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Claude Skills by mdbabumiamssm
github.com/mdbabumiamssm1,581 skills3 installs3,534 views
- Probability StatisticsBayesian OptimizeVotes: 0GitHub stars: 32
- LipidomicsSpecialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipidomics with LipidSearch, MS-DIAL, and LipidMaps annotation. Use when analyzing lipid classes, chain composition, or lipid-specific pathways.Votes: 0GitHub stars: 32
- Normalization QcQuality control and normalization for metabolomics data. Covers QC-based correction, batch effect removal, and data transformation methods. Use when correcting technical variation in metabolomics data before statistical analysis.Votes: 0GitHub stars: 32
- Xcms PreprocessingXCMS3 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.Votes: 0GitHub stars: 32
- Microbiome Cancer AgentAI-powered analysis of microbiome-cancer interactions including tumor microbiome profiling, immunotherapy response prediction, and microbiome-targeted therapeutic opportunities.Votes: 0GitHub stars: 32
- Differential AbundanceDifferential 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.Votes: 0GitHub stars: 32
- Qiime2 WorkflowQIIME2 command-line workflow for 16S/ITS amplicon analysis. Alternative to DADA2/phyloseq R workflow with built-in provenance tracking. Use when preferring CLI over R, needing reproducible provenance, or working within QIIME2 ecosystem.Votes: 0GitHub stars: 32
- Data HarmonizationPreprocessing 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.Votes: 0GitHub stars: 32
- Mixomics AnalysisSupervised 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.Votes: 0GitHub stars: 32
- Mofa IntegrationMulti-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.Votes: 0GitHub stars: 32
- Star AlignmentAlign RNA-seq reads with STAR (Spliced Transcripts Alignment to a Reference). Supports two-pass mode for novel splice junction discovery. Use when aligning RNA-seq data requiring splice-aware alignment.Votes: 0GitHub stars: 32
- Contamination ScreeningDetect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination.Votes: 0GitHub stars: 32
- Quality FilteringFilter reads by quality scores, length, and N content using Trimmomatic and fastp. Apply sliding window trimming, remove low-quality bases from read ends, and discard reads below thresholds. Use when reads have poor quality tails or require minimum quality for downstream analysis.Votes: 0GitHub stars: 32
- Quality ReportsGenerate 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.Votes: 0GitHub stars: 32
- Umi ProcessingExtract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep includes UMIs and accurate molecule counting is needed, such as in single-cell RNA-seq, low-input RNA-seq, or targeted sequencing to distinguish PCR from biological duplicates.Votes: 0GitHub stars: 32
- Cancer Metabolism AgentAI-powered analysis of cancer metabolic reprogramming including Warburg effect, glutamine addiction, lipid metabolism, and metabolic vulnerabilities for therapeutic targeting.Votes: 0GitHub stars: 32
- Chromosomal Instability AgentAI-powered analysis of chromosomal instability (CIN) signatures for cancer prognosis, immunotherapy response prediction, and therapeutic vulnerability identification.Votes: 0GitHub stars: 32
- Exosome EV Analysis AgentAI-powered extracellular vesicle and exosome analysis for cancer biomarker discovery, liquid biopsy applications, and intercellular communication profiling.Votes: 0GitHub stars: 32
- HRD Analysis AgentAI-powered homologous recombination deficiency (HRD) analysis for PARP inhibitor response prediction using genomic scarring signatures and BRCA pathway assessment.Votes: 0GitHub stars: 32
- Cfdna PreprocessingPreprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate removal using fgbio. Applies cfDNA-specific quality thresholds and fragment length filtering. Use when processing plasma cfDNA sequencing data before downstream analysis.Votes: 0GitHub stars: 32
- Ctdna Mutation DetectionDetects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.Votes: 0GitHub stars: 32
- Fragment AnalysisAnalyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin. Extracts nucleosome positioning patterns, fragment ratios, and DELFI-style fragmentation profiles for cancer detection. Use when leveraging fragment patterns for tumor detection or tissue-of-origin analysis.Votes: 0GitHub stars: 32
- Longitudinal MonitoringTracks ctDNA dynamics over time for treatment response monitoring using serial liquid biopsy samples. Analyzes tumor fraction trends, mutation clearance kinetics, and defines molecular response criteria. Use when monitoring patients during therapy or detecting molecular relapse before clinical progression.Votes: 0GitHub stars: 32
- Methylation Based DetectionAnalyzes cfDNA methylation patterns for cancer detection using cfMeDIP-seq or bisulfite sequencing with MethylDackel. Identifies cancer-specific methylation signatures and performs tissue-of-origin deconvolution. Use when using methylation biomarkers for early cancer detection or minimal residual disease.Votes: 0GitHub stars: 32
