- Introduction
- Getting Started
- Installation
- Setup
- Tips and FAQ
- Functions
- search_product
find a genome by ATCC® Product ID - search_text
find a genome(s) by string matching - download_assembly
download an assembly file - download_annotations
download an annotations file - download_metadata
pull the JSON metadata of a singular genome - download_all_genomes
pull the JSON metadata for ALL genomes - deep_search
search ALL genome metadata for a string or value (SUPPORTS FUZZY) - tabulate
convert a list of genome dictionaries to a formatted table
- search_product
- Cookbook
- Citation
- Contact Us
This repository serves as a collection of python scripts that can be used to access the ATCC® Genome Portal through the One Codex REST-API.
Accessing the ATCC® Genome Portal through the API provides extensively more data than can be seen on the ATCC® Genome Portal GUI. While One Codex documentation exists to detail the exact endpoints, this repo serves as prebuilt and formatted wrappers to navigate the REST API. All scripts were created and tested using Python version 3.9.
You will need:
-
An ATCC® Genome Portal Suporting Membership
- As of May 1, 2024, a supporting membership is required to access the REST API
- To purchase a suporting membership, learn more here
-
An API Key. This is required for all scripts.
- A One Codex account at https://app.onecodex.com/ and an account on https://genomes.atcc.org is required to obtain an API Key.
- Log in or create an account on https://app.onecodex.com/ and https://genomes.atcc.org
- Proceed to https://genomes.atcc.org/profile
- Click on “Copy API Key”
-
Python 3.9 or higher.
- Scripts were tested on python 3.9, but use of earlier requirements (v3.7+) should not prohibit usage of the API and functionality.
conda create -n "genome_portal_api" python=3.9
conda activate genome_portal_api
git clone https://github.com/ATCC-Bioinformatics/genome_portal_api.git
pip install /path/to/downloaded/genome_portal_api_folder
python -m venv env
source env/bin/activate
git clone https://github.com/ATCC-Bioinformatics/genome_portal_api.git
pip install /path/to/downloaded/genome_portal_api_folder
Activate your environment, load in all the functions, and get started!
conda activate genome_portal_api
python
# The following code is run from within a python session
from genome_portal_api import * #Loads up all functions of the API
If you have the global_api_key variable set in your environment, you do not need to provide an api_key argument to any function. To verify your API key is set, call the function get_global_apikey().
If this is your first time using the API, we advise to set and export your API key under the global variable ATCC_GENOME_PORTAL_API_KEY in your .bashrc file.
Otherwise, we recommend setting your api_key globally on script startup. This can be done in the following ways:
Best Option:
The API key is added to your .bashrc under variable ATCC_GENOME_PORTAL_API_KEY, and will be automatically detected.
(ex. EXPORT ATCC_GENOME_PORTAL_API_KEY="yourapikeygoeshere")
>>> set_global_api()
2024-07-29 14:20:08,021 - INFO - API key has been found: "yourapikeygoeshere"
Option 2 - "Dedicated manual setting / override"
The API key is provided as an argument to set_global_api(), or is provided as USER input.
If you already have ATCC_GENOME_PORTAL_API_KEY exported, you will need to provide an API key to overwrite.
Will overwrite ANY set API key.
## Manual set OR overwrite any set API key
>>> set_global_api(api_key="testing")
2024-07-29 14:24:13,860 - INFO - API key is now set: 'testing'. Please export this api_key under the variable 'ATCC_GENOME_PORTAL_API_KEY'!
## No API key set, nor exported in the OS environment. Requires USER response
>>> set_global_api()
Please enter the API key: 'testing'
2024-07-29 14:24:13,860 - INFO - API key is now set: 'testing. Please export this api_key under the variable 'ATCC_GENOME_PORTAL_API_KEY'!
Option 3 - "Set as you go; Quickstart"
In this case, you just begin using the API functions at full speed and don't care to set it by a dedicated call. If no API key is set, the functions will automatically run set_global_api().
If you have an API key being exported, it will be found, otherwise it will require user input to set! Here is an example of both:
## No API key found nor exported from .bashrc, requires USER response.
>>> search_product(product_id='TSD-364')
Please enter the API key: 'testing'
2024-07-29 14:24:13,860 - INFO - API key is now set: 'testing. Please export this api_key under the variable 'ATCC_GENOME_PORTAL_API_KEY'!
['ATCC TSD-364': '4a3251f108a044c3']
## Exported OS environment API key found, and is set on first run
>>> search_product(product_id='TSD-364')
2024-07-31 13:34:56,064 - INFO - API key has been found: testing
['ATCC TSD-364': '4a3251f108a044c3']
Option 4 - "Provide a different API key during to a bigger function"
For this option, maybe you wanted to use a different API key during a function call. An example reason why may be to test your groups' API keys, or to troublehsoot if your API key is not activated.
In this case, we are purposely providing a fault API key to demonstrate the return messaging. PROVIDING
>>> search_product(product_id='TSD-364',api_key='badapikey')
2024-07-29 19:40:57,305 - CRITICAL - API access to the ATCC® Genome Portal requires a supporting membership. Please visit https://genomes.atcc.org/plans to subscribe.
Click to Expand
Absolutely not! The recommended way is to set your API key in the global variable ATCC_GENOME_PORTAL_API_KEY. This can be written into and explicitly exported from your .bashrc file. At anytime, you can also overwrite your API Key from within a function by providing it as an argument, making it avalailable for all functions. Please look above to section "Setting a global api key" to learn how to set an API key in more detail.
set_global_api(api_key='yourapikey')
Simply put, whichever is best for you! For most functions, there is a choice of final output type. The output types are broken down here:
Personally this is our favorite output mode. In this mode, a pandas dataframe is returned as the output of the function. Each row is a unique different genome/assembly, and the JSON metadata is meaningfully auto-formatted and named in a human-readable way! We are continually improving the captured and reported metadata, so keep checking back for more updates! We advise using the table output type if you need a human-readable format.
This is like legendary mode...There is much more data behind each genome, but reporting on it for most use-cases is challenging for brevity. We are working on a wiki to explain each metadata field in the JSON, but feel free to raise an issue if you need help! We advise using the json output type if you need access to ALL the data.
Bread and butter! One of the most-used cases in the past API behavior, this output is strictly a list of IDs, so that each list entry is the ATCC® catalog number followed by the genomeID. ex( ['ATCC 10536:996d977f03724ce6', ...]). We advise using the id output type if you only need the ATCC® catalog numbers and their respective genome IDs.
When this happens, please run the function get_global_metadata(). This will output the variable to stdOUT, but is also saving it to a the variable global_genome_metadata. Running this should work:
global_genome_metadata=get_global_metadata()
The search_product() function is intended to give an exact match to an ATCC® Catalog number / product ID.
