데이터·AI 스킬

Preprocess Illumina sequencing reads with fastp by removing adapter sequences, filtering by quality and length, and optionally removing duplicates. Produce cleaned FASTQ files alongside HTML reports for review and JSON statistics for further processing.

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• Remove adapters and low-quality sections from FASTQ reads
• Preprocess paired-end RNA-seq reads
• Remove poly-G sequences from NovaSeq or NextSeq reads

Remove sequencing adapter sequences from FASTQ files with Cutadapt or Trimmomatic. Handle single-end and paired-end reads, select standard or custom adapters, filter trimmed reads, and check the resulting files with FastQC before alignment.

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• Removing adapter contamination detected by FastQC
• Trimming paired-end reads before alignment
• Removing custom adapter sequences

Build and manage spectral libraries, reference collections for identifying peptides in proteomics data. Use measured or predicted spectra, convert library formats, check quality, and merge libraries for DIA, a mass spectrometry acquisition method.

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• Build a spectral library from experimental search results
• Prepare libraries using predicted spectra and retention times
• Convert libraries into formats used by DIA analysis tools

Identify translated regions in Ribo-seq data, which records ribosome activity along RNA. Use RiboCode to detect known and novel open reading frames, then quantify their translation and compare counts across experimental conditions.

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• Find translated regions beyond annotated coding sequences
• Identify upstream open reading frames
• Quantify translation for each open reading frame

Read, write, and convert molecular files with RDKit and Open Babel. Prepare compound libraries for analysis by checking structures and standardizing chemical representations, including charges, salts, and alternative forms of the same molecule.

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• Load a compound library into Python
• Convert molecular files between formats
• Standardize molecular structures before analysis

Import mass spectrometry files and protein quantification results into Python or R for analysis. Access spectra, organize protein measurements by sample, remove flagged contaminants and reverse hits, and assess missing values across proteins and samples.

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• Load mzML or mzXML files for spectrum analysis
• Filter and extract measurements from MaxQuant results
• Reshape DIA-NN reports into protein-by-sample tables

Analyze gene lists against WikiPathways to find biological pathways with more matching genes than expected. Test ranked expression changes, compare gene groups, and create plots or CSV tables using R tools.

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• Finding pathways enriched in differentially expressed genes
• Analyzing ranked expression changes with GSEA
• Comparing pathways across upregulated and downregulated genes

Extract protein abundance estimates from mass spectrometry data for comparisons between samples. Covers label-free measurements, TMT/iTRAQ chemical tags, and SILAC metabolic labels, with normalization, protein-level summaries, and heavy-to-light ratio calculations.

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• Normalize label-free protein measurements
• Summarize TMT/iTRAQ signals by protein
• Calculate SILAC heavy-to-light ratios

Assign taxonomic names to ASVs, distinct DNA sequences obtained from amplicon processing. Use reference databases such as SILVA, GTDB, or UNITE with DADA2, QIIME2, or IDTAXA, set confidence thresholds, and organize results in phyloseq.

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• Assigning taxonomy to ASVs after DADA2 processing
• Classifying fungal ITS sequences with UNITE
• Setting confidence thresholds for taxonomy assignments

Screen sequencing reads against multiple reference genomes with FastQ Screen to investigate contamination from other species, bacteria, viruses, or adapters. Generate visual reports, combine sample results with MultiQC, and select reads based on their reference matches.

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• Investigating unexpected species in sequencing data
• Checking samples for bacterial or viral contamination
• Comparing contamination reports across samples

Use RDKit to turn molecular structures into numerical properties and fingerprints, which encode structural features. Prepare compound data for machine learning, compare molecular similarity, and filter compounds using drug-likeness criteria.

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• Preparing molecular features for machine learning
• Generating fingerprints for compound similarity analysis
• Filtering compound libraries by drug-likeness criteria

Measure distances, angles, and contacts in protein structures using Bio.PDB. Compare structures through superposition and RMSD, a measure of positional differences, and calculate solvent accessible surface area to examine exposed and buried residues.

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• Measuring atomic distances and backbone or sidechain angles
• Superimposing protein structures and comparing RMSD
• Finding nearby atoms and residue contacts

Edit protein structure files with Biopython Bio.PDB. Covers moving and rotating coordinates, adding or removing atoms and residues, extracting or copying chains, updating atom properties, and constructing or merging structures through Python code.

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• Removing water molecules or hydrogen atoms from structures
• Extracting chains or selected residue ranges
• Moving and rotating structure coordinates

Find biological functions overrepresented in a gene list using clusterProfiler. Analyze Gene Ontology categories describing processes, molecular functions, and cellular locations, convert gene identifiers, choose background genes, and export results with redundant terms reduced.

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• Interpret functions associated with differentially expressed genes
• Convert gene identifiers before enrichment analysis
• Use experimentally tested genes as the analysis background

Analyze how cell types are arranged in imaging mass cytometry data, a method for measuring many cellular markers in tissue images. Build maps of neighboring cells, test spatial enrichment, and export plots and tables describing cell proximity patterns.

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• Test which cell types occur near each other more often
• Compare cell type co-occurrence across distances
• Find regions where CD8 T cells and tumor cells are close

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

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

Use Clair3 to detect inherited single-base changes and small insertions or deletions from aligned ONT or PacBio HiFi long reads. Select platform models, restrict analysis to target regions, and filter variant files by quality.

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• Detect variants from aligned ONT or PacBio HiFi reads
• Limit variant detection to regions specified in a BED file
• Filter variant results by quality or variant type

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

Compare bacterial genomes and metagenomes to track strains, which are groups within a species. Use MASH, sourmash, fastANI, and inStrain to examine sample similarities and genetic variation for outbreak, transmission, or within-host studies.

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• Finding closely related samples in an outbreak investigation
• Comparing bacterial strains across samples
• Tracking strain changes within a host

Assign candidate compounds to signals detected in untargeted metabolomics. Match mass measurements and fragmentation spectra against reference databases, check retention time, and organize annotations with confidence levels for export as a compound identification report.

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• Finding metabolite candidates from measured mass values
• Comparing MS/MS spectra with reference libraries
• Checking compound candidates against retention time

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

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

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