gget 0.3.7

Efficient querying of genomic databases directly into programming environments.

gget

✨ Version ≥ 0.3.0: gget alphafold

✨ What's new in version ≥ 0.2.0

• JSON is now the default output format for the command-line interface for modules that previously returned data frame (CSV) format by default (the output can be converted to data frame/CSV using flag [-csv][--csv]). Data frame/CSV remains the default output for Jupyter Lab / Google Colab (and can be converted to JSON with json=True).
• For all modules, the first required argument was converted to a positional argument and should not be named anymore in the command-line, e.g. gget ref -s human → gget ref human.
• gget info: [--expand] is deprecated. The module will now always return all of the available information.
• Slight changes to the output returned by gget info, including the return of versioned Ensembl IDs.
• gget info and gget seq now support 🪱 WormBase and 🪰 FlyBase IDs.
• gget archs4 and gget enrichr now also take Ensembl IDs as input with added flag [-e][--ensembl] (ensembl=True in Jupyter Lab / Google Colab).
• gget seq argument seqtype was replaced by flag [-t][--translate] (translate=True/False in Jupyter Lab / Google Colab) which will return either nucleotide (False) or amino acid (True) sequences.
• gget search argument seqtype was renamed to id_type for clarity (still taking the same arguments 'gene' or 'transcript').
• Version ≥ 0.2.6: gget ref supports plant genomes! 🌱

Note: UniProt changed the structure of their API on June 28, 2022. Please upgrade to gget version ≥ 0.2.5 if you use any of the modules querying data from UniProt (gget info and gget seq).

Note: The Ensembl FTP site changed its structure on August 8, 2022. Please upgrade to gget version ≥ 0.3.7 if you use gget ref.

---

gget is a free, open-source command-line tool and Python package that enables efficient querying of genomic databases. gget consists of a collection of separate but interoperable modules, each designed to facilitate one type of database querying in a single line of code.

If you use gget in a publication, please cite*:

Luebbert, L. & Pachter, L. (2022). Efficient querying of genomic reference databases with gget. bioRxiv 2022.05.17.492392

Read the manuscript here: https://doi.org/10.1101/2022.05.17.492392

gget currently consists of the following modules:

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

Installation

pip install --upgrade gget

For use in Jupyter Lab / Google Colab:

import gget

🪄 Quick start guide

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

# Search 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

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

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

More examples

---

Manual

Jupyter Lab / Google Colab arguments are equivalent to long-option arguments (--arg), unless otherwise specified. Flags are True/False arguments in Jupyter Lab / Google Colab.
The manual for any gget tool can be called from the command-line using the -h --help flag.

gget ref 📖

Fetch FTPs and their respective metadata (or use flag ftp to only return the links) for reference genomes and annotations from Ensembl by species.
Return format: dictionary/JSON.

Positional argument
species
Species for which the FTPs will be fetched in the format genus_species, e.g. homo_sapiens.
Note: Not required when calling flag [--list_species].
Supported shortcuts: 'human', 'mouse'

Optional arguments
-w --which
Defines which results to return. Default: 'all' -> Returns all available results.
Possible entries are one or a combination (as comma-separated list) of the following:
'gtf' - Returns the annotation (GTF).
'cdna' - Returns the trancriptome (cDNA).
'dna' - Returns the genome (DNA).
'cds' - Returns the coding sequences corresponding to Ensembl genes. (Does not contain UTR or intronic sequence.)
'cdrna' - Returns transcript sequences corresponding to non-coding RNA genes (ncRNA).
'pep' - Returns the protein translations of Ensembl genes.

-r --release
Defines the Ensembl release number from which the files are fetched, e.g. 104. Default: latest Ensembl release.

-o --out
Path to the JSON file the results will be saved in, e.g. path/to/directory/results.json. Default: Standard out.
Jupyter Lab / Google Colab: save=True will save the output in the current working directory.

Flags
-l --list_species
Lists all available species. (Jupyter Lab / Google Colab: combine with species=None.)

-ftp --ftp
Returns only the requested FTP links.

-d --download
Command-line only. Downloads the requested FTPs to the current directory (requires curl to be installed).