The product id used in this function must be an exact match to return results. Expected output of this function is a single item list, signifying the matched genome for that catalog number.
This function mimics the GUI ability of using the "Search for a genome" bar on the ATCC® Genome Portal set to the "Catalog Number" filter.
Usage
search_product() is intended for EXACT matching of ATCC® product numbers.
This mimics the function of using the search bar on the ATCC® Genome Portal set to CATALOG NUMBER
--------- USAGE ---------
Required arguments:
product_id = <str>
An ATCC® product_id (ex. "BAA-2889")
Optional arguments:
output = <str>
The API response output format [(id) | json | table]
api_key = <str>
Your Genome Portal APIKey [default looks for global_api_key]
EXAMPLES:
> search_product(product_id='35638') Returns resulting Genome ID for ATCC® 35638
> search_product(product_id='35638', api_key='apikey') Same as above, but this time with a new apikey
> search_product(product_id='35638', output='json') Return resulting JSON metadata for ATCC® 35638
> search_product(product_id='35638', output='table') Return resulting JSON metadata for ATCC® 35638 in a table format
Advanced
Example:
product_metadata = search_product(product_id="BAA-335",output="json")
product_metadata
product_metadata holds the following dictionary:
["attributes": {
"atcc_metadata": {
"amr_intermediate": [],
"amr_resistant": [],
"amr_susceptible": [],
"antibiotic_resistance": null,
"antigenic_prop": null,
"bsl": 2,
"catalog_number": "BAA-335",
"drug_repository": null,
"genotype": null,
"gold": null,
"isolation_new_web": "Infection",
"notes": null,
"other_metadata": {
"catalog_details": {
"ATCC_catalog_number": "ATCC BAA-335",
"ATCC_lot_number": "70055783"
},
"genome_provider": {
"annotatated_by": "ATCC",
"annotations_date": "04/12/2024 05:10:20",
"annotations_software": "PGAP",
"asssembled_by": "One Codex",
"asssembler_software": "Illumina + Oxford Nanopore Unicycler Hybrid Assembly",
"asssembly_date": "12/08/22 00:14:05"
},
},
"collection_name": "bacteriology",
"created_at": "2020-02-26T18:58:47.303264+00:00",
"description": null,
"id": "261b0e41db924d0f",
"name": "ATCC BAA-335",
'preferred_taxonomy_name': None,
'product_id': 'BAA-335',
'product_url': 'https://www.atcc.org/Products/All/BAA-335',
'taxon_name': 'Neisseria meningitidis'}}]
... "Intentionally shortened for brevity"
Example:
search_product_table = search_product(product_id="BAA-335",output="table")
search_product_table is a human-readable dataframe. Each result is populated as a single row, and the relevant JSON metadata is automatically pulled and formatted under each column!
atcc_product_id name taxid genome_id assembly_id collection isolation_new_web biosafety_level notes
0 BAA-335 Neisseria meningitidis 487 261b0e41db924d0f 62a2a65ba33541af ATCC Bacteriology Infection 2 None
The search_text() function can be used to find assemblies and/or associated metadata for genomes that match a search term.
This function mimics default behavior of the "Search for a genome" bar on the ATCC® Genome Portal. The search term can either be a full- or sub-string of an organism name, or an exact match of the ATCC® catalog number as a character string.
The default output of this function is a list of ids formatted like ['ATCC CatalogNo:GenomeID', ... ].
To test out what search terms might help, first try using the "Search for a genome" bar on the ATCC® Genome Portal.
Usage
search_text() is intended for exact string matching or substring matching on taxonomic names. This will also capture partial matching of ATCC® product numbers.
This mimics the function of using the search bar default on the ATCC® Genome Portal, but DOESNOT FULLY SUPPORT FUZZY MATCHING
--------- USAGE ---------
Required arguments:
text = <str>
A free text field to search by (ex. "Salmonella enterica")
Optional arguments:
output = <str>
The API response format "output" [(id) | json | table]
api_key = <str>
Your Genome Portal APIKey [Globally set "global_api_key" or entered "api_key"]
EXAMPLES:
> search_text(text='coli') return resulting Genome IDs that match that string
> search_text(text='coli',output="table") return resulting metadata in TABULAR output for genomes that contain the input text string
> metadata = search_text(api_key='YOUR_API_KEY',text='coli',output="table") Same as above, but with a new API Key
> search_text(text='coli', output="json") return resulting metadata in JSON format for genomes that contain the input text string
Advanced
For the example below, any of the following search terms could have been used to produce a list which contained Escherichia coli: "Escherichia", "Esch", "coli", "richia", or "35401".Example:
search_text_results=search_text(text="coli",output="table")
search_text_results is a human-readable dataframe. Each search result is populated as a new row, and the relevant JSON metadata is automatically pulled and formatted under each column!
atcc_product_id name taxid ... isolation_new_web biosafety_level notes
0 35401 Escherichia coli 562 ... Feces 2 None
1 700928 Escherichia coli 562 ... Blood and urine from woman with acute pyelonep... 2 None
2 27729 Yersinia enterocolitica subsp. enterocolitica 150052 ... Blood; isolated November 7, 1972 2 None
3 8739 Escherichia coli 562 ... Feces 1 None
4 23715 Yersinia enterocolitica subsp. enterocolitica 150052 ... Blood, petechiae, from anterior chamber of eye 2 None
.. ... ... ... ... ... ... ...
