gget

Overview

Installation

# Reproducible install targeting this skill
uv venv .venv
source .venv/bin/activate
uv pip install "gget==0.30.5"

# In Python/Jupyter
import gget

Quick Start

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

# Python
gget.module(arguments, options)

• Command-line: JSON (default) or CSV with -csv flag
• Python: DataFrame or dictionary

• -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

• species: Genus_species format (e.g., 'homo_sapiens', 'mus_musculus'). Shortcuts: 'human', 'mouse'
• -w/--which: Specify return types as comma-separated CLI values or Python list (gtf, cdna, dna, cds, cdrna, pep). Default: all
• -r/--release: Ensembl release number (default: latest)
• -od/--out_dir: Directory for downloaded files
• -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)

# List available species
gget ref --list_species

# Get all reference files for human
gget ref homo_sapiens

# Download GTF and cDNA files for mouse
gget ref -w gtf,cdna -d mouse

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

gget search - Gene Search

• 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
• wrap_text: Python-only display helper for wide DataFrames

# 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

• 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)

# 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

• 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)

# 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

• 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)

# 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

• 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.)

# 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

• fasta: Sequences or path to FASTA/.txt file
• -s5/--super5: Use Super5 algorithm for faster processing (large datasets)

# 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

• Query: Sequences (string/list) or FASTA file path
• -ref/--reference: Reference sequences (string/list) or FASTA file path (required)
• -s/--sensitivity: fast, mid-sensitive, sensitive, more-sensitive, very-sensitive (default), ultra-sensitive
• -t/--threads: CPU threads (default: 1)
• -db/--diamond_db: Save database for reuse
• -x/--translated: Enable nucleotide query to amino acid reference alignment

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

# Translate nucleotide query against amino acid reference
gget diamond query_nt.fasta -ref proteins.fasta --translated

# Python
gget.diamond("GGETISAWESQME", reference="reference.fasta", threads=4)
gget.diamond("ATGGGC...", reference="proteins.fasta", translated=True)

3. Structural & Protein Analysis

gget pdb - Protein Structures

• pdb_id: PDB identifier (e.g., '7S7U')
• -r/--resource: Data type (pdb, entry, pubmed, assembly, entity types)
• -i/--identifier: Assembly, entity, or chain ID

# 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

# Installs modified third-party dependencies and downloads model parameters
gget setup alphafold

• 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)

# 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

gget setup elm

• 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)

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

# 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

• 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

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

# 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

gget setup cellxgene

• --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

# 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

• 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

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

# 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

• 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'

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

# 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

• Ensembl gene ID (required)
• -r/--resource: diseases (default), drugs, tractability, pharmacogenetics, expression, depmap, interactions
• -l/--limit: Cap results count
• --filters: Exact-match filters using returned OpenTargets column names; repeat on the CLI or pass a Python dict
• -or/--or: CLI-only; combine filters with OR logic instead of the default AND logic

• gget 0.30.5 rewrote this module for the newer OpenTargets API; some output column names differ from older releases.
• The older --filter_mode argument was removed upstream.

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

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

# Filter interactions by returned column names
gget opentargets ENSG00000169194 -r interactions --filters protein_a_id=P35225 --filters gene_b_id=ENSG00000077238

# Python
gget.opentargets("ENSG00000169194", resource="diseases", limit=5)
gget.opentargets(
    "ENSG00000169194",
    resource="interactions",
    filters={"protein_a_id": "P35225", "gene_b_id": "ENSG00000077238"},
)

gget cbio - cBioPortal Cancer Genomics

gget cbio search breast lung

• -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

# 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

• 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)

• -d/--download_cosmic: Activate download mode
• -gm/--gget_mutate: Create version for gget mutate
• -cp/--cosmic_project: Database type (cancer, cancer_example, 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 for non-interactive downloads; prefer prompt or Python env vars

# First download database; gget prompts for COSMIC email/password
gget cosmic --download_cosmic --cosmic_project cancer

# Then query
gget cosmic EGFR --cosmic_tsv_path "CancerMutationCensus_AllData_Tsv_v101_GRCh37/CancerMutationCensus_AllData_v101_GRCh37.tsv" -l 10

# Python
import os

gget.cosmic(
    searchterm=None,
    download_cosmic=True,
    cosmic_project="cancer",
    email=os.environ["COSMIC_EMAIL"],
    password=os.environ["COSMIC_PASSWORD"],
)
gget.cosmic("EGFR", cosmic_tsv_path="cosmic_data.tsv", limit=10)