Examples

Use gget ref in combination with kallisto | bustools to build a reference index:

kb ref -i INDEX -g T2G -f1 FASTA $(gget ref --ftp -w dna,gtf homo_sapiens)

→ kb ref builds a reference index using the latest DNA and GTF files of species Homo sapiens passed to it by gget ref.

List all available genomes from Ensembl release 103:

gget ref --list_species -r 103

# Jupyter Lab / Google Colab:
gget.ref(species=None, list_species=True, release=103)

→ Returns a list with all available genomes (checks if GTF and FASTAs are available) from Ensembl release 103.
(If no release is specified, gget ref will always return information from the latest Ensembl release.)

Get the genome reference for a specific species:

gget ref -w gtf,dna homo_sapiens

# Jupyter Lab / Google Colab:
gget.ref("homo_sapiens", which=["gtf", "dna"])

→ Returns a JSON with the latest human GTF and FASTA FTPs, and their respective metadata, in the format:

{
    "homo_sapiens": {
        "annotation_gtf": {
            "ftp": "http://ftp.ensembl.org/pub/release-106/gtf/homo_sapiens/Homo_sapiens.GRCh38.106.gtf.gz",
            "ensembl_release": 106,
            "release_date": "28-Feb-2022",
            "release_time": "23:27",
            "bytes": "51379459"
        },
        "genome_dna": {
            "ftp": "http://ftp.ensembl.org/pub/release-106/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz",
            "ensembl_release": 106,
            "release_date": "21-Feb-2022",
            "release_time": "09:35",
            "bytes": "881211416"
        }
    }
}

More examples

---

gget search 🔎

Fetch genes and transcripts from Ensembl using free-form search terms.
Return format: JSON (command-line) or data frame/CSV (Jupyter Lab / Google Colab).

Positional argument searchwords
One or more free form search words, e.g. gaba nmda. (Note: Search is not case-sensitive.)

Other required arguments
-s --species
Species or database to be searched.
A species can be passed in the format 'genus_species', e.g. 'homo_sapiens'.
To pass a specific database, pass the name of the CORE database, e.g. 'mus_musculus_dba2j_core_105_1'.
All availabale databases can be found here.
Supported shortcuts: 'human', 'mouse'.

Optional arguments
-t --id_type
'gene' (default) or 'transcript'
Returns genes or transcripts, respectively.

-ao --andor
'or' (default) or 'and'
'or': Returns all genes that INCLUDE AT LEAST ONE of the searchwords in their name/description.
'and': Returns only genes that INCLUDE ALL of the searchwords in their name/description.

-l --limit
Limits the number of search results, e.g. 10. Default: None.

-o --out
Path to the csv the results will be saved in, e.g. path/to/directory/results.csv (or .json). Default: Standard out.
Jupyter Lab / Google Colab: save=True will save the output in the current working directory.

Flags
-csv --csv
Command-line only. Returns results in CSV format.
Jupyter Lab / Google Colab: Use json=True to return output in JSON format.

wrap_text
Jupyter Lab / Google Colab only. wrap_text=True displays data frame with wrapped text for easy reading (default: False).

Example

gget search -s human gaba gamma-aminobutyric

# Jupyter Lab / Google Colab:
gget.search(["gaba", "gamma-aminobutyric"], "homo_sapiens")

→ Returns all genes that contain at least one of the search words in their name or Ensembl/external reference description:

ensembl_id	gene_name	ensembl_description	ext_ref_description	biotype	url
ENSG00000034713	GABARAPL2	GABA type A receptor associated protein like 2 [Source:HGNC Symbol;Acc:HGNC:13291]	GABA type A receptor associated protein like 2	protein_coding	https://uswest.ensembl.org/homo_sapiens/Gene/Summary?g=ENSG00000034713
. . .	. . .	. . .	. . .	. . .	. . .

More examples

---

gget info 💡

Fetch extensive gene and transcript metadata from Ensembl, UniProt, and NCBI using Ensembl IDs.
Return format: JSON (command-line) or data frame/CSV (Jupyter Lab / Google Colab).

Positional argument
ens_ids
One or more Ensembl IDs.

Optional arguments
-o --out
Path to the file the results will be saved in, e.g. path/to/directory/results.csv (or .json). Default: Standard out.
Jupyter Lab / Google Colab: save=True will save the output in the current working directory.

