All authors
Claude Skills by mdbabumiamssm
github.com/mdbabumiamssm1,581 skills3 installs3,534 views
- Metaphlan ProfilingMarker gene-based taxonomic profiling using MetaPhlAn 4. Provides accurate species-level relative abundances using clade-specific markers. Use when accurate taxonomic profiling is needed and computational resources are limited, or for comparison with HMP/other MetaPhlAn studies.Votes: 0GitHub stars: 32
- Strain TrackingTrack 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.Votes: 0GitHub stars: 32
- BioMasterOrchestrate BioMaster’s multi-agent pipelines (RNA-seq, ChIP-seq, single-cell, Hi-C) using the provided configs and repos to deliver reproducible outputs.Votes: 0GitHub stars: 32
- Nicheformer Spatial AgentFoundation model-powered spatial transcriptomics analysis leveraging 53M+ spatially resolved cells for cellular architecture modeling and tissue niche discovery.Votes: 0GitHub stars: 32
- PopEVE Variant Predictor AgentAI-powered genetic variant pathogenicity prediction using PopEVE deep learning model for population-aware disease variant identification and rare disease diagnosis.Votes: 0GitHub stars: 32
- RNA Velocity AgentAI-powered RNA velocity analysis for predicting cellular state transitions, differentiation trajectories, and dynamic gene regulation from single-cell RNA sequencing data.Votes: 0GitHub stars: 32
- SIMO Multiomics Integration AgentAI-powered spatial integration of multi-omics datasets using probabilistic alignment for comprehensive tissue atlas construction and cellular state mapping.Votes: 0GitHub stars: 32
- CellAgentUse CellTypeAgent to interpret marker genes, annotate scRNA-seq clusters, and coordinate multi-agent workflows for downstream analysis.Votes: 0GitHub stars: 32
- RNAAnnotate scRNA-seqVotes: 0GitHub stars: 32
- Cell CommunicationInfer cell-cell communication networks from scRNA-seq data using CellChat, NicheNet, and LIANA for ligand-receptor interaction analysis. Use when inferring ligand-receptor interactions between cell types.Votes: 0GitHub stars: 32
- ClusteringscRNA-seq clustering analysisVotes: 0GitHub stars: 32
- Data IoRead, write, and create single-cell data objects using Seurat (R) and Scanpy (Python). Use for loading 10X Genomics data, importing/exporting h5ad and RDS files, creating Seurat objects and AnnData objects, and converting between formats. Use when loading, saving, or converting single-cell data formats.Votes: 0GitHub stars: 32
- Doublet DetectionDetect and remove doublets (multiple cells captured in one droplet) from single-cell RNA-seq data. Uses Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering to avoid artificial cell populations. Use when identifying and removing doublets from scRNA-seq data.Votes: 0GitHub stars: 32
- Lineage TracingReconstruct cell lineage trees from CRISPR barcode tracing or mitochondrial mutations. Use when studying clonal dynamics, cell fate decisions, or developmental trajectories.Votes: 0GitHub stars: 32
- Metabolite CommunicationAnalyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when studying metabolic crosstalk between cell populations or metabolite-receptor interactions.Votes: 0GitHub stars: 32
- Perturb SeqAnalyze Perturb-seq and CROP-seq CRISPR screening data integrated with scRNA-seq. Use when identifying gene function through pooled genetic perturbations in single cells.Votes: 0GitHub stars: 32
- PreprocessingQuality control, filtering, and normalization for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for calculating QC metrics, filtering cells and genes, normalizing counts, identifying highly variable genes, and scaling data. Use when filtering, normalizing, and selecting features in single-cell data.Votes: 0GitHub stars: 32
- Scatac AnalysisSingle-cell ATAC-seq analysis with Signac (R/Seurat) and ArchR. Process 10X Genomics scATAC data, perform QC, dimensionality reduction, clustering, peak calling, and motif activity scoring with chromVAR. Use when analyzing single-cell ATAC-seq data.Votes: 0GitHub stars: 32
- Trajectory InferenceInfer developmental trajectories and pseudotime from single-cell RNA-seq data using Monocle3, Slingshot, and scVelo for RNA velocity analysis. Use when inferring developmental trajectories or pseudotime.Votes: 0GitHub stars: 32
- Spatial Epigenomics AgentAI-powered spatial epigenomics analysis combining chromatin accessibility, histone modifications, and DNA methylation with spatial coordinates for tissue architecture mapping.Votes: 0GitHub stars: 32
- STAgentRun STAgent to align histology images with expression matrices, perform clustering/SVG detection, and generate literature-backed spatial reports.Votes: 0GitHub stars: 32
- SpatialAgentAn agent that interprets spatial transcriptomics data to propose mechanistic hypotheses and analyze tissue organization.Votes: 0GitHub stars: 32
- Image AnalysisProcess and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.Votes: 0GitHub stars: 32
- Spatial Data IoLoad spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data.Votes: 0GitHub stars: 32
- Spatial MultiomicsAnalyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.Votes: 0GitHub stars: 32
- Spatial ProteomicsAnalyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.Votes: 0GitHub stars: 32
- Variant InterpretationClassifies genetic variants according to ACMG (American College of Medical Genetics) guidelines.Votes: 0GitHub stars: 32
