PyHGNC Documentation¶

for version: 0.2.4

PyHGNC is a python software interface developed by the Department of Bioinformatics at the Fraunhofer Institute for Algorithms and Scientific Computing SCAI for the data provided by the European Bioinformatics Institute (EMBL-EBI) on their HGNC website. Thanks to the significant and important work of the HUGO Gene Nomenclature Committee the scientific community has an essential standardised nomenclature for human genes in hand. Because in many software projects a local installation with fast programmatic access is required, we have developed a Python library which allows to access and query HGNC data locally with very limited programming skills.

For the impatient user: on your console

pip install pyhgnc
pyhgnc update

… in your Python console

import pyhgnc
query = pyhgnc.query()
query.hgnc()

The content of HGNC made easy accessible by PyHGNC supports successfully scientists in the IMI funded projects AETIONOMY and PHAGO. It is used for the identification of potential drugs in complex disease networks with several thousand relationships compiled from BEL statements.

Aim of this software project is to provide an programmatic access to locally stored HGNC data and allow a filtered export in several formats used in the scientific community. Query functions allow to search in the data and use it as pandas.DataFrame in Jupyter notebooks. We will focus our software development on the analysis and extension of biological disease knowledge networks. PyHGNC is an ongoing project and needs improvement. We are happy if you want to support our project or start a scientific cooperation with us.

ToDo: Add Figure of PyHGNC ER

Fig. 1: ER model of PyHGNC database

supported by IMI, AETIONOMY, PHAGO.

Installation¶

System requirements¶

After complete installation of HGNC (gene symbols and names) and HCOP (orthology) data by PyHGNC ~1,441,250 rows in 22 tables need only ~380 MB of disk storage (depending on the used RDMS).

Tests were performed on Ubuntu 16.04, 4 x Intel Core i7-6560U CPU @ 2.20Ghz with 16 GiB of RAM. In general PyHGNC should work also on other systems like Windows, other Linux distributions or Mac OS. Installation were complete after ~4 min. For systems with low memory the option –low_memory was added in the update method.

Supported databases¶

PyHGNC uses SQLAlchemy to cover a wide spectrum of RDMSs (Relational database management systems). For best performance MySQL or MariaDB is recommended. But if you have no possibility to install software on your system, SQLite - which needs no further installation - also works. The following RDMSs are supported (by SQLAlchemy):

Install software¶

The following instructions are written for Linux/MacOS. The way you install python software on Windows could be different.

Often it makes sense to avoid conflicts with other python installations by using different virtual environments. Read here about easy setup and management of different virtual environments.

If you want to install pyhgnc system wide use superuser (sudo for Ubuntu):

sudo pip install pyhgnc

If you have no sudo rights install as user

pip install --user pyhgnc

If you want to make sure you install pyhgnc in python3 environment:

sudo python3 -m pip install pyhgnc

MySQL/MariaDB setup¶

In general you don’t have to setup any database, because pyhgnc uses file based SQLite by default. But we strongly recommend to use MySQL/MariaDB.

Log in MySQL/MariaDB as root user and create a new database, create a user, assign the rights and flush privileges.

CREATE DATABASE pyhgnc CHARACTER SET utf8 COLLATE utf8_general_ci;
GRANT ALL PRIVILEGES ON pyhgnc.* TO 'pyhgnc_user'@'%' IDENTIFIED BY 'pyhgnc_passwd';
FLUSH PRIVILEGES;

The simplest way to set the configurations of pyhgnc for MySQL/MariaDB is to use the command …

pyhgnc mysql

… and accept all default values.

Another way is to open a python shell and set the MySQL configuration. If you have not changed anything in the SQL statements …

import pyhgnc
pyhgnc.set_mysql_connection()

If you have used you own settings, please adapt the following command to you requirements.

import pyhgnc
pyhgnc.set_mysql_connection(host='localhost', user='pyhgnc_user', passwd='pyhgnc_passwd', db='pyhgnc')

Updating¶

During the updating process PyHGNC will download HGNC and HCOP files from the EBI ftp server.

Downloaded files will take no space on your disk after the update process.

To update from command line or terminal:

pyhgnc update

Update options are available aswell, type pyhgnc update –help to get a full list with descriptions.