296 49920 Mycobacterium confluentis 28047 ... Sputum from healthy male 2 None
297 BAA-3232 Escherichia coli 562 ... None 1 None
298 BAA-3237 Escherichia coli 562 ... Red clover sprout 2 None
299 23014 Mycobacterium vaccae 1810 ... Cattle 2 None
300 51798 Campylobacter coli 195 ... Terminal ileum of pig with proliferative enter... 2 None
Example:
search_text_results=search_text(text="coli",output="json")
search_text_results is a list of dictionary objects. The first element search_text_results[0] is:
{"attributes": {
"atcc_metadata": {
"amr_intermediate": [],
"amr_resistant": [],
"amr_susceptible": [],
"antibiotic_resistance": null,
"antigenic_prop": null,
"bsl": 2,
"catalog_number": "35401",
"drug_repository": null,
"genotype": null,
"gold": true,
"isolation_new_web": "Feces",
"notes": null,
"other_metadata": {
"catalog_details": {
"ATCC_catalog_number": "ATCC 35401",
"ATCC_lot_number": "57972906"
},
"genome_provider": {
"annotatated_by": "ATCC",
"annotations_date": "04/08/2024 13:35:34",
"annotations_software": "PGAP",
"asssembled_by": "One Codex",
"asssembler_software": "Illumina + Oxford Nanopore Unicycler Hybrid Assembly",
"asssembly_date": "06/17/19 18:38:25"
},
"genome_stats": {
"filtered_contig_count": 6,
"filtered_contig_length": 5393109,
"number_of_n_bases": 0
},
"illumina_metadata": {
"barcoding_kit": "Nextera XT Index Kit (96 Indexes 384 Samples)",
"basecaller_model": "MiSeq Reporter Software",
"basecaller_version": "2.6.2.3",
"library_kit": "Nextera XT",
"sequencer": "Illumina MiSeq"
},
"ont_metadata": {
"barcoding_kit": "EXP-NBD103",
"basecaller_model": "albacore",
"basecaller_version": "2.3.4",
"flowcell_type": "FLO-MIN106D",
"library_kit": "SQK-LSK109",
"sequencer": "Oxford Nanopore MinION"}
}
"id": "07905137c2314f3f",
"visibility": "public"
},
"product_id": "35401",
"product_url": "https://www.atcc.org/Products/All/35401",
"taxon_id": "562",
"taxon_name": "Escherichia coli"
}}
... "Intentionally shortened for brevity"
Specific values from each dictionary can be accessed as follows:
[(e['taxon_name'],e['attributes']['atcc_metadata']['catalog_number']) for e in search_text_results]
DEFAULT MODE ID output:
>>> search_text_results=search_text(text="coli")
>>>
Output:
['ATCC 35401:50cf24c55cc943c6', 'ATCC 700928:bad734779799489e', 'ATCC 27729:099e5acebc284d19', 'ATCC 8739:b9d91f150db449de', 'ATCC 23715:7df0c95fcc1f4b9a', 'ATCC 10536:996d977f03724ce6', 'ATCC 13706:c8c61b38f71249fe', 'ATCC 35218:92c302c2e2f34245', 'ATCC BAA-1061:82f4cdb84df547e8', 'ATCC BAA-2775:14440a65caf249d2', 'ATCC BAA-2776:8fbfddd1b7d54dea', 'ATCC BAA-2777:ca654a1656c9442e', 'ATCC BAA-2779:2dff3bccaa724f0b', 'ATCC BAA-2781:5c691565033045d5', 'ATCC 35638:0e56bc78536746d0', 'ATCC BAA-2774:d13a73ffd124484e', 'ATCC BAA-1429:a374dd646c7f42b2', 'ATCC BAA-1431:0a8241dfa0aa49b9', 'ATCC BAA-2523:0553bec8639740aa', ... ]
The download_assembly() function is the only function that can query and download the fasta assembly of an item on the ATCC® Genome Portal.
This function takes an genome id as input to either download an assembly directly to a filepath, or store an assembly as a dictionary.
Usage
download_assembly() is function to download the fasta assemblies on the ATCC® Genome Portal. The genomes files can be downloaded, or output directly to stdout.
--------- USAGE ---------
Required arguments:
id = <str>
An ATCC® Genome ID (https://genomes.atcc.org/genome<genomeid>)
Optional arguments:
output = <str>
The API response format "output" [ (dict) | fasta ]
download_dir = [Path <str>]
A directory to download the fasta file to. The fasta file will be named automatically.
api_key = <str>
Your Genome Portal APIKey [(global_api_key) | overwrite if provided ]
EXAMPLES:
> download_assembly(id='assemblyid', output='fasta', download_dir="/directory/for/download/") downloads an assembly file to provided path
> download_assembly(id='assemblyid', output='dict') return a dictionary of the assembly. [Key=Header : Value=Seq].
Advanced
download_assembly(id='8df308b788704bed',output='fasta',download_dir="/path/to/folder")
This will download the fasta file directly to the folder provided for the "download_assembly_path" argument
assembly=download_assembly(id='8df308b788704bed',output="dict")
for contig in assembly:
print(contig)
print(assembly[contig][0:200])
assembly is a dictionary where each key is the contig header and each value is the contig sequence. The output from above (200 bases extended from each contig) is shown below:
>128666ac42774942_1 assembly_id="128666ac42774942" genome_id="8df308b788704bed" atcc_catalog_number="ATCC BAA-2481" species="Liberibacter crescens" contig_number="1" topology="circular"
TTTTTCCTTCTGCTCATGTCTATTTTATGGAAAATAAAGGTCGTGATATAAAGCCTTTTTTGACTTTGCTTGAATCTGGGAAACTCGATCAGTATGATTATATTTGCAAGATTCATGGCAAGGAGTCGAGACATCAAAAGCGTTCTCCGATTGAAGGAACCTTATGGAGACGTTGGTTATTTTATGATCTTCTTGGAGCA
download_annotations() is the only function that can query and download the annotations of an item on the ATCC® Genome Portal via GenBank format.
Similar to download_assembly(), an assembly id is required for the download_annotations(), which can be used to obtain the annotations to stdOUT or save as a file directly. The annotations are in .gbk filetype.
Usage
download_annotations() is a function to download the fasta assemblies on the ATCC® Genome Portal.
The annotation files can be downloaded, or output directly to stdout.
-------- USAGE ---------
Required arguments:
id = <str>
An ATCC® Genome ID (https://genomes.atcc.org/genomes/<genomeid>)
Optional arguments:
output = <str>
The API response format "output" [ (dict) | gbk ]
download_dir = [Path <str>]
A directory to download the GenBank files to. The file will be named automatically.
api_key = <str>
Your Genome Portal APIKey [(global_api_key) | overwrite if provided ]
EXAMPLES:
> download_annotations(id='genomeid', output='gbk', download_dir='/directory/for/download/') downloads a GenBank file to provided path
> download_annotations(id='genomeid', output='dict') returns the raw genbank file
Advanced
>>> download_annotations(id='8df308b788704bed', output="gbk", download_dir="/path/to/folder")
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 3537k 100 3537k 0 0 5733k 0 --:--:-- --:--:-- --:--:-- 5724k
SUCCESS! File: /path/to/folder/Liberibacter_crescens_ATCC_BAA_2481.gbk now exists!
The above will directly download a GenBank file to your provided path.
annotations=download_annotations(id='8df308b788704bed',output='dict')
for line in annotations.split("\n"):
print(line)
The output from the above code block prints each line of the .gbk file.
LOCUS assembly_128666ac42774942_1 1513871 bp DNA circular BCT 30-APR-2024
DEFINITION Liberibacter crescens ATCC® BAA-2481™, contig 1.
ACCESSION assembly_128666ac42774942_1
VERSION assembly_128666ac42774942_1
DBLINK assembly: 128666ac42774942
annotation_set: a000cf31c34f4e43
genome: 8df308b788704bed
KEYWORDS .