Flags
-q --quiet
Command-line only. Prevents progress information from being displayed.
Jupyter Lab / Google Colab: Use verbose=False to prevent progress information from being displayed.

-csv --csv
Command-line only. Returns results in CSV format.
Jupyter Lab / Google Colab: Use json=True to return output in JSON format.

wrap_text
Jupyter Lab / Google Colab only. wrap_text=True displays data frame with wrapped text for easy reading (default: False).

Example

gget info ENSG00000034713 ENSG00000104853 ENSG00000170296

# Jupyter Lab / Google Colab:
gget.info(["ENSG00000034713", "ENSG00000104853", "ENSG00000170296"])

→ Returns extensive information about each requested Ensembl ID:

	uniprot_id	ncbi_gene_id	primary_gene_name	synonyms	protein_names	ensembl_description	uniprot_description	ncbi_description	biotype	canonical_transcript	...
ENSG00000034713	P60520	11345	GABARAPL2	[ATG8, ATG8C, FLC3A, GABARAPL2, GATE-16, GATE16, GEF-2, GEF2]	Gamma-aminobutyric acid receptor-associated protein like 2 (GABA(A) receptor-associated protein-like 2)...	GABA type A receptor associated protein like 2 [Source:HGNC Symbol;Acc:HGNC:13291]	FUNCTION: Ubiquitin-like modifier involved in intra- Golgi traffic (By similarity). Modulates intra-Golgi transport through coupling between NSF activity and ...	Enables ubiquitin protein ligase binding activity. Involved in negative regulation of proteasomal protein catabolic process and protein...	protein_coding	ENST00000037243.7	...
. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .	...

More examples

---

gget seq 🧬

Fetch nucleotide or amino acid sequence of a gene (and all its isoforms) or a transcript by Ensembl ID.
Return format: FASTA.

Positional argument
ens_ids
One or more Ensembl IDs.

Optional arguments
-o --out
Path to the file the results will be saved in, e.g. path/to/directory/results.fa. Default: Standard out.
Jupyter Lab / Google Colab: save=True will save the output in the current working directory.

Flags
-t --translate
Returns amino acid (instead of nucleotide) sequences.
Nucleotide sequences are fetched from Ensembl.
Amino acid sequences are fetched from UniProt.

-iso --isoforms
Returns the sequences of all known transcripts.
(Only for gene IDs.)

Examples

gget seq ENSG00000034713 ENSG00000104853 ENSG00000170296

# Jupyter Lab / Google Colab:
gget.seq(["ENSG00000034713", "ENSG00000104853", "ENSG00000170296"])

→ Returns the nucleotide sequences of ENSG00000034713, ENSG00000104853, and ENSG00000170296 in FASTA format.

gget seq -t -iso ENSG00000034713

# Jupyter Lab / Google Colab:
gget.seq("ENSG00000034713", translate=True, isoforms=True)

→ Returns the amino acid sequences of all known transcripts of ENSG00000034713 in FASTA format.

More examples

---

gget blast 💥

BLAST a nucleotide or amino acid sequence to any BLAST database.
Return format: JSON (command-line) or data frame/CSV (Jupyter Lab / Google Colab).

Positional argument
sequence
Nucleotide or amino acid sequence, or path to FASTA or .txt file.

Optional arguments
-p --program
'blastn', 'blastp', 'blastx', 'tblastn', or 'tblastx'.
Default: 'blastn' for nucleotide sequences; 'blastp' for amino acid sequences.

-db --database
'nt', 'nr', 'refseq_rna', 'refseq_protein', 'swissprot', 'pdbaa', or 'pdbnt'.
Default: 'nt' for nucleotide sequences; 'nr' for amino acid sequences.
More info on BLAST databases

-l --limit
Limits number of hits to return. Default: 50.

-e --expect
Defines the expect value cutoff. Default: 10.0.

-o --out
Path to the file the results will be saved in, e.g. path/to/directory/results.csv (or .json). Default: Standard out.
Jupyter Lab / Google Colab: save=True will save the output in the current working directory.

Flags
-lcf --low_comp_filt
Turns on low complexity filter.