To update from Python shell:

import pyhgnc
pyhgnc.update()

Changing database configuration¶

Following functions allow to change the connection to your RDBMS (relational database management system). The connection settings will be used by default on the next time pyhgnc is executed.

To set a new MySQL/MariaDB connection use the interactive command line interface (bash, terminal, cmd) …

pyhgnc mysql

… or in Python shell …

import pyhgnc
pyhgnc.set_mysql_connection(host='localhost', user='pyhgnc_user', passwd='pyhgnc_passwd', db='pyhgnc')

To set connection to other database systems use the database.set_connection().

For more information about connection strings go to the SQLAlchemy documentation.

Examples for valid connection strings are:

mysql+pymysql://user:passwd@localhost/database?charset=utf8
postgresql://scott:tiger@localhost/mydatabase
mssql+pyodbc://user:passwd@database
oracle://user:passwd@127.0.0.1:1521/database
Linux: sqlite:////absolute/path/to/database.db
Windows: sqlite:///C:\path\to\database.db

You could use the following code to connect pyhgnc to an oracle database:

import pyhgnc
pyhgnc.set_connection('oracle://user:passwd@127.0.0.1:1521/database')

Tutorial¶

In this tutorial we will use a new python environment using virualenvwrapper and make a fresh install of pyhgnc. Then we will perform an update and store the data in a SQLite database. Afterwards we will perform some queries to analyze the data we received and stored in our database.

Setup¶

New environment
install pyhgnc

Update¶

Setup connection
perform update

Query¶

Use python shell to perform some basic queries.

Query functions ¶

Contents

Before you query ¶

1. You can use % as a wildcard.¶

import pyhgnc
query = pyhgnc.query()

# exact search
query.hgnc(name='amyloid beta precursor protein')

# starts with 'amyloid beta'
query.hgnc(name='amyloid beta %')

# ends with 'precursor protein'
query.hgnc(name='% precursor protein')

# contains 'precursor'
query.hgnc(name='%precursor%')

2. limit to restrict number of results ¶

import pyhgnc
query = pyhgnc.query()

query.hgnc(limit=10)

Use an offset by paring a tuple (page_number, number_of_results_per_page) to the parameter limit.

page_number starts with 0!

import pyhgnc
query = pyhgnc.query()

# first page with 3 results (every page have 3 results)
query.hgnc(limit=(0,3))
# fourth page with 10 results (every page have 10 results)
query.hgnc(limit=(4,10))

3. Return `pandas.DataFrame` as result¶

This is very useful if you want to profit from amazing pandas functions.

import pyhgnc
query = pyhgnc.query()

query.hgnc(as_df=True)

4. show all columns as dict ¶

import pyhgnc
query = pyhgnc.query()

first_entry = query.hgnc(limit=1)[0]
first_entry.to_dict()

5. Return single values with key name ¶

import pyhgnc
query = pyhgnc.query()

query.hgnc(name='%kinase')[0].name

6. Access to the linked data models (1-n, n-m)¶

From results of pyhgnc.query().hgnc() you can access

alias_symbols
alias_names
rgds
omims
ccdss
lsdbs
orthology_predictions
enzymes
gene_families
refseq_accessions
mgds
uniprots
pubmeds
enas

import pyhgnc
query = pyhgnc.query()

r = query.hgnc(limit=1)[0]

r.alias_symbols
r.alias_names
r.rgds
r.omims
r.ccdss
r.lsdbs
r.orthology_predictions
r.enzymes
r.gene_families
r.refseq_accessions
r.mgds
r.uniprots
r.pubmeds
r.enas

But for example from pyhgnc.query().uniprot() you can go back to hgnc

import pyhgnc
query = pyhgnc.query()

uniprot = query.uniprot(uniprotid='Q9BTE6')[0]
uniprot.hgncs
# [AARSD1, PTGES3L-AARSD1]
# following is crazy but possible, again go back to ec_number
uniprot.hgncs[0].uniprots
# [Q9BTE6]

7. HGNC identifier and symbol is available in all methods ¶

Hint

In all query functions (except hgnc) you have the parameters - hgnc_identifier - hgnc_symbol even it is not part of the model.

