数据与 AI技能

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

Analyze time-series RNA-seq data to find genes whose expression changes over time. Compare temporal patterns between conditions, detect transient responses, group similar profiles, and visualize results with expression curves and heatmaps using R analysis methods.

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• Finding genes whose expression changes over time
• Comparing temporal expression patterns between treatment and control
• Detecting transient increases or decreases in gene expression

Identify cell populations in flow cytometry data using manual boundaries or automated methods. Organize populations into a hierarchy, extract matching cells, summarize counts, visualize boundaries, and save or export the analysis.

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• Extract cells that meet marker conditions
• Apply the same gating template across samples
• Inspect population counts and gate plots

Normalize guide RNA counts from CRISPR screens performed in separate batches. Assess technical differences with principal component analysis and replicate correlations, apply batch correction, and calculate treatment changes while accounting for batch structure.

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• Combine CRISPR screens from separate experimental batches
• Normalize differences in sequencing depth
• Check correlations between technical replicates

Visualize copy number variation, changes in the number of DNA copies, using genome-wide plots, chromosome views, and sample heatmaps. Turn CNVkit or GATK results into figures for inspection, comparison, and publication.

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• Inspect DNA copy number changes across the genome
• Examine a specific chromosome or genomic region
• Compare copy number patterns across samples

Analyze RNA-seq count data with DESeq2 in R to find genes whose expression differs between conditions. Define experimental factors, account for batch effects, moderate noisy fold-change estimates, and export significant genes and normalized counts.

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• Find genes with expression changes between treatment and control
• Analyze condition effects while accounting for batch differences
• Moderate noisy fold-change estimates for gene ranking

Adjust RNA-seq expression data for batch effects, unwanted differences between measurement groups. Use methods suited to raw or normalized data, compare plots before and after correction, and account for batches during differential expression testing.

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• Correcting batch effects before clustering expression data
• Accounting for known batches in differential expression tests
• Estimating hidden sources of variation

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 CRISPR sequencing data with CRISPResso2 to measure editing efficiency, insertions and deletions, and template-directed repair. Generate editing reports and allele frequency tables, process multiple samples, and compare outcomes between experiments.

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• Measure editing efficiency at a target site
• Quantify template-directed repair efficiency
• Process multiple samples or pooled amplicons

Convert biological sequence files between FASTA, FASTQ, GenBank, and EMBL using Biopython. Handle required annotations, process multiple files, and identify when conversion discards information such as sequence quality scores or annotated biological features.

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• Converting GenBank files to FASTA
• Preparing sequence files for another analysis tool
• Batch converting files in a directory

Filter and rank variants from exome or genome sequencing for rare disease analysis. Combine population frequency, clinical annotations, predicted effects, and inheritance patterns to produce prioritized candidate lists and evidence-based tiers for further review.

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• Filter rare variants using gnomAD and ClinVar annotations
• Rank candidates with CADD and REVEL prediction scores
• Narrow candidates by dominant or recessive inheritance

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 inherited single-base changes and small insertions or deletions with GATK. The skill covers single-sample analysis, joint analysis across samples, and quality filtering, with workflows for both large cohorts and smaller datasets.

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• Calling germline variants from a single BAM file
• Joint genotyping across multiple samples
• Filtering variants in small datasets

Identify significant gene hits from CRISPR screen count data after quality checks. Use BAGEL2, drugZ, or Python methods to find essential and resistance gene candidates, compare results across methods, and visualize gene rankings.

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• Identify essential gene candidates with BAGEL2
• Find drug sensitivity or resistance gene candidates
• Compare hits across MAGeCK, BAGEL2, and drugZ

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

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

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

Normalize CyTOF and high-parameter flow cytometry data using calibration beads, particles with reference signals. Covers signal drift correction, alignment across experimental batches, before-and-after plots, and export of normalized, bead-free FCS files.

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• Correcting signal drift during long CyTOF runs
• Aligning measurement batches with shared reference samples
• Comparing marker distributions before and after normalization

Analyze multiple CRISPR screens together with JACKS to estimate gene effects while accounting for differences in guide RNA effectiveness. Identify essential genes, assess guide performance, and visualize results for comparison across experiments.

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• Joint analysis of multiple CRISPR screens
• Ranking candidate essential genes
• Finding guide RNAs with low effectiveness

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

Compare cell population frequencies and marker expression between experimental groups in cytometry data. Use statistical tests, including models for repeated measurements, and summarize differences with heatmaps, abundance plots, and exported result tables.

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• Compare cell population frequencies between conditions
• Test marker expression differences within cell clusters
• Analyze paired or repeated measurements

Assign bacterial strain types using MLST, which compares multiple genes, and cgMLST, which compares core genome genes. Compare isolate profiles, cluster related samples, and extract selected single-base variants for SNP-based typing.

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• Assigning sequence types to bacterial isolates
• Comparing core genome allele profiles across isolates
• Clustering isolates to investigate potential outbreak groups

Classify viral sequences into genetic lineages with Nextclade or pangolin, then summarize their distribution and quality checks. Use collection dates to track weekly variant proportions and flag mutations whose frequency has increased between earlier and recent samples.

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• Assign genetic lineages to viral sequences
• Track weekly variant proportions
• Find mutations increasing in frequency