Data & AI skills

Analyze gene lists against Reactome, a curated database of biological pathways. Find pathways enriched among selected genes or across ranked genes, compare multiple lists, visualize results, and export tables for further review.

• Find pathways enriched among differentially expressed genes
• Analyze ranked genes with GSEA
• Compare pathways across upregulated and downregulated gene lists

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.

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

Analyze chemical modifications added to proteins after production, including phosphorylation, acetylation, and ubiquitination. Filter modification sites by confidence, examine surrounding sequence patterns, and compare site quantities while accounting for changes in overall protein abundance.

• Filtering confident phosphorylation sites from MaxQuant output
• Examining sequence patterns around modification sites
• Comparing modification levels between experimental groups

Integrate RNA, protein, and other molecular measurements with mixOmics to find related features and distinguish sample groups. Covers feature selection, visualizations, classification performance checks, and export of selected features for further analysis.

• Finding related features across RNA and protein measurements
• Selecting features that distinguish experimental groups
• Comparing sample separation and feature networks in plots

Check proteomics data before further analysis by summarizing detected proteins, missing values, measurement distributions, and agreement between replicates. Generate quality plots and summary metrics, and investigate whether processing batches explain differences between samples.

• Review sample quality before downstream proteomics analysis
• Inspect missing values by sample and protein
• Compare replicate correlations and measurement variability

Turn clusterProfiler enrichment results, which identify overrepresented biological functions or pathways, into figures with enrichplot in R. Create summary charts, gene relationship networks, and gene set enrichment plots, customize their appearance, and export PDF or PNG files.

• Summarizing GO or KEGG enrichment results
• Visualizing links between genes and biological terms
• Plotting GSEA scores and gene set distributions

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.

• 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

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.

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

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.

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

Integrate RNA-seq, protein, methylation, and other omics measurements with MOFA2. Discover hidden patterns of variation shared across data types or specific to one type, visualize contributing features, and export results for further analysis.

• Find shared patterns across multiple omics datasets
• Identify features contributing to each factor
• Explore factor patterns by experimental condition

Coordinate bioinformatics work by identifying input files and selecting specialized skills. Plan analyses with several steps, combine results into Markdown reports, and collect commands, environment details, and data checksums so the analysis can be repeated.

• Identify biological data files and select an analysis skill
• Chain variant annotation with population diversity scoring
• Combine analysis methods and results in one report

Process paired FASTQ files containing reads from both ends of DNA fragments with Biopython. Keep R1/R2 records together while filtering by quality or length, check pair IDs and counts, and combine or split alternating records.

• Checking R1/R2 record IDs and counts
• Filtering read pairs by quality or length
• Combining paired reads into alternating records

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.

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

Prepare RNA, protein, and methylation datasets for joint analysis in R. Normalize each data type, correct differences between measurement batches, match samples and gene identifiers, handle missing values, and export consistently scaled datasets for integration tools.

• Preparing RNA, protein, and methylation data for joint analysis
• Correcting differences between measurement batches
• Matching samples and gene identifiers across datasets

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.

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

Detect chromatin loops, contacts between distant DNA regions, from Hi-C data using cooltools or chromosight. Filter candidates by score, compare conditions, check CTCF binding sites, and export results for further analysis and visualization.

• Detecting chromatin loops in Hi-C data
• Checking loop anchors against CTCF binding sites
• Finding contacts between promoters and enhancers

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.

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

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.

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

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.

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

Identify genomic regions with DNA methylation differences between conditions or cell types using methylKit, bsseq, and DMRcate. Add gene annotations, visualize methylation patterns, merge nearby regions, and export results for further analysis.

• Finding methylation differences between experimental conditions
• Analyzing methylation regions in WGBS or array data
• Linking detected regions to genes and promoters

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.

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

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.

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

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.

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

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.

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