可以通过运行“pip install gget”从命令行安装 gget。下图描述了每个 gget 工具的一个用例和相应的输出。每个 gget 工具都有一个详尽的手册，可作为 Python 环境中的函数文档或在命令行中使用帮助标志 [-h] 作为标准输出。

gget工具地址

gget地址：https://pachterlab.github.io/gget/
gget 示例存储库：https://github.com/pachterlab/gget_examples

gget安装

pip install --upgrade gget

或者

conda install -c bioconda gget

在 Jupyter Lab / Google Colab中调用

import gget

gget模块

• gget ref
  Fetch File Transfer Protocols (FTPs) and metadata for reference genomes and annotations from Ensembl by species.

• gget search
  Fetch genes and transcripts from Ensembl using free-form search terms.

• gget info
  Fetch extensive gene and transcript metadata from Ensembl, UniProt, and NCBI using Ensembl IDs.

• gget seq
  Fetch nucleotide or amino acid sequences of genes or transcripts from Ensembl or UniProt, respectively.

• gget blast
  BLAST a nucleotide or amino acid sequence to any BLAST database.

• gget blat
  Find the genomic location of a nucleotide or amino acid sequence using BLAT.

• gget muscle
  Align multiple nucleotide or amino acid sequences to each other using Muscle5.

• gget enrichr
  Perform an enrichment analysis on a list of genes using Enrichr.

• gget archs4
  Find the most correlated genes to a gene of interest or find the gene's tissue expression atlas using ARCHS4.

• gget pdb
  Get the structure and metadata of a protein from the RCSB Protein Data Bank.

• gget alphafold
  Predict the 3D structure of a protein from its amino acid sequence using a simplified version of DeepMind’s AlphaFold2.

gget快速入门

命令行

# Fetch all Homo sapiens reference and annotation FTPs from the latest Ensembl release
$ gget ref homo_sapiens

# Get Ensembl IDs of human genes with "ace2" or "angiotensin converting enzyme 2" in their name/description
$ gget search -s homo_sapiens 'ace2' 'angiotensin converting enzyme 2'

# Look up gene ENSG00000130234 (ACE2) and its transcript ENST00000252519
$ gget info ENSG00000130234 ENST00000252519

# Fetch the amino acid sequence of the canonical transcript of gene ENSG00000130234
$ gget seq --translate ENSG00000130234

# Quickly find the genomic location of (the start of) that amino acid sequence
$ gget blat MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS

# BLAST (the start of) that amino acid sequence
$ gget blast MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS

# Align nucleotide or amino acid sequences stored in a FASTA file
$ gget muscle path/to/file.fa

# Use Enrichr for an ontology analysis of a list of genes
$ gget enrichr -db ontology ACE2 AGT AGTR1 ACE AGTRAP AGTR2 ACE3P

# Get the human tissue expression of gene ACE2
$ gget archs4 -w tissue ACE2

# Get the protein structure (in PDB format) of ACE2 as stored in the Protein Data Bank (PDB ID returned by gget info)
$ gget pdb 1R42 -o 1R42.pdb

# Predict the protein structure of GFP from its amino acid sequence
$ gget setup alphafold # setup only needs to be run once
$ gget alphafold MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK

Python (Jupyter Lab / Google Colab):

import gget
gget.ref("homo_sapiens")
gget.search(["ace2", "angiotensin converting enzyme 2"], "homo_sapiens")
gget.info(["ENSG00000130234", "ENST00000252519"])
gget.seq("ENSG00000130234", translate=True)
gget.blat("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS")
gget.blast("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS")
gget.muscle("path/to/file.fa")
gget.enrichr(["ACE2", "AGT", "AGTR1", "ACE", "AGTRAP", "AGTR2", "ACE3P"], database="ontology", plot=True)
gget.archs4("ACE2", which="tissue")
gget.pdb("1R42", save=True)

gget.setup("alphafold") # setup only needs to be run once
gget.alphafold("MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK")

Call gget from R using reticulate:

system("pip install gget")
install.packages("reticulate")
library(reticulate)
gget <- import("gget")

gget$ref("homo_sapiens")
gget$search(list("ace2", "angiotensin converting enzyme 2"), "homo_sapiens")
gget$info(list("ENSG00000130234", "ENST00000252519"))
gget$seq("ENSG00000130234", translate=TRUE)
gget$blat("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS")
gget$blast("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS")
gget$muscle("path/to/file.fa", out="path/to/out.afa")
gget$enrichr(list("ACE2", "AGT", "AGTR1", "ACE", "AGTRAP", "AGTR2", "ACE3P"), database="ontology")
gget$archs4("ACE2", which="tissue")
gget$pdb("1R42", save=TRUE)