데이터·AI 스킬

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.

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• Map nucleosome positions from ATAC-seq data
• Identify nucleosome-free regions near promoters
• Visualize patterns around transcription start sites

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.

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• Estimating exposure effects from GWAS summary statistics
• Comparing estimates across Mendelian randomization methods
• Checking instrument strength and sensitivity to individual variants

Design protein binders for a target structure by jointly optimizing their backbone and amino acid sequence. Run configurable design campaigns with built-in AF2 validation, then review candidate structures, sequences, and quality metrics for experimental follow-up.

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• Designing protein binders for a target structure
• Specifying binding sites and binder length ranges
• Choosing design protocols for screening or final candidates

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.

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• Preparing HLA typing data for transplant matching
• Determining HLA types for neoantigen prediction
• Checking HLA alleles against listed drug associations

Predict how candidate compounds may be absorbed, processed, and cleared by the body, along with toxicity risks. Combine these estimates with drug-likeness scores and structural alerts to filter compounds and prioritize leads for further study.

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• Comparing predicted ADMET properties across candidate compounds
• Flagging structures that may cause false positives in assays
• Filtering compounds by drug-likeness criteria

Convert raw Nanopore sequencing signals into DNA sequences with Dorado. Covers choosing accuracy models, configuring GPU processing, detecting chemically modified bases, filtering reads by quality, and generating reports before sequence alignment.

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• Converting raw Nanopore signals into DNA sequences
• Choosing a model for speed or accuracy
• Detecting modified bases

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.

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• Check whether GWAS and gene expression signals share a causal variant
• Assess how prior assumptions affect colocalization results
• Analyze regions with multiple causal variants

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.

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• Detecting narrow peaks for transcription factors
• Finding broad regions of histone enrichment
• Calling peaks from paired-end sequencing data

Retrieve predicted protein structures from AlphaFold DB using UniProt identifiers. Download structure files, examine confidence scores that indicate prediction reliability, and summarize multiple proteins or obtain species datasets for larger structural analyses.

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• Downloading protein structures by UniProt identifier
• Checking confidence across protein regions
• Summarizing confidence scores for multiple proteins

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.

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• Test whether gene expression mediates a variant's effect on disease
• Compare mediation results across candidate genes
• Explore pathways linking methylation, expression, and disease

Manage measurement matrices alongside descriptive information about cells and genes in Python. Create and save AnnData objects, filter annotated data, combine experimental batches, and handle large datasets using sparse storage or disk-backed access.

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• Organizing single-cell RNA-seq data with metadata
• Reading and saving h5ad or zarr files
• Combining datasets with batch labels

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.

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• Compare two sequences across their full length
• Find the best matching region between sequences
• Measure identical positions, mismatches, and gaps

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.

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• Check ChIP-seq quality before downstream analysis
• Measure read enrichment within peaks
• Assess agreement between replicate experiments

Identify open chromatin regions, where DNA is accessible, from aligned ATAC-seq data using MACS3. Apply ATAC-specific settings, process multiple samples, generate peak and signal files, and assess reproducibility across experimental replicates.

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• Finding open chromatin regions in aligned ATAC-seq data
• Processing multiple samples with consistent peak-calling settings
• Detecting peaks from nucleosome-free fragments

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.

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• Checking MR results for bias from alternative biological pathways
• Comparing causal estimates before and after outlier removal
• Checking the direction of genetic variant effects

Assess ATAC-seq data quality using fragment sizes, signal enrichment near transcription start sites, reads within peaks, and library diversity. Create diagnostic plots and summary reports to help identify samples with quality problems.

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• Checking sample quality before or after peak calling
• Inspecting fragment sizes and transcription start site signals
• Assessing mitochondrial reads and library diversity

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.

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• Predict transcription factor binding sites
• Compare binding between two conditions
• Plot average signals around binding sites

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.

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• Converting Clustal alignments to PHYLIP
• Reading multiple alignments from one Stockholm file
• Extracting selected sequences or alignment regions

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.

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• Plotting signal heatmaps around gene starts
• Comparing average signal profiles across samples
• Viewing signals alongside genes in a selected region

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.

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• Discover enriched DNA motifs in ChIP-seq peaks
• Analyze motifs in ATAC-seq footprint regions
• Compare discovered motifs with known databases

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.

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• Merging FASTA files while recording their source filenames
• Splitting sequence files by record count or ID prefix
• Converting GenBank files to FASTA in bulk

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.

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• Prioritize likely causal variants at a GWAS locus
• Analyze summary statistics with an LD reference matrix
• Build credible sets for functional validation

Prepare plasma cell-free DNA sequencing data for downstream analysis with fgbio. Use unique molecular identifiers, tags that distinguish original DNA molecules, to group reads and generate consensus sequences, then filter by quality and inspect fragment lengths.

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• Preparing plasma cfDNA data for downstream analysis
• Grouping reads by molecular tags and generating consensus sequences
• Filtering consensus reads by quality

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.

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• Investigating where users abandon a signup flow
• Interpreting changes in user retention
• Checking tracking issues before changing a design