research
scientific-computing
471
bio-clinical-databases-myvariant-queries
Query myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously.
GPTomics
research
bioinformatics
471
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.
GPTomics
research
bioinformatics
471
bio-chipseq-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.
GPTomics
research
bioinformatics
471
bio-crispr-screens-library-design
CRISPR library design for genetic screens. Covers sgRNA selection, library composition, control design, and oligo ordering. Use when designing custom sgRNA libraries for knockout, activation, or interference screens.
GPTomics
research
scientific-computing
471
bio-chipseq-peak-calling
ChIP-seq peak calling using MACS3 and HOMER findPeaks. Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports single-caller and multi-caller consensus approaches, input control, fragment size modeling, and various output formats. Use when calling peaks from ChIP-seq alignments.
GPTomics
research
computational-chemistry
471
bio-clinical-databases-clinvar-lookup
Query ClinVar for variant pathogenicity classifications, review status, and disease associations via REST API or local VCF. Use when determining clinical significance of variants for diagnostic or research purposes.
GPTomics
research
scientific-computing
471
bio-chipseq-peak-annotation
Annotate ChIP-seq peaks to genomic features and nearest genes. Classify peaks as promoter, exon, intron, or intergenic using ChIPseeker (R), HOMER annotatePeaks.pl (CLI), or Python (pandas/pyranges). Supports pre-built annotation databases and custom GTF files. Handles promoter definition, feature priority, category collapsing, and signed distance-to-TSS. Use when assigning genomic context to ChIP-seq peaks or linking peaks to target genes.
GPTomics
research
scientific-computing
471
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.
GPTomics
research
bioinformatics
471
bio-alignment-files-bam-statistics
Generate alignment statistics using samtools flagstat, stats, depth, and coverage. Use when assessing alignment quality, calculating coverage, or generating QC reports.
GPTomics
research
bioinformatics
471
bio-alignment-sorting
Sort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.
GPTomics
research
bioinformatics
471
bio-alignment-indexing
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam. Use when enabling random access to alignment files or fetching specific genomic regions.
GPTomics
research
bioinformatics
471
bio-chipseq-motif-analysis
De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences.
GPTomics
research