gget

Overview

gget is a command-line bioinformatics tool and Python package providing unified access to 20+ genomic databases and analysis methods. Query gene information, sequence analysis, protein structures, expression data, and disease associations through a consistent interface. All gget modules work both as command-line tools and as Python functions.

Important: The databases queried by gget are continuously updated, which sometimes changes their structure. gget modules are tested automatically on a biweekly basis and updated to match new database structures when necessary.

Installation

Install gget in a clean virtual environment to avoid conflicts:

# Using uv (recommended)
uv uv pip install gget

# Or using pip
uv pip install --upgrade gget

# In Python/Jupyter
import gget

Quick Start

Basic usage pattern for all modules:

# Command-line
gget <module> [arguments] [options]

# Python
gget.module(arguments, options)

Most modules return:

• Command-line: JSON (default) or CSV with

  -csv

  flag
• Python: DataFrame or dictionary

Common flags across modules:

• -o/--out

  : Save results to file

• -q/--quiet

  : Suppress progress information

• -csv

  : Return CSV format (command-line only)

Module Categories

1. Reference & Gene Information

gget ref - Reference Genome Downloads

Retrieve download links and metadata for Ensembl reference genomes.

Parameters:

• species

  : Genus_species format (e.g., 'homo_sapiens', 'mus_musculus'). Shortcuts: 'human', 'mouse'

• -w/--which

  : Specify return types (gtf, cdna, dna, cds, cdrna, pep). Default: all

• -r/--release

  : Ensembl release number (default: latest)

• -l/--list_species

  : List available vertebrate species

• -liv/--list_iv_species

  : List available invertebrate species

• -ftp

  : Return only FTP links

• -d/--download

  : Download files (requires curl)

Examples:

# List available species
gget ref --list_species

# Get all reference files for human
gget ref homo_sapiens

# Download only GTF annotation for mouse
gget ref -w gtf -d mouse

# Python
gget.ref("homo_sapiens")
gget.ref("mus_musculus", which="gtf", download=True)

gget search - Gene Search

Locate genes by name or description across species.

Parameters:

• searchwords

  : One or more search terms (case-insensitive)

• -s/--species

  : Target species (e.g., 'homo_sapiens', 'mouse')

• -r/--release

  : Ensembl release number

• -t/--id_type

  : Return 'gene' (default) or 'transcript'

• -ao/--andor

  : 'or' (default) finds ANY searchword; 'and' requires ALL

• -l/--limit

  : Maximum results to return

Returns: ensembl_id, gene_name, ensembl_description, ext_ref_description, biotype, URL

Examples:

# Search for GABA-related genes in human
gget search -s human gaba gamma-aminobutyric

# Find specific gene, require all terms
gget search -s mouse -ao and pax7 transcription

# Python
gget.search(["gaba", "gamma-aminobutyric"], species="homo_sapiens")

gget info - Gene/Transcript Information

Retrieve comprehensive gene and transcript metadata from Ensembl, UniProt, and NCBI.

Parameters:

• ens_ids

  : One or more Ensembl IDs (also supports WormBase, Flybase IDs). Limit: ~1000 IDs

• -n/--ncbi

  : Disable NCBI data retrieval

• -u/--uniprot

  : Disable UniProt data retrieval

• -pdb

  : Include PDB identifiers (increases runtime)

Returns: UniProt ID, NCBI gene ID, primary gene name, synonyms, protein names, descriptions, biotype, canonical transcript

Examples:

# Get info for multiple genes
gget info ENSG00000034713 ENSG00000104853 ENSG00000170296

# Include PDB IDs
gget info ENSG00000034713 -pdb

# Python
gget.info(["ENSG00000034713", "ENSG00000104853"], pdb=True)

gget seq - Sequence Retrieval

Fetch nucleotide or amino acid sequences for genes and transcripts.

Parameters:

• ens_ids

  : One or more Ensembl identifiers

• -t/--translate

  : Fetch amino acid sequences instead of nucleotide

• -iso/--isoforms

  : Return all transcript variants (gene IDs only)

Returns: FASTA format sequences

Examples:

# Get nucleotide sequences
gget seq ENSG00000034713 ENSG00000104853

# Get all protein isoforms
gget seq -t -iso ENSG00000034713

# Python
gget.seq(["ENSG00000034713"], translate=True, isoforms=True)

2. Sequence Analysis & Alignment

gget blast - BLAST Searches

BLAST nucleotide or amino acid sequences against standard databases.

