“duplication”的搜索结果

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.

中文简介正在准备中。当前显示原文说明。

Improves code quality across duplication, efficiency, and architectural fit. Use when code passes tests but quality is poor or before a major release.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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

中文简介正在准备中。当前显示原文说明。

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".

中文简介正在准备中。当前显示原文说明。

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

中文简介正在准备中。当前显示原文说明。

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

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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

中文简介正在准备中。当前显示原文说明。

帮助编写和编辑AI代理阅读的技能及指令文档，整理操作步骤、参考信息和完成条件，明确何时阅读相关资料，并减少重复内容。

• 编写或编辑技能文档
• 整理AGENTS.md和CLAUDE.md
• 明确阅读参考资料的条件

按统一标准检查变量和函数命名、代码可读性、数据更新方式及错误处理，用于新项目开发、代码审查和重构，帮助团队统一便于维护的写法。

• 制定新项目的编码规范
• 审查代码可读性
• 整理重复逻辑和深层条件分支

梳理GitHub、Linear及本地通知，按重要程度和负责人设计接收渠道，合并重复事件，并明确哪些应即时提醒、汇总发送、进入待办列表或仅记录日志。

• 统一多个工具的通知流程
• 减少CI失败的重复提醒
• 区分紧急提醒与汇总通知