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gget

使用 gget CLI 和 Python 工作流程快速查詢基因組資料庫、序列查找、BLAST 風格搜尋、富集分析檢查以及可重現的生物資訊證據記錄。當任務需要透過 gget CLI 或 Python 套件快速進行基因組參考資料庫的生物資訊查詢時使用。

gget 是什麼?

gget 是一個工具,提供命令列介面與 Python 套件,讓開發者與生物資訊學家能快速查詢多個基因組參考資料庫,無需建立完整的本地管道。它支援多種常見查詢,包括尋找 Ensembl ID、基因元數據、轉錄本細節、序列,並能執行快速的 BLAST/BLAT 比對。重要的蛋白質結構、路徑、癌症、表現及疾病關聯模組,如 AlphaFold、Enrichr 與 OpenTargets,都可透過統一介面存取。其設計強調可重現性,鼓勵使用者記錄查詢的版本、參數與資料庫假設。gget 適合初步探索與證據記錄,但若任務涉及嚴謹的臨床判讀或大規模生產流程,則建議使用 Biopython、Nextflow 或 BLAST+ 等專用工具。它支援在 Unix、macOS 與 Windows 環境中安裝,並且透過 pip 與 Conda 進行套件管理。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/scientific-pkg-gget

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說明文件

gget 是做什麼的?

當任務需要透過 gget CLI 或 Python 套件快速查詢基因組參考資料庫時,請使用此技能。

使用時機

  • 尋找 Ensembl ID、基因元數據、轉錄本細節或序列。
  • 執行快速 BLAST 或 BLAT 查詢,無需建立完整的本地管道。
  • 從 Ensembl 擷取參考基因組連結與註釋。
  • 透過單一介面查詢蛋白質結構、路徑、癌症、表現或疾病關聯模組。
  • 在轉移至較重的工具(如 Biopython、Snakemake、Nextflow、BLAST+ 或特定資料庫用戶端)之前,建立可重現的初步證據記錄。

當任務需要受規範的臨床判讀、高產量生產流程,或對資料庫版本與本地索引進行精細控制時,請使用專用工作流程而非 gget。

安裝

使用乾淨的 Python 環境。

python -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install --upgrade gget
gget --help

如果有 uv 可用:

uv venv
. .venv/bin/activate
uv pip install gget

在依賴舊環境之前,請先升級 gget 並重新檢查模組文件。gget 查詢的上游資料庫會隨時間變動。

基本模式

CLI 格式:

gget <module> [arguments] [options]

Python 格式:

import gget

result = gget.search(["BRCA1"], species="human")
print(result)

常見工作流程:

  1. 識別所需的物種、組裝版本、基因 ID 類型與資料庫。
  2. 檢查目前模組文件中的參數。
  3. 先執行一個小型查詢。
  4. 使用明確的檔名與日期儲存輸出。
  5. 記錄模組名稱、版本、參數與資料庫假設。

常見模組

請使用最新的上游文件來確認確切參數。以下是常見的首選模組:

  • gget search:從搜尋詞找出 Ensembl ID。
  • gget info:擷取 Ensembl、UniProt 或相關 ID 的元數據。
  • gget seq:擷取核苷酸或胺基酸序列。
  • gget ref:擷取參考基因組下載連結。
  • gget blast:執行快速 BLAST 查詢。
  • gget blat:比對序列與支援的基因組組裝版本。
  • gget muscle:執行多序列比對。
  • gget diamond:針對參考序列執行本地序列比對。
  • gget alphafold 與 gget pdb:檢查蛋白質結構參考。
  • gget enrichr、gget opentargets、gget archs4、gget bgee、gget cbio 與 gget cosmic:探索富集分析、目標、表現、癌症與疾病關聯資料。

請勿假設每個模組都支援所有 Python 版本或依賴組合。某些選用的科學運算相依套件的版本支援範圍比核心套件更窄。

快速範例

尋找基因:

gget search -s human brca1 dna repair -o brca1-search.json

擷取基因元數據:

gget info ENSG00000012048 -o brca1-info.json

擷取序列:

gget seq ENSG00000012048 -o brca1-seq.fa

執行小型 BLAST 查詢:

gget blast "MEEPQSDPSVEPPLSQETFSDLWKLLPEN" -l 10 -o blast-results.json

Python 範例:

import gget

genes = gget.search(["BRCA1", "DNA repair"], species="human")
info = gget.info(["ENSG00000012048"])
sequence = gget.seq("ENSG00000012048")

可重現性記錄

對於科學輸出,請包含足夠的元數據以重播查詢。

| 日期 | gget 版本 | 模組 | 查詢 | 物種/組裝 | 輸出 | 備註 |
| --- | --- | --- | --- | --- | --- | --- |
| 2026-05-11 | `gget --version` | search | `BRCA1 DNA repair` | human | `brca1-search.json` | 執行前已檢查文件 |

同時記錄:

  • Python 版本與環境管理器。
  • 透過 gget setup 安裝的任何選用相依套件。
  • 查詢回傳的資料庫特定識別碼。
  • 輸出格式為 JSON、CSV、FASTA 或 DataFrame 匯出。
  • 任何透過升級 gget 解決的失敗。

審查清單

  • 是否已升級或驗證已安裝的 gget 版本?
  • 在使用參數前是否檢查了最新的上游模組文件?
  • 物種或組裝版本是否明確指定?
  • 識別碼是否完全保留,包括 Ensembl/UniProt 前綴?
  • 結果是否標記為資料庫輸出,而非臨床判讀?
  • 查詢是否可以從儲存的命令或 Python 程式碼片段中重現?
  • 選用相依套件是否安裝在隔離環境中?

參考資料

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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