import pyhgnc
query = pyhgnc.query()

query.alias_symbol(hgnc_identifier=620)
# [AD1]
query.alias_symbol(hgnc_symbol='APP')
# [AD1]

hgnc ¶

import pyhgnc
query = pyhgnc.query()

query.hgnc(entrez=503538)

Check documentation of pyhgnc.manager.query.QueryManager.hgnc() for all available parameters.

orthology_prediction ¶

import pyhgnc
query = pyhgnc.query()

query.orthology_prediction(ortholog_species=10090, hgnc_symbol='APP')
# [10090: amyloid beta (A4) precursor protein: App]

Check documentation of pyhgnc.manager.query.QueryManager.orthology_prediction() for all available parameters.

alias_symbol ¶

import pyhgnc
query = pyhgnc.query()

result = query.alias_symbol(alias_symbol='AD1')[0]
result.hgnc
# APP

Check documentation of pyhgnc.manager.query.QueryManager.alias_symbol() for all available parameters.

alias_name ¶

import pyhgnc
query = pyhgnc.query()

result = query.alias_name(alias_name='peptidase nexin-II')[0]
result.hgnc.name
# 'amyloid beta precursor protein'

Check documentation of pyhgnc.manager.query.QueryManager.alias_name() for all available parameters.

gene_family ¶

import pyhgnc
query = pyhgnc.query()

    result = query.gene_family(family_name='Parkinson%')[0]
result
# 'Parkinson disease associated genes'
result.hgncs
# [ATP13A2, EIF4G1, FBXO7, HTRA2, LRRK2, PARK3, PARK7, PARK10, PARK11, PARK12, PARK16, PINK1,\
# PLA2G6, PRKN, SNCA, UCHL1, VPS35]

Check documentation of pyhgnc.manager.query.QueryManager.gene_family() for all available parameters.

ref_seq ¶

import pyhgnc
query = pyhgnc.query()

query.ref_seq(hgnc_symbol='APP')
# [NM_000484]

Check documentation of pyhgnc.manager.query.QueryManager.ref_seq() for all available parameters.

rgd ¶

import pyhgnc
query = pyhgnc.query()

query.rgd(rgdid=2139)[0].hgncs
# [APP]

Check documentation of pyhgnc.manager.query.QueryManager.rgd() for all available parameters.

omim ¶

import pyhgnc
query = pyhgnc.query()

query.omim(omimid=104760)[0].hgnc.name
# 'amyloid beta precursor protein'

Check documentation of pyhgnc.manager.query.QueryManager.omim() for all available parameters.

mgd ¶

import pyhgnc
query = pyhgnc.query()

query.mgd(mgdid=88059)[0].hgncs
# [APP]

Check documentation of pyhgnc.manager.query.QueryManager.mgd() for all available parameters.

uniprot ¶

import pyhgnc
query = pyhgnc.query()

query.uniprot(uniprotid='P05067')[0].hgncs
# [APP]

Check documentation of pyhgnc.manager.query.QueryManager.uniprot() for all available parameters.

ccds ¶

import pyhgnc
query = pyhgnc.query()

query.ccds(ccdsid='CCDS13576')[0].hgnc
# APP

Check documentation of pyhgnc.manager.query.QueryManager.ccds() for all available parameters.

pubmed ¶

import pyhgnc
query = pyhgnc.query()

query.pubmed(hgnc_symbol='A1CF')
# [11815617, 11072063]

Check documentation of pyhgnc.manager.query.QueryManager.pubmed() for all available parameters.

ena ¶

import pyhgnc
query = pyhgnc.query()

query.ena(hgnc_identifier=620)
# [AD1]

Check documentation of pyhgnc.manager.query.QueryManager.ena() for all available parameters.

enzyme ¶

import pyhgnc
query = pyhgnc.query()

query.enzyme(hgnc_symbol='PRKCA')
# [2.7.11.1]

Check documentation of pyhgnc.manager.query.QueryManager.enzyme() for all available parameters.

lsdb ¶

import pyhgnc
query = pyhgnc.query()

query.lsdb(hgnc_symbol='APP')
# [Alzheimer Disease & Frontotemporal Dementia Mutation Database]

Check documentation of pyhgnc.manager.query.QueryManager.lsdb() for all available parameters.

RESTful API¶

PyUniProt provides also a RESTful API web server.