Parameters:

• sequence

  : Sequence string or path to FASTA/.txt file

• -p/--program

  : blastn, blastp, blastx, tblastn, tblastx (auto-detected)

• -db/--database

  :
  • Nucleotide: nt, refseq_rna, pdbnt
  • Protein: nr, swissprot, pdbaa, refseq_protein

• -l/--limit

  : Max hits (default: 50)

• -e/--expect

  : E-value cutoff (default: 10.0)

• -lcf/--low_comp_filt

  : Enable low complexity filtering

• -mbo/--megablast_off

  : Disable MegaBLAST (blastn only)

Examples:

# BLAST protein sequence
gget blast MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR

# BLAST from file with specific database
gget blast sequence.fasta -db swissprot -l 10

# Python
gget.blast("MKWMFK...", database="swissprot", limit=10)

gget blat - BLAT Searches

Locate genomic positions of sequences using UCSC BLAT.

Parameters:

• sequence

  : Sequence string or path to FASTA/.txt file

• -st/--seqtype

  : 'DNA', 'protein', 'translated%20RNA', 'translated%20DNA' (auto-detected)

• -a/--assembly

  : Target assembly (default: 'human'/hg38; options: 'mouse'/mm39, 'zebrafinch'/taeGut2, etc.)

Returns: genome, query size, alignment positions, matches, mismatches, alignment percentage

Examples:

# Find genomic location in human
gget blat ATCGATCGATCGATCG

# Search in different assembly
gget blat -a mm39 ATCGATCGATCGATCG

# Python
gget.blat("ATCGATCGATCGATCG", assembly="mouse")

gget muscle - Multiple Sequence Alignment

Align multiple nucleotide or amino acid sequences using Muscle5.

Parameters:

• fasta

  : Sequences or path to FASTA/.txt file

• -s5/--super5

  : Use Super5 algorithm for faster processing (large datasets)

Returns: Aligned sequences in ClustalW format or aligned FASTA (.afa)

Examples:

# Align sequences from file
gget muscle sequences.fasta -o aligned.afa

# Use Super5 for large dataset
gget muscle large_dataset.fasta -s5

# Python
gget.muscle("sequences.fasta", save=True)

gget diamond - Local Sequence Alignment

Perform fast local protein or translated DNA alignment using DIAMOND.

Parameters:

• Query: Sequences (string/list) or FASTA file path

• --reference

  : Reference sequences (string/list) or FASTA file path (required)

• --sensitivity

  : fast, mid-sensitive, sensitive, more-sensitive, very-sensitive (default), ultra-sensitive

• --threads

  : CPU threads (default: 1)

• --diamond_db

  : Save database for reuse

• --translated

  : Enable nucleotide-to-amino acid alignment

Returns: Identity percentage, sequence lengths, match positions, gap openings, E-values, bit scores

Examples:

# Align against reference
gget diamond GGETISAWESQME -ref reference.fasta --threads 4

# Save database for reuse
gget diamond query.fasta -ref ref.fasta --diamond_db my_db.dmnd

# Python
gget.diamond("GGETISAWESQME", reference="reference.fasta", threads=4)

3. Structural & Protein Analysis

gget pdb - Protein Structures

Query RCSB Protein Data Bank for structure and metadata.

Parameters:

• pdb_id

  : PDB identifier (e.g., '7S7U')

• -r/--resource

  : Data type (pdb, entry, pubmed, assembly, entity types)

• -i/--identifier

  : Assembly, entity, or chain ID

Returns: PDB format (structures) or JSON (metadata)

Examples:

# Download PDB structure
gget pdb 7S7U -o 7S7U.pdb

# Get metadata
gget pdb 7S7U -r entry

# Python
gget.pdb("7S7U", save=True)

gget alphafold - Protein Structure Prediction

Predict 3D protein structures using simplified AlphaFold2.