-mbo --megablast_off
Turns off MegaBLAST algorithm. Default: MegaBLAST on (blastn only).

-q --quiet
Command-line only. Prevents progress information from being displayed.
Jupyter Lab / Google Colab: Use verbose=False to prevent progress information from being displayed.

-csv --csv
Command-line only. Returns results in CSV format.
Jupyter Lab / Google Colab: Use json=True to return output in JSON format.

wrap_text
Jupyter Lab / Google Colab only. wrap_text=True displays data frame with wrapped text for easy reading (default: False).

Example

gget blast MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR

# Jupyter Lab / Google Colab:
gget.blast("MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR")

→ Returns the BLAST result of the sequence of interest. gget blast automatically detects this sequence as an amino acid sequence and therefore sets the BLAST program to blastp with database nr.

Description	Scientific Name	Common Name	Taxid	Max Score	Total Score	Query Cover	...
PREDICTED: gamma-aminobutyric acid receptor-as...	Colobus angolensis palliatus	NaN	336983	180	180	100%	...
. . .	. . .	. . .	. . .	. . .	. . .	. . .	...

BLAST from .fa or .txt file:

gget blast fasta.fa

# Jupyter Lab / Google Colab:
gget.blast("fasta.fa")

→ Returns the BLAST results of the first sequence contained in the fasta.fa file.

More examples

---

gget blat 🎯

Find the genomic location of a nucleotide or amino acid sequence using BLAT.
Return format: JSON (command-line) or data frame/CSV (Jupyter Lab / Google Colab).

Positional argument
sequence
Nucleotide or amino acid sequence, or path to FASTA or .txt file.

Optional arguments
-st --seqtype
'DNA', 'protein', 'translated%20RNA', or 'translated%20DNA'.
Default: 'DNA' for nucleotide sequences; 'protein' for amino acid sequences.

-a --assembly
'human' (hg38) (default), 'mouse' (mm39), 'zebrafinch' (taeGut2),
or any of the species assemblies available here (use short assembly name).

-o --out
Path to the file the results will be saved in, e.g. path/to/directory/results.csv (or .json). Default: Standard out.
Jupyter Lab / Google Colab: save=True will save the output in the current working directory.

Flags
-csv --csv
Command-line only. Returns results in CSV format.
Jupyter Lab / Google Colab: Use json=True to return output in JSON format.

Example

gget blat -a taeGut2 MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR

# Jupyter Lab / Google Colab:
gget.blat("MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR", assembly="taeGut2")

→ Returns BLAT results for assembly taeGut2 (zebra finch). In the above example, gget blat automatically detects this sequence as an amino acid sequence and therefore sets the BLAT seqtype to protein.

genome	query_size	aligned_start	aligned_end	matches	mismatches	%_aligned	...
taeGut2	88	12	88	77	0	87.5	...

More examples

---

gget muscle 🦾

Align multiple nucleotide or amino acid sequences to each other using Muscle5.
Return format: ClustalW formatted standard out or aligned FASTA (.afa).

Positional argument
fasta
Path to FASTA or .txt file containing the nucleotide or amino acid sequences to be aligned.

Optional arguments
-o --out
Path to the aligned FASTA file the results will be saved in, e.g. path/to/directory/results.afa. Default: Standard out.
Jupyter Lab / Google Colab: save=True will save the output in the current working directory.

Flags
-s5 --super5
Aligns input using the Super5 algorithm instead of the Parallel Perturbed Probcons (PPP) algorithm to decrease time and memory.
Use for large inputs (a few hundred sequences).

Example

gget muscle fasta.fa

# Jupyter Lab / Google Colab:
gget.muscle("fasta.fa")

→ Returns an overview of the aligned sequences with ClustalW coloring. (To return an aligned FASTA (.afa) file, use --out argument (or save=True in Jupyter Lab/Google Colab).) In the above example, the 'fasta.fa' includes several sequences to be aligned (e.g. isoforms returned from gget seq).

More examples

---

gget enrichr 💰

Perform an enrichment analysis on a list of genes using Enrichr.
Return format: JSON (command-line) or data frame/CSV (Jupyter Lab / Google Colab).