Start the server with

pyhgnc web

Open PyHGNC Web API in a web browser.

HGNC¶

We want to pay tribute to the HGNC database of human gene names and HUGO Gene Nomenclature Committee team for their amazing resource their provide to the scientific community. pyhgnc only provides methods to download and locally query open accessible HGNC data available on EBI ftp server.

About¶

Citation from EBI HGNC website [23/11/2017]:: “HGNC is the only worldwide authority assigning standardised human gene symbols and names. Its key goals are to provide unique standardised nomenclature for every human gene; to ensure this information is freely available, widely disseminated and universally used; and to coordinate the expansion and utilisation of this nomenclature across vertebrates.”

Citation¶

Latest HGNC publication:

Gray KA, Yates B, Seal RL, Wright MW, Bruford EA. genenames.org: the HGNC resources in 2015. Nucleic Acids Res. 2015 Jan;43(Database issue):D1079-85. doi: 10.1093/nar/gku1071. PMID:25361968

HGNC Database, HUGO Gene Nomenclature Committee (HGNC), EMBL Outstation - Hinxton, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK www.genenames.org.

Links¶

Link to data: EBI ftp server

Check the HGNC database of human gene names for more information.

HCOP¶

PyHGNC integrates also HGNC Comparison of Orthology Predictions (HCOP).

About¶

HCOP is a tool that integrates orthology assertions predicted for a specified human gene, or set of human genes, by

An indication of the reliability of a prediction is provided by the number of databases which concur. HCOP was originally designed to show orthology predictions between human and mouse, but has been expanded to include data from chimp, macaque, rat, dog, horse, cow, pig, opossum, platypus, chicken, anole lizard, xenopus, zebrafish, C. elegans, Drosophila and S. cerevisiae, meaning that there are currently 18 genomes available for comparison in HCOP.

Citation¶

Wright MW, Eyre TA, Lush MJ, Povey S and Bruford EA.

HCOP: The HGNC Comparison of Orthology Predictions Search Tool. Mamm Genome. 2005 Nov; 16(11):827-828. PMID:16284797 PDF
Eyre TA, Wright MW, Lush MJ and Bruford EA.

HCOP: a searchable database of human orthology predictions. Brief Bioinform. 2007 Jan;8(1):2-5. PMID: 16951416
Gray KA, Yates, B, Seal RL, Wright MW, Bruford EA.

Genenames.org: the HGNC resources in 2015. Nucleic Acids Res. 2015 Jan;43(Database issue):D1079-85. PMID: 25361968

Links¶

HCOP: Orthology Predictions Search

Benchmarks¶

All benchmarks created on a standard notebook:

OS: Linux Ubuntu 16.04.2 LTS (xenial)
Python: 3.5.2
Hardware: x86_64, Intel(R) Core(TM) i7-6560U CPU @ 2.20GHz, 4 CPUs, Mem 16Gb
MariaDB: Server version: 10.0.29-MariaDB-0ubuntu0.16.04.1 Ubuntu 16.04

MySQL/MariaDB¶

Database created with following command in MySQL/MariaDB as root:

CREATE DATABASE pyhgnc CHARACTER SET utf8 COLLATE utf8_general_ci;

User created with following command in MySQL/MariaDB:

GRANT ALL PRIVILEGES ON pyhgnc.* TO 'pyhgnc_user'@'%' IDENTIFIED BY 'pyhgnc_passwd';
FLUSH PRIVILEGES;

Update¶

To import HGNC data executed following commands in python console:

import pyhgnc
pyhgnc.set_mysql_connection()
pyhgnc.update()

The other possibility is to use the command line interface

pyhgnc mysql # accept all default values
pyhgnc update

CPU times:
- real 3m52.311s
- user 3m4.964s
- sys 0m6.340s

Low memory option¶

If you have low memory available please use the –low_memory option in command line

pyhgnc mysql # accept all default values
pyhgnc update --low_memory
# or short
pyhgnc update -l

CPU times:
- real 6m40.913s
- user 5m22.724s
- sys 0m9.016s

Query ¶

Contents

Query
- Query interface
- Query Manager Reference

Query interface ¶

PyHGNC provides a powerfull query interface for the stored data. It can be accessed from python shell:

import pyhgnc
query = pyhgnc.query()

You can use the query interface instance to issue a query to any model defined in pyhgnc.manager.models:

# Issue query on hgnc table:
query.hgnc()

# Issue query on pubmed table:
query.pubmed()

Hint

See Query functions for more examples and check out pyhgnc.manager.query.QueryManager (below) for all possible parameters for the different models.