Setup Required:

# Install OpenMM first
uv pip install openmm

# Then setup AlphaFold
gget setup alphafold

Parameters:

• sequence

  : Amino acid sequence (string), multiple sequences (list), or FASTA file. Multiple sequences trigger multimer modeling

• -mr/--multimer_recycles

  : Recycling iterations (default: 3; recommend 20 for accuracy)

• -mfm/--multimer_for_monomer

  : Apply multimer model to single proteins

• -r/--relax

  : AMBER relaxation for top-ranked model

• plot

  : Python-only; generate interactive 3D visualization (default: True)

• show_sidechains

  : Python-only; include side chains (default: True)

Returns: PDB structure file, JSON alignment error data, optional 3D visualization

Examples:

# Predict single protein structure
gget alphafold MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR

# Predict multimer with higher accuracy
gget alphafold sequence1.fasta -mr 20 -r

# Python with visualization
gget.alphafold("MKWMFK...", plot=True, show_sidechains=True)

# Multimer prediction
gget.alphafold(["sequence1", "sequence2"], multimer_recycles=20)

gget elm - Eukaryotic Linear Motifs

Predict Eukaryotic Linear Motifs in protein sequences.

Setup Required:

gget setup elm

Parameters:

• sequence

  : Amino acid sequence or UniProt Acc

• -u/--uniprot

  : Indicates sequence is UniProt Acc

• -e/--expand

  : Include protein names, organisms, references

• -s/--sensitivity

  : DIAMOND alignment sensitivity (default: "very-sensitive")

• -t/--threads

  : Number of threads (default: 1)

Returns: Two outputs:

1. ortholog_df: Linear motifs from orthologous proteins
2. regex_df: Motifs directly matched in input sequence

Examples:

# Predict motifs from sequence
gget elm LIAQSIGQASFV -o results

# Use UniProt accession with expanded info
gget elm --uniprot Q02410 -e

# Python
ortholog_df, regex_df = gget.elm("LIAQSIGQASFV")

4. Expression & Disease Data

gget archs4 - Gene Correlation & Tissue Expression

Query ARCHS4 database for correlated genes or tissue expression data.

Parameters:

• gene

  : Gene symbol or Ensembl ID (with

  --ensembl

  flag)

• -w/--which

  : 'correlation' (default, returns 100 most correlated genes) or 'tissue' (expression atlas)

• -s/--species

  : 'human' (default) or 'mouse' (tissue data only)

• -e/--ensembl

  : Input is Ensembl ID

Returns:

• Correlation mode: Gene symbols, Pearson correlation coefficients
• Tissue mode: Tissue identifiers, min/Q1/median/Q3/max expression values

Examples:

# Get correlated genes
gget archs4 ACE2

# Get tissue expression
gget archs4 -w tissue ACE2

# Python
gget.archs4("ACE2", which="tissue")

gget cellxgene - Single-Cell RNA-seq Data

Query CZ CELLxGENE Discover Census for single-cell data.

Setup Required:

gget setup cellxgene

Parameters:

• --gene

  (-g): Gene names or Ensembl IDs (case-sensitive! 'PAX7' for human, 'Pax7' for mouse)

• --tissue

  : Tissue type(s)

• --cell_type

  : Specific cell type(s)

• --species

  (-s): 'homo_sapiens' (default) or 'mus_musculus'

• --census_version

  (-cv): Version ("stable", "latest", or dated)

• --ensembl

  (-e): Use Ensembl IDs

• --meta_only

  (-mo): Return metadata only
• Additional filters: disease, development_stage, sex, assay, dataset_id, donor_id, ethnicity, suspension_type

Returns: AnnData object with count matrices and metadata (or metadata-only dataframes)

Examples:

# Get single-cell data for specific genes and cell types
gget cellxgene --gene ACE2 ABCA1 --tissue lung --cell_type "mucus secreting cell" -o lung_data.h5ad

# Metadata only
gget cellxgene --gene PAX7 --tissue muscle --meta_only -o metadata.csv

# Python
adata = gget.cellxgene(gene=["ACE2", "ABCA1"], tissue="lung", cell_type="mucus secreting cell")

gget enrichr - Enrichment Analysis

Perform ontology enrichment analysis on gene lists using Enrichr.

