swaruplab/operon - 12ページ

tumor-mutational-burden-agent

Calculates and harmonizes Tumor Mutational Burden (TMB) across platforms to predict immunotherapy response.

原文の言語: 英語

universal-single-cell-annotator

Annotate scRNA-seq

原文の言語: 英語

scte

Quantify transposable element expression from single-cell RNA/ATAC-seq BAM files at locus or family level.

原文の言語: 英語

solote

Locus-specific transposable element quantification from single-cell RNA-seq BAMs, producing a gene+TE 10x-style count matrix.

原文の言語: 英語

enhanced-volcano-plot

Publication-quality volcano plots from DE results using EnhancedVolcano (R) or matplotlib (Python).

原文の言語: 英語

medical-specialty-briefs

Generate daily or on-demand medical research briefs for any medical specialty. Searches latest research from top-tier journals, delivers concise summaries with 1-sentence takeaways, images when available, and direct links. Use when user asks for medical news,…

原文の言語: 英語

tooluniverse-antibody-engineering

Comprehensive antibody engineering and optimization for therapeutic development. Covers humanization, affinity maturation, developability assessment, and immunogenicity prediction. Use when asked to optimize antibodies, humanize sequences, or engineer…

原文の言語: 英語

tooluniverse-clinical-trial-design

Strategic clinical trial design feasibility assessment using ToolUniverse. Evaluates patient population sizing, biomarker prevalence, endpoint selection, comparator analysis, safety monitoring, and regulatory pathways. Creates comprehensive feasibility…

原文の言語: 英語

tooluniverse-drug-drug-interaction

Comprehensive drug-drug interaction (DDI) prediction and risk assessment. Analyzes interaction mechanisms (CYP450, transporters, pharmacodynamic), severity classification, clinical evidence grading, and provides management strategies. Supports single drug…

原文の言語: 英語

tooluniverse-epigenomics

Production-ready genomics and epigenomics data processing for BixBench questions. Handles methylation array analysis (CpG filtering, differential methylation, age-related CpG detection, chromosome-level density), ChIP-seq peak analysis (peak calling, motif…

原文の言語: 英語

tooluniverse-gene-enrichment

Perform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ ToolUniverse tools. Supports GO enrichment (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB Hallmark, and 220+ Enrichr libraries.…

原文の言語: 英語

tooluniverse-gwas-study-explorer

Compare GWAS studies, perform meta-analyses, and assess replication across cohorts. Integrates NHGRI-EBI GWAS Catalog and Open Targets Genetics to compare study designs, effect sizes, ancestry diversity, and heterogeneity statistics. Use when comparing GWAS…

原文の言語: 英語

tooluniverse-gwas-trait-to-gene

Discover genes associated with diseases and traits using GWAS data from the GWAS Catalog (500,000+ associations) and Open Targets Genetics (L2G predictions). Identifies genetic risk factors, prioritizes causal genes via locus-to-gene scoring, and assesses…

原文の言語: 英語

tooluniverse-multiomic-disease-characterization

Comprehensive multi-omics disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers for systems-level understanding. Produces a detailed multi-omics report with quantitative confidence scoring (0-100),…

原文の言語: 英語

tooluniverse-phylogenetics

Production-ready phylogenetics and sequence analysis skill for alignment processing, tree analysis, and evolutionary metrics. Computes treeness, RCV, treeness/RCV, parsimony informative sites, evolutionary rate, DVMC, tree length, alignment gap statistics, GC…

原文の言語: 英語

tooluniverse-rnaseq-deseq2

Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates…

原文の言語: 英語

tooluniverse-single-cell

Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type…

原文の言語: 英語

tooluniverse-systems-biology

Comprehensive systems biology and pathway analysis using multiple pathway databases (Reactome, KEGG, WikiPathways, Pathway Commons, BioModels). Performs pathway enrichment, protein-pathway mapping, keyword searches, and systems-level analysis. Use when…