Query Manager Reference ¶

class pyhgnc.manager.query.QueryManager(connection=None, echo=False)[source]¶

Query interface to database.

alias_name(alias_name=None, is_previous_name=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.AliasName objects in database

Parameters:

alias_name (str or tuple(str) or None) – alias name(s)
is_previous_name (bool or tuple(bool) or None) – flag for ‘is previous’
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.AliasSymbol)
if as_df == True -> pandas.DataFrame

Return type:

list(models.AliasSymbol) or pandas.DataFrame

alias_symbol(alias_symbol=None, is_previous_symbol=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.AliasSymbol objects in database

Parameters:

alias_symbol (str or tuple(str) or None) – alias symbol(s)
is_previous_symbol (bool or tuple(bool) or None) – flag for ‘is previous’
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.AliasSymbol)
if as_df == True -> pandas.DataFrame

Return type:

list(models.AliasSymbol) or pandas.DataFrame

ccds(ccdsid=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.CCDS objects in database

Parameters:

ccdsid (str or tuple(str) or None) – Consensus CDS ID(s)
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.CCDS)
if as_df == True -> pandas.DataFrame

Return type:

list(models.CCDS) or pandas.DataFrame

ena(enaid=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.ENA objects in database

Parameters:

enaid (str or tuple(str) or None) – European Nucleotide Archive (ENA) identifier(s)
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.ENA)
if as_df == True -> pandas.DataFrame

Return type:

list(models.ENA) or pandas.DataFrame

enzyme(ec_number=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.Enzyme objects in database

Parameters:

ec_number (str or tuple(str) or None) – Enzyme Commission number (EC number)(s)
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.Enzyme)
if as_df == True -> pandas.DataFrame

Return type:

list(models.Enzyme) or pandas.DataFrame

gene_family(family_identifier=None, family_name=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.GeneFamily objects in database

Parameters:

family_identifier (int or tuple(int) or None) – gene family identifier(s)
family_name (str or tuple(str) or None) – gene family name(s)
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.AliasSymbol)
if as_df == True -> pandas.DataFrame

Return type:

list(models.AliasSymbol) or pandas.DataFrame

get_model_queries(query_obj, model_queries_config)[source]¶: use this if your are searching for a field in the same model

hgnc(name=None, symbol=None, identifier=None, status=None, uuid=None, locus_group=None, orphanet=None, locus_type=None, date_name_changed=None, date_modified=None, date_symbol_changed=None, pubmedid=None, date_approved_reserved=None, ensembl_gene=None, horde=None, vega=None, lncrnadb=None, uniprotid=None, entrez=None, mirbase=None, iuphar=None, ucsc=None, snornabase=None, gene_family_name=None, mgdid=None, pseudogeneorg=None, bioparadigmsslc=None, locationsortable=None, ec_number=None, refseq_accession=None, merops=None, location=None, cosmic=None, imgt=None, enaid=None, alias_symbol=None, alias_name=None, rgdid=None, omimid=None, ccdsid=None, lsdbs=None, ortholog_species=None, gene_family_identifier=None, limit=None, as_df=False)[source]¶

Method to query pyhgnc.manager.models.Pmid

Parameters:

