3,745 件 ・ 関連度順
概要と使いどころ
Query Reactome for biological pathways and reactions. Use when user asks about signaling cascades, biological processes, pathway diagrams, or reaction details. Triggers on "reactome", "signaling cascade", "biological pathway", "pathway diagram", "reaction mechanism".
日本語の概要は準備中です。原文の説明を表示しています。
Query STRING for protein-protein interactions. Use when user asks about protein interactions, interaction networks, binding partners, or interactome. Triggers on "string", "protein interaction", "interaction network", "binding partners", "interactome", "PPI".
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for constraint-based metabolic modeling, context-specific models, gene essentiality, and systems-level interpretation.
日本語の概要は準備中です。原文の説明を表示しています。
Query UniProt protein database. Use when user asks about protein sequences, functions, annotations, domains, or protein identifiers. Triggers on "uniprot", "protein function", "protein sequence", "gene product", "protein info".
日本語の概要は準備中です。原文の説明を表示しています。
Publication-quality PDF report generation using Typst templates. Produces professional scientific reports with colored section bands, styled tables, figure captions, callout boxes, and page headers/footers.
日本語の概要は準備中です。原文の説明を表示しています。
SPR and BLI assay planning, kinetic interpretation, and troubleshooting guidance. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
日本語の概要は準備中です。原文の説明を表示しています。
Query NCBI GEO for gene expression datasets. Use when user asks about RNA-seq datasets, microarray data, expression data, GEO accessions, or finding public datasets. Triggers on "geo", "gene expression omnibus", "expression dataset", "RNA-seq dataset", "microarray dataset", "GSE", "GDS".
日本語の概要は準備中です。原文の説明を表示しています。
Standard scRNA-seq preprocessing and clustering with Scanpy. Use for QC, normalization, HVG selection, PCA, neighbor graph construction, UMAP, Leiden clustering, and export of an analysis-ready AnnData object.
日本語の概要は準備中です。原文の説明を表示しています。
Query Ensembl for genomic data. Use when user asks about gene coordinates, genomic sequences, variants, gene structure, exons, transcripts, or species comparison. Triggers on "ensembl", "gene coordinates", "genomic location", "exon", "transcript", "variant location", "rsid", "rs number".
日本語の概要は準備中です。原文の説明を表示しています。
Review SDS-PAGE or protein purification gel images using DNA sequence, protein sequence, base-pair length, expected protein size, and lane labels. Use when the user wants to judge whether a gel ran well, whether the main band matches the expected product, or whether there may be impurities, degradation, aggregation, or low expression.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for haplotype phasing, genotype imputation, reference-panel matching, and imputation QC.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for small-variant and structural-variant discovery, filtering, annotation, and interpretation from sequencing data.
日本語の概要は準備中です。原文の説明を表示しています。
SEC (size-exclusion chromatography) analysis with peak detection, oligomer classification, and publication-quality PDF report generation via Typst templates. Triggers on "SEC", "size exclusion", "chromatography", "oligomer analysis", "protein assembly", "SEC report".
日本語の概要は準備中です。原文の説明を表示しています。
Analyze DNA/RNA/protein sequences. Use when the user provides a sequence and asks for analysis, translation, GC content, ORFs, motifs, restriction sites, or primer design. Triggers on "sequence", "translate", "GC content", "ORF", "primer", "restriction", "complement", "reverse complement".
日本語の概要は準備中です。原文の説明を表示しています。
Browse and install community skills from the BioClaw Skills Hub. Use when a user's task is not covered by built-in skills, or when the user asks about available skills, advanced workflows, or specialized analysis pipelines. Triggers on "skills hub", "more skills", "install skill", "community skills", "find a skill for".
日本語の概要は準備中です。原文の説明を表示しています。
Structure retrieval, confidence-aware AlphaFold DB usage, coordinate download, PAE and pLDDT interpretation, and structure-guided biological annotation.
日本語の概要は準備中です。原文の説明を表示しています。
Species abundance estimation using Bracken with Kraken2 output. Redistributes reads from higher taxonomic levels to species for more accurate estimates. Use when accurate species-level abundances are needed from Kraken2 classification output.
日本語の概要は準備中です。原文の説明を表示しています。
Applies ACMG/AMP 2015 framework with ClinGen SVI specifications, Tavtigian 2018/2020 Bayesian point system, Abou Tayoun 2018 PVS1 decision tree, Pejaver 2022 calibrated PP3/BP4 thresholds for REVEL/BayesDel/AlphaMissense, Brnich 2020 PS3/BS3 OddsPath, Walker 2023 SpliceAI splicing framework, and AMP/ASCO/CAP 2017 tumor tiers. Use when classifying germline variants P / LP / VUS / LB / B, applying VCEP-specific CSpec rules, computing Whiffin BS1, or assigning cancer Tier I-IV per Li 2017.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for spatial transcriptomics preprocessing, domain detection, deconvolution, neighborhood analysis, and publication-ready maps.
日本語の概要は準備中です。原文の説明を表示しています。
Remove sequencing adapters from FASTQ files using Cutadapt and Trimmomatic. Supports single-end and paired-end reads, Illumina TruSeq, Nextera, and custom adapter sequences. Use when FastQC shows adapter contamination or before alignment of short reads.
日本語の概要は準備中です。原文の説明を表示しています。
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
日本語の概要は準備中です。原文の説明を表示しています。
Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.
日本語の概要は準備中です。原文の説明を表示しています。
Automated and marker-guided single-cell cell type annotation using CellTypist, marker review, reference transfer, and confidence-aware label curation.
日本語の概要は準備中です。原文の説明を表示しています。
Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.
日本語の概要は準備中です。原文の説明を表示しています。