Data & AI skills

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.

• Find genes with expression changes between treatment and control
• Analyze condition effects while accounting for batch differences
• Moderate noisy fold-change estimates for gene ranking

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.

• Discover cell populations without predefined gates
• Label cell clusters using marker expression
• Compare cell population proportions across samples

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.

• Combine CRISPR screens from separate experimental batches
• Normalize differences in sequencing depth
• Check correlations between technical replicates

Turn DESeq2 or edgeR results into annotated lists of genes with expression differences. Filter by statistical significance and change size, compare methods, and export CSV files, Excel workbooks, or ranked gene lists for further analysis.

• Filter genes by significance and expression change
• Add gene names and descriptions to result tables
• Share results in CSV or multi-sheet Excel files

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.

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

Visualize copy number variation, changes in the number of DNA copies, using genome-wide plots, chromosome views, and sample heatmaps. Turn CNVkit or GATK results into figures for inspection, comparison, and publication.

• Inspect DNA copy number changes across the genome
• Examine a specific chromosome or genomic region
• Compare copy number patterns across samples

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.

• Correcting signal drift during long CyTOF runs
• Aligning measurement batches with shared reference samples
• Comparing marker distributions before and after normalization

Analyze cell-free DNA fragment patterns with FinaleToolkit, Griffin, or pysam. Extract length distributions, short-to-long fragment ratios, and DNA packaging patterns to produce features for research into tumor detection and tissue of origin.

• Summarize cfDNA fragment lengths and short-to-long ratios
• Build genome-wide fragmentation profiles
• Analyze nucleosome patterns around regulatory sites

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.

• Correcting signal overlap between fluorescent labels
• Calculating compensation matrices from single-stained controls
• Transforming flow cytometry or CyTOF data

Evaluate guide RNA specificity by searching for unintended CRISPR cutting sites across a genome with Cas-OFFinder. Summarize sequence mismatches and use CFD scoring, an estimate of cutting likelihood, to compare potential off-target sites.

• Search a genome for potential CRISPR off-target sites
• Summarize off-target counts by mismatch number
• Rank candidate sites using CFD scores

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.

• Read compressed FASTA or FASTQ files
• Save sequence records directly in compressed form
• Convert gzip files to indexable BGZF

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.

• Assign genetic lineages to viral sequences
• Track weekly variant proportions
• Find mutations increasing in frequency

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.

• Create a sample-specific sequence from VCF variants
• Export each haplotype as a separate sequence
• Mask regions with insufficient sequencing coverage

Detect copy number variants, regions where DNA copies increase or decrease, from whole-genome or exome data using GATK. Follow workflows for tumor-normal comparisons or germline analysis, with read counting, noise removal, segment calling, and plots.

• Detecting copy number changes in tumor-normal samples
• Analyzing germline copy number variants across a cohort
• Adding copy number analysis to a GATK pipeline

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.

• Assigning sequence types to bacterial isolates
• Comparing core genome allele profiles across isolates
• Clustering isolates to investigate potential outbreak groups

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.

• Selecting candidate sgRNAs for a target gene list
• Adding control guides to a screening library
• Filtering guides with excessive unintended genomic matches

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.

• Filtering FASTQ reads by average or minimum quality
• Trimming low-quality read ends
• Checking quality changes across read positions

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.

• Filter rare variants using gnomAD and ClinVar annotations
• Rank candidates with CADD and REVEL prediction scores
• Narrow candidates by dominant or recessive inheritance

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.

• Screen bacterial genomes for resistance genes
• Summarize detected genes by drug class
• Track resistance gene prevalence over time

Calculate tumor mutational burden, the number of protein-altering mutations per megabase, from tumor sequencing files. Apply quality filters, normalize by the tested region, classify results using selected thresholds, and summarize multiple samples.

• Calculate tumor mutational burden from panel or exome data
• Filter variants by depth, allele frequency, and population frequency
• Summarize mutation burden across multiple tumor samples

Detect DNA copy number gains and losses in exome or targeted sequencing data with CNVkit. Process tumor-normal pairs or tumor-only samples, adjust calls for tumor purity and ploidy, and produce plots and exported result files.

• Detect copy number changes in exome data
• Analyze tumors without a matched normal sample
• Plot copy number profiles across samples

Adjust RNA-seq expression data for batch effects, unwanted differences between measurement groups. Use methods suited to raw or normalized data, compare plots before and after correction, and account for batches during differential expression testing.

• Correcting batch effects before clustering expression data
• Accounting for known batches in differential expression tests
• Estimating hidden sources of variation

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.

• Finding genes whose expression changes over time
• Comparing temporal expression patterns between treatment and control
• Detecting transient increases or decreases in gene expression

Reconstruct likely pathogen transmission chains from genomic data and sampling dates with TransPhylo. Estimate possible transmission pairs and unsampled cases, visualize the resulting network, and identify cases associated with many inferred secondary infections.

• Reconstructing likely transmission chains during an outbreak
• Estimating unsampled cases in a transmission chain
• Visualizing inferred transmission pairs as a network