name (str or tuple(str) or None) – HGNC approved name for the gene
symbol (str or tuple(str) or None) – HGNC approved gene symbol
identifier (int or tuple(int) or None) – HGNC ID. A unique ID created by the HGNC for every approved symbol
status (str or tuple(str) or None) – Status of the symbol report, which can be either “Approved” or “Entry Withdrawn”
uuid (str or tuple(str) or None) – universally unique identifier
locus_group (str or tuple(str) or None) – group name for a set of related locus types as defined by the HGNC
orphanet (int ot tuple(int) or None) – Orphanet database identifier (related to rare diseases and orphan drugs)
locus_type (str or tuple(str) or None) – locus type as defined by the HGNC (e.g. RNA, transfer)
date_name_changed (str or tuple(str) or None) – date the gene name was last changed (format: YYYY-mm-dd, e.g. 2017-09-29)
date_modified (str or tuple(str) or None) – date the entry was last modified (format: YYYY-mm-dd, e.g. 2017-09-29)
date_symbol_changed (str or tuple(str) or None) – date the gene symbol was last changed (format: YYYY-mm-dd, e.g. 2017-09-29)
date_approved_reserved (str or tuple(str) or None) – date the entry was first approved (format: YYYY-mm-dd, e.g. 2017-09-29)
pubmedid (int ot tuple(int) or None) – PubMed identifier
ensembl_gene (str or tuple(str) or None) – Ensembl gene ID. Found within the “GENE RESOURCES” section of the gene symbol report
horde (str or tuple(str) or None) – symbol used within HORDE for the gene (not available in JSON)
vega (str or tuple(str) or None) – Vega gene ID. Found within the “GENE RESOURCES” section of the gene symbol report
lncrnadb (str or tuple(str) or None) – Noncoding RNA Database identifier
uniprotid (str or tuple(str) or None) – UniProt identifier
entrez (str or tuple(str) or None) – Entrez gene ID. Found within the “GENE RESOURCES” section of the gene symbol report
mirbase (str or tuple(str) or None) – miRBase ID
iuphar (str or tuple(str) or None) – The objectId used to link to the IUPHAR/BPS Guide to PHARMACOLOGY database
ucsc (str or tuple(str) or None) – UCSC gene ID. Found within the “GENE RESOURCES” section of the gene symbol report
snornabase (str or tuple(str) or None) – snoRNABase ID
gene_family_name (int or tuple(int) or None) – Gene family name
gene_family_identifier – Gene family identifier
mgdid (int ot tuple(int) or None) – Mouse Genome Database identifier
imgt (str or tuple(str) or None) – Symbol used within international ImMunoGeneTics information system
enaid (str or tuple(str) or None) – European Nucleotide Archive (ENA) identifier
alias_symbol (str or tuple(str) or None) – Other symbols used to refer to a gene
alias_name (str or tuple(str) or None) – Other names used to refer to a gene
pseudogeneorg (str or tuple(str) or None) – Pseudogene.org ID
bioparadigmsslc (str or tuple(str) or None) – Symbol used to link to the SLC tables database at bioparadigms.org for the gene
locationsortable (str or tuple(str) or None) – locations sortable
ec_number (str or tuple(str) or None) – Enzyme Commission number (EC number)
refseq_accession (str or tuple(str) or None) – RefSeq nucleotide accession(s)
merops (str or tuple(str) or None) – ID used to link to the MEROPS peptidase database
location (str or tuple(str) or None) – Cytogenetic location of the gene (e.g. 2q34).
cosmic (str or tuple(str) or None) – Symbol used within the Catalogue of somatic mutations in cancer for the gene
rgdid (int or tuple(int) or None) – Rat genome database gene ID
omimid (int or tuple(int) or None) – Online Mendelian Inheritance in Man (OMIM) ID
ccdsid (str or tuple(str) or None) – Consensus CDS ID
lsdbs (str or tuple(str) or None) – Locus Specific Mutation Database Name
ortholog_species (int or tuple(int) or None) – Ortholog species NCBI taxonomy identifier
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.Keyword)
if as_df == True -> pandas.DataFrame

Return type:

list[models.HGNC]

lsdb(lsdb=None, url=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.LSDB objects in database

Parameters:

lsdb (str or tuple(str) or None) – name(s) of the Locus Specific Mutation Database
url (str or tuple(str) or None) – URL of the Locus Specific Mutation Database
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.LSDB)
if as_df == True -> pandas.DataFrame

Return type:

list(models.LSDB) or pandas.DataFrame

mgd(mgdid=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.MGD objects in database

Parameters:

mgdid (str or tuple(str) or None) – Mouse genome informatics database ID(s)
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.MGD)
if as_df == True -> pandas.DataFrame

Return type:

list(models.MGD) or pandas.DataFrame

omim(omimid=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.OMIM objects in database

Parameters:

omimid (str or tuple(str) or None) – Online Mendelian Inheritance in Man (OMIM) ID(s)
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.OMIM)
if as_df == True -> pandas.DataFrame

