Data analysis, machine learning, LLM apps, and prompt design (9.1K skills)
Analyze ATAC-seq data with TOBIAS to predict where transcription factors bind DNA. Correct cutting bias, score protected regions, compare binding between conditions, and plot average signals around predicted binding sites.
Retrieve variant frequencies from gnomAD to support rare disease analysis. Check how often a genetic change appears overall or within ancestry groups, compare exome and genome records, and filter candidate variants using a chosen frequency threshold.
Retrieve clinical classifications, population frequencies, and other genetic variant annotations from multiple databases through myvariant.info. Query individual variants or batches, select relevant fields, and organize results into tables for further analysis.
Analyze ATAC-seq fragment lengths to estimate nucleosome positions and occupancy, showing where DNA is wrapped around proteins. Use NucleoATAC and ATACseqQC to identify nucleosome-free regions and visualize patterns around gene transcription start sites.
Look up genetic variants by rsID, a variant identifier, or genomic position. Retrieve dbSNP details and related ClinVar and gnomAD annotations, with examples for batch searches and extracting key fields.
Process collections of biological sequence files with Biopython. Merge or split records, convert file formats, produce CSV summaries of sequence counts and lengths, and organize files by GC content, the proportion of G and C bases.
Look up how genes and genetic variants relate to drug response using PharmGKB annotations and CPIC dosing guidelines. Includes Python examples for classifying drug metabolism from allele pairs and collecting drug–gene information into tables.
Read, write, and convert multiple sequence alignments, files containing biological sequences arranged for comparison, using Biopython. Convert between formats such as Clustal, FASTA, and PHYLIP, extract selected sequences or regions, and process files in batches.
Annotate ChIP-seq peaks with ChIPseeker to connect detected regions to nearby genes and genomic features. Export annotation tables, plot distances from transcription start sites, compare experiments, and examine biological processes associated with peak-linked genes.
Identify enriched regions in aligned ChIP-seq data using MACS3 or MACS2. Choose narrow or broad peaks, compare against input controls, adjust fragment settings, and export peak coordinates or signal tracks for visualization.
Assess ChIP-seq experiment quality before further analysis. Calculate enrichment, read duplication, and agreement between replicate experiments, then review quality metrics and plots to understand sample quality and identify potential problems.
Estimate causal effects between exposures and outcomes from GWAS summary statistics using genetic variants as instruments, or indirect measures of exposure. Compare methods, assess instrument strength, examine sensitivity, and visualize results with TwoSampleMR.
Infer HLA types, genetic variants involved in immune recognition, from DNA or RNA sequencing data using OptiType, HLA-HD, or arcasHLA. Extract relevant reads, organize allele calls, merge sample results, and check listed drug associations.
Compare ChIP-seq peaks, genomic regions enriched for protein binding, across experimental conditions with DiffBind. Use replicate samples to calculate binding changes and statistical significance, visualize sample relationships, and export results as CSV or BED files.
Narrow genetic association signals from GWAS, a genome-wide association study, to likely causal variants using SuSiE or FINEMAP. Estimate probabilities for individual variants and build candidate sets to prioritize functional follow-up experiments.
Test whether trait associations and gene expression signals share a causal genetic variant. Using R, the skill compares probabilities for competing explanations, checks sensitivity to prior assumptions, and supports analyses involving multiple causal variants or traits.
Check Mendelian randomization results for bias from genetic variants that affect an outcome through other pathways. Use outlier removal, regression diagnostics, and direction checks to compare corrected estimates and assess how consistently different methods support the findings.
Analyze whether gene expression, DNA methylation, or other molecular measurements carry the effect of a genetic variant to an outcome. Estimate direct and indirect effects, their uncertainty, and the proportion explained by the intermediate measurement.
Analyze ChIP-seq or ATAC-seq peak regions for DNA motifs, recurring sequence patterns. Use HOMER and MEME-ChIP to discover motifs, test known motif enrichment, locate matches, and summarize results with sequence logos.
Turn ChIP-seq signals and peaks into heatmaps, average signal plots, and genome browser tracks. Use deepTools and R tools to compare samples and examine patterns around gene starts, peaks, or selected genomic regions.
Compare two DNA, RNA, or protein sequences with Biopython. Align entire sequences or find matching regions, adjust scoring rules, measure identical positions and gaps, and export alignments in formats used by other analysis tools.
Analyze ATAC-seq data with chromVAR to compare accessibility at motifs, DNA patterns recognized by transcription factors. Calculate sample scores, rank variable motifs, test group differences, and visualize patterns with heatmaps and PCA.
Calculate statistics for aligned DNA or protein sequences using Python. Measure sequence identity, conserved positions, substitutions, and gaps to compare alignment quality and identify regions with little variation or substantial differences.
Read conversion steps, retention curves, and recorded user actions to investigate why people leave a product. Check tracking issues first, then produce ranked design hypotheses with clear evidence that would support or rule out each explanation.