- Consensus SequencesGenerate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.Votes: 0GitHub stars: 32
- DeepvariantDeep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller.Votes: 0GitHub stars: 32
- Filtering Best PracticesComprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices.Votes: 0GitHub stars: 32
- Joint CallingJoint genotype calling across multiple samples using GATK CombineGVCFs and GenotypeGVCFs. Essential for cohort studies, population genetics, and leveraging VQSR. Use when performing joint genotyping across multiple samples.Votes: 0GitHub stars: 32
- Structural Variant CallingCall structural variants (SVs) from short-read sequencing using Manta, Delly, and LUMPY. Detects deletions, insertions, inversions, duplications, and translocations that are too large for standard SNV callers. Use when detecting structural variants from short-read data.Votes: 0GitHub stars: 32
- Variant AnnotationComprehensive variant annotation using bcftools annotate/csq, VEP, SnpEff, and ANNOVAR. Add database annotations, predict functional consequences, and assess clinical significance. Use when annotating variants with functional and clinical information.Votes: 0GitHub stars: 32
- Variant CallingCall SNPs and indels from aligned reads using bcftools mpileup and call. Use when detecting variants from BAM files or generating VCF from alignments.Votes: 0GitHub stars: 32
- Variant NormalizationNormalize indel representation and split multiallelic variants using bcftools norm. Use when comparing variants from different callers or preparing VCF for downstream analysis.Votes: 0GitHub stars: 32
- Vcf BasicsView, query, and understand VCF/BCF variant files using bcftools and cyvcf2. Use when inspecting variants, extracting specific fields, or understanding VCF format structure.Votes: 0GitHub stars: 32
- Vcf ManipulationMerge, concatenate, sort, intersect, and subset VCF files using bcftools. Use when combining variant files, comparing call sets, or restructuring VCF data.Votes: 0GitHub stars: 32
- Vcf StatisticsGenerate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.Votes: 0GitHub stars: 32
- Assembly PolishingPolish genome assemblies to reduce errors using short reads (Pilon), long reads (Racon), or ONT-specific tools (medaka). Essential for improving long-read assembly accuracy. Use when improving assembly accuracy with polishing tools.Votes: 0GitHub stars: 32
- Hifi AssemblyHigh-quality genome assembly from PacBio HiFi reads using hifiasm with phasing support. Use when building reference-quality diploid assemblies from HiFi data, especially with trio or Hi-C phasing for fully resolved haplotypes.Votes: 0GitHub stars: 32
- Metagenome AssemblyMetagenome assembly from long reads using metaFlye and metaSPAdes with binning strategies. Use when reconstructing genomes from microbial communities, recovering metagenome-assembled genomes (MAGs), or resolving strain-level variation in complex samples.Votes: 0GitHub stars: 32
- ScaffoldingScaffold contigs into chromosome-level assemblies using Hi-C data with YaHS, 3D-DNA, SALSA2, and validate with BUSCO and contact maps. Use when scaffolding contigs to chromosome-level assemblies.Votes: 0GitHub stars: 32
- Short Read AssemblyDe novo genome assembly from Illumina short reads using SPAdes. Covers bacterial, fungal, and small eukaryotic genome assembly, as well as metagenome and transcriptome assembly modes. Use when assembling genomes from Illumina reads.Votes: 0GitHub stars: 32
- Base Editing DesignDesign guides for cytosine and adenine base editing using editing window optimization and BE-Hive outcome prediction. Select optimal positions for C-to-T or A-to-G conversions without double-strand breaks. Use when designing base editor experiments for precise nucleotide changes.Votes: 0GitHub stars: 32
- Hdr Template DesignDesign homology-directed repair donor templates for CRISPR knock-ins using primer3-py. Create ssODN, dsDNA, or plasmid templates with optimized homology arms. Use when designing donor templates for precise insertions, tagging, or allele replacement.Votes: 0GitHub stars: 32
- Off Target PredictionPredict CRISPR off-target sites using Cas-OFFinder and CFD scoring algorithms. Identify potential unintended cleavage sites genome-wide and assess guide specificity. Use when evaluating guide RNA specificity or selecting guides with minimal off-target risk.Votes: 0GitHub stars: 32
- Prime Editing DesignDesign pegRNAs for prime editing using PrimeDesign algorithms. Generate spacer, PBS, and RT template sequences for precise genomic modifications without double-strand breaks. Use when designing prime editing experiments for precise insertions, deletions, or point mutations.Votes: 0GitHub stars: 32
- Bed File BasicsBED file format fundamentals, creation, validation, and basic operations. Covers BED3 through BED12 formats, coordinate systems, sorting, and format conversion using bedtools and pybedtools. Use when working with genomic coordinates or preparing interval files for downstream tools.Votes: 0GitHub stars: 32
- Bedgraph HandlingCreate, 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.Votes: 0GitHub stars: 32
- Coverage AnalysisCalculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.Votes: 0GitHub stars: 32