'duplication' 검색 결과

Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Perform a comprehensive DRY (Don't Repeat Yourself) code review on a codebase. Identifies duplicated code, repeated logic, redundant patterns, and opportunities for abstraction. Use this skill whenever the user asks to review code for duplication, reduce repetition, apply DRY principles, refactor for reusability, find copy-pasted code, deduplicate logic, or audit code quality with a focus on redundancy. Also trigger when users say things like "clean up my code", "find repeated patterns", "too much boilerplate", "reduce code duplication", or "refactor for maintainability". Works on any language or framework.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit server-side paid-media measurement including server-side tag management, platform conversion APIs, event taxonomy, browser/server deduplication, consent, hashing, data quality, observability, and privacy. Use for server-side tracking, sGTM, server-side tagging, Meta CAPI, TikTok Events API, event_id, deduplication, pixel debugging, first-party measurement, or conversion data loss.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML reconciliation), AleRax (Morel 2024 co-estimation), Whale.jl (Bayesian DL+WGD), RANGER-DTL 2 parsimony, NOTUNG, ecceTERA, and Treerecs. Use when inferring ancestral gene-family content, distinguishing duplication from horizontal transfer from differential loss, rooting deep species trees from gene-content signals (STRIDE / Williams 2017 ALE-rooting), counting DTL events per branch, refining noisy gene trees against a species tree, modeling WGD events jointly with DTL, or producing publication-grade gene-family histories for phylogenomic / comparative analyses.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect syntenic blocks and structural rearrangements between genomes using MCScanX (Wang 2012), JCVI/MCScan (Tang 2008 Python), GENESPACE (Lovell 2022) for orthology-anchored riparian visualization, SyRI for structural variation, AnchorWave for sequence-level synteny, i-ADHoRe 3.0 for highly diverged species, SynNet for synteny networks, and ntSynt for multi-genome macrosynteny. Use when identifying collinear gene blocks across species, distinguishing macrosynteny from microsynteny, detecting inversions/translocations/duplications, anchoring orthology in WGD lineages, producing publication riparian plots, computing synteny block age via Ks (cross-references whole-genome-duplication), or running synteny-aware ortholog inference in polyploids.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploy DefectDojo as a centralized vulnerability management dashboard with scanner integrations, deduplication, metrics tracking, and Jira ticketing workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create an Estuary derivation with custom SQLite state (internal tables) to make per-document decisions based on accumulated history — account balances, inventory levels, approval workflows, deduplication. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "stateful derivation", "derivation to track balance", "transformation to approve based on state", "derive an approval workflow", "deduplication derivation", or "build a stateful derivation".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Data Quality Specialist IA — Expert en qualité des données (profiling, cleaning, déduplication, validation de schéma, Great Expectations)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploy DefectDojo as a centralized vulnerability management dashboard with scanner integrations, deduplication, metrics tracking, and Jira ticketing workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect syntenic blocks and structural rearrangements between genomes using MCScanX (Wang 2012), JCVI/MCScan (Tang 2008 Python), GENESPACE (Lovell 2022) for orthology-anchored riparian visualization, SyRI for structural variation, AnchorWave for sequence-level synteny, i-ADHoRe 3.0 for highly diverged species, SynNet for synteny networks, and ntSynt for multi-genome macrosynteny. Use when identifying collinear gene blocks across species, distinguishing macrosynteny from microsynteny, detecting inversions/translocations/duplications, anchoring orthology in WGD lineages, producing publication riparian plots, computing synteny block age via Ks (cross-references whole-genome-duplication), or running synteny-aware ortholog inference in polyploids.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML reconciliation), AleRax (Morel 2024 co-estimation), Whale.jl (Bayesian DTL+WGD), RANGER-DTL 2 parsimony, NOTUNG, ecceTERA, and Treerecs. Use when inferring ancestral gene-family content, distinguishing duplication from horizontal transfer from differential loss, rooting deep species trees from gene-content signals (STRIDE / Williams 2017 ALE-rooting), counting DTL events per branch, refining noisy gene trees against a species tree, modeling WGD events jointly with DTL, or producing publication-grade gene-family histories for phylogenomic / comparative analyses.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Improve code structure, readability, and maintainability without changing external behavior through systematic refactoring techniques like extracting functions, removing duplication, simplifying conditionals, and applying design patterns. Use when reducing technical debt, extracting functions or classes, removing code duplication, simplifying complex conditionals, renaming for clarity, applying design patterns, improving code organization, reducing coupling, increasing cohesion, or maintaining test coverage during structural improvements.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect syntenic blocks and structural rearrangements between genomes using MCScanX (Wang 2012), JCVI/MCScan (Tang 2008 Python), GENESPACE (Lovell 2022) for orthology-anchored riparian visualization, SyRI for structural variation, AnchorWave for sequence-level synteny, i-ADHoRe 3.0 for highly diverged species, SynNet for synteny networks, and ntSynt for multi-genome macrosynteny. Use when identifying collinear gene blocks across species, distinguishing macrosynteny from microsynteny, detecting inversions/translocations/duplications, anchoring orthology in WGD lineages, producing publication riparian plots, computing synteny block age via Ks (cross-references whole-genome-duplication), or running synteny-aware ortholog inference in polyploids.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML reconciliation), AleRax (Morel 2024 co-estimation), Whale.jl (Bayesian DTL+WGD), RANGER-DTL 2 parsimony, NOTUNG, ecceTERA, and Treerecs. Use when inferring ancestral gene-family content, distinguishing duplication from horizontal transfer from differential loss, rooting deep species trees from gene-content signals (STRIDE / Williams 2017 ALE-rooting), counting DTL events per branch, refining noisy gene trees against a species tree, modeling WGD events jointly with DTL, or producing publication-grade gene-family histories for phylogenomic / comparative analyses.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploy DefectDojo as a centralized vulnerability management dashboard with scanner integrations, deduplication, metrics tracking, and Jira ticketing workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

