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gget

gget CLI 和 Python 工作流,用于快速基因组数据库查询、序列查找、BLAST 样式搜索、富集检查以及可重现的生物信息学证据日志。当任务需要通过 gget CLI 或 Python 包跨基因组参考数据库进行快速生物信息学查找时使用。

gget 是什么?

gget 是一个开源命令行和 Python 工具,旨在简化生物信息学查询过程,允许用户快速从多个基因组参考数据库获取信息。它支持一系列常见任务,包括搜索 Ensembl 基因 ID、检索基因元数据、获取核苷酸或氨基酸序列、运行 BLAST/BLAT 相似性搜索以及执行富集分析。gget 支持包括 Ensembl、UniProt、AlphaFold、Enrichr、OpenTargets 等多种数据库,涵盖基因注释、蛋白质结构、癌症和疾病关联等领域。该技能有助于创建可重现的第一手证据日志,适合在过渡到更重的工具(如 Biopython、Snakemake、Nextflow、BLAST+)之前使用。gget 在 macOS、Linux 和 Windows 上均可运行,核心依赖关系简单,但一些可选的科学依赖可能对版本要求更严格。

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文档

gget 是做什么的?

当任务需要通过 gget CLI 或 Python 包跨基因组参考数据库进行快速生物信息学查找时,使用此技能。

何时使用

  • 查找 Ensembl ID、基因元数据、转录本详细信息或序列。
  • 无需构建完整的本地管道即可运行快速的 BLAST 或 BLAT 查找。
  • 从 Ensembl 获取参考基因组链接和注释。
  • 通过单一接口查询蛋白质结构、通路、癌症、表达或疾病关联模块。
  • 在转入更重的工具(如 Biopython、Snakemake、Nextflow、BLAST+ 或特定数据库客户端)之前,创建可重现的第一手证据日志。

当任务需要受监管的临床解释、高吞吐量生产管道或对数据库版本和本地索引进行精细控制时,请使用专用工作流代替 gget。

安装

使用干净的 Python 环境。

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

如果 uv 可用:

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

在依赖旧环境之前,请升级 gget 并重新检查模块文档。 gget 查询的上游数据库会随时间变化。

基本模式

CLI 格式:

gget <module> [arguments] [options]

Python 格式:

import gget

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

通用工作流:

  1. 确定所需的物种、组装、基因 ID 类型和数据库。
  2. 查看当前模块文档以获取参数。
  3. 先运行一个小型查询。
  4. 使用明确的文件名和日期保存输出。
  5. 记录模块名称、版本、参数和数据库假设。

常用模块

使用最新的上游文档获取精确参数。以下模块是常见的首选:

  • gget search:从搜索词查找 Ensembl ID。
  • gget info:检索 Ensembl、UniProt 或相关 ID 的元数据。
  • gget seq:获取核苷酸或氨基酸序列。
  • gget ref:检索参考基因组下载链接。
  • gget blast:运行快速 BLAST 查询。
  • gget blat:针对支持的基因组组装定位序列。
  • gget muscle:运行多序列比对。
  • gget diamond:针对参考序列运行本地序列比对。
  • gget alphafold 和 gget pdb:检查蛋白质结构参考。
  • gget enrichr、gget opentargets、gget archs4、gget bgee、gget cbio 和 gget cosmic:探索富集、靶点、表达、癌症和疾病关联数据。

不要假设每个模块都支持所有 Python 版本或依赖集。某些可选的科学依赖相对于核心包具有更窄的版本支持范围。

快速示例

查找基因:

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

获取基因元数据:

gget info ENSG00000012048 -o brca1-info.json

获取序列:

gget seq ENSG00000012048 -o brca1-seq.fa

运行小型 BLAST 查询:

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

Python 示例:

import gget

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

可重现性日志

对于科学输出,请包含足够的元数据以重放查询。

| 日期 | gget 版本 | 模块 | 查询 | 物种/组装 | 输出 | 备注 |
| --- | --- | --- | --- | --- | --- | --- |
| 2026-05-11 | `gget --version` | search | `BRCA1 DNA repair` | human | `brca1-search.json` | 运行前已查看文档 |

同时记录:

  • Python 版本和环境管理器。
  • 通过 gget setup 安装的任何可选依赖。
  • 查询返回的特定数据库标识符。
  • 输出是 JSON、CSV、FASTA 还是 DataFrame 导出。
  • 通过升级 gget 解决的任何失败。

审查清单

  • 您是否升级或验证了已安装的 gget 版本?
  • 您是否在使用参数前检查了当前上游模块文档?
  • 物种或组装是否明确?
  • 标识符是否完全保留,包括 Ensembl/UniProt 前缀?
  • 结果是否标注为数据库输出而非临床解释?
  • 查询是否可以从保存的命令或 Python 片段重现?
  • 可选依赖是否安装在隔离环境中?

参考

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/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

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.

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