Data & Analytics

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

skills in category: 12,625
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Showing 12,409–12,432 of 12,625 skills

Heatmaps ClusteringACreate clustered heatmaps with row/column annotations using ComplexHeatmap, pheatmap, and seaborn for gene expression and omics data visualization. Use when visualizing expression patterns across samples or identifying co-expressed gene clusters.datapythonexpressVotes: 0GitHub stars: 1,198
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: 1,198
Color PalettesASelect and apply colorblind-friendly palettes for scientific figures using viridis, RColorBrewer, and custom color schemes. Use when selecting colorblind-friendly palettes for figures.datapythongoVotes: 0GitHub stars: 1,198
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: 1,198
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: 1,198
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: 1,198
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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: 1,198
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.datapythongoVotes: 0GitHub stars: 1,198
Batch CorrectionABatch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation.datapythonVotes: 0GitHub stars: 1,198
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: 1,198
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: 1,198
Cnv VisualizationAVisualize copy number profiles, segments, and compare across samples. Create publication-quality plots of CNV data from CNVkit, GATK, or other callers. Use when creating genome-wide CNV plots, sample heatmaps, or chromosome-level visualizations.datapythonbashVotes: 0GitHub stars: 1,198
Cnv AnnotationAAnnotate CNVs with genes, pathways, and clinical significance. Use when interpreting CNV calls or identifying affected genes from copy number analysis.datapythongoVotes: 0GitHub stars: 1,198
Synteny AnalysisAAnalyze genome collinearity and syntenic blocks using MCScanX, SyRI, and JCVI for comparative genomics. Detect conserved gene order, chromosomal rearrangements, and whole-genome duplications. Use when comparing genome structure between species or identifying conserved genomic regions.datapythongoVotes: 0GitHub stars: 1,198
Ancestral ReconstructionAReconstruct ancestral sequences at phylogenetic nodes using PAML and IQ-TREE marginal likelihood methods. Infer ancient protein sequences and trace evolutionary trajectories through sequence history. Use when inferring ancestral states for protein resurrection or tracing evolutionary history.datapythonshellVotes: 0GitHub stars: 1,198
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: 1,198
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: 1,198
Tumor Mutational BurdenACalculate tumor mutational burden from panel or WES data with proper normalization and clinical thresholds. Use when assessing immunotherapy eligibility or characterizing tumor immunogenicity.datapythondatabaseVotes: 0GitHub stars: 1,198
Super EnhancersAIdentifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools. Use when studying cell identity genes, cancer-associated regulatory elements, or master transcription factor binding regions that cluster into large enhancer domains.datapythonbashVotes: 0GitHub stars: 1,198
Peak CallingAChIP-seq peak calling using MACS3 (or MACS2). Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports input control, fragment size modeling, and various output formats including narrowPeak and broadPeak BED files. Use when calling peaks from ChIP-seq alignments.databashVotes: 0GitHub stars: 1,198
Peak AnnotationAAnnotate ChIP-seq peaks to genomic features and genes using ChIPseeker. Assign peaks to promoters, exons, introns, and intergenic regions. Find nearest genes and calculate distance to TSS. Generate annotation plots and statistics. Use when annotating ChIP-seq peaks to genomic features.datagoapiVotes: 0GitHub stars: 1,198
Motif AnalysisADe novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences.datapythongoVotes: 0GitHub stars: 1,198
Differential BindingADifferential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.datagoexpressVotes: 0GitHub stars: 1,198
Virtual ScreeningAPerforms structure-based virtual screening using AutoDock Vina 1.2 for molecular docking. Prepares receptor PDBQT files, generates ligand conformers, defines binding site boxes, and ranks compounds by predicted binding affinity. Use when screening chemical libraries against a protein structure to find potential binders.datapythonVotes: 0GitHub stars: 1,198