'annotation' 검색 결과

Queries S3 object metadata, tracks bucket activity, audits object changes, searches annotations, and analyzes storage metrics using S3 Metadata system tables (journal, inventory, annotation) and S3 Storage Lens tables via Athena SQL. Applies when counting objects, finding recent uploads or deletions, identifying who wrote to a prefix, breaking down storage classes, finding objects by tag, searching annotation content, analyzing storage lens metrics, or enabling S3 Metadata tracking. Prefers system tables over raw S3 APIs (list-objects-v2, head-object) at scale. Trigger phrases: bucket activity, object count, who uploaded, track deletions, storage class breakdown, find by tag, search annotations, storage lens metrics, audit bucket changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-vs-de-novo validation. Use when annotating a new assembly of a species with an existing reference, harmonizing coordinates across builds, or mapping annotations across related species.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

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Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-vs-de-novo validation. Use when annotating a new assembly of a species with an existing reference, harmonizing coordinates across builds, or mapping annotations across related species.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-vs-de-novo validation. Use when annotating a new assembly of a species with an existing reference, harmonizing coordinates across builds, or mapping annotations across related species.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Map a mobile app journey across three or four phone frames in one HTML board. Simple gray content blocks, dashed arrows, numbered actions, and short annotations show how users move between screens before detailed visual design.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Mapping onboarding through the home screen
• Reviewing the path from item details to confirmation
• Discussing screen transitions before visual design

Edit screen recordings from the command line with zoom effects, speed changes, cuts, cropping, and text annotations. Adjust backgrounds and framing, review rendered previews, and export the finished video as MP4 or GIF.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a demo video from a screen recording
• Zoom in on important steps
• Cut or speed up waiting time

Fit probabilistic models to single-cell measurements for integration across experiments, cell annotation, combined RNA and protein or ATAC analysis, and gene expression comparisons. The skill covers input requirements, training checks, reference mapping, and interpretation of model uncertainty.

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• Integrating single-cell RNA data across experimental batches
• Assigning cell labels using partial annotations or a reference
• Analyzing RNA with protein or ATAC measurements

Inspect microscopy data stored on OMERO.server, export selected image metadata, and plan imports or exports. The skill uses explicit data scopes and limits, with dry-run helpers and reviewed targets for workflows that change server data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inventory selected OMERO objects with result limits
• Export annotations and regions of interest as JSON
• Plan microscopy image imports and exports

Helps organize biological datasets and models in LaminDB. Covers data registration, table validation, annotation with standardized biological terms, metadata searches, and tracking analysis inputs and outputs so researchers can preserve the context needed to repeat their work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Register and organize biological datasets
• Validate DataFrame or AnnData metadata
• Standardize annotations with biological vocabularies

Retrieve and combine protein annotations, biological pathways, and chemical identifiers from services such as UniProt, KEGG, and UniChem using Python. Supports cross-database lookups and batch identifier conversion while preserving multiple matches and failed requests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Collect protein sequences and functional annotations
• Retrieve pathway information for genes
• Match compound identifiers across databases

Make focused interface changes from screenshots, annotations, or visually selected elements. Helps locate the responsible component and apply small style or markup edits while preserving existing behavior, accessibility features, and surrounding design.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adjust a button identified in a screenshot
• Change only one selected card
• Apply interface fixes from visual annotations

Keep Kilo-specific changes small in files shared with upstream OpenCode. The skill guides where to place custom logic and tests, how to mark changes, and how to check annotations while preserving behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add Kilo-specific features to shared upstream code
• Move additive logic into Kilo-owned files
• Clean up outdated or overly broad change markers

Open documents, text configuration files, HTML, URLs, or folders in Plannotator for browser review. The agent responds to returned annotations and can use an approval session when you need to accept a plan, specification, or document.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding document feedback in a browser
• Reviewing text configuration files or HTML
• Approving a generated plan or specification

Plan or run DNA analysis to find small genetic changes in tumors, using tumor samples alone or paired with normal tissue. Check sample pairing, contamination, data quality, filtering, and annotations that add information to detected variants.

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• Planning analysis of paired tumor and normal samples
• Reviewing filtering and limitations for tumor-only analysis
• Checking quality in whole-genome, exome, or cancer-panel data

Retrieve genes, genetic variants, clinical annotations, and dosing guidelines from the PharmGKB API, then turn the results into concise summaries. Save the full response when you need the underlying data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Look up a gene or genetic variant by ID
• Find clinical annotations related to a drug
• Retrieve and summarize dosing guidelines

Prepare single-cell or single-nucleus RNA-seq analysis by checking inputs and selecting workflows that turn FASTQ sequencing files into count matrices. Route quality checks, cell annotation, clustering, and UMAP visualization to the embedded downstream skill.

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• Start single-cell RNA-seq analysis from FASTQ files
• Choose a workflow for generating count matrices
• Route existing matrices to quality checks and cell annotation

Look up existing predictions and protein information in the AlphaFold Protein Structure Database. Retrieve concise structure metadata, UniProt summaries, sequence summaries, and annotations, with an option to save the full response for later inspection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Look up an existing protein structure prediction
• Check a UniProt summary
• Review sequence summaries and annotations