데이터·AI 스킬

Create sample-specific FASTA sequences by applying recorded genetic variants to a reference with bcftools consensus. Select haplotypes, representing alternative inherited sequences, mask low-coverage regions, and export coordinate mappings between the reference and resulting sequence.

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• Create a sample-specific sequence from VCF variants
• Export each haplotype as a separate sequence
• Mask regions with insufficient sequencing coverage

Find cell populations in flow or mass cytometry data without predefined selection boundaries. Use FlowSOM, Phenograph, and CATALYST to group cells, interpret marker patterns, assign cell type labels, and visualize population proportions across samples.

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• Discover cell populations without predefined gates
• Label cell clusters using marker expression
• Compare cell population proportions across samples

Read and write compressed sequence files with Biopython, including FASTA and FASTQ. Convert between compression formats, count sequence records, and use BGZF, an indexable compression format, to retrieve individual records by ID.

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• Read compressed FASTA or FASTQ files
• Save sequence records directly in compressed form
• Convert gzip files to indexable BGZF

Analyze FASTQ sequencing read quality with Biopython. Summarize quality scores, filter reads, trim low-quality bases, and inspect quality at each position. Convert older FASTQ quality encodings to the standard format when preparing data for further analysis.

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• Filtering FASTQ reads by average or minimum quality
• Trimming low-quality read ends
• Checking quality changes across read positions

Detect copy number variants, regions where DNA copies increase or decrease, from whole-genome or exome data using GATK. Follow workflows for tumor-normal comparisons or germline analysis, with read counting, noise removal, segment calling, and plots.

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• Detecting copy number changes in tumor-normal samples
• Analyzing germline copy number variants across a cohort
• Adding copy number analysis to a GATK pipeline

Select sequence records by length, ID, GC content—the proportion of G and C bases—or matching patterns using Biopython. Combine conditions, exclude unwanted records, sample sequences, and save selected records to separate files.

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• Removing sequences shorter than a chosen length
• Extracting sequences from an ID list
• Selecting sequences by GC content or motifs

Analyze pooled CRISPR screens with MAGeCK, from sequencing read counts to ranked genes. Compare enriched or depleted guides to identify candidate essential or resistance genes, visualize results, and examine pathways across simple or multi-condition experiments.

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• Counting sgRNA reads from FASTQ files
• Finding candidate essential or drug-resistance genes
• Analyzing multi-condition or time-course screens

Prepare flow cytometry data by correcting signal overlap between fluorescent labels and transforming measurement scales for analysis. Calculate or load compensation matrices, apply Logicle or arcsinh transformations, compare results visually, and save processed data and parameters.

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• Correcting signal overlap between fluorescent labels
• Calculating compensation matrices from single-stained controls
• Transforming flow cytometry or CyTOF data

Analyze plasma DNA to detect tumor mutations present at low frequencies. The skill covers variant calling with molecular tags that help suppress sequencing errors, annotation of detected variants, and measurement of known mutation frequencies across samples.

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• Detecting low-frequency mutations in plasma DNA
• Analyzing DNA data with molecular tags
• Tracking known mutation frequencies over time

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

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

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.

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• Finding drug-response annotations for a gene
• Looking up gene-related dosing guidelines
• Classifying metabolism from allele pairs

Analyze tumor mutation data to extract mutational signatures, recurring patterns linked to DNA damage and repair processes. Use SigProfiler or MutationalPatterns to compare results with COSMIC references and visualize each signature’s contribution across samples.

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• Extracting mutational signatures from tumor VCF files
• Comparing extracted patterns with COSMIC references
• Visualizing signature contributions across samples

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

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

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

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.

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• Look up variant details by rsID
• Find rsIDs from genomic coordinates
• Retrieve annotations for multiple variants

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

Infer potential relationships between transcription factors, proteins that control gene expression, and target genes from RNA-seq data. Use GRNBoost2 or GENIE3 to produce scored regulatory networks, filter links, and scale computation across local cores or clusters.

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• Inferring regulatory networks from single-cell RNA-seq data
• Restricting bulk RNA-seq inference to a transcription factor list
• Generating separate networks for different experimental conditions

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.

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• Find transcription factor motifs that vary across samples
• Compare motif accessibility between experimental groups
• Visualize sample patterns with heatmaps and PCA

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.

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• Checking how common a variant is in the population
• Filtering rare disease candidates by frequency
• Comparing frequencies across ancestry groups

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

Compare ATAC-seq samples to find chromatin regions where DNA accessibility changes between groups. Uses DiffBind or DESeq2 to test differences, visualize results, and connect significant regions with nearby genes.

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• Compare chromatin accessibility between treatment and control groups
• Find accessibility changes across cell types or developmental stages
• Visualize differential regions with heatmaps and volcano plots

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