「aria」の検索結果

Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization.

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Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

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Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.

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Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.

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Implements missing-data sensitivity analyses for confirmatory clinical trials including MMRM under MAR (with Kenward-Roger correction), reference-based multiple imputation (J2R, CR, CIR, LMCF per Carpenter-Roger 2013), Permutt delta-adjustment / tipping-point analysis, pattern-mixture identifying restrictions (CCMV, NCMV, ACMV), and the Cro vs Bartlett variance debate. Use when handling missing primary or secondary endpoint data in regulatory submissions following NRC 2010 and ICH E9(R1).

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Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.

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Sort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.

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Analyze TF motif accessibility variability across samples or single cells using chromVAR. Use when identifying TF motifs whose accessibility correlates with conditions, computing per-sample motif z-scores after matched background correction, comparing to ArchR / Signac equivalents, or distinguishing motif-accessibility signal from per-site footprinting.

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Mark and remove PCR/optical duplicates using samtools fixmate and markdup. Use when preparing alignments for variant calling or when duplicate reads would bias analysis.

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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.

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Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.

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Installs 425 bioinformatics skills covering sequence analysis, RNA-seq, single-cell, variant calling, metagenomics, structural biology, and 56 more categories. Use when setting up bioinformatics capabilities or when a bioinformatics task requires specialized skills not yet installed.

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Add MAUI DevFlow to a .NET MAUI project with agent package references, MauiProgram.cs registration, Blazor WebView support, GTK variants, Central Package Management guidance, and verification commands. USE FOR: first-time DevFlow setup, reviewing what files to edit, choosing DevFlow packages, or continuing after `maui devflow init` installs skills. DO NOT USE FOR: troubleshooting an already-integrated app that cannot connect, iterative app debugging, UI inspection, or generic MAUI build failures (use maui-devflow-debug). INVOKES: maui devflow CLI and dotnet CLI.

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Use this skill to understand the geographic patterns of FMCSA inspections. Covers high-inspection states, why they're high-inspection, and operational planning.

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Use this skill when a driver has vision in only one eye or significantly reduced vision in one eye. Covers § 391.41 vision standards, monocular driver variance, and depth-perception assessment.

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Use when a carrier asks how to calculate cost-per-mile (CPM) at the fleet, truck, lane, or driver level — separating fixed vs variable costs, allocating overhead, accounting for empty miles, and using CPM to negotiate rates, build pricing tiers, or evaluate lane profitability.

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Build a compact Coval test set from product requirements, observed conversation failures or a coverage gap. Design realistic scenarios with separate expected behaviors, provenance and controlled voice/persona variation.

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Extract exactly what the person typed in a Claude Code session (script, no agent context spent), have a fresh agent write a faithful structured summary under it, and optionally have an adversarial agent judge whether the working agent is still on track. Use at a checkpoint, before a handoff, after a compaction, when a long session may have drifted from what the owner asked, or when told to 'summarize what I said' or 'check we're on track'.

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Orchestrator that runs one UI brief through all four ui-* posture skills in parallel, then picks and merges a winner. Use when a screen matters enough to explore several directions, or the user says 'build it a few ways', 'design options', 'bake-off', 'show me variations', or 'compare approaches'.

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Filesystem auto-discovery of App Router pages into navigable index pages. Use when creating an index/listing page, adding route directories, converting hard-coded route configs to auto-discovery, or adding a route-listing display variant.

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Pre-commitment attack on a plan by an agent that did not write it. Use when presenting a feature-deep-dive plan, assigning vision-to-fleet briefs, moving a register to build mode, ratifying a scope list, or coding a new contract or table. NOT for built work or DONE reports (use subagent-dispatch).

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Real tests around a big code change: written first, proven to fail on a planted break, run by a fresh agent in the live product before and after. Use when told to use safety-net, or before a change wide enough that one agent cannot see every way it breaks. NOT for one test (forcing-function-tests).

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Locate CCSPlayerPawn::SetModelFromLoadout in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayerPawn::SetModelFromLoadout" (symbol CCSPlayerPawn_SetModelFromLoadout). Shortlist alongside SetModelFromClass (same file/call region — sibling paths in pawn model selection). This variant resolves the model from the player's loadout/inventory items (agent skin selection), referencing inventory services and item definition indexes before calling SetModel. Trigger: CCSPlayerPawn_SetModelFromLoadout, CCSPlayerPawn::SetModelFromLoadout

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