Return type:

list(models.OMIM) or pandas.DataFrame

orthology_prediction(ortholog_species=None, human_entrez_gene=None, human_ensembl_gene=None, human_name=None, human_symbol=None, human_chr=None, human_assert_ids=None, ortholog_species_entrez_gene=None, ortholog_species_ensembl_gene=None, ortholog_species_db_id=None, ortholog_species_name=None, ortholog_species_symbol=None, ortholog_species_chr=None, ortholog_species_assert_ids=None, support=None, hgnc_identifier=None, hgnc_symbol=None, limit=None, as_df=False)[source]¶

Method to query pyhgnc.manager.models.OrthologyPrediction

Parameters:

ortholog_species (int) – NCBI taxonomy identifier
human_entrez_gene (str) – Entrez gene identifier
human_ensembl_gene (str) – Ensembl identifier
human_name (str) – human gene name
human_symbol (str) – human gene symbol
human_chr (str) – human chromosome
human_assert_ids (str) –
ortholog_species_entrez_gene (str) – Entrez gene identifier for ortholog
ortholog_species_ensembl_gene (str) – Ensembl gene identifier for ortholog
ortholog_species_db_id (str) – Species specific database identifier (e.g. MGI:1920453)
ortholog_species_name (str) – gene name of ortholog
ortholog_species_symbol (str) – gene symbol of ortholog
ortholog_species_chr (str) – chromosome identifier (ortholog)
ortholog_species_assert_ids (str) –
support (str) –
hgnc_identifier (int) – HGNC identifier
hgnc_symbol (str) – HGNC symbol
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.Keyword)
if as_df == True -> pandas.DataFrame

Return type:

list(models.Keyword) or pandas.DataFrame

pubmed(pubmedid=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.PubMed objects in database

Parameters:

pubmedid (str or tuple(str) or None) – alias symbol(s)
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.PubMed)
if as_df == True -> pandas.DataFrame

Return type:

list(models.PubMed) or pandas.DataFrame

ref_seq(accession=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.RefSeq objects in database

Parameters:

accession (str or tuple(str) or None) – RefSeq accessionl(s)
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.RefSeq)
if as_df == True -> pandas.DataFrame

Return type:

list(models.RefSeq) or pandas.DataFrame

rgd(rgdid=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.RGD objects in database

Parameters:

rgdid (str or tuple(str) or None) – Rat genome database gene ID(s)
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.RGD)
if as_df == True -> pandas.DataFrame

Return type:

list(models.RGD) or pandas.DataFrame

uniprot(uniprotid=None, hgnc_symbol=None, hgnc_identifier=None, limit=None, as_df=False)[source]¶

Method to query models.UniProt objects in database

Parameters:

uniprotid (str or tuple(str) or None) – UniProt identifier(s)
hgnc_symbol (str or tuple(str) or None) – HGNC symbol(s)
hgnc_identifier (int or tuple(int) or None) – identifiers(s) in models.HGNC
limit (int or tuple(int) or None) –
- if isinstance(limit,int)==True -> limit
- if isinstance(limit,tuple)==True -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
as_df (bool) – if True results are returned as pandas.DataFrame

Returns:

if as_df == False -> list(models.UniProt)
if as_df == True -> pandas.DataFrame

Return type:

list(models.UniProt) or pandas.DataFrame

Data Models ¶

PyHGNC uses SQLAlchemy to store the data in the database. You can use an instance of pyhgnc.manager.query.QueryManager to query the content of the database.

Entity–relationship model:

ToDo: Add ER figure here!