Parameters:

• genes

  : Gene symbols or Ensembl IDs

• -db/--database

  : Reference database (supports shortcuts: 'pathway', 'transcription', 'ontology', 'diseases_drugs', 'celltypes')

• -s/--species

  : human (default), mouse, fly, yeast, worm, fish

• -bkg_l/--background_list

  : Background genes for comparison

• -ko/--kegg_out

  : Save KEGG pathway images with highlighted genes

• plot

  : Python-only; generate graphical results

Database Shortcuts:

• 'pathway' → KEGG_2021_Human
• 'transcription' → ChEA_2016
• 'ontology' → GO_Biological_Process_2021
• 'diseases_drugs' → GWAS_Catalog_2019
• 'celltypes' → PanglaoDB_Augmented_2021

Examples:

# Enrichment analysis for ontology
gget enrichr -db ontology ACE2 AGT AGTR1

# Save KEGG pathways
gget enrichr -db pathway ACE2 AGT AGTR1 -ko ./kegg_images/

# Python with plot
gget.enrichr(["ACE2", "AGT", "AGTR1"], database="ontology", plot=True)

gget bgee - Orthology & Expression

Retrieve orthology and gene expression data from Bgee database.

Parameters:

• ens_id

  : Ensembl gene ID or NCBI gene ID (for non-Ensembl species). Multiple IDs supported when

  type=expression

• -t/--type

  : 'orthologs' (default) or 'expression'

Returns:

• Orthologs mode: Matching genes across species with IDs, names, taxonomic info
• Expression mode: Anatomical entities, confidence scores, expression status

Examples:

# Get orthologs
gget bgee ENSG00000169194

# Get expression data
gget bgee ENSG00000169194 -t expression

# Multiple genes
gget bgee ENSBTAG00000047356 ENSBTAG00000018317 -t expression

# Python
gget.bgee("ENSG00000169194", type="orthologs")

gget opentargets - Disease & Drug Associations

Retrieve disease and drug associations from OpenTargets.

Parameters:

• Ensembl gene ID (required)

• -r/--resource

  : diseases (default), drugs, tractability, pharmacogenetics, expression, depmap, interactions

• -l/--limit

  : Cap results count
• Filter arguments (vary by resource):
  • drugs:

    --filter_disease

  • pharmacogenetics:

    --filter_drug

  • expression/depmap:

    --filter_tissue

    ,

    --filter_anat_sys

    ,

    --filter_organ

  • interactions:

    --filter_protein_a

    ,

    --filter_protein_b

    ,

    --filter_gene_b

Examples:

# Get associated diseases
gget opentargets ENSG00000169194 -r diseases -l 5

# Get associated drugs
gget opentargets ENSG00000169194 -r drugs -l 10

# Get tissue expression
gget opentargets ENSG00000169194 -r expression --filter_tissue brain

# Python
gget.opentargets("ENSG00000169194", resource="diseases", limit=5)

gget cbio - cBioPortal Cancer Genomics

Plot cancer genomics heatmaps using cBioPortal data.

Two subcommands:

search - Find study IDs:

gget cbio search breast lung

plot - Generate heatmaps:

Parameters:

• -s/--study_ids

  : Space-separated cBioPortal study IDs (required)

• -g/--genes

  : Space-separated gene names or Ensembl IDs (required)

• -st/--stratification

  : Column to organize data (tissue, cancer_type, cancer_type_detailed, study_id, sample)

• -vt/--variation_type

  : Data type (mutation_occurrences, cna_nonbinary, sv_occurrences, cna_occurrences, Consequence)

• -f/--filter

  : Filter by column value (e.g., 'study_id:msk_impact_2017')

• -dd/--data_dir

  : Cache directory (default: ./gget_cbio_cache)

• -fd/--figure_dir

  : Output directory (default: ./gget_cbio_figures)

• -dpi

  : Resolution (default: 100)

• -sh/--show

  : Display plot in window

• -nc/--no_confirm

  : Skip download confirmations

Examples:

# Search for studies
gget cbio search esophag ovary

# Create heatmap
gget cbio plot -s msk_impact_2017 -g AKT1 ALK BRAF -st tissue -vt mutation_occurrences

# Python
gget.cbio_search(["esophag", "ovary"])
gget.cbio_plot(["msk_impact_2017"], ["AKT1", "ALK"], stratification="tissue")

gget cosmic - COSMIC Database

Search COSMIC (Catalogue Of Somatic Mutations In Cancer) database.

Important: License fees apply for commercial use. Requires COSMIC account credentials.