SOURCE https://genomes.atcc.org/genomes/8df308b788704bed
ORGANISM Liberibacter crescens
cellular organisms; Bacteria; Pseudomonadota; Alphaproteobacteria;
Hyphomicrobiales; Rhizobiaceae; Liberibacter; Liberibacter crescens.
COMMENT ##Genome-Annotation-Data-START##
Annotation Provider :: ATCC Sequencing & Bioinformatics Center
Annotation Date :: 04/29/2024 16:44:51
Annotation Pipeline :: NCBI Prokaryotic Genome Annotation Pipeline
(PGAP)
Annotation Method :: Best-placed reference protein set; GeneMarkS-2+
Annotation Software revision :: 2022-12-13.build6494
ATCC Item Catalog :: ATCC BAA-2481
ATCC Item Lot Number :: 70048410
ATCC Item Name :: Liberibacter crescens
ATCC Collection :: Bacteriology
Features Annotated :: Gene; CDS; rRNA; tRNA; ncRNA
Genes (total) :: 1,386
CDSs (total) :: 1,327
Genes (coding) :: 1,305
CDSs (with protein) :: 1,305
Genes (RNA) :: 59
rRNAs :: 3, 3, 3 (5S, 16S, 23S)
complete rRNAs :: 3, 3, 3 (5S, 16S, 23S)
tRNAs :: 46
ncRNAs :: 4
Pseudo Genes (total) :: 22
CDSs (without protein) :: 22
Pseudo Genes (ambiguous residues) :: 0 of 22
Pseudo Genes (frameshifted) :: 3 of 22
Pseudo Genes (incomplete) :: 19 of 22
Pseudo Genes (internal stop) :: 3 of 22
Pseudo Genes (multiple problems) :: 2 of 22
##Genome-Annotation-Data-END##
The annotation was added by the assembly submitters using the NCBI
Prokaryotic Genome Annotation Pipeline (PGAP). Information about
stand-alone PGAP can be found here: https://github.com/ncbi/pgap/
FEATURES Location/Qualifiers
source 1..1513871
/organism="Liberibacter crescens"
/mol_type="genomic DNA"
/strain="BAA-2481"
/db_xref="taxon:1273132"
gene join(1513283..1513871,1..587)
/locus_tag="pgap_annot_000001"
CDS join(1513283..1513871,1..587)
/locus_tag="pgap_annot_000001"
/inference="COORDINATES: ab initio prediction:GeneMarkS-2+"
/note="Derived by automated computational analysis using
gene prediction method: GeneMarkS-2+."
/codon_start=1
/transl_table=11
/product="hypothetical protein"
/protein_id="extdb:pgap_annot_000001"
...
...
... "Intentionally Shortened for Readability"
download_metadata() is a function to download the full metadata of a genome on the portal.
This function is used to obtain the detailed metadata including qc statistics, contig length(s), etc. for a specific genome id.
Usage
download_metadata() is a function intended to download the JSON metadata behind each assembly on the portal.
The metadata can be output as a dictionary, or as an informative table with the correct fields already formatted.
--------- USAGE ---------
Required arguments:
id = <str>
An ATCC® Genome ID (https://genomes.atcc.org/genomes/<genomeid>)
Optional arguments:
output = <str>
The API response format "output" [ (dict) | table ]
api_key = <str>
Your Genome Portal APIKey [(global_api_key) | overwrite if provided ]
Advanced
The detailed metadata can be downloaded as follows:
>>> assembly_metadata = download_metadata(id='8df308b788704bed')
>>> assembly_metadata
assembly_metadata is a dictionary:
{"attributes": {
"atcc_metadata": {
"amr_intermediate": [],
"amr_resistant": [],
"amr_susceptible": [],
"antigenic_prop": null,
"bsl": 1,
"catalog_number": "BAA-2481",
"genotype": null,
"gold": true,
"isolation_new_web": "Cultivated mountain papaya (Babaco)",
"notes": null,
"other_metadata": {
"catalog_details": {
"ATCC_catalog_number": "ATCC BAA-2481",
"ATCC_lot_number": "70048410"
},
"genome_provider": {
"annotatated_by": "ATCC",
"annotations_date": "04/29/2024 16:44:51",
"annotations_software": "PGAP",
"asssembled_by": "One Codex",
"asssembler_software": "Illumina + Oxford Nanopore Unicycler Hybrid Assembly",
"asssembly_date": "08/12/23 12:35:01"
},
"genome_stats": {
"filtered_contig_count": 1,
"filtered_contig_length": 1513871,
"number_of_n_bases": 0
},
"illumina_metadata": {
"barcoding_kit": "IDTIlmnDNARNAUDISetATagmentation",
"basecaller_model": "NextSeq 1000/2000 Control Software",
"basecaller_version": "1.5.0.42699",
"library_kit": "IlluminaDNAPrep",
"sequencer": "Illumina NextSeq"
},
"ont_metadata": {
"barcoding_kit": "EXP-NBD196",
"basecaller_model": "HAC",
"basecaller_version": "6.4.6+ae70e8f",
"flowcell_type": "FLO-MIN106",
"library_kit": "SQK-LSK109",
"sequencer": "Oxford Nanopore GridION X5"
...
"Intentionally Shortened For Example"
...
download_all_genomes() is a function that allows the user to download all genomic JSON metadata available on https://genomes.atcc.org.
If you are using functions like deep_search(), this is not needed to run manually.
This can be ran without prior knowledge of any associated metadata.
This function defaults to storing all genomes to the variable global_genome_metadata as well as piping to stdOUT for variable storage. You may need to run the following line to extract this global variable
global_genome_metadata=get_global_metadata()
Usage
download_all_genomes() is a function intended to download the JSON metadata for ALL ATCC® genomes currently available on the ATCC® Genome Portal.
This function will store the results as the variable "global_genome_metadata", and will also report to stdOUT.
--------- USAGE ---------
Required arguments: NONE
Optional arguments:
api_key = <str>
Your Genome Portal APIKey [(global_api_key) | overwrite if provided ] \n
EXAMPLES:
> download_all_genomes() downloads all genomes as a list of JSON dictionaries to the variable "global_genome_metadata".
> secondary_var=download_all_genomes() downloads all genomes as a list of JSON dictionaries to the variable "global_genome_metadata" AND "secondary_var".
