「apply」の検索結果

Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

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。

Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.

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。

Call protein-RNA binding sites from CLIP-seq BAM with CLIPper, PureCLIP, Skipper, Piranha, omniCLIP, CTK, CLAM, or Paraclu. Use when choosing between coverage-based, HMM-based, beta-binomial window-based, and crosslink-site-based peak callers; applying ENCODE eCLIP thresholds (log2 IP/SMInput >= 3, -log10 p >= 3); deciding when SMInput is mandatory; or reconciling peak-set discordance between callers for the same RBP.

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。

Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.

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。

Identify direct miRNA-target interactions from AGO HITS-CLIP, AGO-CLEAR-CLIP (chimeric reads), HEAP (Halo-Ago2 mouse), chimeric eCLIP / miR-eCLIP (deep miRNA-target profiling), or CLASH using chimeric-read processing pipelines, seed-pairing analysis, and 3' auxiliary pairing rules. Use when distinguishing direct miRNA targets from indirect, integrating CLIP-derived target maps with TargetScan / miRDB / DIANA predictions, applying canonical 7mer-8mer seed matching with 3' UTR context, or recovering miRNA-mRNA chimeras at scale.

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。

Calls ChIP-seq peaks with MACS3, MACS2, HOMER, or SPP across narrow (TF) and broad (histone) modes. Handles input control matching, fragment-size modeling vs --nomodel, effective genome size, ENCODE-style IDR vs naive overlap, hyper-ChIPable artifacts, and aligner-specific shifts. Use when calling peaks from ChIP-seq alignments, choosing between narrow vs broad mode for a histone mark, deciding model vs nomodel for low-depth data, applying ENCODE pseudoreplicate IDR, or reconciling MACS vs HOMER vs SPP 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。

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 2025 Nat Genet; 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。

Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential 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。

Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection rather than two-group comparison. Standard tool in EU rare-disease (Solve-RD) and NIH UDN programs. Use when applying RNA-seq to undiagnosed Mendelian disease, validating predicted splice variants in clinical samples, or detecting cryptic splicing in disease tissue.

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 this skill when a driver does not meet federal medical standards and needs to apply for a variance. Covers application steps, required documentation, and review timeline.

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 this skill when the user asks how to determine whether a CMV operation is interstate vs intrastate, why it matters (federal vs state rules apply), the "continuation of journey" rule, state-specific intrastate exemptions, and what happens when an intrastate carrier picks up an interstate load. Cite 49 CFR 390.5 + state-specific exemptions.

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 this skill to understand the value and limits of CVSA decals. Covers when they apply, what they signal, and inspector discretion.

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。

Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.

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。

Recipe for making an existing surface agent-writable via manifest writeTargets. Use when asked to 'let agents write/update/fill X on this page', 'add write targets', or 'wire apply_surface_write', or for an avalanche-campaign item. NOT for a brand-new surface (use surface-authoring).

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。

Audit-and-fix method for one user setting area across every repo. Use when a setting seems ignored, duplicated or confusing, on 'audit the X settings' or 'why doesn't my X setting apply', or as a lane of the settings sweep. NOT for adding a new setting (use build-sub-feature).

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。

Bringing an existing table onto the platform base standard. Use when asked to canonicalize, retrofit, or 'Wave 3' a table, run apply_rls or an org backfill, retire a bespoke *_versions table, or move it onto platform comments/associations/permissions. NOT for a schema move (use db-move-table-schema).

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。

When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.

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。

Applies Anthropic brand colors and typography to artifacts that need a consistent look-and-feel. Use when brand colors, style guidelines, visual formatting, or company design standards apply.

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。

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

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 37signals/DHH Rails conventions when writing Ruby on Rails code. Use when building Rails applications, reviewing Rails code, or making architectural decisions. Covers various aspects of Rails application architecture, design and dependencies.

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 timeless programming wisdom from "97 Things Every Programmer Should Know" when writing, reviewing, or refactoring code. Use for design decisions, code quality checks, professional development guidance, testing strategies, and workflow 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。

Create documentation with gaming-specific examples, retro styling, and 8-bit terminology. Apply when documenting gaming blocks, RPG components, or retro-styled UI elements.

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。

[52] EXECUTE. Three-stage refactoring workflow: (1) iterative research of refactor/modularization options, (2) plan + risk/edge-case analysis + Scope150 validation, then implement with tests after user confirmation, and (3) apply Scout105 cleanup protocol. Use when asked to refactor, modularize, or restructure code safely.

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 you need to review, improve, or refactor Java code for generics quality — including avoiding raw types, applying the PECS (Producer Extends Consumer Super) principle for wildcards, using bounded type parameters, designing effective generic methods, leveraging the diamond operator, understanding type erasure implications, handling generic inheritance correctly, preventing heap pollution with @SafeVarargs, and integrating generics with modern Java features like Records, sealed types, and pattern matching. Part of the skills-for-java project

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。