데이터·AI 스킬

Analyze CRISPR base editing and prime editing results from sequencing data for a target region. Use CRISPResso2 to quantify intended changes, unintended nearby edits, and DNA insertions or deletions, and compare editing outcomes.

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• Quantify editing efficiency from target-region sequencing data
• Compare ABE and CBE editing efficiency
• Assess unintended nearby edits and insertion or deletion rates

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

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

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

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

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

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

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 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

Design custom CRISPR screening libraries by selecting sgRNAs, short guides that direct editing to target sequences. Covers guide scoring, control selection, checks for unintended genomic matches, library quality summaries, and oligo tables for ordering.

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• Selecting candidate sgRNAs for a target gene list
• Adding control guides to a screening library
• Filtering guides with excessive unintended genomic matches

Load and inspect FCS files containing flow or mass cytometry measurements of cells. Explore measurement channels and sample metadata, extract or combine data, create basic plots, and save files for subsequent analysis.

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• Inspecting FCS measurement channels and metadata
• Loading multiple samples together
• Extracting selected cells or measurement channels

Analyze Hi-C data, which records contacts between DNA regions, to identify active A and inactive B compartments. Visualize their genomic positions, measure compartment strength, compare samples, and export region classifications for further analysis.

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• Classifying A/B compartments from Hi-C data
• Visualizing and measuring compartment strength
• Finding compartment switches between samples

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

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

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

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

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

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

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

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

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

Turn DESeq2 or edgeR results into annotated lists of genes with expression differences. Filter by statistical significance and change size, compare methods, and export CSV files, Excel workbooks, or ranked gene lists for further analysis.

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• Filter genes by significance and expression change
• Add gene names and descriptions to result tables
• Share results in CSV or multi-sheet Excel files

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

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