scientific-pkg-gget
用 gget 快速查基因體資料庫並留下可重現紀錄
中文翻譯
---
name: scientific-pkg-gget
description: 用 gget CLI 與 Python 工作流程快速查詢基因體資料庫、序列、BLAST 類型搜尋、富集檢查，並留下可重現的生物資訊證據紀錄。任務需要用 gget CLI 或 Python 套件，在基因體參考資料庫之間快速做生物資訊查找時使用。
metadata:
origin: community
---
# gget
任務需要用 `gget` CLI 或 Python 套件，在基因體參考資料庫之間快速做生物資訊查找時，使用這個 skill。
## 何時使用
- 找 Ensembl ID、基因 metadata、轉錄本細節或序列。
- 不用建完整的本地 pipeline，就快速跑 BLAST 或 BLAT 查找。
- 從 Ensembl 取得參考基因體連結和註解。
- 透過單一介面查詢蛋白質結構、pathway、癌症、表現量或疾病關聯模組。
- 在轉用 Biopython、Snakemake、Nextflow、BLAST+ 或資料庫專用 client 等較重的工具之前，先建立可重現的第一輪證據紀錄。
任務需要受監管的臨床判讀、高吞吐量的正式 pipeline，或需要精細控制資料庫版本與本地索引時，改用專屬的工作流程，不要用 `gget`。
## 安裝
用乾淨的 Python 環境。
```bash
python -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install --upgrade gget
gget --help
```
有 `uv` 的話：
```bash
uv venv
. .venv/bin/activate
uv pip install gget
```
依賴比較舊的環境之前，先升級 `gget` 並重新確認模組文件。`gget` 查詢的上游資料庫會隨時間改變。
## 基本模式
CLI 的形式：
```bash
gget <module> [arguments] [options]
```
Python 的形式：
```python
import gget
result = gget.search(["BRCA1"], species="human")
print(result)
```
常見流程：
1. 確定需要的物種、組裝版本、基因 ID 類型和資料庫。
2. 查看目前的模組文件，確認參數。
3. 先跑一個小查詢。
4. 輸出存成有明確檔名和日期的檔案。
5. 記錄模組名稱、版本、參數和對資料庫的假設。
## 常用模組
精確的參數請看最新的上游文件。這些模組是常見的首選：
- `gget search`：用搜尋詞找 Ensembl ID。
- `gget info`：取得 Ensembl、UniProt 或相關 ID 的 metadata。
- `gget seq`：抓取核苷酸或胺基酸序列。
- `gget ref`：取得參考基因體的下載連結。
- `gget blast`：跑快速的 BLAST 查詢。
- `gget blat`：在支援的基因體組裝版本中定位序列。
- `gget muscle`：執行多序列比對。
- `gget diamond`：對參考序列執行本地序列比對。
- `gget alphafold` 和 `gget pdb`：查看蛋白質結構的參考資料。
- `gget enrichr`、`gget opentargets`、`gget archs4`、`gget bgee`、`gget cbio` 和 `gget cosmic`：探索富集、標靶、表現量、癌症和疾病關聯資料。
不要假設每個模組都支援每個 Python 版本或每組相依套件。有些選用的科學計算相依套件，支援的版本範圍比核心套件窄。
## 快速範例
找基因：
```bash
gget search -s human brca1 dna repair -o brca1-search.json
```
取得基因 metadata：
```bash
gget info ENSG00000012048 -o brca1-info.json
```
抓序列：
```bash
gget seq ENSG00000012048 -o brca1-seq.fa
```
跑一個小的 BLAST 查詢：
```bash
gget blast "MEEPQSDPSVEPPLSQETFSDLWKLLPEN" -l 10 -o blast-results.json
```
Python 範例：
```python
import gget
genes = gget.search(["BRCA1", "DNA repair"], species="human")
info = gget.info(["ENSG00000012048"])
sequence = gget.seq("ENSG00000012048")
```
## 可重現性紀錄
科學產出要附上足夠的 metadata，讓查詢能重播。
```markdown
| Date | gget version | Module | Query | Species/assembly | Output | Notes |
| --- | --- | --- | --- | --- | --- | --- |
| 2026-05-11 | `gget --version` | search | `BRCA1 DNA repair` | human | `brca1-search.json` | Docs checked before run |
```
另外也要記錄：
- Python 版本和環境管理工具。
- 透過 `gget setup` 安裝的任何選用相依套件。
- 查詢回傳的資料庫專屬識別碼。
- 輸出是 JSON、CSV、FASTA，還是 DataFrame 匯出。
- 靠升級 `gget` 才解決的任何失敗。
## Review 檢查清單
- 有升級或確認已安裝的 `gget` 版本嗎？
- 使用參數之前，有查看目前的上游模組文件嗎？
- 物種或組裝版本有明確寫出嗎？
- 識別碼有原封不動保留，包含 Ensembl/UniProt 前綴嗎？
- 結果有標示為資料庫輸出，而不是臨床判讀嗎？
- 查詢能從儲存的指令或 Python 片段重現嗎？
- 選用相依套件有裝在隔離的環境裡嗎？
## 參考資料
- [gget documentation](https://pachterlab.github.io/gget/)
- [gget updates](https://pachterlab.github.io/gget/en/updates.html)
- [gget GitHub repository](https://github.com/pachterlab/gget)
- [gget Bioinformatics paper](https://doi.org/10.1093/bioinformatics/btac836)英文原文
---
name: scientific-pkg-gget
description: gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs. Use when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.
metadata:
origin: community
---
# gget
Use this skill when a task needs quick bioinformatics lookup across genomic
reference databases with the `gget` CLI or Python package.
