Data & Analytics

Data analysis, BI, visualization, datasets, statistics, and ML workflows

skills in category: 13,012
pages available: 543

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Showing 1,897–1,920 of 13,012 skills

Bio De Edger BasicsAPerform differential expression analysis using edgeR in R/Bioconductor. Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing. Use when performing DE analysis with edgeR.datagoexpressVotes: 0GitHub stars: 2
Bio De Deseq2 BasicsAPerform differential expression analysis using DESeq2 in R/Bioconductor. Use for analyzing RNA-seq count data, creating DESeqDataSet objects, running the DESeq workflow, and extracting results with log fold change shrinkage. Use when performing DE analysis with DESeq2.dataexpressVotes: 0GitHub stars: 2
Bio Data Visualization Genome TracksACreate genome browser-style visualizations showing multiple data tracks (coverage, peaks, genes) using pyGenomeTracks, Gviz, and IGV. Use when visualizing genomic data at specific loci with multiple aligned tracks.datapythonbashVotes: 0GitHub stars: 2
Bio Data Visualization Circos PlotsACreate circular genome visualizations with Circos and pyCircos. Display multi-track data including ideograms, genes, variants, CNVs, and interaction arcs. Use when creating circular genome visualizations.datapythonbashVotes: 0GitHub stars: 2
Bio Crispr Screens Screen QcAQuality control for pooled CRISPR screens. Covers library representation, read distribution, replicate correlation, and essential gene recovery. Use when assessing screen quality before hit calling or diagnosing poor screen performance.datapythongoVotes: 0GitHub stars: 2
Bio Crispr Screens Mageck AnalysisAMAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.datapythongoVotes: 0GitHub stars: 2
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Bio Crispr Screens Jacks AnalysisAJACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments.datapythongoVotes: 0GitHub stars: 2
Bio Crispr Screens Hit CallingAStatistical methods for calling hits in CRISPR screens. Covers MAGeCK, BAGEL2, drugZ, and custom approaches for identifying essential and resistance genes. Use when identifying significant genes from screen count data after QC passes.datapythonbashVotes: 0GitHub stars: 2
Bio Crispr Screens Crispresso EditingACRISPResso2 for analyzing CRISPR gene editing outcomes. Quantifies indels, HDR efficiency, and generates comprehensive editing reports. Use when analyzing amplicon sequencing data from CRISPR editing experiments to assess editing efficiency.datapythonbashVotes: 0GitHub stars: 2
Bio Crispr Library DesignACRISPR library design for genetic screens. Covers sgRNA selection, library composition, control design, and oligo ordering. Use when designing custom sgRNA libraries for knockout, activation, or interference screens.datapythongoVotes: 0GitHub stars: 2
Bio Copy Number Gatk CnvACall copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.datagobashVotes: 0GitHub stars: 2
Bio Copy Number Cnvkit AnalysisADetect copy number variants from targeted/exome sequencing using CNVkit. Supports tumor-normal pairs, tumor-only, and germline CNV calling. Use when detecting CNVs from WES or targeted panel sequencing data.datapythonbashVotes: 0GitHub stars: 2
Bio Clip Seq Clip Peak CallingACall protein-RNA binding site peaks from CLIP-seq data using CLIPper, PureCLIP, or Piranha. Use when identifying RBP binding sites from aligned CLIP reads.datapythongoVotes: 0GitHub stars: 2
Bio Clinical Databases Variant PrioritizationAFilter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.datapythongoVotes: 0GitHub stars: 2
Bio Clinical Databases Myvariant QueriesAQuery myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously.datapythonapiVotes: 0GitHub stars: 2
Bio Clinical Databases Gnomad FrequenciesAQuery gnomAD for population allele frequencies to assess variant rarity. Use when filtering variants by population frequency for rare disease analysis or determining if a variant is common in the general population.datapythongoVotes: 0GitHub stars: 2
Bio Bedgraph HandlingACreate, manipulate, and convert bedGraph files for genome browser visualization. Covers bedGraph format, conversion to/from bigWig, normalization, and signal processing. Use when handling coverage and signal tracks from ChIP-seq, ATAC-seq, or RNA-seq.datapythonbashVotes: 0GitHub stars: 2
Bio Atac Seq Atac QcAQuality control metrics for ATAC-seq data including fragment size distribution, TSS enrichment, FRiP, and library complexity. Use when assessing ATAC-seq library quality before or after peak calling to identify problematic samples.datapythongoVotes: 0GitHub stars: 2
Bio Alignment ValidationAValidate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.datapythongoVotes: 0GitHub stars: 2
Bio Alignment StatisticsACalculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.datapythonVotes: 0GitHub stars: 2
Bio Alignment Msa ParsingAParse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.datapythonVotes: 0GitHub stars: 2
BigqueryABigQuery Expert Engineer Skill - Comprehensive guide for GoogleSQL queries, data management, performance optimization, and cost managementdatagosqlVotes: 0GitHub stars: 2
Bigquery 2ABigQuery query optimization, feature engineering, and BigQuery ML models for recommendation systems. Use when writing optimized SQL queries, implementing partitioning/clustering, creating materialized views, engineering user/product/interaction features, handling schema evolution, setting up streaming with Storage Write API, or building Matrix Factorization models.datapythongoVotes: 0GitHub stars: 2
Batch Processing 1APatterns for processing multiple samples efficiently. Trigger when analyzing cohorts, processing sample batches, or parallelizing analyses. Provides patterns for sample sheets, parallel processing, and result aggregation while maintaining streaming memory efficiency.datapythonVotes: 0GitHub stars: 2