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概要と使いどころ
Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
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Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
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。
Run remote BLAST searches against NCBI servers using Biopython Bio.Blast.NCBIWWW. Use when identifying unknown sequences, finding homologs, picking the correct BLAST program (blastn/blastp/blastx/tblastn/tblastx/psiblast/megablast/dc-megablast), interpreting Karlin-Altschul E-values, avoiding the max_target_seqs trap (Shah 2019), choosing composition-based statistics, or limiting searches by organism. Covers RID lifecycle, database choice (nt/nr/refseq_select/swissprot), word-size and CBS taxonomy.
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Reconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML reconciliation), AleRax (Morel 2024 co-estimation), Whale.jl (Bayesian DL+WGD), RANGER-DTL 2 parsimony, NOTUNG, ecceTERA, and Treerecs. Use when inferring ancestral gene-family content, distinguishing duplication from horizontal transfer from differential loss, rooting deep species trees from gene-content signals (STRIDE / Williams 2017 ALE-rooting), counting DTL events per branch, refining noisy gene trees against a species tree, modeling WGD events jointly with DTL, or producing publication-grade gene-family histories for phylogenomic / comparative analyses.
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。
Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.
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。
Queries gnomAD v4 (807k samples), v3, v2.1.1, and constraint metrics with grpmax FAF95, bottleneck-group exclusion, LOEUF interpretation, SV/CNV/mtDNA catalogs, and Whiffin max-credible-AF framework. Use when filtering rare variants, applying ACMG BS1/BA1, ranking genes by LoF intolerance, or selecting between v2 (GRCh37 + chrX/Y constraint) and v4 (GRCh38 + 807k samples).
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Analyzes alternative splicing from PacBio Iso-Seq (HiFi, Kinnex/MAS-Iso-seq) and Oxford Nanopore (direct cDNA, direct RNA, R10.4.1+) long-read RNA-seq with full-isoform resolution. Tools include FLAIR (correct/collapse/quantify/diffSplice for PacBio + ONT), IsoQuant (de-novo or annotation-guided isoform discovery 2024 SOTA), Bambu (annotation-aware Bayesian discovery + quantification with Novel Discovery Rate), SQANTI3 (isoform classification: FSM/ISM/NIC/NNC + artifact flags), rMATS-long (event calling on long-read isoforms), and minimap2 (-ax splice:hq for HiFi; -ax splice -k14 for ONT cDNA; add -uf only for direct RNA or stranded cDNA preps). Solves microexon detection, recursive splicing, complex multi-exon isoforms, and DTU without transcript-quantification uncertainty. Use when short-read AS limitations (anchor length, complex isoforms, microexons, recursive splicing, transcript ambiguity) demand full-isoform resolution.
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。
Use when the user mentions simulator testing, visual verification, push notification testing, location simulation, screenshot capture, OR live accessibility validation (VoiceOver announcements, Dynamic Type, ADA checks) on the simulator.
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。
Use when storing credentials securely, encrypting data, implementing passkeys, securing AI/agentic features against prompt injection, code signing, or managing certificates and provisioning profiles.
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Use when writing ANY test, debugging flaky tests, making tests faster, or choosing Swift Testing vs XCTest. Covers unit tests, UI tests, async testing, test architecture.
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。
Use when implementing ANY computer vision feature — image analysis, pose detection, person segmentation, subject lifting, text recognition, barcode scanning.
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。
Use when the user wants to simplify Swift code, reduce boilerplate, or make Swift more readable and idiomatic without changing behavior.
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。
Use when the user mentions security review, App Store submission prep, Privacy Manifest requirements, hardcoded credentials, or sensitive data storage.
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。
Use when the user wants to run XCUITests, parse test results, view test failures, or export test attachments.
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。
Use when building, fixing, or improving ANY SwiftUI UI — views, navigation, layout, animations, gestures, debugging, iOS 26 features, iPhone Duo, view-level performance, feature architecture.
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。
Use when connecting to Xcode via MCP, using xcrun mcpbridge or the headless mcp-server, or working with ANY Xcode MCP tool (XcodeRead, BuildProject, RunSomeTests, RenderPreview). Covers setup, tool reference, workflows, troubleshooting.
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。
Use when bridging UIKit and SwiftUI, modernizing UIKit apps (scene lifecycle, resizability), debugging Auto Layout, Combine, TextKit, PencilKit, or UIKit animations.
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。
Use when the user wants automated performance profiling, headless Instruments analysis, or CLI-based trace collection.
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。
Use when an Apple-platform app feels slow, memory grows, battery drains, or ANY performance issue needs diagnosing. Covers memory leaks, profiling, Instruments workflows, retain cycles, optimization.
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。
Use when building ANY watchOS app — app structure, independent apps, Watch Connectivity, Smart Stack widgets, complications, controls, RelevanceKit, background tasks, ClockKit migration.
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。
Use when the user mentions SPM resolution failures, "no such module" errors, duplicate symbol linker errors, version conflicts between packages, or Swift 6 package compatibility issues.
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Use when the question is about Swift or its toolchain rather than a framework — modern idioms, noncopyable types, drag and drop, debug deep links, tvOS targets.
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。
Use when preparing an app for App Store submission, handling rejections or appeals, managing App Store Connect and Xcode Cloud, or triaging TestFlight/Sentry crash corpora. Covers checklists, metadata, privacy manifests, age ratings, export compliance.
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。
Use when the user mentions App Store screenshot validation, screenshot review, checking screenshots before submission, or verifying screenshot dimensions and content.
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。