- Tumor Fraction EstimationEstimates circulating tumor DNA fraction from shallow whole-genome sequencing using ichorCNA. Detects copy number alterations via HMM segmentation and calculates ctDNA percentage. Requires 0.1-1x sWGS coverage. Use when quantifying tumor burden from liquid biopsy or monitoring treatment response.Votes: 0GitHub stars: 32
- Liquid Biopsy Analytics AgentAI-powered comprehensive liquid biopsy analysis integrating ctDNA, CTCs, exosomes, and cfRNA for cancer detection, monitoring, and treatment guidance.Votes: 0GitHub stars: 32
- MRD EDGE Detection AgentUltra-sensitive AI-powered molecular residual disease detection using MRD-EDGE deep learning for sub-0.001% VAF ctDNA detection and early relapse prediction.Votes: 0GitHub stars: 32
- Organoid Drug Response AgentAI-powered analysis of patient-derived organoid (PDO) drug screening for personalized oncology treatment selection and biomarker discovery.Votes: 0GitHub stars: 32
- PDX Model Analysis AgentAI-powered analysis of patient-derived xenograft (PDX) models for drug response prediction, translational research, and personalized treatment selection.Votes: 0GitHub stars: 32
- Pan Cancer MultiOmics AgentAI-powered pan-cancer analysis integrating genomic, transcriptomic, proteomic, and epigenomic data for cancer subtyping, driver identification, and cross-cancer pattern discovery.Votes: 0GitHub stars: 32
- Radiomics Pathomics Fusion AgentAI-powered multimodal fusion of radiology (CT/MRI/PET) and pathology (H&E/IHC) imaging with clinical and genomic data for comprehensive cancer diagnostics and treatment prediction.Votes: 0GitHub stars: 32
- Tumor Clonal Evolution AgentAI-powered analysis of tumor clonal architecture, subclonal dynamics, and evolutionary trajectories from multi-region sequencing and longitudinal liquid biopsy data.Votes: 0GitHub stars: 32
- Tumor Heterogeneity AgentAI-powered intratumor heterogeneity analysis for clonal architecture reconstruction, subclonal evolution tracking, and therapy resistance prediction using multi-region and longitudinal sequencing.Votes: 0GitHub stars: 32
- Tumor Mutational Burden AgentAI-powered tumor mutational burden (TMB) analysis for immunotherapy response prediction, harmonization across platforms, and integration with other biomarkers.Votes: 0GitHub stars: 32
- CtDNA Dynamics MRD AgentAI-powered circulating tumor DNA dynamics analysis for molecular residual disease detection, treatment response monitoring, and early relapse prediction using liquid biopsy.Votes: 0GitHub stars: 32
- Computational Pathology Agent**Version:** 1.0.0 **Author:** MD BABU MIA, PhD **Date:** February 2026Votes: 0GitHub stars: 32
- Drug InteractionChecks for potential drug-drug interactions (DDIs) between a list of medications.Votes: 0GitHub stars: 32
- Regulatory AffairsAutomates the drafting of regulatory documents (e.g., FDA CTD sections) with citation management and audit trails.Votes: 0GitHub stars: 32
- BioKernelBiomedical OS Core & MCP ServerVotes: 0GitHub stars: 32
- Meta PrompterAutomatic prompt engineering & optimizationVotes: 0GitHub stars: 32
- Distance CalculationsCompute 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.Votes: 0GitHub stars: 32
- Modern Tree InferenceBuild 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.Votes: 0GitHub stars: 32
- Tree IoRead, write, and convert phylogenetic tree files using Biopython Bio.Phylo. Use when parsing Newick, Nexus, PhyloXML, or NeXML tree formats, converting between formats, or handling multiple trees.Votes: 0GitHub stars: 32
- Tree ManipulationModify phylogenetic tree structure using Biopython Bio.Phylo. Use when rooting trees with outgroups or midpoint, pruning taxa, collapsing clades, ladderizing branches, or extracting subtrees.Votes: 0GitHub stars: 32
- Tree VisualizationDraw and export phylogenetic trees using Biopython Bio.Phylo with matplotlib. Use when creating publication-quality tree figures, customizing colors and labels, or exporting to image formats.Votes: 0GitHub stars: 32
- Association TestingGenome-wide association studies (GWAS) with PLINK. Perform case-control and quantitative trait association testing using logistic/linear regression with covariates, generate Manhattan and QQ plots for result visualization. Use when running GWAS or association tests.Votes: 0GitHub stars: 32
- Linkage DisequilibriumCalculate linkage disequilibrium statistics (r², D'), perform LD pruning for population structure analysis, identify haplotype blocks, and visualize LD patterns using PLINK, scikit-allel, and LDBlockShow. Use when calculating LD or pruning variants.Votes: 0GitHub stars: 32
- Plink BasicsPLINK 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.Votes: 0GitHub stars: 32
- Population StructureAnalyze population structure using PCA and admixture analysis with PLINK and ADMIXTURE. Identify population clusters, assess ancestry proportions, visualize genetic structure, and choose optimal K for admixture models. Use when analyzing population stratification with PCA or admixture.Votes: 0GitHub stars: 32
- Scikit Allel AnalysisPython 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.Votes: 0GitHub stars: 32