Advanced
Running the below code will generate a list of every genome's metadata. Because the output was assigned to a variable, it will store as the assigned variable genomes as well as global_genome_metadata by default.
genomes=download_all_genomes()
This will output a stored variable genomes. This object is a list of dictionaries representing all genomes available in the portal. This will also print a message about the collection:
Fetched 4,750 genomes
genomes visibility='public': 4,750
This list can then be leveraged into download_assembly or download_annotations in order to download each item to file. Each genome's ID can be found in the "id" keys of each dictionary:
>>> genomes[0]['id']
'21846cfe916b4f18'
Alternatively, you can create a secondary list of these genomes, now indexed by genomeid:
>>> genomes_named=convert_to_genomeid(list=genomes)
>>> genomes_named['21846cfe916b4f18']['id']
'21846cfe916b4f18'
Instead of a list of all genomes, one can convert the collection into a table like so!
If you wanted to do this from the global variable global_genome_metadata, you may need to run this first.
global_genome_metadata=get_global_metadata()
Otherwise, you can tabulate your list from the example above, or any list of genomic metadata like this:
final_table=tabulate(genomes)
-or-
final_table=tabulate(global_genome_metadata)
deep_search() allows the user to search for a term in a list of genome JSON metadata. The function searches through every key and value in the metadata nested dictionary. By default, it runs in 'text' mode, where the term is searched within the entire JSON as a string. 'json' mode enables full text matching as either a key or dict value.
This function also supports fuzzy matching with the search term. By adding fuzz_on=value to the command, the 'value' becomes the fuzzy ratio value.
To use this function, you must have downloaded all genomic metadata using download_all_genomes() or must assign your personal list to the variable global_genome_metadata.
Usage
deep_search() is intended for deep searching of ATCC® JSON metadata behind each genome.
THIS FUNCTION SUPPORTS EXACT KEY|VALUE|STRING MATCHING, WITH "text" METHOD CAPTURE SUBSTRINGS.
There is currently no similar function on the ATCC® Genome Portal.
This function will search through the JSON metadata of all available genomes for matching strings or values.
--------- USAGE ---------
Required arguments:
text = <str>
any text field caputured within the JSON of the portal.
-- a list of genomes (Globally set variable "global_genome_metadata")
Optional arguments:
fuzz_on = <str>
If provided with a value, enables fuzzy matching [(75) | value 0-100]
output = <str>
The API response format "output" [(id) | json | table]
mode = <str>
Choice to search based on a dictionary structure or just raw text [(text) | json]
api_key = <str>
Your Genome Portal APIKey [Globally set "global_api_key"or entered "api_key"]
STORE global_genome_metadata
A list of genomes can be set as "global_genome_metadata" variable. Not needed as an argument (Globally set variable "global_genome_metadata")
EXAMPLES:
> deep_search(text="PGAP") will return a list of genomeIDs that had "PGAP" somewhere in the JSON
> deep_search(text="Lake", output="id") return resulting assembly IDs that contain "Lake"in the metadata
> deep_search(text="Lake", output="id", fuzz_on='75') Same as the previous command, but will also output a fuzzy score to lake
> deep_search(mode='json',text="Lake", output="table") Same as the previous command, but with an included manually entered API Key and output as an informational table.
> deep_search(api_key="<apikey>",text="Lake",output="table",fuzz_on='75') Same as the previous command, but now fuzzy match to '75'
Advanced
For example, searching for the term "yursinia" (a misspelling of yersinia) works as follows:
match_list=deep_search(text="yursinia",fuzz_on='50',output='json')
[e['taxon_name'] for e in match_list]
match_list is a list of dictionaries. The taxon_name for each element output from the above code is listed below:
['Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia pestis', 'Yersinia ruckeri', 'Yersinia pestis', 'Human rhinovirus 35', 'Phytobacter ursingii', 'Yersinia rohdei', 'Yersinia aldovae', 'Yersinia rohdei', 'Yersinia rohdei', 'Yersinia rohdei', 'Yersinia aldovae', 'Yersinia ruckeri', 'Bovine respiratory syncytial virus', 'Yersinia aldovae', 'Yersinia aldovae', 'Yersinia rohdei', 'Phytobacter ursingii', 'Yersinia sp.', ... ]
Example:
>>> search_results = deep_search(text="'bsl': 2",output='table')
Which returns a tabular output of all genomes that have a BSL level of 2 in the metadata!:
>>> search_results
atcc_product_id name taxid ... isolation_new_web biosafety_level notes
0 700824 Helicobacter pylori 210 ... Patient with a duodenal ulcer 2 None
1 700294 Streptococcus pyogenes 1314 ... Infected wound 2 None
2 19606 Acinetobacter baumannii 470 ... Urine 2 None
3 BAA-1605 Acinetobacter baumannii 470 ... Sputum of military personnel returning from Af... 2 None
4 49882 Bartonella henselae 38323 ... Blood of HIV-positive male 2 None
... ... ... ... ... ... ... ...
2623 VR-240 Human echovirus 6 12062 ... Child with aseptic meningitis. 2 None
2624 VR-47 Human echovirus 17 47505 ... Rectal swab from healthy child. 2 None
2625 VR-583 Human Coxsackievirus A 24 variant 12089 ... Isolated from infant with diarrhea. 2 None
2626 VR-645 Adeno-associated virus 1 85106 ... Pool of RhMk TC infected with SV-15 virus. 2 None
2627 VR-692 Human echovirus 30 41846 ... None 2 None
[2628 rows x 44 columns]
tabulate() is a function that is intended to reformat your list of genome metadata into a table.
This function allows the user to input a genome metadata list, and return a formatted dataframe that has pre-converted the JSON values into important columns.
Usage
tabulate() is a helper function used to convert a list of JSON-formatted metadata into a dataframe.
This function then calls on "format_qc()" to pull relevant metadata fields as dedicated columns.
--------- USAGE ---------
Required arguments:
genome_list = [list]
This is not a kwarg, and is just the object of the function to be called. You must pass a list of genomic metadata.
Advanced
Example:
>>> global_genome_metadata_table = tabulate(global_genome_metadata) # Covert the global list of all genomes into a table
By providing a list of genomes, tabulate() will then convert the inputed listed to stdOUT. Redirecting to global_genome_metadata_table provides the following output.
>>> global_genome_metadata_table
atcc_product_id name taxid ... isolation_new_web biosafety_level notes
0 700824 Helicobacter pylori 210 ... Patient with a duodenal ulcer 2.0 None
1 4357 Lactobacillus acidophilus 1579 ... None 1.0 None
2 700294 Streptococcus pyogenes 1314 ... Infected wound 2.0 None
3 700610 Streptococcus mutans 1309 ... Child with active caries 1.0 None
4 19606 Acinetobacter baumannii 470 ... Urine 2.0 None
... ... ... ... ... ... ... ...