原文の言語: 英語

archr

Single-cell ATAC-seq analysis with ArchR (R). The mature R-based scATAC pipeline — Arrow files, doublet inference, iterative LSI + Harmony, clustering, gene scores, MACS2 peak calling, motif enrichment, chromVAR deviations, footprinting, scRNA-seq integration…

原文の言語: 英語

cellbender

Remove ambient RNA from raw scRNA-seq count matrices using CellBender's remove-background. GPU-strongly-recommended (~30 min on GPU vs hours on CPU for typical data). Takes raw 10X h5 (or h5ad with all-barcodes-included), trains a variational model that…

原文の言語: 英語

cellchat

Cell-cell communication analysis with CellChat (R). Covers (1) the standard single-dataset pipeline — database setup, communication inference, pathway-level aggregation, centrality, communication-pattern discovery, similarity-based clustering; (2)…

原文の言語: 英語

hdwgcna

Co-expression network analysis for single-cell and spatial transcriptomics using hdWGCNA (R/Seurat). Covers the full pipeline — metacell construction, soft-power selection, network construction, module identification, module eigengenes (MEs/hMEs), hub gene…

原文の言語: 英語

kallisto-bustools

scRNA-seq quantification with kallisto + bustools via kb-python. Pseudoalignment-based — orders of magnitude faster than full alignment (STAR / cellranger) while producing comparable count matrices. Covers index generation (kb ref), per-sample quantification…

原文の言語: 英語

mrvi

MrVI — multi-resolution variational inference for multi-sample scRNA-seq. Two-level hierarchical model that learns both a sample-unaware cell-state latent (u) and a sample-aware latent (z). Outputs per-cell sample-distance matrices for stratification…

原文の言語: 英語

resolvi

ResolVI — variational autoencoder for denoising imaging-based spatial transcriptomics (Xenium, MERFISH, CosMx). Removes ambient background and resolves wrong segmentation by jointly modeling true cell expression, mis-assigned neighbor counts, and unspecific…

原文の言語: 英語

scanpy

Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning…

原文の言語: 英語

scvelo

RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory…

原文の言語: 英語

seurat

scRNA-seq analysis with Seurat v5 (R) — the standard R-based pipeline. Covers QC, normalization (LogNormalize + SCTransform), HVG selection, scaling, PCA, neighbors, leiden/Louvain clustering, UMAP/t-SNE, marker gene identification (FindMarkers /…

原文の言語: 英語

singlecellexperiment

SingleCellExperiment (SCE) — the canonical Bioconductor S4 container for single-cell genomics. Covers SCE construction, assay/colData/rowData/reducedDims/altExps accessors, sizeFactors and labels, iteration via applySCE, and the standard scater + scran…

原文の言語: 英語

snapatac2

Single-cell ATAC-seq analysis with SnapATAC2 (scverse). Covers the full pipeline — fragment import, TSS enrichment QC, tile-matrix construction, doublet filtering, spectral embedding, UMAP/leiden, MACS3 peak calling, gene activity matrices, differentially…

原文の言語: 英語

stellar-atlas

Build deployable interactive web atlases from single-cell RNA-seq data using STELLAR. Turns a .h5ad (or Seurat .rds) into a UMAP + gene-expression + DE + hdWGCNA + CellChat + Milo + enrichment + AI-chat browser SPA. Covers the four-step CLI (init → ingest →…

原文の言語: 英語

adaptyv

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence…

原文の言語: 英語

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and…

原文の言語: 英語

benchling-integration

Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.

原文の言語: 英語

biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick…

原文の言語: 英語

biorxiv-database

Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.

原文の言語: 英語

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database…

原文の言語: 英語

brenda-database

Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information for biochemical research and metabolic pathway analysis.

原文の言語: 英語

cellxgene-census

Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your…

原文の言語: 英語

chembl-database

Query ChEMBL bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.

原文の言語: 英語