Parameters:

• searchterm

  : Gene name, Ensembl ID, mutation notation, or sample ID

• -ctp/--cosmic_tsv_path

  : Path to downloaded COSMIC TSV file (required for querying)

• -l/--limit

  : Maximum results (default: 100)

Database download flags:

• -d/--download_cosmic

  : Activate download mode

• -gm/--gget_mutate

  : Create version for gget mutate

• -cp/--cosmic_project

  : Database type (cancer, census, cell_line, resistance, genome_screen, targeted_screen)

• -cv/--cosmic_version

  : COSMIC version

• -gv/--grch_version

  : Human reference genome (37 or 38)

• --email

  ,

  --password

  : COSMIC credentials

Examples:

# First download database
gget cosmic -d --email user@example.com --password xxx -cp cancer

# Then query
gget cosmic EGFR -ctp cosmic_data.tsv -l 10

# Python
gget.cosmic("EGFR", cosmic_tsv_path="cosmic_data.tsv", limit=10)

5. Additional Tools

gget mutate - Generate Mutated Sequences

Generate mutated nucleotide sequences from mutation annotations.

Parameters:

• sequences

  : FASTA file path or direct sequence input (string/list)

• -m/--mutations

  : CSV/TSV file or DataFrame with mutation data (required)

• -mc/--mut_column

  : Mutation column name (default: 'mutation')

• -sic/--seq_id_column

  : Sequence ID column (default: 'seq_ID')

• -mic/--mut_id_column

  : Mutation ID column

• -k/--k

  : Length of flanking sequences (default: 30 nucleotides)

Returns: Mutated sequences in FASTA format

Examples:

# Single mutation
gget mutate ATCGCTAAGCT -m "c.4G>T"

# Multiple sequences with mutations from file
gget mutate sequences.fasta -m mutations.csv -o mutated.fasta

# Python
import pandas as pd
mutations_df = pd.DataFrame({"seq_ID": ["seq1"], "mutation": ["c.4G>T"]})
gget.mutate(["ATCGCTAAGCT"], mutations=mutations_df)

gget gpt - OpenAI Text Generation

Generate natural language text using OpenAI's API.

Setup Required:

gget setup gpt

Important: Free tier limited to 3 months after account creation. Set monthly billing limits.

Parameters:

• prompt

  : Text input for generation (required)

• api_key

  : OpenAI authentication (required)
• Model configuration: temperature, top_p, max_tokens, frequency_penalty, presence_penalty
• Default model: gpt-3.5-turbo (configurable)

Examples:

gget gpt "Explain CRISPR" --api_key your_key_here

# Python
gget.gpt("Explain CRISPR", api_key="your_key_here")

gget setup - Install Dependencies

Install/download third-party dependencies for specific modules.

Parameters:

• module

  : Module name requiring dependency installation

• -o/--out

  : Output folder path (elm module only)

Modules requiring setup:

• alphafold

  - Downloads ~4GB of model parameters

• cellxgene

  - Installs cellxgene-census (may not support latest Python)

• elm

  - Downloads local ELM database

• gpt

  - Configures OpenAI integration

Examples:

# Setup AlphaFold
gget setup alphafold

# Setup ELM with custom directory
gget setup elm -o /path/to/elm_data

# Python
gget.setup("alphafold")

Common Workflows

Workflow 1: Gene Discovery to Sequence Analysis

Find and analyze genes of interest:

# 1. Search for genes
results = gget.search(["GABA", "receptor"], species="homo_sapiens")

# 2. Get detailed information
gene_ids = results["ensembl_id"].tolist()
info = gget.info(gene_ids[:5])

# 3. Retrieve sequences
sequences = gget.seq(gene_ids[:5], translate=True)

Workflow 2: Sequence Alignment and Structure

Align sequences and predict structures:

# 1. Align multiple sequences
alignment = gget.muscle("sequences.fasta")

# 2. Find similar sequences
blast_results = gget.blast(my_sequence, database="swissprot", limit=10)

# 3. Predict structure
structure = gget.alphafold(my_sequence, plot=True)

# 4. Find linear motifs
ortholog_df, regex_df = gget.elm(my_sequence)

Workflow 3: Gene Expression and Enrichment

Analyze expression patterns and functional enrichment:

