メニュー
このリポジトリの skills
SkillsMP は FreedomIntelligence/OpenClaw-Medical-Skills から 465 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
FreedomIntelligence/OpenClaw-Medical-Skills収集済み skill 465 件中 40 件を表示しています。
Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and…
原文の言語: 英語
Gene Set Enrichment Analysis using clusterProfiler gseGO and gseKEGG. Use when analyzing ranked gene lists to find coordinated expression changes in gene sets without arbitrary significance cutoffs. Detects subtle but coordinated expression changes.
原文の言語: 英語
KEGG pathway and module enrichment analysis using clusterProfiler enrichKEGG and enrichMKEGG. Use when identifying metabolic and signaling pathways over-represented in a gene list. Supports 4000+ organisms via KEGG online database.
原文の言語: 英語
Reactome pathway enrichment using ReactomePA package. Use when analyzing gene lists against Reactome's curated peer-reviewed pathway database. Performs over-representation analysis and GSEA with visualization and pathway hierarchy exploration.
原文の言語: 英語
WikiPathways enrichment using clusterProfiler and rWikiPathways. Use when analyzing gene lists against community-curated open-source pathways. Performs over-representation analysis and GSEA for 30+ species.
原文の言語: 英語
Perform geometric calculations on protein structures using Biopython Bio.PDB. Use when measuring distances, angles, and dihedrals, superimposing structures, calculating RMSD, or computing solvent accessible surface area (SASA).
原文の言語: 英語
Parse and write protein structure files using Biopython Bio.PDB. Use when reading PDB, mmCIF, and MMTF files, downloading structures from RCSB PDB, or writing structures to various formats.
原文の言語: 英語
Modify protein structures using Biopython Bio.PDB. Use when transforming coordinates, removing atoms or residues, adding new entities, modifying B-factors and occupancies, or building structures programmatically.
原文の言語: 英語
Navigate protein structure hierarchy using Biopython Bio.PDB SMCRA model. Use when accessing models, chains, residues, and atoms, iterating over structure levels, or extracting sequences from PDB files.
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
Statistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups.
原文の言語: 英語
Peptide-spectrum matching and protein identification from MS/MS data. Use when identifying peptides from tandem mass spectra. Covers database searching, spectral library matching, and FDR estimation using target-decoy approaches.
原文の言語: 英語
Protein grouping and inference from peptide identifications. Use when resolving protein ambiguity from shared peptides. Handles protein groups and protein-level FDR control using parsimony and probabilistic approaches.
原文の言語: 英語
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.
原文の言語: 英語
Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Covers site localization, motif analysis, and quantitative PTM analysis. Use when analyzing phosphoproteomic data or other modification-enriched samples.
原文の言語: 英語
Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.
原文の言語: 英語
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.
原文の言語: 英語
Enumerates chemical libraries through reaction SMARTS transformations using RDKit. Generates virtual compound libraries from building blocks using defined chemical reactions with product validation. Use when creating combinatorial libraries or enumerating…
原文の言語: 英語
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.
原文の言語: 英語
Detect 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…
原文の言語: 英語
All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic,…
原文の言語: 英語
Filter 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…
原文の言語: 英語
Generate 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…
原文の言語: 英語
Extract, 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…
原文の言語: 英語
Read biological sequence files (FASTA, FASTQ, GenBank, EMBL, ABI, SFF) using Biopython Bio.SeqIO. Use when parsing sequence files, iterating multi-sequence files, random access to large files, or high-performance parsing.
原文の言語: 英語
Detect and quantify translated ORFs from Ribo-seq data including uORFs and novel ORFs using RiboCode and ORFquant. Use when identifying translated regions beyond annotated coding sequences or quantifying ORF-level translation.
原文の言語: 英語
Preprocess ribosome profiling data including adapter trimming, size selection, rRNA removal, and alignment. Use when preparing Ribo-seq reads for downstream analysis of translation.
原文の言語: 英語
Validate Ribo-seq data quality by checking 3-nucleotide periodicity and calculating P-site offsets. Use when assessing library quality or determining read offsets for downstream analysis.
原文の言語: 英語
Detect ribosome pausing and stalling sites from Ribo-seq data at codon resolution. Use when studying translational regulation, identifying pause sites, or analyzing codon-specific translation dynamics.
原文の言語: 英語
Calculate translation efficiency (TE) as the ratio of ribosome occupancy to mRNA abundance. Use when comparing translational regulation between conditions or identifying genes with altered translation independent of transcription.
原文の言語: 英語
RNA-seq specific quality control including rRNA contamination detection, strandedness verification, gene body coverage, and transcript integrity metrics. Use when validating RNA-seq libraries before differential expression analysis.
原文の言語: 英語
Creates sashimi plots showing RNA-seq read coverage and splice junction counts using ggsashimi or rmats2sashimiplot. Visualizes differential splicing events with grouped samples and junction read support. Use when visualizing specific splicing events or…
原文の言語: 英語
Calculate sequence statistics (N50, length distribution, GC content, summary reports) using Biopython. Use when analyzing sequence datasets, generating QC reports, or comparing assemblies.
原文の言語: 英語
Performs molecular similarity searches using Tanimoto coefficient on fingerprints via RDKit. Finds structurally similar compounds using ECFP or MACCS keys and clusters molecules by structural similarity using Butina clustering. Use when finding analogs of a…
原文の言語: 英語
Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets.
原文の言語: 英語
Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets.
原文の言語: 英語
Infer 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.
原文の言語: 英語
Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when…
原文の言語: 英語
Read, 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,…
原文の言語: 英語