'aria' 검색 결과

Plan or run inherited DNA variant detection from whole-genome, exome, or targeted-panel sequencing. Check reference compatibility, sample quality, and combined analysis across samples, with documented commands, resource versions, and paths to variant and annotation outputs.

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• Planning inherited DNA variant detection from sequencing data
• Preparing joint variant analysis for cohorts or families
• Checking reference resources and sequencing quality

Plan DNA variant detection workflows by identifying the assay and sample model, then routing to germline, cancer somatic, or molecular-barcode panel skills. It also supports focused local BAM/CRAM checks with variant files, depth summaries, and optional filters.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a workflow for WGS, WES, or DNA panels
• Routing inherited, cancer, and molecular-barcode analyses
• Checking reference genomes and required input files

Look up TPMI associations for a single genetic variant using an rsID or genome coordinates. Get a concise summary showing the queried variant, the total number of associations, and whether the displayed results are incomplete.

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• Check TPMI associations for a variant identified by rsID
• Look up a variant using GRCh37 genome coordinates
• Save the full association response to a file

Inspect and edit Figma files through JavaScript using the Plugin API. Guides changes to text, layout, variables, and components, with incremental steps and visual checks to verify the resulting design.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect pages, components, and variables in a Figma file
• Update text and colors across matching elements
• Adjust auto-layout containers and child sizing

Use JavaScript through Figma's Plugin API to inspect file structure and edit design elements, components, variables, and layouts. Follow incremental steps with font loading, returned element IDs, and visual checks to validate changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Edit shapes and text in a Figma file
• Set up components, variants, and shared design values
• Adjust auto-layout, fills, and variable bindings

Extract structured product records from Amazon URLs across regional sites, including price, stock, specifications, rating breakdowns, sellers, and visible variants. Use the records for competitor research, catalog audits, or tracking product prices and availability.

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• Collect product specifications for competitor research
• Prepare records for a brand catalog audit
• Check prices and stock for selected products

Generate a Stata .do file that compares every subset of optional control variables across parallel instances. The script ranks combinations by the focal variable’s absolute t-statistic, reports the top ten, and fits the highest-ranked model.

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• Comparing all subsets of optional control variables
• Keeping mandatory controls in every regression
• Preparing parallel regression runs in Stata

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Post a comment (or a formal PR review) to a GitHub issue or pull request in mariadb-operator/mariadb-operator. Use whenever the user wants to publish something to GitHub for this repo — "comment on issue #123", "post this as a PR comment", "leave a note on #456", "reply on the PR" — and especially when they want the output of the mariadb-operator-pr-review skill delivered to GitHub instead of just shown in chat, e.g. "review PR #1234 and post it as a comment" or "post the review results to the PR". Always confirms the exact comment text with the user before publishing — never posts autonomously.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller or when highest germline calling accuracy is required.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Unity Prefab / Scene / Asset の壊れた参照・Variant override・配線ミスを構造化応答で診断し、 dry-run → confirm 経路で安全に修復するための MCP ツールリファレンス。 Editor Bridge のセットアップ手順もここに含む。 トリガー: .prefab, .unity, .asset, .mat, Prefab, Variant, 配線, 参照, 壊れた参照, broken reference, GUID, fileID, override, パッチ, patch plan, validate refs, validate structure, where-used, inspect variant, inspect wiring, inspect hierarchy, wiring, null参照, null reference, MonoBehaviour, 階層, hierarchy, Transform整合, 孤立, orphan, 重複fileID, duplicate fileID, Editor Bridge, Bridge セットアップ, bridge setup, deploy_bridge, UNITYTOOL_BRIDGE_WATCH_DIR, watch ディレクトリ のいずれかに該当する作業で使用する。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Locate CCSCustomHudLayout_SetDialogVariableStringForPlayer in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSCustomHudLayout_SetDialogVariableStringForPlayer" (symbol CCSCustomHudLayout_SetDialogVariableStringForPlayer). The gamedata consumer stores a BYTE SIGNATURE for this entry - ALWAYS emit the func schema (func_sig), never vfunc fields. The function may be virtual; locating it via the owning class vtable (RTTI) is a fine strategy, but once found, read its head bytes and emit func_sig. Locate via cross-references, distinctive constants/strings, callers of related symbols, or vtable slots; verify by decompilation before committing. Trigger: CCSCustomHudLayout_SetDialogVariableStringForPlayer, CCSCustomHudLayout_SetDialogVariableStringForPlayer

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Locate CCSCustomHudLayout_SetDialogVariableString in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSCustomHudLayout_SetDialogVariableString" (symbol CCSCustomHudLayout_SetDialogVariableString). The gamedata consumer stores a BYTE SIGNATURE for this entry - ALWAYS emit the func schema (func_sig), never vfunc fields. The function may be virtual; locating it via the owning class vtable (RTTI) is a fine strategy, but once found, read its head bytes and emit func_sig. Locate via cross-references, distinctive constants/strings, callers of related symbols, or vtable slots; verify by decompilation before committing. Trigger: CCSCustomHudLayout_SetDialogVariableString, CCSCustomHudLayout_SetDialogVariableString

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

현재 성능 측정, 가설별 변경안 비교, 정확성 검증으로 최적화를 진행합니다. 시간과 비용 한도 안에서 가장 빠르고 안전한 안을 고르고 재실행 절차를 남깁니다.

• 느린 작업을 측정하며 속도 개선
• 같은 입력 조건에서 구현안 비교
• 응답 시간, 처리량, 실행 비용 평가

Review HTML list structure so screen readers can identify lists and announce item counts. Find list items without valid containers, check custom accessibility roles, and guide fixes to markup used for navigation or styling.

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• Review list markup split across components
• Fix navigation lists with invalid nesting
• Check parent roles for custom lists

Check form fields for duplicate labels that may confuse screen readers or voice control. Identify affected inputs and suggest consolidating labels or using aria-labelledby, an attribute that connects a field to multiple pieces of naming text.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding duplicate labels on form inputs
• Combining multiple text sources into a field name
• Reviewing form labels with screen readers

Review decorative images and icons to prevent unnecessary screen reader announcements. Check empty alt text, aria-hidden, and relevant keyboard behavior, then suggest fixes for concrete accessibility issues without flagging decorative assets solely because they exist.

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• Checking decorative images for unnecessary announcements
• Reviewing accessibility of SVGs and icons
• Checking shared decorative component patterns

Review and fix breadcrumb HTML so readers, including people using screen readers, can understand site hierarchy and their current location. Check navigation labels, ordered lists, current-page attributes, and consistency with structured data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing breadcrumbs for screen reader accessibility
• Checking breadcrumb HTML produced by shared components
• Aligning structured data with visible breadcrumbs