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概要と使いどころ
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.
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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.
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Statistical visualization on matplotlib with native pandas support. Auto aggregation, CIs, grouping for distributions (histplot, kdeplot), categorical (boxplot, violinplot), relational (scatterplot, lineplot), regression (regplot, lmplot), matrix (heatmap, clustermap), grids (pairplot, FacetGrid). Use for quick statistical summaries; matplotlib for fine control; plotly for interactive HTML.
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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.
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Python library for biology: sequence manipulation (DNA/RNA/protein), pairwise/multiple alignment, phylogenetic trees (NJ, UPGMA), diversity (Shannon, Faith PD, Bray-Curtis, UniFrac), ordination (PCoA, CCA, RDA), stats (PERMANOVA, ANOSIM, Mantel), file I/O (FASTA, FASTQ, Newick, BIOM). Use for microbiome, community ecology, or phylogenetics.
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Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.
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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.
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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.
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Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.
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Hardware-agnostic Python liquid-handler library: portable scripts run on Hamilton STAR, Tecan Freedom EVO, Opentrons OT-2, or a simulator without vendor lock-in. For protocol automation, method dev, plate reformatting, serial dilutions, and Python lab workflows.
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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.
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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.
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Computer vision for bio-image preprocessing, feature detection, real-time microscopy. Color conversion, morphology, contour/blob detection, template matching, optical flow on fluorescence/brightfield. 10-100× faster than pure Python via C++. Use scikit-image for scientific morphometry/regionprops; OpenCV for real-time, video, classical feature extraction.
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Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data. C-index, Brier, time-dependent AUC; Kaplan-Meier, Nelson-Aalen, competing risks. Pipeline/GridSearchCV compatible. Use statsmodels for frequentist, pymc for Bayesian, lifelines for parametric.
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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.
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De novo and known TF motif enrichment in ChIP-seq/ATAC-seq peaks via HOMER. findMotifsGenome.pl finds over-represented patterns vs background; annotatePeaks.pl assigns context (TSS distance, gene, repeat). Use after MACS3 to identify enriched TFs, annotate peaks with nearest genes, and validate ChIP-seq via the target motif.
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Parse HMDB (Human Metabolome Database) local XML for metabolite info, chemical properties, biological context, disease links, spectra, and cross-DB mapping. No REST API — uses ~6 GB XML download. Use drugbank-database-access for drugs; pubchem-compound-search for live lookups.
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Poisson-model peak caller for ChIP-seq/ATAC-seq BAMs. MACS3 callpeak finds enriched regions (TF sites or histone marks) vs input/IgG; outputs BED narrowPeak/broadPeak for motif analysis, annotation, and differential binding. Use narrow peaks for TF ChIP-seq and ATAC-seq; broad for H3K27me3, H3K9me3, and other broad marks.
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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.
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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.
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Interactive scientific visualization with Plotly. Two APIs: plotly.express (px) for one-liner DataFrame plots, plotly.graph_objects (go) for trace-level control. 40+ chart types with hover, zoom, pan, animation. Exports HTML or static PNG/SVG/PDF via kaleido. Use for volcano plots with gene hover, dose-response dashboards, expression heatmaps, 3D molecular views. Use seaborn for stats; matplotlib for publication figures.
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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.
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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.
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Protocols and best practices for western blot quantification and analysis including band detection, normalization, and statistical methods.
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