# 1. Get tissue expression
tissue_expr = gget.archs4("ACE2", which="tissue")

# 2. Find correlated genes
correlated = gget.archs4("ACE2", which="correlation")

# 3. Get single-cell data
adata = gget.cellxgene(gene=["ACE2"], tissue="lung", cell_type="epithelial cell")

# 4. Perform enrichment analysis
gene_list = correlated["gene_symbol"].tolist()[:50]
enrichment = gget.enrichr(gene_list, database="ontology", plot=True)

Workflow 4: Disease and Drug Analysis

Investigate disease associations and therapeutic targets:

# 1. Search for genes
genes = gget.search(["breast cancer"], species="homo_sapiens")

# 2. Get disease associations
diseases = gget.opentargets("ENSG00000169194", resource="diseases")

# 3. Get drug associations
drugs = gget.opentargets("ENSG00000169194", resource="drugs")

# 4. Query cancer genomics data
study_ids = gget.cbio_search(["breast"])
gget.cbio_plot(study_ids[:2], ["BRCA1", "BRCA2"], stratification="cancer_type")

# 5. Search COSMIC for mutations
cosmic_results = gget.cosmic("BRCA1", cosmic_tsv_path="cosmic.tsv")

Workflow 5: Comparative Genomics

Compare proteins across species:

# 1. Get orthologs
orthologs = gget.bgee("ENSG00000169194", type="orthologs")

# 2. Get sequences for comparison
human_seq = gget.seq("ENSG00000169194", translate=True)
mouse_seq = gget.seq("ENSMUSG00000026091", translate=True)

# 3. Align sequences
alignment = gget.muscle([human_seq, mouse_seq])

# 4. Compare structures
human_structure = gget.pdb("7S7U")
mouse_structure = gget.alphafold(mouse_seq)

Workflow 6: Building Reference Indices

Prepare reference data for downstream analysis (e.g., kallisto|bustools):

# 1. List available species
gget ref --list_species

# 2. Download reference files
gget ref -w gtf -w cdna -d homo_sapiens

# 3. Build kallisto index
kallisto index -i transcriptome.idx transcriptome.fasta

# 4. Download genome for alignment
gget ref -w dna -d homo_sapiens

Best Practices

Data Retrieval

• Use

  --limit

  to control result sizes for large queries
• Save results with

  -o/--out

  for reproducibility
• Check database versions/releases for consistency across analyses
• Use

  --quiet

  in production scripts to reduce output

Sequence Analysis

• For BLAST/BLAT, start with default parameters, then adjust sensitivity
• Use

  gget diamond

  with

  --threads

  for faster local alignment
• Save DIAMOND databases with

  --diamond_db

  for repeated queries
• For multiple sequence alignment, use

  -s5/--super5

  for large datasets

Expression and Disease Data

• Gene symbols are case-sensitive in cellxgene (e.g., 'PAX7' vs 'Pax7')
• Run

  gget setup

  before first use of alphafold, cellxgene, elm, gpt
• For enrichment analysis, use database shortcuts for convenience
• Cache cBioPortal data with

  -dd

  to avoid repeated downloads

Structure Prediction

• AlphaFold multimer predictions: use

  -mr 20

  for higher accuracy
• Use

  -r

  flag for AMBER relaxation of final structures
• Visualize results in Python with

  plot=True

• Check PDB database first before running AlphaFold predictions

Error Handling

• Database structures change; update gget regularly:

  uv pip install --upgrade gget

• Process max ~1000 Ensembl IDs at once with gget info
• For large-scale analyses, implement rate limiting for API queries
• Use virtual environments to avoid dependency conflicts

Output Formats

Command-line

• Default: JSON
• CSV: Add

  -csv

  flag
• FASTA: gget seq, gget mutate
• PDB: gget pdb, gget alphafold
• PNG: gget cbio plot

Python

• Default: DataFrame or dictionary
• JSON: Add

  json=True

  parameter
• Save to file: Add

  save=True

  or specify

  out="filename"

• AnnData: gget cellxgene

Resources

This skill includes reference documentation for detailed module information:

references/

• module_reference.md

  - Comprehensive parameter reference for all modules

• database_info.md

  - Information about queried databases and their update frequencies

• workflows.md

  - Extended workflow examples and use cases

For additional help:

• Official documentation: https://pachterlab.github.io/gget/
• GitHub issues: https://github.com/pachterlab/gget/issues
• Citation: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836

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