Positional argument
genes
Short names (gene symbols) of genes to perform enrichment analysis on, e.g. PHF14 RBM3 MSL1 PHF21A.
Alternatively: use flag --ensembl to input a list of Ensembl gene IDs, e.g. ENSG00000106443 ENSG00000102317 ENSG00000188895.

Other required arguments
-db --database
Database to use as reference for the enrichment analysis.
Supports any database listed here under 'Gene-set Library' or one of the following shortcuts:
'pathway'       (KEGG_2021_Human)
'transcription'     (ChEA_2016)
'ontology'      (GO_Biological_Process_2021)
'diseases_drugs'   (GWAS_Catalog_2019)
'celltypes'      (PanglaoDB_Augmented_2021)
'kinase_interactions'  (KEA_2015)

Optional arguments
-o --out
Path to the file the results will be saved in, e.g. path/to/directory/results.csv (or .json). Default: Standard out.
Jupyter Lab / Google Colab: save=True will save the output in the current working directory.

figsize
Jupyter Lab / Google Colab only. (width, height) of plot in inches. (Default: (10,10))

ax
Jupyter Lab / Google Colab only. Pass a matplotlib axes object for plot customization. (Default: None)

Flags
-e --ensembl
Add this flag if genes are given as Ensembl gene IDs.

-csv --csv
Command-line only. Returns results in CSV format.
Jupyter Lab / Google Colab: Use json=True to return output in JSON format.

plot
Jupyter Lab / Google Colab only. plot=True provides a graphical overview of the first 15 results (default: False).

Example

gget enrichr -db ontology ACE2 AGT AGTR1

# Jupyter Lab / Google Colab:
gget.enrichr(["ACE2", "AGT", "AGTR1"], database="ontology", plot=True)

→ Returns pathways/functions involving genes ACE2, AGT, and AGTR1 from the GO Biological Process 2021 database. In Jupyter Lab / Google Colab, plot=True returns a graphical overview of the results:

More examples

---

gget archs4 🐁

Find the most correlated genes to a gene of interest or find the gene's tissue expression atlas using ARCHS4.
Return format: JSON (command-line) or data frame/CSV (Jupyter Lab / Google Colab).

Positional argument
gene
Short name (gene symbol) of gene of interest, e.g. STAT4.
Alternatively: use flag --ensembl to input an Ensembl gene IDs, e.g. ENSG00000138378.

Optional arguments
-w --which
'correlation' (default) or 'tissue'.
'correlation' returns a gene correlation table that contains the 100 most correlated genes to the gene of interest. The Pearson correlation is calculated over all samples and tissues in ARCHS4.
'tissue' returns a tissue expression atlas calculated from human or mouse samples (as defined by 'species') in ARCHS4.

-s --species
'human' (default) or 'mouse'.
Defines whether to use human or mouse samples from ARCHS4.
(Only for tissue expression atlas.)

-o --out
Path to the file the results will be saved in, e.g. path/to/directory/results.csv (or .json). Default: Standard out.
Jupyter Lab / Google Colab: save=True will save the output in the current working directory.

Flags
-e --ensembl
Add this flag if gene is given as an Ensembl gene ID.

-csv --csv
Command-line only. Returns results in CSV format.
Jupyter Lab / Google Colab: Use json=True to return output in JSON format.

Examples

gget archs4 ACE2

# Jupyter Lab / Google Colab:
gget.archs4("ACE2")

→ Returns the 100 most correlated genes to ACE2:

gene_symbol	pearson_correlation
SLC5A1	0.579634
CYP2C18	0.576577
. . .	. . .

gget archs4 -w tissue ACE2

# Jupyter Lab / Google Colab:
gget.archs4("ACE2", which="tissue")

→ Returns the tissue expression of ACE2 (by default, human data is used):

id	min	q1	median	q3	max
System.Urogenital/Reproductive System.Kidney.RENAL CORTEX	0.113644	8.274060	9.695840	10.51670	11.21970
System.Digestive System.Intestine.INTESTINAL EPITHELIAL CELL	0.113644	5.905560	9.570450	13.26470	13.83590
. . .	. . .	. . .	. . .	. . .	. . .

More examples

---

gget setup ⚙️

Function to install/download third-party dependencies for a specified gget module.

