Data analysis, machine learning, LLM apps, and prompt design (9.1K skills)
Analyze DNA methylation data in R with methylKit. Import Bismark files, filter and normalize read coverage, compare samples, and identify methylation differences at individual DNA sites or across fixed genomic regions.
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.
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.
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.
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.
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.
Label cell types in imaging mass cytometry images using napari, an image viewer, and marker overlays. Export labeled cells as training data, extend labels to similar cells, and generate image panels for annotation review.
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.
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.
Identify microbial taxa whose abundance differs between experimental groups while accounting for data measured as relative proportions. Provides R workflows using ALDEx2, ANCOM-BC2, and MaAsLin2 to filter statistical results, handle additional variables, and visualize differences.
Identify nuclei and whole cells in preprocessed IMC or MIBI tissue images using Cellpose or Mesmer. Extract marker intensities, cell shapes, and positions, inspect segmentation overlays, and save labeled images and tables for further single-cell analysis.
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.
Screen bacterial genomes for antimicrobial resistance genes using AMRFinderPlus or ResFinder. The skill helps filter detections, summarize affected drug classes, track changes over time, identify newly observed genes, and prepare surveillance reports.
Assign bacterial strain types using MLST, which compares multiple genes, and cgMLST, which compares core genome genes. Compare isolate profiles, cluster related samples, and extract selected single-base variants for SNP-based typing.
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.
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.
Analyze RNA-seq count data with DESeq2 in R to find genes whose expression differs between conditions. Define experimental factors, account for batch effects, moderate noisy fold-change estimates, and export significant genes and normalized counts.
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.
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.
Select sequence records by length, ID, GC content—the proportion of G and C bases—or matching patterns using Biopython. Combine conditions, exclude unwanted records, sample sequences, and save selected records to separate files.
Analyze RNA-seq count data with edgeR to identify genes whose expression differs between groups. Covers filtering, normalization, statistical testing, adjustment for batch differences, and export of ranked gene results and normalized counts.
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.
Compare alternative splicing, differences in how RNA segments are joined, between experimental groups. Use BAM alignment files or TPM expression estimates to detect changed events and prioritize them by statistical significance and change in inclusion rates.
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.