5. Viral & Mouse Specificity Data

gget virus - Viral Sequence Downloads

• virus: Virus taxon name, taxon ID, accession, space-separated accessions, or path to a text file of accessions
• -a/--is_accession: Treat virus as accession input
• --is_sars_cov2, --is_alphainfluenza: Use optimized cached NCBI datasets paths for SARS-CoV-2 or Influenza A
• --host: Host organism name or NCBI taxonomy ID
• --nuc_completeness: complete or partial
• --min_seq_length, --max_seq_length: Sequence length filters
• -g/--genbank_metadata: Fetch detailed GenBank metadata; auto-enabled by some annotation filters
• --segment, --vaccine_strain, --annotated, --lab_passaged, --source_database: Common viral metadata filters
• --download_all_accessions: Apply filters across all viral accessions
• --baseline, --merge-results: Resume or merge with prior metadata from partial/previous runs

# Complete Zika genomes from human hosts
gget virus "Zika virus" --nuc_completeness complete --host human --out zika_data

# SARS-CoV-2 reference genome by accession
gget virus NC_045512.2 --is_accession --is_sars_cov2

# Python
gget.virus(
    "SARS-CoV-2",
    host="human",
    nuc_completeness="complete",
    min_seq_length=29000,
    genbank_metadata=True,
    is_sars_cov2=True,
    outfolder="covid_data",
)

gget 8cube - Mouse Specificity & Expression

• gget 8cube specificity <genes...>: Return gene-level psi/zeta specificity statistics
• gget 8cube psi_block <genes...> --analysis_level <level> --analysis_type <type>: Return block-level specificity
• gget 8cube expression <genes...> --analysis_level <level> --analysis_type <type>: Return mean/variance normalized expression

gget 8cube specificity Acsm2 ENSMUSG00000046623.9
gget 8cube psi_block Acsm2 --analysis_level Kidney --analysis_type "Sex:Celltype"
gget 8cube expression Gjb4 --analysis_level Across_tissues --analysis_type Strain

# Python
from gget import specificity, psi_block, gene_expression

specificity(["Acsm2", "ENSMUSG00000046623.9"])
psi_block(["Acsm2"], analysis_level="Kidney", analysis_type="Sex:Celltype")
gene_expression(["Gjb4"], analysis_level="Across_tissues", analysis_type="Strain")

6. Additional Tools

gget mutate - Generate Mutated Sequences

• sequences: FASTA file path or direct nucleotide sequence input (string/list)
• -m/--mutations: Mutation string/list, CSV/TSV path, 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 (default: same as mut_column)
• -k/--k: Length of flanking sequences (default: 30 nucleotides)
• -o/--out: Output FASTA path; without it Python returns a list of mutated sequences

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

# Multiple sequences with one mutation per sequence
gget mutate ATCGCTAAGCT TAGCTA -m "c.4G>T" "c.1_3inv" -o mutated.fasta

# Python
gget.mutate("ATCGCTAAGCT", "c.4G>T")
gget.mutate(["ATCGCTAAGCT", "TAGCTA"], ["c.4G>T", "c.1_3inv"], out="mutated.fasta")

gget gpt - OpenAI Text Generation

gget setup gpt

• prompt: Text input for generation (required)
• api_key: OpenAI authentication (required by the upstream API)
• Model configuration: model, temperature, top_p, stop, max_tokens, frequency_penalty, presence_penalty, logit_bias
• Default model: gpt-3.5-turbo (upstream default; verify available models in your OpenAI account)

# Python
import os

gget.gpt("Explain CRISPR", api_key=os.environ["OPENAI_API_KEY"])

gget setup - Install Dependencies

• module: Module name requiring dependency installation
• -o/--out: Output folder path (elm module only)

• alphafold - Downloads ~4GB of model parameters
• cellxgene - Installs cellxgene-census (may require Python 3.9/3.10 if the latest Python is unsupported)
• elm - Downloads local ELM database
• gpt - Installs/configures OpenAI integration dependencies

# 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

# 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

# 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

# 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

# 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

# 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

# 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
• For OpenTargets, inspect returned column names before writing filters; gget 0.30.5 follows the newer OpenTargets API schema

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

Viral Data

• Use restrictive filters with gget virus before requesting broad viral datasets
• Keep command_summary.txt with downstream results for reproducibility and recovery after partial downloads
• Use --baseline and --merge-results to resume interrupted viral metadata/sequence downloads

Error Handling

• Database structures change; when an adapter breaks, check upstream release notes and pin the newer fixed version explicitly
• Pin the known-good version for reproducible environments: uv pip install "gget==0.30.5"
• 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
• Keep COSMIC and OpenAI credentials in named environment variables or interactive prompts; do not write real credentials into examples, notebooks, or logs

Output Formats

Command-line

• Default: JSON
• CSV: Add -csv flag
• FASTA: gget seq, gget mutate
• PDB: gget pdb, gget alphafold
• PNG: gget cbio plot
• FASTA/CSV/JSONL folder: gget virus

Python

• Default: DataFrame or dictionary
• JSON: Add json=True parameter
• Save to file: Add save=True or specify out="filename"
• AnnData: gget cellxgene
• DataFrame/JSON: gget 8cube specificity, psi_block, expression

Resources

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

• 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