4885 VR-583 Human Coxsackievirus A 24 variant 12089 ... Isolated from infant with diarrhea. 2.0 None
4886 VR-645 Adeno-associated virus 1 85106 ... Pool of RhMk TC infected with SV-15 virus. 2.0 None
4887 VR-692 Human echovirus 30 41846 ... None 2.0 None
4888 VR-74 Japanese encephalitis virus 11072 ... Spinal fluid from a fatally infected 6-year-ol... 3.0 None
4889 TSD-366 Methanoglobus nevadensis 1872626 ... Hot springs by Gerlach, Nevada (N40° 39.684′ W... 1.0 None
[4890 rows x 44 columns]
Click to view cookbook
First, we search for all Escherichia coli using search_text(). Then we iterate through the results list, create a dictionary entry for each assembly, and then download and store the assembly, annotations, and metadata. The first 3 assemblies are downloaded below.
search_text_results=search_text(text="Escherichia coli",output='json')
e_coli_data = {}
# Download assembly, annotations, and metadata for first 3
for e in search_text_results[:3]:
id = e['id']
e_coli_data[id] = {}
e_coli_data[id]["assembly"] = download_assembly(id=e['id'],output='dict')
e_coli_data[id]["annotations"] = download_annotations(id=e['id'],output='dict')
e_coli_data[id]["metadata"] = download_metadata(id=e['id'])
# Print the data from each of the assemblies
for id in e_coli_data.keys():
print("First 150 nts of each contig")
for contig in e_coli_data[id]["assembly"].keys():
print(contig)
print(e_coli_data[id]["assembly"][contig][0:150])
print("\nFirst 10 lines of the annotation data:")
for line in e_coli_data[id]["annotations"].split("\n")[:10]:
print(line)
print("\nSome example metadata:")
print("contig_lengths:",e_coli_data[id]["metadata"]["primary_assembly"]["attributes"]["contig_lengths"])
print("checkm_results:",e_coli_data[id]["metadata"]["primary_assembly"]["attributes"]["qc_statistics"]["checkm_results"])
break
Output:
>>> for id in e_coli_data.keys():
... print("First 150 nts of each contig")
... for contig in e_coli_data[id]["assembly"].keys():
... print(contig)
... print(e_coli_data[id]["assembly"][contig][0:150])
... print("\nFirst 10 lines of the annotation data:")
... for line in e_coli_data[id]["annotations"].split("\n")[:10]:
... print(line)
... print("\nSome example metadata:")
... print("contig_lengths:",e_coli_data[id]["metadata"]["primary_assembly"]["attributes"]["contig_lengths"])
... print("checkm_results:",e_coli_data[id]["metadata"]["primary_assembly"]["attributes"]["qc_statistics"]["checkm_results"])
... break
...
First 150 nts of each contig
>07905137c2314f3f_1 assembly_id="07905137c2314f3f" genome_id="50cf24c55cc943c6" atcc_catalog_number="ATCC 35401" species="Escherichia coli" contig_number="1" topology="circular"
GTGTCACTTTCGCTTTGGCAGCAGTGTCTTGCCCGATTGCAGGATGAGTTACCAGCCACAGAATTCAGTATGTGGATACGCCCATTGCAGGCGGAACTGAGCGATAACACGCTGGCCCTGTACGCGCCAAACCGTTTTGTCCTCGATTGG
>07905137c2314f3f_2 assembly_id="07905137c2314f3f" genome_id="50cf24c55cc943c6" atcc_catalog_number="ATCC 35401" species="Escherichia coli" contig_number="2" topology="circular"
GTGACTGATCTTCAACAAACGTATTACCGCCAGGTAAAGAACCCGAATCCGGTGTTTACACCCCGTGAAGGTGCCGGAACGCTGAAGTTCTGCGAAAAACTGATGGAAAAGGCGGTGGGCTTCACTTCTCGTTTTGATTTCGCCATTCAT
>07905137c2314f3f_3 assembly_id="07905137c2314f3f" genome_id="50cf24c55cc943c6" atcc_catalog_number="ATCC 35401" species="Escherichia coli" contig_number="3" topology="circular"
GTGACTGATCTTCAACAAACGTATTACCGCCAGGTAAAGAACCCGAATCCGGTGTTTACACCCCGTAAAGGTGCCGGAACGCTGAAGTTCTGCGAAAAACTGATGGAAAAGGCGGTGGGCTTCACTTCCCGTTTTGATTTCGCCATTCAT
>07905137c2314f3f_4 assembly_id="07905137c2314f3f" genome_id="50cf24c55cc943c6" atcc_catalog_number="ATCC 35401" species="Escherichia coli" contig_number="4" topology="circular"
GTGACTGATCTTCAACAAACGTATTACCGCCAGGTAAAGAACCCGAATCCGGTGTTTACACCCCGTAAAGGTGCCGGAACGCTGAAGTTCTGCGAAAAACTGATGGAAAAGGCGGTGGGCTTCACTTCCCGTTTTGATTTCGCCATTCAT
>07905137c2314f3f_5 assembly_id="07905137c2314f3f" genome_id="50cf24c55cc943c6" atcc_catalog_number="ATCC 35401" species="Escherichia coli" contig_number="5" topology="circular"
TTCCTCAGGGATGAAAAAGGTAATATACATTGAACCTTTTGATAAAAGCTTAGCTTTAGATTTACATGACGATGCAATAACCACCACAGAAGACCCGTCGAGAGTTATTTTCTCGAAATTTGAGGGTGTTGCACCAAGAAGATACAATAA
>07905137c2314f3f_6 assembly_id="07905137c2314f3f" genome_id="50cf24c55cc943c6" atcc_catalog_number="ATCC 35401" species="Escherichia coli" contig_number="6" topology="circular"
ATCTATGCTGAGCGGGAAGGTAATGGGTTGCTTGTGAGAGTAGATCTGAGAACGTGCTATCTGAATCCAGGTCAAGCTGAAATGTTTAATATAGCTATTGGATATACGCGTTCAGTTCACGGGAATCATCTATAAGCTGTCTACAAAAAG
First 10 lines of the annotation data:
LOCUS assembly_07905137c2314f3f_1 5152467 bp DNA circular BCT 17-APR-2024
DEFINITION Escherichia coli ATCC® 35401™, contig 1.
ACCESSION assembly_07905137c2314f3f_1
VERSION assembly_07905137c2314f3f_1
DBLINK assembly: 07905137c2314f3f
annotation_set: 7966cc374f2a4a3a
genome: 50cf24c55cc943c6
KEYWORDS .
