2,944 件 ・ 関連度順
概要と使いどころ
WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed imaging.
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。
ETE Toolkit (ETE3): Python phylogenetic tree analysis and visualization. Parse Newick/NHX/PhyloXML, traverse/annotate nodes, render figures with TreeStyle/NodeStyle, integrate NCBI taxonomy, run PhyloTree comparative genomics. Use for species trees, gene family evolution, annotated tree figures.
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 InterPro REST API for protein domain architecture, family classification, and member-DB integration. Search entries, retrieve a protein's domains, list family members, get taxonomic distribution, link to PDB. Unifies Pfam, PANTHER, PIRSF, PRINTS, PROSITE, SMART, CDD, NCBIfam. Use uniprot-protein-database for sequences; pdb-database for 3D structures.
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。
Guided statistical analysis: test choice, assumption checks, effect sizes, power, APA reporting. Pick tests, verify assumptions, or format results for publication. Covers frequentist (t-test, ANOVA, chi-square, regression, correlation, survival, count, reliability) and Bayesian. Use statsmodels or pymc-bayesian-modeling to fit.
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。
Open-source bio-image data management. Use the omero-py client to connect to an OMERO server, retrieve images as numpy arrays, annotate with tags and key-value pairs, manage ROIs, and feed image data into Python analysis pipelines — programmatically, no GUI.
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。
Biopython sequence analysis: parse FASTA/FASTQ/GenBank/GFF (SeqIO), NCBI Entrez (esearch/efetch/elink), remote/local BLAST, pairwise/MSA alignment (PairwiseAligner, MUSCLE/ClustalW), phylogenetic trees (Phylo). Use for gene family studies, phylogenomics, comparative genomics, NCBI pipelines. For PCR/restriction/cloning use biopython-molecular-biology; for SAM/BAM use pysam.
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。
protocols.io REST API: search and fetch wet-lab, bioinformatics, and clinical protocols by keyword, DOI, or category, with steps, reagents, materials, equipment, timing. Public access free; auth needed for private or publishing. Pair with opentrons-protocol-api or benchling-integration to execute.
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。
Molecular biology toolkit: sequence manipulation, FASTA/GenBank/PDB I/O, NCBI Entrez, BLAST automation, pairwise/MSA alignment, Bio.PDB, phylogenetic trees. Use for batch processing, custom pipelines, format conversion, PubMed/GenBank queries. For quick gene lookups use gget; for multi-service REST APIs use bioservices.
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。
omics-plotting: publication-style figure authoring for omics / bioinformatics results with matplotlib / seaborn. Read this before writing any plotting or figure code in any omics analysis — RNA-seq, proteomics, single-cell, variant, or database results — not only when a plot is explicitly requested: whenever an analysis will produce a figure, load this first and follow its recipes. Covers volcano, MA, expression / correlation heatmap, GSEA bar / dot plot, box / violin / bar / ridgeline, PCA / UMAP / t-SNE scatter, Kaplan–Meier, Manhattan / QQ / forest. Supplies a shared journal-ready style and copy-paste recipes so every figure looks like one consistent system. To combine several plots into ONE multi-panel composite figure, use the sibling `multipanel` skill.
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。
Protocols and best practices for western blot quantification and analysis including band detection, normalization, and statistical methods.
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 whole-slide images (WSIs): load slides, extract tiles, stain normalization, nuclear segmentation, feature extraction, and ML training. Supports H&E and multiplex. For end-to-end pipelines from raw WSIs to quantitative outputs.
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。
MS spectral matching and metabolite ID with matchms. Import spectra (mzML, MGF, MSP, JSON), filter/normalize peaks, score similarity (cosine, modified cosine, fingerprint), build reproducible pipelines, identify unknowns vs spectral libraries. Use pyopenms for full LC-MS/MS 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。
Guide for annotating statistical significance (p-value asterisks) on comparison plots. Covers standard notation (ns, *, **, ***, ****), matplotlib bracket+asterisk implementation, and use with seaborn box/violin/bar plots. Use when preparing publication-ready figures with significance markers.
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。
Pure Python DICOM for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM, pixels as NumPy, edit tags, windowing (VOI LUT), PHI anonymization, build DICOM, series→3D volumes. Use histolab for WSI pathology; nibabel for NIfTI.
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。
Assemble multiple plots into ONE publication-ready multi-panel journal figure (e.g. Figure 1 with panels A, B, C). Use whenever the user asks to combine, compose, or lay out several plots as a single composite figure — newly plotted from data or from already-rendered panels the user supplies (PNG/PDF). Ask the user to pick one of two approaches: (1) redraw every panel into one unified figure using independent, tightly packed `subfigures` (each sized to its own labels, so axes need NOT align), consistent style, correctly placed panel letters, and per-panel legends/colorbars; (2) composite already-rendered PNG/PDF panels onto a mosaic canvas and add panel letters (image compositing, not plotting). Both export vector PDF + high-DPI PNG. For a SINGLE plot from a data table, use the sibling `omics-plotting` skill instead.
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。
3Dmol.js WebGL molecular visualization emitted as self-contained HTML. Render structures (PDB/SDF/XYZ/MOL2/cube) with stick, sphere, cartoon, line, and surface styles; animate trajectories with a frame-delay (interval, ms) control; and animate vibrational normal modes via vibrate() from per-atom dx/dy/dz displacements or from precomputed frames. Output standalone HTML that loads 3Dmol from a CDN, with optional play/pause and speed controls. Use for transition-state imaginary-mode animations, MD or reaction-path playback, docking poses, and orbital/density isosurfaces. For static 2D chemical structure drawings use rdkit-chemdraw-cdxml; for 2D statistical plots use matplotlib or plotly.
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。
API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.
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。
Parse/write FCS (Flow Cytometry) files v2.0-3.1. Events as NumPy, channel metadata, multi-dataset files, CSV/FCS export. Use FlowKit for gating/compensation.
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。
Benchling R&D Python SDK: CRUD on registry entities (DNA, RNA, proteins, custom), inventory, ELN, workflow automation. Needs Benchling account and API key. Use biopython for local sequence analysis; pubchem for chemical DBs.
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。
Interactive viewer for microscopy. Displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, Tracks layers; supports annotation, plugin analysis, headless screenshots. Core visualization for Python bioimage workflows. Use ImageJ/FIJI for macro processing; napari for Python-native interactive visualization and DL segmentation review.
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 library for single-particle tracking (SPT) in video microscopy via the Crocker-Grier algorithm. Locate particles (fluorescent spots, colloids, vesicles, cells) per frame, link into trajectories, filter short tracks, and compute MSD for diffusion analysis. 2D/3D with subpixel accuracy; reads TIF stacks, AVI, image series via pims. Use for quantitative SPT and diffusion coefficient extraction from fluorescence or brightfield video.
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。
Three-tiered sgRNA design guide using validated Addgene sequences, CRISPick pre-computed datasets, or de novo design rules for CRISPR experiments
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 Metabolomics Workbench REST API (4,200+ NIH studies) for metabolite ID, study discovery, RefMet standardization, m/z precursor searches, and gene/protein annotations. Quirks: compound input_item rejects `name` (use pubchem_cid/kegg_id/inchi_key/etc.); free-text → compound is a two-step refmet/match→refmet/name flow; moverz endpoint returns TSV text, not JSON. Use hmdb-database for local XML; pubchem-compound-search for general compound lookup.
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。