「junction」の検索結果

Manage agent-skill lifecycles across local, global, project, Skill Hub, skills.sh, GitHub, plugin-cache, and system roots. Use when searching, recommending, installing, updating, upgrading, syncing, merging, auditing, deduplicating, cleaning, packaging, publishing, or validating skills; choosing project versus global scope; verifying upstream source, stars, or installs; or working with skill-upgrade, check_upstreams.ps1, sync-pull/push, junctions, skills-lock.json, or skill-upstreams.json. Do not use for ordinary software-package upgrades or non-skill file cleanup.

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。

End-to-end alternative splicing analysis from FASTQ to differential splicing results for short-read bulk RNA-seq. Aligns with STAR 2-pass cohort-style, performs junction QC (RSeQC, MaxEntScan, SpliceAI), runs rMATS-turbo and leafcutter for concordant differential analysis, optionally MAJIQ V3 for complex events / heterogeneous cohorts, isoform-switching with NMD/ORF/domain consequences (IsoformSwitchAnalyzeR v2 + DRIMSeq+DEXSeq+stageR DTU), and sashimi visualizations. Use when performing comprehensive splicing analysis from raw bulk RNA-seq data; for variant-driven splice prediction see splice-variant-prediction; for rare-disease single-patient outlier detection see outlier-splicing-detection; for full-isoform PacBio/ONT analysis see long-read-splicing.

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。

Drives a vehicle to a destination autonomously using CARLA's navigation agents (BasicAgent/BehaviorAgent/ConstantVelocityAgent), or plans a route between two points with GlobalRoutePlanner. The agent handles routing, obstacle avoidance and traffic-light obedience. Use when the user asks to "drive the ego to (x,y,z)", "navigate to the junction", "go to that location", "plan a route from A to B", or "make the car drive itself somewhere specific". Manual control is control-vehicle; autopilot roaming is control-traffic.

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。

Runs a single ScenarioRunner Python scenario (or a whole group) against a running CARLA server — FollowLeadingVehicle, ControlLoss, DynamicObjectCrossing, SignalizedJunctionLeftTurn and the rest of srunner/examples — in sync or async mode, with result output, recording and manual/agent control of the ego. Lists what is runnable on the checked-out branch and on the loaded map. Use when the user asks to "run a scenario", "test FollowLeadingVehicle", "list scenarios", "run a CARLA traffic scenario", or reports a scenario that is "not supported".

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。

Runs a route through ScenarioRunner's RouteScenario mode (--route / --route-id) with an autonomous agent driving the ego — the same mechanism the CARLA Leaderboard uses, and the only way to reach the ~34 route-only scenario classes (Accident, ParkedObstacle, HazardAtSideLane, InvadingTurn, HardBreakRoute, YieldToEmergencyVehicle, junction and actor-flow scenarios). Inspects route files, lists route ids and their scenarios, and runs one or all of them. Use when the user asks to "run a route", "drive a route with my agent", "run leaderboard scenarios without the leaderboard", or names a route-only scenario type.

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。

Controls the traffic-light actors on a running CARLA server — set a light green/red/yellow/off, freeze all lights (optionally all-green), set phase timing (green/yellow/red durations), and reset to the normal cycle. Target all lights, one by id, all in a junction, or the nearest. Use when the user asks to "make the light red", "freeze all lights green", "change the light timing", "turn the traffic lights off", or "reset the lights". This is the light actors, not the Traffic Manager (that's control-traffic).

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。

Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

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。

Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/SUPPA2/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality 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。

Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normalization. Use when measuring splice-site usage or isoform inclusion ratios from short-read RNA-seq.

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。

Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

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。

Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normalization. Use when measuring splice-site usage or isoform inclusion ratios from short-read RNA-seq.

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。

Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/SUPPA2/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality 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。

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。

The one association system (platform.associations) and schema-qualified table refs. Use when touching an M2M junction table, an associate_*/attach_*/tag_* RPC, FK columns used as tags, a 'things attached to X' list or card, or a PGRST205 / 42703 table-not-found on a bare .from().

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。