494 件 ・ 関連度順
概要と使いどころ
Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.
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Load gene expression count matrices from various formats including CSV, TSV, featureCounts, Salmon, kallisto, and 10X. Use when importing quantification results for downstream analysis.
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。
Visualize differential expression results using DESeq2/edgeR built-in functions. Covers plotMA, plotDispEsts, plotCounts, plotBCV, sample distance heatmaps, and p-value histograms. Use when visualizing differential expression results.
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。
Compare gene regulatory and co-expression networks between biological conditions to identify rewired regulatory relationships using DiffCorr. Detects gained, lost, and reversed gene-gene correlations between conditions. Use when comparing co-expression networks between disease vs control, treatment conditions, or developmental stages.
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Remove batch effects from RNA-seq data using ComBat, ComBat-Seq, limma removeBatchEffect, and SVA for unknown batch variables. Use when correcting batch effects in expression data.
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。
Bulk transcriptomics differential expression with count-aware modeling, design validation, contrast handling, thresholded exports, and publication-ready DE 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。
Cell-free protein synthesis (CFPS) planning and optimization guidance. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).
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。
Guidance for cell-free protein synthesis (CFPS) optimization. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).
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。
Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.
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-first workflow for bulk RNA-seq expression intake, normalization, sample QC, and downstream-ready matrices.
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。
Creating memorable, readable, and emotionally resonant game characters that work at every scale and in every contextUse when "character design, design a character, character art, character concept, character sheet, turnaround, expression sheet, character silhouette, shape language, character proportions, iconic character, memorable character, character lineup, character family, hero design, villain design, npc design, protagonist design, character, art-direction, visual-design, game-art, concept-art, silhouette, shape-language, color-theory, costume, expression, turnaround, iconic, readable" mentioned.
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。
Automate Adobe After Effects workflows. Use when a user asks to script After Effects with ExtendScript or CEP, batch render compositions, automate motion graphics templates (MOGRTs), build render pipelines with aerender, create expressions for animations, manage project files programmatically, automate text and image replacements in templates, build data-driven motion graphics, integrate After Effects with CI/CD, or control AE via command line. Covers ExtendScript, CEP panels, expressions, aerender CLI, and template automation.
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。
This skill should be used when the user asks anything about Adobe After Effects — to "write an After Effects expression", "make a wiggle / loopOut / inertial bounce", "rig a control with a slider / null", "build a MOGRT / Essential Graphics template", "export AE to Lottie or Media Encoder"; OR to "export from After Effects", "export with transparency / an alpha channel", "fix a black background on export", "choose ProRes vs H.264", "which codec / what bitrate for 1080p / 4K", "stop H.264 disappearing from the Render Queue"; OR when "After Effects is slow / laggy / crashing / froze", "RAM preview won't play in real time", "AE is using all my memory / render takes forever", "how do I speed up After Effects"; OR to "organize my After Effects project", "relink missing footage", "fix broken links after moving files", "set a folder structure / naming conventions", "batch rename layers", or "write an ExtendScript / .jsx script". Covers expressions & rigging, export recipes, performance triage, and project hygiene.
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。
Animate objects, cameras, lights, and properties in Blender — keyframes, F-curves, easing (Bezier, Linear, Sine, Bounce, Elastic), shape keys (morph targets / blendshapes / visemes), drivers (Python expressions on properties), NLA actions for reuse and layering. Use whenever the user asks to "animate this", "make it move / rotate / scale over time", "add keyframes", "loop / oscillate", "shape key / morph / blendshape", "visemes for lip sync", or any time-based property change. Make sure to use this skill even if the user does not say "animate" — also covers "spin it slowly", "make it wave", "fade in / out", "pulse", "facial expression".
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。
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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。
Modern PHP idioms for any PHP project (PHP 8.1+): readonly properties, enums, match expressions, constructor property promotion, typed properties, named arguments, nullsafe operator, first-class callable syntax, strict_types, PSR-12 style, and null discipline. Apply when the project is a PHP 8.1+ project. Stack-agnostic — referenced by every PHP plugin in the marketplace. Use this skill to: - Write strongly-typed, immutable-by-default value objects with readonly properties and enums. - Replace switch ladders and magic constants with match expressions and backed enums. - Cut constructor boilerplate with property promotion and express optionality with typed nullable returns. - Keep code PSR-12 compliant with declare(strict_types=1) in every file. Do NOT use this skill for: - Framework-specific idioms (Eloquent, Doctrine, Symfony services, Laravel facades — those live in framework plugin skills). - Composer / autoloading / dependency management — see php-foundation:composer-tooling. - Testing patterns — see php-foundation:php-testing.
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。
"Apply spiral of silence theory (Noelle-Neumann) to analyze how perceived opinion climate suppresses minority expression. Use this skill when the user needs to understand why certain viewpoints disappear from public discourse, evaluate the role of fear of isolation in opinion expression, or assess opinion climate dynamics — even if they say 'why are people afraid to speak up', 'silent majority', or 'social pressure on opinions'.".
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Search, query, and cross-reference NCBI GEO (Gene Expression Omnibus) datasets with ENCODE experiments. Use when the user wants to find GEO accessions for ENCODE experiments, search GEO for complementary datasets, download GEO metadata or series matrices, cross-reference ENCODE and GEO data, find supplementary files from GEO, or link GEO series to ENCODE experiments for provenance tracking. Also use when the user mentions GEO, GSE, GSM, GPL, GDS, series matrix, SOFT format, or needs to find expression data in GEO that complements their ENCODE analysis.
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 integrating CellxGene Census single-cell data with ENCODE bulk experiments. Use when users need cell-type-specific expression context for ENCODE regulatory data, want to deconvolve bulk ENCODE signals, or validate regulatory elements at single-cell resolution. Trigger on: CellxGene, single-cell atlas, cell type expression, Census, cell type specificity, single-cell context, scRNA-seq atlas.
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Execute ENCODE RNA-seq pipeline from FASTQ to gene quantification and signal tracks. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing RNA-seq data with STAR alignment, RSEM/Kallisto quantification, or generating expression matrices. Trigger on: RNA-seq pipeline, gene expression, STAR alignment, RSEM quantification, transcript quantification, TPM, FPKM, RNA processing, run RNA-seq.
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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native 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。
Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.
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。
Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).
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。
Imports gene expression count matrices from featureCounts, HTSeq, STAR ReadsPerGene, Salmon/kallisto via tximport or tximeta, RSEM, 10X Genomics MTX/H5, AnnData H5AD, and RDS. Handles silent-miscounting traps (featureCounts -p v2.0.2 API break, STAR strandedness column choice, salmon NumReads-sum without tximport, RSEM non-integer expected_count, GENCODE _PAR_Y suffix, zero-length-transcript TPM divide-by-zero), and encodes the tximport countsFromAbundance decision tree with the "lengthScaledTPM is not TPM" warning. Use when assembling a gene-by-sample count matrix from aligner or quantifier output, importing salmon/kallisto for DESeq2 vs limma-voom, choosing strandedness column for STAR, debugging zero-count panics, or building tx2gene mapping.
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。