AI 에이전트가 읽는 스킬과 지침 문서를 작성하고 편집하도록 돕습니다. 절차, 참고 정보, 완료 조건을 정리하고 자료를 읽을 조건과 중복 내용을 검토합니다.

• 스킬 문서 작성과 편집
• AGENTS.md와 CLAUDE.md 정리
• 참고 자료를 읽을 조건 명확화

변수와 함수 이름, 가독성, 데이터 갱신, 오류 처리를 공통 기준으로 검토합니다. 새 개발과 코드 리뷰에서 유지보수하기 쉬운 작성 방식을 맞춥니다.

• 새 프로젝트의 코딩 규칙 정하기
• 코드 가독성 리뷰하기
• 중복 로직과 깊은 조건 분기 정리하기

GitHub, Linear와 로컬 알림을 정리하고 중요도와 담당자에 따라 전달 경로를 설계합니다. 중복 알림을 통합하고 즉시 알림, 요약, 대기 목록, 로그로 분류합니다.

• 여러 도구의 알림 흐름 통합
• CI 실패의 중복 알림 줄이기
• 긴급 알림과 요약 알림 구분

공통 기준으로 코드의 명명, 가독성, 원본 데이터를 바꾸지 않는 갱신 방식과 오류 처리를 검토합니다. 중복과 복잡한 구조를 찾아 유지보수하기 쉽게 정리합니다.

• 기존 코드의 명명과 가독성 검토
• 긴 함수와 깊은 조건 분기 정리
• 새 모듈의 코딩 규칙 통일

코드 변경과 호출부를 읽고 버그, 보안, 부하, 테스트, 속도, 불필요한 구현을 점검합니다. 구체적인 문제 사례와 최소 수정안, 미해결 시 영향을 우선순위에 따라 보고합니다.

• PR이나 최근 커밋을 검토합니다
• 보안과 데이터 손실 위험을 확인합니다
• 예상 부하의 문제와 테스트 누락을 확인합니다