'annotation' 검색 결과

Gets actor and level 3D bounding boxes and projects them into a camera image as 2D boxes — the dataset-annotation workflow. Lists box geometry, draws 3D boxes in the world via debug, or captures one camera frame and writes an annotated PNG plus a JSON of 2D boxes for matching actors. Use when the user asks to "get bounding boxes", "draw boxes around the cars", "project boxes onto the camera", or "generate a detection dataset / annotations".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition selection, the one-isoform-per-locus and missing-UTR traps, merge/split errors, and reference bias against orphan genes. Use when annotating a newly assembled eukaryotic genome, choosing a gene-prediction pipeline based on available evidence, or diagnosing a poor annotation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Discovers, classifies, and masks repetitive elements and transposable elements with RepeatModeler2 (de novo family library), RepeatMasker (masking against a library), EDTA (plant/structural TEs), or EarlGrey (auto-curating wrapper), and quantifies TE expression from RNA-seq with TEtranscripts/SQuIRE. Covers de-novo-library-as-curation-project, soft-vs-hard masking, the domesticated-gene over-masking massacre, Dfam-vs-RepBase, TE classification (Class I/II, family-vs-copy), Kimura repeat landscapes, LAI, and the RNA-seq multimapping problem. Use when masking repeats before gene prediction, building a TE library for a non-model genome, or analyzing transposable-element content or expression.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes alternative splicing from PacBio Iso-Seq (HiFi, Kinnex/MAS-Iso-seq) and Oxford Nanopore (direct cDNA, direct RNA, R10.4.1+) long-read RNA-seq with full-isoform resolution. Tools include FLAIR (correct/collapse/quantify/diffSplice for PacBio + ONT), IsoQuant (de-novo or annotation-guided isoform discovery 2024 SOTA), Bambu (annotation-aware Bayesian discovery + quantification with Novel Discovery Rate), SQANTI3/SQANTI-LR (isoform classification: FSM/ISM/NIC/NNC + artifact flags), rMATS-long (event calling on long-read isoforms), and minimap2 (-ax splice:hq for HiFi; -ax splice -k14 for ONT cDNA; add -uf only for direct RNA or stranded cDNA preps). Solves microexon detection, recursive splicing, complex multi-exon isoforms, and DTU without transcript-quantification uncertainty. Use when short-read AS limitations (anchor length, complex isoforms, microexons, recursive splicing, transcript ambiguity) demand full-isoform resolution.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Discovers, classifies, and masks repetitive elements and transposable elements with RepeatModeler2 (de novo family library), RepeatMasker (masking against a library), EDTA (plant/structural TEs), or EarlGrey (auto-curating wrapper), and quantifies TE expression from RNA-seq with TEtranscripts/SQuIRE. Covers de-novo-library-as-curation-project, soft-vs-hard masking, the domesticated-gene over-masking massacre, Dfam-vs-RepBase, TE classification (Class I/II, family-vs-copy), Kimura repeat landscapes, LAI, and the RNA-seq multimapping problem. Use when masking repeats before gene prediction, building a TE library for a non-model genome, or analyzing transposable-element content or expression.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition selection, the one-isoform-per-locus and missing-UTR traps, merge/split errors, and reference bias against orphan genes. Use when annotating a newly assembled eukaryotic genome, choosing a gene-prediction pipeline based on available evidence, or diagnosing a poor annotation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes alternative splicing from PacBio Iso-Seq (HiFi, Kinnex/MAS-Iso-seq) and Oxford Nanopore (direct cDNA, direct RNA, R10.4.1+) long-read RNA-seq with full-isoform resolution. Tools include FLAIR (correct/collapse/quantify/diffSplice for PacBio + ONT), IsoQuant (de-novo or annotation-guided isoform discovery 2024 SOTA), Bambu (annotation-aware Bayesian discovery + quantification with Novel Discovery Rate), SQANTI3/SQANTI-LR (isoform classification: FSM/ISM/NIC/NNC + artifact flags), rMATS-long (event calling on long-read isoforms), and minimap2 (-ax splice:hq for HiFi; -ax splice -k14 for ONT cDNA; add -uf only for direct RNA or stranded cDNA preps). Solves microexon detection, recursive splicing, complex multi-exon isoforms, and DTU without transcript-quantification uncertainty. Use when short-read AS limitations (anchor length, complex isoforms, microexons, recursive splicing, transcript ambiguity) demand full-isoform resolution.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when a driver needs to edit their own ELD record. Covers what edits are allowed, what edits trigger annotations, the unassigned-driving-time procedure, and when carriers must accept or reject driver edits.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze development-set disagreements between human annotations and one Coval text LLM judge, then propose a focused prompt revision. Use coval-calibrate-metric for independent trust measurements.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Adds accessibility annotations (ARIA labels, roles, alt text) to make web content accessible. Use when user asks to "add accessibility", "make accessible", "add aria labels", "wcag compliance", or "screen reader support".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Complete guide for AutoFixture advanced customization techniques. Use when you need to customize AutoFixture builders or handle special type test data generation rules. Covers DataAnnotations automatic integration, ISpecimenBuilder implementation, priority management. Includes DateTime/numeric range builders, generic design, and fluent extension methods. Keywords: autofixture customization, autofixture customize, autofixture customization, specimen builder, ISpecimenBuilder, RandomDateTimeSequenceGenerator, NumericRangeBuilder, DataAnnotations autofixture, fixture.Customizations, Insert(0), custom builder, NoSpecimen, generic builder

