数据与 AI技能

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

Detect DNA copy number gains and losses in exome or targeted sequencing data with CNVkit. Process tumor-normal pairs or tumor-only samples, adjust calls for tumor purity and ploidy, and produce plots and exported result files.

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• Detect copy number changes in exome data
• Analyze tumors without a matched normal sample
• Plot copy number profiles across samples

Calculate tumor mutational burden, the number of protein-altering mutations per megabase, from tumor sequencing files. Apply quality filters, normalize by the tested region, classify results using selected thresholds, and summarize multiple samples.

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• Calculate tumor mutational burden from panel or exome data
• Filter variants by depth, allele frequency, and population frequency
• Summarize mutation burden across multiple tumor 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

Check flow cytometry and CyTOF data quality before analysis. Use R examples to detect unstable flow rates, changing signals, measurements at detector limits, and dead cells, then produce cleaned data, plots, and sample summaries.

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• Check flow rate and signal stability before analysis
• Remove measurements at detector limits and exclude dead cells
• Identify samples with quality issues across a batch

Evaluate guide RNA specificity by searching for unintended CRISPR cutting sites across a genome with Cas-OFFinder. Summarize sequence mismatches and use CFD scoring, an estimate of cutting likelihood, to compare potential off-target sites.

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• Search a genome for potential CRISPR off-target sites
• Summarize off-target counts by mismatch number
• Rank candidate sites using CFD scores

Detect and filter doublets, measurements containing multiple cells, in flow cytometry and CyTOF data. Use scatter signals, DNA measurements, or event length to prepare single-cell data for clustering and quantitative analysis.

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• Remove doublets before clustering
• Select single cells using scatter signals
• Filter CyTOF data using DNA or event length

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

Assess pooled CRISPR screen quality before selecting candidate genes. Check guide RNA coverage, read-count balance, agreement between repeated experiments, and recovery of known essential genes, then produce diagnostic plots and a summary of quality checks.

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• Checking screen quality before selecting candidate genes
• Investigating missing guides and uneven read counts
• Comparing agreement between repeated experiments

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

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

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

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

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

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

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

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

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

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 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

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

Calculate polygenic risk scores, which combine effects from many genetic variants, using GWAS summary statistics and individual genotypes. The skill covers PRSice-2, LDpred2, PRS-CS, score normalization, population percentiles, and predictive performance evaluation.

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• Calculate polygenic risk scores for a cohort
• Choose a workflow for PRSice-2, LDpred2, or PRS-CS
• Convert scores to standardized values and percentiles

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