64 件 ・ 関連度順
概要と使いどころ
Quality 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.
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。
Unified analysis pipeline for affinity-based proteomics platforms — Olink (PEA, NPX) and SomaLogic SomaScan (SOMAmer, RFU). Platform-aware QC, normalisation, differential abundance, volcano plots, heatmaps, and PCA.
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。
End-to-end proteomics workflow from MaxQuant output to differential protein abundance. Orchestrates data import, normalization, imputation, and statistical testing with limma (default) or MSstats for complex feature-level designs. Use when processing mass spectrometry proteomics.
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。
Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.
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。
Build, 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.
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。
Load 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.
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。
Data-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.
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。
MaxQuant + Perseus proteomics pipeline: run MaxQuant for LFQ and SILAC; parse proteinGroups.txt in Python; filter contaminants/decoys; log2 + median-normalize; impute MNAR; t-test with FDR; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo-native processing; FragPipe/MSFragger for GPU-accelerated DB search.
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。
Data-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.
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。
Load 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.
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。
Mass spectrometry proteomics QC, quantification, comparative analysis, and export for DDA, DIA, and protein-level result tables.
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。
Discover and invoke 1,676 deduplicated biomedical AI agent skills from the Awesome Bio Agent Skills repository (20 source repos, 15 categories). Use this skill as a router whenever a user needs a bioinformatics/biomedical task (genomics, transcriptomics, single-cell, proteomics, protein design, clinical, epigenomics, multi-omics, pathway, metagenomics, database queries, visualization, workflows): search the index, locate the best-matching skill, fetch its SKILL.md, and follow it.
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。
Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.
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。
Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for "pathway analysis", "enrichment analysis", "GO enrichment", "KEGG/Reactome pathways", "GSEA", "over-representation", "functional annotation", or "what pathways are my genes in".
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。
Processes mass spectrometry data with pyOpenMS. Supports proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.
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。
Analyzes and engineers protein glycosylation by scanning canonical N-glycosylation sequons, describing S/T-rich regions, checking curated glycan evidence, and preparing NetNGlyc, NetOGlyc and GlycoSHIELD workflows. Use for glycoprotein engineering, antibody Fc glycosylation, glycan shielding, and site-specific glycoproteomics 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。
Processes, cleans, compares, and searches tandem mass spectra with matchms. Use for MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity, library matching, score matrices, and molecular-similarity networks. Use pyopenms instead for LC-MS feature detection or proteomics pipelines.
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。
Submit compact PRIDE Archive API requests for proteomics project discovery and project-level metadata. Use when a user wants concise PRIDE summaries
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。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
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。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
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。
Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.
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。
Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
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。
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
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。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
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。