데이터·AI 스킬

Prepare Hi-C matrices, which record contacts between DNA regions, using cooler and cooltools. Balance measurement bias, calculate expected contacts by distance, and create observed-to-expected ratios for examining enriched interactions.

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• Balance Hi-C contact matrices
• Create observed-to-expected ratio matrices
• Combine raw matrices from replicates

Use HUMAnN3 to estimate the functional potential of microbial communities from metagenomic reads. Generate gene family and metabolic pathway abundance tables, combine samples, examine species contributions, and compare pathway abundances across conditions.

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• Estimate metabolic pathway abundances
• Combine and normalize results across samples
• Examine species contributions to pathways

Analyze microbial diversity within and between samples using R. Calculate diversity measures, visualize community differences, and test whether sample groups differ in composition, with checks for sequencing depth and variation within groups.

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• Compare microbial diversity across sample groups
• Visualize differences in community composition
• Check whether sequencing depth is sufficient

Classify sequencing reads from mixed biological samples with Kraken2 to identify represented organisms. Covers reference database setup, paired reads, filtering settings, separating unclassified reads, and summarizing species or genus results before abundance estimation.

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• Identifying organisms in metagenomic samples
• Summarizing classifications by species or genus
• Separating classified and unclassified reads

Turn Hi-C data, which measures contacts between genome regions, into heatmaps and comparison figures. Overlay domain boundaries and loops, plot contacts from a chosen location, and combine contact maps with genomic annotation tracks.

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• Plot contact maps for a selected genome region
• Overlay domain boundaries and loops
• Compare contact patterns between two samples

Analyze serial liquid biopsy measurements to track ctDNA, tumor-derived DNA in blood. Summarize tumor fraction trends, mutation clearance, and rises after the lowest measurement through plots and structured reports using specified response thresholds.

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• Plot ctDNA trends during treatment
• Track mutation decline and clearance
• Flag tumor fraction rises after the lowest measurement

Analyze methylation, a chemical marking of DNA, in cell-free DNA samples for cancer detection research. Extract measurements, compare cancer and normal samples, and estimate tissue contributions using reference methylation profiles.

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• Extract methylation measurements from bisulfite sequencing data
• Compare methylation in cancer and normal samples
• Estimate tissue contributions to cell-free DNA

Analyze metabolite measurements in R to find differences between experimental groups and visualize sample patterns. Use statistical tests, classification models, and performance measures to rank relevant metabolites and explore potential biomarkers, which are measurable indicators of biological conditions.

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• Finding metabolites that differ between experimental groups
• Exploring sample patterns and outliers with PCA
• Building and evaluating group classification models

Use medaka to correct errors in genome assemblies and call haploid variants from Oxford Nanopore reads. Select a matching neural network model, process existing alignments or raw reads, and filter variant results by quality.

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• Polishing an assembly using only Oxford Nanopore reads
• Calling haploid variants from an existing BAM alignment
• Polishing a selected genomic region

Assess ONT or PacBio long-read sequencing data with read length plots, quality distributions, and summary reports. Filter reads by length and quality thresholds, and trim read ends or adapters before assembly or alignment.

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• Assessing the quality of an ONT or PacBio sequencing run
• Filtering reads before assembly or alignment
• Reviewing read length and quality distributions

Calculate concentrations of selected metabolites from MRM/SRM measurements, which track specific molecular signals. Use calibration curves and internal reference compounds, apply dilution corrections, and assess measurement accuracy, variability, detection limits, and quantification limits.

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• Calculate metabolite concentrations from calibration curves
• Normalize signals using internal standards
• Assess accuracy, precision, and detection limits

Evaluate imaging mass cytometry (IMC) images before analysis. Measure signal relative to noise, compare channels, inspect tissue coverage and acquisition artifacts, and summarize cell segmentation quality across samples in tables and visual reports.

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• Check IMC image quality before analysis
• Investigate noisy channels and unexpected correlations
• Inspect tissue fragmentation and cell segmentation quality

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

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

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

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

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

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

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

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

Add gene names and biological context to CNVs, regions where DNA copy numbers change. Cross-reference cancer genes and ClinVar records, analyze affected pathways, and produce tables that support interpretation of copy number results.

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• Adding overlapping gene names to CNV regions
• Finding cancer-associated genes in CNVs
• Checking ClinVar records within CNV regions

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