SOURCE https://genomes.atcc.org/genomes/50cf24c55cc943c6
ORGANISM Escherichia coli
Some example metadata:
contig_lengths: [5152467.0, 94797.0, 67898.0, 66609.0, 5800.0, 5538.0]
checkm_results: {'completeness': 99.96693121693121, 'contamination': 0.4836309523809524}
First, we will have to set up a string to search on for deep_search()
## This search works because we explicitly quote the key and value for "bsl".
bsl_hits=deep_search(text="'bsl': 2", output='table')
## Now we have a table called "bsl_hits".
## Lets sort some ABX columns and pull the top 5
bsl_hits['amr_resistant_gene_count']=bsl_hits['amr_resistant'].apply(
lambda x: 0 if x is None
else len(x) if isinstance(x, list) and not (len(x) == 1 and '|' in x[0])
else len(x[0].split('|')) if isinstance(x, list) and len(x) == 1 and '|' in x[0]
else len(x.split(',')) if ',' in x
else len(x.split('|'))
)
Output:
>>> bsl_hits.sort_values(by='amr_resistant_gene_count',ascending=False)[0:5][['atcc_product_id','name','amr_resistant']]
atcc_product_id name amr_resistant
382 BAA-1799 Acinetobacter baumannii [Ampicillin-Sulbactam, Cefazolin, Cefepime, Ce...
456 BAA-196 Escherichia coli [Ampicillin, Ampicillin-Sulbactam, Aztreonam, ...
1800 BAA-1797 Acinetobacter baumannii [Cefazolin, Cefotaxime, Ceftazidime, Ceftriaxo...
1200 BAA-1794 Acinetobacter baumannii [Cefazolin, Cefotaxime, Ceftazidime, Ceftriaxo...
1202 BAA-3197 Pseudomonas aeruginosa [Cefazolin, Cefepime, Cefotaxime, Ceftazidime,...
Now, let's download these genbanks and annotations for each of the top 5 most ABX resistant BSL-2 items:
for i, row in bsl_hits_sorted.iterrows():
download_assembly(id=row['genome_id'], output='fasta',download_dir='/test/download_folder')
download_annotations(id=row['genome_id'], output='gbk',download_dir='/test/download_folder')
Output:
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 388 0 388 0 0 4172 0 --:--:-- --:--:-- --:--:-- 4217
SUCCESS! File: /test/download_dir/Pseudomonas_aeruginosa_ATCC_BAA_2114.fasta now exists!
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 14.2M 100 14.2M 0 0 47.7M 0 --:--:-- --:--:-- --:--:-- 47.8M
SUCCESS! File: /test/download_dir/Pseudomonas_aeruginosa_ATCC_BAA_2114.gbk now exists!
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 387 0 387 0 0 5528 0 --:--:-- --:--:-- --:--:-- 5528
SUCCESS! File: /test/download_dir/Klebsiella_pneumoniae_ATCC_BAA_2790.fasta now exists!
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 13.5M 100 13.5M 0 0 45.9M 0 --:--:-- --:--:-- --:--:-- 46.1M
SUCCESS! File: /test/download_dir/Klebsiella_pneumoniae_ATCC_BAA_2790.gbk now exists!
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 387 0 387 0 0 5229 0 --:--:-- --:--:-- --:--:-- 5301
SUCCESS! File: /test/download_dir/Klebsiella_pneumoniae_ATCC_BAA_2783.fasta now exists!
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 14.0M 100 14.0M 0 0 55.1M 0 --:--:-- --:--:-- --:--:-- 55.1M
SUCCESS! File: /test/download_dir/Klebsiella_pneumoniae_ATCC_BAA_2783.gbk now exists!
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 377 0 377 0 0 4333 0 --:--:-- --:--:-- --:--:-- 4333
SUCCESS! File: /test/download_dir/Serratia_sp_ATCC_BAA_2808.fasta now exists!
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 13.2M 100 13.2M 0 0 38.4M 0 --:--:-- --:--:-- --:--:-- 38.4M
SUCCESS! File: /test/download_dir/Serratia_sp_ATCC_BAA_2808.gbk now exists!
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 383 0 383 0 0 5471 0 --:--:-- --:--:-- --:--:-- 5471
SUCCESS! File: /test/download_dir/Proteus_mirabilis_ATCC_BAA_2791.fasta now exists!
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 10.0M 100 10.0M 0 0 51.1M 0 --:--:-- --:--:-- --:--:-- 51.4M
SUCCESS! File: /test/download_dir/Proteus_mirabilis_ATCC_BAA_2791.gbk now exists!
First, we use search_product to download the assembly metadata from which we pull out the assembly id. Then, we download the assembly, annotations, and metadata.
>>> search_products_results = search_product(product_id="700822",output='table')
>>> id = search_products_results.loc[0,'genome_id']
>>> assembly = download_assembly(id=id,output='dict')
>>> annotations = download_annotations(id=id,output='dict')
>>> metadata = download_metadata(id=id)
print("First 150 nts of each contig")
for contig in assembly.keys():
print(contig)
print(assembly[contig][0:150])
print("First 10 lines of the annotation data:")
for line in annotations.split("\n")[:10]:
print(line)
print('Some example metadata:')
print("Filtered contig N50 value:",metadata["primary_assembly"]["attributes"]["qc_statistics"]["assembly_statistics"]["filtered"]["n50"])
print("Filtered contig GC content:",metadata["primary_assembly"]["attributes"]["qc_statistics"]["assembly_statistics"]["filtered"]["gc_content"])
print("Illumina data:",metadata["primary_assembly"]["attributes"]["qc_statistics"]["sequencing_statistics"]["illumina"]["depth"])
print("ONT data:",metadata["primary_assembly"]["attributes"]["qc_statistics"]["sequencing_statistics"]["ont"]["reads"])
Output:
First 150 nts of each contig
>4a3fc3892d33411f_1 assembly_id="4a3fc3892d33411f" genome_id="a614b8c4a4664441" atcc_catalog_number="ATCC 700822" species="Yersinia enterocolitica subsp. enterocolitica" contig_number="1" topology="circular"
GTGTCACTTTCGCTTTGGCAGCAGTGTCTTGCCCGATTGCAGGATGAGTTACCTGCCACAGAATTTAGTATGTGGATACGCCCCTTACAGGCGGAACTGAGTGACAATACTCTGGCGCTTTACGCACCTAATCGTTTTGTACTGGACTGG
>4a3fc3892d33411f_2 assembly_id="4a3fc3892d33411f" genome_id="a614b8c4a4664441" atcc_catalog_number="ATCC 700822" species="Yersinia enterocolitica subsp. enterocolitica" contig_number="2" topology="linear"
TTCAATGAATCCATTCTGCTGCGGGTTTACCCGGTTGAATATGGCACAAAGTAATACCATTATATTCACAGTAATTCAGTAAGTTAACCGATATCAGTTCCGGACCATTATCAACTCTAATTTGCTGAGGCTGTCCACGTTCTTCTTTCA
First 10 lines of the annotation data:
LOCUS 1 4533095 bp DNA linear UNK 07-DEC-2021
DEFINITION Yersinia enterocolitica subsp. enterocolitica ATCC® 700822™, contig
1.
