Data & Analytics

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

skills in category: 12,625
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Showing 1,345–1,368 of 12,625 skills

Bio Read Qc Quality ReportsAGenerate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.datapythongoVotes: 0GitHub stars: 2
Bio Proteomics Spectral LibrariesABuild, manage, and search spectral libraries for proteomics. Use when creating or working with spectral libraries for DIA analysis. Covers DDA-based library generation, predicted libraries (Prosit, DeepLC), and library formats.datapythonbashVotes: 0GitHub stars: 2
Bio Proteomics QuantificationAProtein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.datapythongoVotes: 0GitHub stars: 2
Bio Proteomics Ptm AnalysisAPost-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Covers site localization, motif analysis, and quantitative PTM analysis. Use when analyzing phosphoproteomic data or other modification-enriched samples.datapythongoVotes: 0GitHub stars: 2
Bio Proteomics Proteomics QcAQuality control and assessment for proteomics data. Use when evaluating proteomics data quality before downstream analysis. Covers sample metrics, missing value patterns, replicate correlation, batch effects, and intensity distributions.datapythongoVotes: 0GitHub stars: 2
Bio Proteomics Peptide IdentificationAPeptide-spectrum matching and protein identification from MS/MS data. Use when identifying peptides from tandem mass spectra. Covers database searching, spectral library matching, and FDR estimation using target-decoy approaches.datapythongoVotes: 0GitHub stars: 2
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Bio Proteomics Differential AbundanceAStatistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups.datapythonexpressVotes: 0GitHub stars: 2
Bio Proteomics Dia AnalysisAData-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling.datapythonbashVotes: 0GitHub stars: 2
Bio Proteomics Data ImportALoad and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt. Use when starting a proteomics analysis with raw or processed MS data. Handles contaminant filtering and missing value assessment.datapythonexpressVotes: 0GitHub stars: 2
Bio Population Genetics Scikit Allel AnalysisAPython population genetics with scikit-allel. Read VCF files, compute allele frequencies, calculate diversity statistics, perform PCA, and run selection scans using GenotypeArray and HaplotypeArray data structures. Use when analyzing population genetics in Python.datapythongoVotes: 0GitHub stars: 2
Bio Population Genetics Plink BasicsAPLINK file formats, format conversion, and quality control filtering for population genetics. Convert between VCF, BED/BIM/FAM, and PED/MAP formats, apply MAF, genotyping rate, and HWE filters using PLINK 1.9 and 2.0. Use when working with PLINK format files or running QC.datapythongoVotes: 0GitHub stars: 2
Bio Phylo Modern Tree InferenceABuild maximum likelihood phylogenetic trees using IQ-TREE2 and RAxML-ng. Use when inferring publication-quality trees with model selection, ultrafast bootstrap, or partitioned analyses from sequence alignments.datagobashVotes: 0GitHub stars: 2
Bio Phylo Distance CalculationsACompute evolutionary distances and build phylogenetic trees using Biopython Bio.Phylo.TreeConstruction. Use when creating distance matrices from alignments, building NJ/UPGMA trees, or generating bootstrap consensus trees.datapythongoVotes: 0GitHub stars: 2
Bio Pdb Structure ModificationAModify protein structures using Biopython Bio.PDB. Use when transforming coordinates, removing atoms or residues, adding new entities, modifying B-factors and occupancies, or building structures programmatically.datapythonVotes: 0GitHub stars: 2
Bio Multi Omics Mofa IntegrationAMulti-Omics Factor Analysis (MOFA2) for unsupervised integration of multiple data modalities. Identifies shared and view-specific sources of variation. Use when integrating RNA-seq, proteomics, methylation, or other omics to discover latent factors driving biological variation across modalities.dataVotes: 0GitHub stars: 2
Bio Multi Omics Mixomics AnalysisASupervised and unsupervised multi-omics integration with mixOmics. Includes sPLS for pairwise integration and DIABLO for multi-block discriminant analysis. Use when performing supervised multi-omics integration or identifying features that discriminate between groups.dataexpressperformanceVotes: 0GitHub stars: 2
Bio Multi Omics Data HarmonizationAPreprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration analysis.dataexpressapiVotes: 0GitHub stars: 2
Bio Microbiome Taxonomy AssignmentATaxonomic classification of ASVs using reference databases like SILVA, GTDB, or UNITE. Covers naive Bayes classifiers (DADA2, IDTAXA) and exact matching approaches. Use when assigning taxonomy to ASVs after DADA2 amplicon processing.databashdatabaseVotes: 0GitHub stars: 2
Bio Microbiome Differential AbundanceADifferential abundance testing for microbiome data using compositionally-aware methods like ALDEx2, ANCOM-BC2, and MaAsLin2. Use when identifying taxa that differ between experimental groups while accounting for the compositional nature of microbiome data.datagoexpressVotes: 0GitHub stars: 2
Bio Methylation MethylkitADNA methylation analysis with methylKit in R. Import Bismark coverage files, filter by coverage, normalize samples, and perform statistical comparisons. Use when analyzing single-base methylation patterns, comparing samples, or preparing data for DMR detection.dataVotes: 0GitHub stars: 2
Bio Methylation Dmr DetectionADifferentially methylated region (DMR) detection using methylKit tiles, bsseq BSmooth, and DMRcate. Use when identifying contiguous genomic regions with methylation differences between experimental conditions or cell types.dataexpressapiVotes: 0GitHub stars: 2
Bio Metagenomics Strain TrackingATrack bacterial strains using MASH, sourmash, fastANI, and inStrain. Compare genomes, detect contamination, and monitor strain-level variation. Use when needing sub-species resolution for outbreak tracking, transmission analysis, or within-host strain dynamics.datapythonbashVotes: 0GitHub stars: 2
Bio Metabolomics Xcms PreprocessingAXCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.datagoapiVotes: 0GitHub stars: 2
Bio Metabolomics Targeted AnalysisATargeted metabolomics analysis using MRM/SRM with standard curves. Covers absolute quantification, method validation, and quality assessment. Use when quantifying specific metabolites using calibration curves and internal standards.datapythonVotes: 0GitHub stars: 2