## When to Use
- Finding Ensembl IDs, gene metadata, transcript details, or sequences.
- Running quick BLAST or BLAT lookups without building a full local pipeline.
- Fetching reference genome links and annotations from Ensembl.
- Querying protein structure, pathway, cancer, expression, or disease-association
modules through a single interface.
- Creating a reproducible first-pass evidence log before moving to heavier
tools such as Biopython, Snakemake, Nextflow, BLAST+, or database-specific
clients.
Use a dedicated workflow instead of `gget` when the task requires regulated
clinical interpretation, high-throughput production pipelines, or fine-grained
control over database versions and local indexes.
## Installation
Use a clean Python environment.
```bash
python -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install --upgrade gget
gget --help
```
If `uv` is available:
```bash
uv venv
. .venv/bin/activate
uv pip install gget
```
Before relying on an older environment, upgrade `gget` and re-check the module
docs. The upstream databases queried by `gget` change over time.
## Basic Patterns
CLI shape:
```bash
gget <module> [arguments] [options]
```
Python shape:
```python
import gget
result = gget.search(["BRCA1"], species="human")
print(result)
```
Common workflow:
1. Identify the species, assembly, gene ID type, and database needed.
2. Check the current module documentation for arguments.
3. Run a small query first.
4. Save output with an explicit filename and date.
5. Record module name, version, arguments, and database assumptions.
## Common Modules
Use current upstream docs for exact arguments. These modules are common first
choices:
- `gget search`: find Ensembl IDs from search terms.
- `gget info`: retrieve metadata for Ensembl, UniProt, or related IDs.
- `gget seq`: fetch nucleotide or amino-acid sequences.
- `gget ref`: retrieve reference genome download links.
- `gget blast`: run a quick BLAST query.
- `gget blat`: locate a sequence against supported genome assemblies.
- `gget muscle`: run multiple sequence alignment.
- `gget diamond`: run local sequence alignment against reference sequences.
- `gget alphafold` and `gget pdb`: inspect protein-structure references.
- `gget enrichr`, `gget opentargets`, `gget archs4`, `gget bgee`, `gget cbio`,
and `gget cosmic`: explore enrichment, target, expression, cancer, and disease
association data.
Do not assume every module supports every Python version or dependency set.
Some optional scientific dependencies have narrower version support than the
core package.
## Quick Examples
Find genes:
```bash
gget search -s human brca1 dna repair -o brca1-search.json
```
Fetch gene metadata:
```bash
gget info ENSG00000012048 -o brca1-info.json
```
Fetch a sequence:
```bash
gget seq ENSG00000012048 -o brca1-seq.fa
```
Run a small BLAST query:
```bash
gget blast "MEEPQSDPSVEPPLSQETFSDLWKLLPEN" -l 10 -o blast-results.json
```
Python example:
```python
import gget
genes = gget.search(["BRCA1", "DNA repair"], species="human")
info = gget.info(["ENSG00000012048"])
sequence = gget.seq("ENSG00000012048")
```
## Reproducibility Log
For scientific outputs, include enough metadata to replay the query.
```markdown
| Date | gget version | Module | Query | Species/assembly | Output | Notes |
| --- | --- | --- | --- | --- | --- | --- |
| 2026-05-11 | `gget --version` | search | `BRCA1 DNA repair` | human | `brca1-search.json` | Docs checked before run |
```
Also record:
- Python version and environment manager.
- Any optional dependency installed through `gget setup`.
- Database-specific identifiers returned by the query.
- Whether output is JSON, CSV, FASTA, or a DataFrame export.
- Any failures that were resolved by upgrading `gget`.
## Review Checklist
- Did you upgrade or verify the installed `gget` version?
- Did you check the current upstream module docs before using arguments?
- Is the species or assembly explicit?
- Are identifiers preserved exactly, including Ensembl/UniProt prefixes?
- Is the result labeled as database output rather than clinical interpretation?
- Is the query reproducible from the saved command or Python snippet?
- Are optional dependencies installed in an isolated environment?
## References
- [gget documentation](https://pachterlab.github.io/gget/)
- [gget updates](https://pachterlab.github.io/gget/en/updates.html)
- [gget GitHub repository](https://github.com/pachterlab/gget)
- [gget Bioinformatics paper](https://doi.org/10.1093/bioinformatics/btac836)