데이터·AI 스킬

Assign cell types to segmented imaging mass cytometry (IMC) data using protein marker expression. Apply marker thresholds, group similar cells, or classify against labeled reference data, then visualize cell locations and summarize cell type proportions.

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• Assigning cell types using CD45, CD3, CD8, and CD4 thresholds
• Annotating cell clusters from their protein marker patterns
• Predicting cell types using labeled reference IMC data

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

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

Align Oxford Nanopore and PacBio sequencing reads to a reference genome with minimap2. Choose settings for each read type and produce sorted alignment files for downstream variant detection or analysis of sequencing coverage.

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• Align Oxford Nanopore reads to a reference genome
• Prepare PacBio HiFi or CLR alignments
• Align long RNA reads across splice sites

Preprocess LC-MS metabolomics data with MS-DIAL, from detecting peaks and matching them across samples to assigning metabolite candidates. Prepare filtered measurement tables for further analysis in R or Python, or export data for MetaboAnalyst.

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• Preprocessing LC-MS data through the MS-DIAL GUI
• Filtering and transforming MS-DIAL exports in Python
• Preparing measurement tables for MetaboAnalyst

Process PacBio Iso-Seq data to discover and quantify full-length transcript variants, called isoforms. Follow read cleanup, clustering, genome alignment, and quality checks, then classify known and novel variants and examine differences in isoform usage.

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• Discovering full-length transcript variants in Iso-Seq data
• Classifying known and novel splice variants
• Checking isoform quality and filtering artifacts

Analyze DNA methylation in Oxford Nanopore data without bisulfite conversion. Use modkit to summarize modified-base information into per-site methylation frequencies, inspect sample statistics, and focus analysis on CpG islands or promoter regions.

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• Calculate methylation frequencies at individual sites
• Summarize methylation in CpG islands
• Analyze methylation in promoter regions

Prepare metabolomics measurements for statistical analysis in R. Correct signal drift and differences between measurement batches, handle missing values, normalize intensities, and compare quality before and after correction using reproducibility metrics and sample plots.

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• Correct signal drift using quality control samples
• Adjust differences between measurement batches
• Filter and fill missing measurements

Predict how strongly peptides bind to MHC proteins, which present fragments to T cells. Scan protein sequences and compare HLA types to identify candidate immune targets for vaccine design or neoantigen research.

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• Screening protein sequences for candidate T-cell epitopes
• Comparing peptide binding across HLA types
• Selecting candidates for vaccine design

Explore which antigen fragments T-cell receptors may recognize. The skill covers ERGO-II setup, matching receptor sequences against known examples in VDJdb, grouping similar sequences, and summarizing candidate recognition patterns across a receptor collection.

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• Prepare ERGO-II for receptor–antigen recognition predictions
• Match receptor sequences to known epitopes in VDJdb
• Group receptors with similar CDR3 sequences

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

Prepare IMC and MIBI images for cell segmentation, the process of separating individual cells. Load MCD or TIFF data, remove unusually bright pixels, normalize channel signals, and correct signal leakage between channels.

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• Extracting TIFF images from raw MCD files
• Removing unusually bright pixels and normalizing signals
• Correcting signal leakage between channels

Compare Hi-C matrices, which record contacts between genome regions, across experimental conditions. Calculate contact changes, test differences using biological replicates, and produce comparison maps and tables of contacts with statistically significant changes.

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• Comparing Hi-C contact maps between conditions
• Testing contact differences using biological replicates
• Examining compartment switches and TAD boundary changes

Turn raw LC-MS metabolomics data into a table of measured signal intensities using R and xcms. Detect peaks, align their measurement times across samples, match corresponding signals, and recover missed peaks for downstream analysis.

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• Build a feature table from raw LC-MS data
• Align retention times across samples
• Check preprocessing quality with plots and PCA

Analyze 16S or ITS amplicon data, sequences from amplified marker regions, through the QIIME2 command line. Import reads, remove noise, assign taxonomy, compare microbial diversity and abundance between groups, and retain a record of processing steps.

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• Analyze 16S or ITS data through the QIIME2 command line
• Track processing history for reproducible analysis
• Compare microbial diversity and abundance between groups

Identify TADs, genome regions with frequent internal contacts, from Hi-C data using cooltools or HiCExplorer. Calculate boundary scores, extract regions, compare boundaries across conditions, and export results as plots or genomic coordinate files.

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• Detecting TAD regions and boundaries in Hi-C data
• Comparing boundary positions across conditions
• Summarizing and plotting TAD sizes

Screen bacterial genomes and metagenomes for antimicrobial resistance genes using AMRFinderPlus, ResFinder, and CARD/RGI. Compare database results, summarize resistance by drug class, and organize findings across samples for resistance profiling.

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• Screening clinical isolates for resistance genes
• Comparing resistance profiles across surveillance samples
• Measuring resistance gene abundance in metagenomes

Estimate microbial species abundance from Kraken2 classification reports using Bracken. Reassign reads from broader taxonomic groups to species, calculate relative abundance, filter low-count groups, and combine sample results into a table for comparison.

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• Estimate species abundance from Kraken2 reports
• Combine multiple samples into an abundance matrix
• Filter taxa with low read counts

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 pVACtools to predict neoantigens, potential immune targets created by tumor mutations. Combine patient HLA types with annotated variants, then rank peptide candidates using predicted binding, mutation frequency, and available gene expression data.

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• Find neoantigen candidates from tumor variants
• Predict peptide binding for patient HLA types
• Prioritize candidates for cancer vaccine research

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

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

Identify genes whose main transcript form changes between conditions using IsoformSwitchAnalyzeR. Assess predicted effects on protein domains, protein production, and transcript degradation, then visualize individual switches and summarize patterns across the analyzed genes.

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• Comparing transcript usage between control and treatment
• Investigating protein domain changes caused by transcript switches
• Assessing changes in coding potential and degradation sensitivity

Analyze lipid measurements from LC-MS, a method combining separation and mass measurement. Organize lipid classes and chain composition, compare abundance between conditions, visualize changes, and relate results to lipid pathways using lipidr and annotation resources.

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• Analyze lipid measurements exported from LipidSearch or MS-DIAL
• Find lipids whose abundance differs between conditions
• Compare lipid classes, chain lengths, and saturation patterns