Positional argument
module
gget module for which dependencies should be installed.

Example

gget setup alphafold

# Jupyter Lab / Google Colab:
gget.setup("alphafold")

→ Installs all (modified) third-party dependencies and downloads model parameters (~4GB) required to run gget alphafold.

---

gget alphafold 🪢

Predict the 3D structure of a protein from its amino acid sequence using a simplified version of DeepMind’s AlphaFold2 originally released and benchmarked for AlphaFold Colab.
Returns: Predicted structure (PDB) and alignment error (json).

Before using gget alphafold for the first time, run gget setup alphafold / gget.setup("alphafold") once (also see gget setup above).

Positional argument
sequence
Amino acid sequence (str), list of sequences (for multimers), or path to FASTA file.

Optional arguments
-o --out
Path to folder to save prediction results in (str). Default: "./[date_time]_gget_alphafold_prediction".

Flags
-r --relax
AMBER relax the best model.

plot
Jupyter Lab / Google Colab only. plot=True provides an interactive, 3D graphical overview of the predicted structure and alignment quality using py3Dmol and matplotlib (default: True).

show_sidechains
Jupyter Lab / Google Colab only. show_sidechains=True includes side chains in the plot (default: True).

Example

gget alphafold MAAHKGAEHHHKAAEHHEQAAKHHHAAAEHHEKGEHEQAAHHADTAYAHHKHAEEHAAQAAKHDAEHHAPKPH

# Jupyter Lab / Google Colab:
gget.alphafold("MAAHKGAEHHHKAAEHHEQAAKHHHAAAEHHEKGEHEQAAHHADTAYAHHKHAEEHAAQAAKHDAEHHAPKPH")

→ Returns the predicted structure (PDB) and predicted alignment error (.json) in a new folder ("./[date_time]_gget_alphafold_prediction"). The Python interface also returns the following plots:

https://user-images.githubusercontent.com/56094636/182939299-36ac2a8f-0560-4a64-b1f8-6cff39ef2a75.mp4

More examples

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Cite

If you use gget in a publication, please cite:
Luebbert L. & Pachter L. (2022). Efficient querying of genomic reference databases with gget. bioRxiv 2022.05.17.492392; doi: https://doi.org/10.1101/2022.05.17.492392

• If using gget archs4, please also cite:
  Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Wang L, Silverstein MC, Ma’ayan A. Massive mining of publicly available RNA-seq data from human and mouse. Nature Communications 9. Article number: 1366 (2018), doi:10.1038/s41467-018-03751-6

  Bray NL, Pimentel H, Melsted P and Pachter L, Near optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, p 525--527 (2016). https://doi.org/10.1038/nbt.3519

• If using gget enrichr, please also cite:
  Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013; 128(14).

  Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, Ma'ayan A. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research. 2016; gkw377.

  Xie Z, Bailey A, Kuleshov MV, Clarke DJB., Evangelista JE, Jenkins SL, Lachmann A, Wojciechowicz ML, Kropiwnicki E, Jagodnik KM, Jeon M, & Ma’ayan A. Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. 2021. doi: 10.1002/cpz1.90.

• If using gget blast, please also cite:
  Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990 Oct 5;215(3):403-10. doi: 10.1016/S0022-2836(05)80360-2. PMID: 2231712.

• If using gget blat, please also cite:
  Kent WJ. BLAT--the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. doi: 10.1101/gr.229202. PMID: 11932250; PMCID: PMC187518.

• If using gget muscle, please also cite:
  Edgar RC (2021), MUSCLE v5 enables improved estimates of phylogenetic tree confidence by ensemble bootstrapping, bioRxiv 2021.06.20.449169. https://doi.org/10.1101/2021.06.20.449169.

• If using gget alphafold, please also cite:
  Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2

  And, if applicable:
  Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv 2021.10.04.463034; https://doi.org/10.1101/2021.10.04.463034

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Disclaimer

gget is only as accurate as the databases/servers/APIs it queries from. The accuracy or reliability of the data is not guaranteed or warranted in any way and the providers disclaim liability of any kind whatsoever, including, without limitation, liability for quality, performance, merchantability and fitness for a particular purpose arising out of the use, or inability to use the data.