ACCESSION assembly_4a3fc3892d33411f_1
VERSION assembly_4a3fc3892d33411f_1
DBLINK assembly: 4a3fc3892d33411f
annotation_set: 0518695e1d044c30
genome: a614b8c4a4664441
KEYWORDS .
SOURCE https://genomes.atcc.org/genomes/a614b8c4a4664441
Some example metadata:
Filtered contig N50 value: 4533095
Filtered contig GC content: 0.46992943761839084
Illumina data: {'max': 1154, 'mean': 303.12891886668774, 'median': 304.0, 'min': 12, 'stdev': 43.0585172469467}
ONT data: {'N50': 30777, 'ambiguous_bases': 0, 'median_quality': 25, 'median_quantized_percent_gc': 40, 'median_read_length': 9478, 'position_specific_quality_scores': {}, 'quantized_percent_gc_histogram': {'20': 49, '30': 2514, '40': 66891, '50': 8085, '60': 12}, 'total_bases': 1255401398, 'total_gc_bases': 589992714, 'total_reads': 77551}
First, we use deep_search to download the assembly metadata for all items that fuzzy match "yersinia." Then, we pull out the assembly id for each and download the assembly, annotations, and metadata.
match_list=deep_search(text="yersinia",fuzz_on='50',output='json')
yersinia_data = {}
for e in match_list[:3]:
id = e['id']
yersinia_data[id] = {}
yersinia_data[id]["assembly"] = download_assembly(id=id,output='dict')
yersinia_data[id]["annotations"] = download_annotations(id=id,output='dict')
yersinia_data[id]["metadata"] = download_metadata(id=id)
for id in yersinia_data.keys():
print("First 150 nts of each contig")
for contig in yersinia_data[id]["assembly"].keys():
print(contig)
print(yersinia_data[id]["assembly"][contig][0:150])
print("\nFirst 10 lines of the annotation data:")
for line in yersinia_data[id]["annotations"].split("\n")[:10]:
print(line)
print("\nSome example metadata:")
print("taxon:",yersinia_data[id]["metadata"]["taxon"])
break
Output:
2024-07-31 11:18:03,556 - INFO - API key has been found: 'testing'
Fetched 4,890 genomes
genomes visibility='public': 4,750
First 150 nts of each contig
>02048bb2e6674f9c_1 assembly_id="02048bb2e6674f9c" genome_id="099e5acebc284d19" atcc_catalog_number="ATCC 27729" species="Yersinia enterocolitica" contig_number="1" topology="circular"
GTGTCACTTTCGCTTTGGCAGCAGTGTCTTGCCCGATTGCAGGATGAGTTACCTGCCACAGAATTTAGTATGTGGATACGCCCCTTACAGGCGGAACTGAGTGACAATACTCTGGCGCTTTACGCACCTAATCGTTTTGTACTGGACTGG
>02048bb2e6674f9c_2 assembly_id="02048bb2e6674f9c" genome_id="099e5acebc284d19" atcc_catalog_number="ATCC 27729" species="Yersinia enterocolitica" contig_number="2" topology="circular"
GTGGATAAGCAGAAATTCTCTAATTTTTCTAAAGATCATTCTTGGGAAGATATTGATTTTGAAGCACTAGAACGAGCTTCAATCGAATATTTTCAAGAACAGACTTCTTTCGATACATCCAAAACTGAAAAGAAAAGAACGCTTCGGAAA
First 10 lines of the annotation data:
LOCUS 1 4551126 bp DNA linear UNK 19-SEP-2019
DEFINITION Yersinia enterocolitica ATCC® 27729™, contig 1.
ACCESSION assembly_02048bb2e6674f9c_1
VERSION assembly_02048bb2e6674f9c_1
DBLINK assembly: 02048bb2e6674f9c
annotation_set: 4d8f7c47c7c44494
genome: 099e5acebc284d19
KEYWORDS .
SOURCE https://genomes.atcc.org/genomes/099e5acebc284d19
ORGANISM Yersinia enterocolitica
Some example metadata:
taxon: {'name': 'Yersinia enterocolitica', 'parents': [{'name': 'Yersinia', 'rank': 'genus', 'tax_id': 629}, {'name': 'Yersiniaceae', 'rank': 'family', 'tax_id': 1903411}, {'name': 'Enterobacterales', 'rank': 'order', 'tax_id': 91347}, {'name': 'Gammaproteobacteria', 'rank': 'class', 'tax_id': 1236}, {'name': 'Proteobacteria', 'rank': 'phylum', 'tax_id': 1224}, {'name': 'Bacteria', 'rank': 'superkingdom', 'tax_id': 2}, {'name': 'cellular organisms', 'rank': 'no rank', 'tax_id': 131567}], 'rank': 'species', 'tax_id': 630}
To properly cite the ATCC® Genome Portal, please use the reference below:
Benton B, King S, Greenfield SR, et al. The ATCC® Genome Portal: Microbial Genome Reference Standards with Data Provenance. Thrash JC, ed. Microbiol Resour Announc. 2021;10(47):e00818-21. https://journals.asm.org/doi/10.1128/msphere.00077-22
Additional recent peer reviewed research from ATCC®’s Sequencing & Bioinformatics Center includes:
Yarmosh DA, Lopera JG, Puthuveetil NP, et al. Comparative Analysis and Data Provenance for 1,113 Bacterial Genome Assemblies. Suen G, ed. mSphere. Published online May 2, 2022:e00077-22. https://journals.asm.org/doi/10.1128/MRA.00818-21
Jacobs SE, Jacobs JL, Dennis EK, et al. Candida auris Pan-Drug-Resistant to Four Classes of Antifungal Agents. Antimicrob Agents Chemother. Published online June 30, 2022:e00053-22. doi:10.1128/aac.00053-22
Harmon CL, Castlebury L, Boundy-Mills K, et al. Standards of Diagnostic Validation: Recommendations for reference collections. PhytoFrontiersTM. Published online August 25, 2022:PHYTOFR-05-22-0050-FI. doi:10.1094/PHYTOFR-05-22-0050-FI
If you found a bug with this REST-API, would like new features, or have questions related to this GitHub, please fill out an Issue Template form if applicable, and post it to the issues section of this repository.
For questions, concerns, or anything ATCC® Genome Portal related, please e-mail us at: nextgen@atcc.org
For anything else, please reach out to the main ATCC® Contact portal: Contact ATCC®