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

[BETA] AEM Cloud Service expert skill — migrate javax.inject.@Inject fields in Sling Model (@Model) classes to injector-specific annotations (@ValueMapValue / @OSGiService / @SlingObject). Self-discoverable (scan for @Model classes with field-level @Inject); the replacement is chosen deterministically from each field's declared type. Use for "fix @Inject in HeroModel.java", "modernize my Sling Models", or when scanning an AEM project for @Inject misuse. Detailed decision table, companion-annotation handling, import management, and file-level skip policy are in recipe.md. This skill is in beta. Verify all outputs before applying them to production projects.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MyBatis를 사용하는 Java 데이터 저장·조회 코드를 설계하고 검토합니다. 결과 매핑, 동적 SQL과 입력 안전성, 트랜잭션, 페이지 나누기와 검색 성능을 다룹니다.

• 매퍼와 XML의 대응 관계 설계
• 동적 SQL과 입력값 안전성 확인
• 여러 갱신을 묶는 트랜잭션 검토

데스크톱 랜딩 페이지의 구조를 회색 상자로 그립니다. 번호 주석과 사양 패널을 연결해 배치 및 구현 시 참고할 내용을 공유하는 와이어프레임을 만듭니다.

• 데스크톱 랜딩 페이지 구조를 검토할 때
• 개발 담당자에게 사양 메모를 전달할 때
• 내비게이션과 주요 홍보 영역 배치를 확인할 때

데스크톱 대시보드 구성을 회색 블록으로 표현한 단일 HTML 와이어프레임을 만듭니다. 색상과 사진을 정하기 전에 정보 우선순위와 배치를 확인할 수 있습니다.

• 대시보드 배치 검토
• 탐색 메뉴와 정보 우선순위 검토
• KPI·차트·표 배치 검토

Choose an approach for map animations in Remotion, from satellite image overlays to globe views and terrain flythroughs. The skill compares five techniques and directs the agent to the implementation guide for the intended shot.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Animate overlays on a static satellite image
• Create globe views or show 3D buildings
• Choose a map approach without an API key

Find biological pathways and functions enriched in gene lists or ranked gene data. Choose an appropriate statistical method, align gene identifiers, define comparison backgrounds, and turn results into interpretable tables and plots with multiple-testing corrections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding enriched pathways among differentially expressed genes
• Running GSEA on a complete ranked gene list
• Calculating gene-set scores for individual samples or cells

Create scientific charts in Python with detailed control over axes, colors, labels, legends, and layout. Arrange multiple plots in one figure and export PNG, PDF, or SVG files for papers and presentations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create charts from experimental data
• Arrange multiple plots in one figure
• Adjust colors, labels, and annotations