본문으로 건너뛰기
cccskills
무료GitHub에 공개

comparative-genomics

Workflow for orthology, synteny, ancestral reconstruction, and evolutionary comparison across genomes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(3)

  • SKILL.md4.1 KB
  • README.md461 B
  • references/technical_reference.md2.0 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Comparative Genomics

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially orthology and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

  • Python: python -c "import <module>; print(<module>.__version__)"
  • CLI: <tool> --version
  • If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for orthology, synteny, ancestral reconstruction, and evolutionary comparison across genomes.

When To Use This Skill

  • use when the task is cross-genome comparison or evolutionary inference
  • use when assembled genomes and annotations are available for multiple taxa or strains
  • use when the user needs orthologs, synteny blocks, or positive-selection style summaries

Quick Route

  • If the input is raw or minimally processed data, start with validation and QC before any modeling.
  • If the input is already processed, skip directly to the first workflow step that matches the user goal.
  • If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

  • Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
  • Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

  • Prefer Python-first workflows unless the task explicitly requires something else.
  • Keep intermediate and final outputs separated.
  • Record software versions, reference builds, and key parameters when they affect interpretation.
  • Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

  • assemblies
  • gene annotations
  • optional phylogenetic context

Expected Outputs

  • ortholog tables
  • synteny outputs
  • evolutionary comparison summaries

Preferred Tools

  • orthology tools
  • alignment and phylogeny utilities
  • pandas

Starter Pattern

Preferred starting point: orthology
Inputs: assemblies, gene annotations, optional phylogenetic context
Outputs: ortholog tables, synteny outputs, evolutionary comparison summaries

Workflow

1. Define comparison scale

Clarify whether the task is gene-level, synteny-level, or phylogenomic.

2. Standardize annotations

Use consistent naming, feature models, and assemblies before comparing genomes.

3. Infer shared and divergent elements

Run orthology, synteny, or evolutionary analyses appropriate to the question.

4. Interpret in biological context

Separate technical annotation differences from genuine biological divergence.

5. Export concise comparison artifacts

Save tables and figures that highlight conserved versus lineage-specific patterns.

Output Artifacts

  • Recommended output layout:
    • results/ for final tables and serialized objects
    • figures/ for plots and static visual exports
    • qc/ for checks that justify downstream interpretation
  • Minimum expected outputs for this skill:
  • ortholog tables
  • synteny outputs
  • evolutionary comparison summaries

Quality Review

  • Confirm identifiers and metadata join correctly before modeling or summarizing.
  • Generate at least one QC artifact before final biological interpretation.
  • Keep raw or minimally processed inputs separate from transformed outputs.
  • Record reference build, caller assumptions, and filtering rules in the final outputs.
  • Separate raw calls from filtered or interpreted results.

Anti-Patterns

  • comparing genomes with incompatible annotation quality without caveats
  • overstating adaptive evolution from weak evidence
  • mixing orthology and homology claims carelessly

Related Skills

  • Variant Calling
  • Copy Number
  • Long-Read Genomics
  • Genome Assembly

Optional Supplements

  • None required for the first pass.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

alphafold

무료

Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

atac-seq

무료

ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

bindcraft

무료

End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

BioTender-max의 스킬 모두 보기

이 스킬의 문제 신고