Contents

Data Models
- HGNC
- AliasSymbol
- AliasName
- GeneFamily
- RefSeq
- RGD
- OMIM
- MGD
- UniProt
- CCDS
- PubMed
- ENA
- Enzyme
- LSDB
- OrthologyPrediction
Database functions

HGNC ¶

class pyhgnc.manager.models.HGNC(**kwargs)[source]¶

Root class (table, model) for all other classes (tables, models) in PyHGNC. Basic information with 1:1 relationship to identifier are stored here

Warning

homeodb (Homeobox Database ID)
horde_id (Symbol used within HORDE for the gene)

described in README, but not found in HGNC JSON file

Hint

To link to IUPHAR/BPS Guide to PHARMACOLOGY database only use the number (only use 1 from the result objectId:1)

Variables:

name (str) – HGNC approved name for the gene. Equates to the “APPROVED NAME” field within the gene symbol report
symbol (str) – The HGNC approved gene symbol. Equates to the “APPROVED SYMBOL” field within the gene symbol report
orphanet (int) – Orphanet ID
identifier (str) – Unique ID created by the HGNC for every approved symbol (HGNC ID)
status (str) – Status of the symbol report, which can be either “Approved” or “Entry Withdrawn”
uuid (str) – universally unique identifier
locus_group (str) – Group name for a set of related locus types as defined by the HGNC (e.g. non-coding RNA)
locus_type (str) – Locus type as defined by the HGNC (e.g. RNA, transfer)
date_name_changed (date) – date the gene name was last changed
date_modified (date) – date the entry was last modified
date_symbol_changed (date) – date the gene symbol was last changed
date_approved_reserved (date) – date the entry was first approved
ensembl_gene (str) – Ensembl gene ID. Found within the “GENE RESOURCES” section of the gene symbol report
horde (str) – symbol used within HORDE for the gene (not available in JSON)
vega (str) – Vega gene ID. Found within the “GENE RESOURCES” section of the gene symbol report
lncrnadb (str) – Long Noncoding RNA Database identifier
entrez (str) – Entrez gene ID. Found within the “GENE RESOURCES” section of the gene symbol report
mirbase (str) – miRBase ID
iuphar (str) – The objectId used to link to the IUPHAR/BPS Guide to PHARMACOLOGY database
ucsc (str) – UCSC gene ID. Found within the “GENE RESOURCES” section of the gene symbol report
snornabase (str) – snoRNABase ID
imgt (str) – Symbol used within international ImMunoGeneTics information system
pseudogeneorg (str) – Pseudogene.org ID
bioparadigmsslc (str) – Symbol used to link to the SLC tables database at bioparadigms.org for the gene
locationsortable (str) – locations sortable
merops (str) – ID used to link to the MEROPS peptidase database
location (str) – Cytogenetic location of the gene (e.g. 2q34).
cosmic (str) – Symbol used within the Catalogue of somatic mutations in cancer for the gene
rgds (list) – relationship to RGD
omims (list) – relationship to OMIM
ccdss (list) – relationship to CCDS
lsdbs (list) – relationship to LSDB
orthology_predictions (list) – relationship to OrthologyPrediction
enzymes (list) – relationship to Enzyme
gene_families (list) – relationship to GeneFamily
refseq_accessions (list) – relationship to RefSeq
mgds (list) – relationship to MGD
uniprots (list) – relationship to UniProt
pubmeds (list) – relationship to PubMed
enas (list) – relationship to ENA

AliasSymbol ¶

class pyhgnc.manager.models.AliasSymbol(**kwargs)[source]¶

Other symbols used to refer to this gene as seen in the “SYNONYMS” field in the symbol report.

Attention

Symbols previously approved by the HGNC for this gene are tagged with is_previous_symbol==True. Equates to the “PREVIOUS SYMBOLS & NAMES” field within the gene symbol report.

Variables:	alias_symbol (str) – other symbol is_previous_symbol (bool) – previously approved hgnc – back populates to `HGNC`

AliasName ¶

class pyhgnc.manager.models.AliasName(**kwargs)[source]¶

Other names used to refer to this gene as seen in the “SYNONYMS” field in the gene symbol report.

Attention

Gene names previously approved by the HGNC for this gene are tagged with is_previous_name==True.. Equates to the “PREVIOUS SYMBOLS & NAMES” field within the gene symbol report.

Variables:	alias_name (str) – other name is_previous_name (bool) – previously approved hgnc – back populates to `HGNC`

GeneFamily ¶

class pyhgnc.manager.models.GeneFamily(**kwargs)[source]¶

Name and identifier given to a gene family or group the gene has been assigned to. Equates to the “GENE FAMILY” field within the gene symbol report.

Variables:	familyid (int) – family identifier familyname (str) – family name hgncs (list) – back populates to `HGNC`

RefSeq ¶

class pyhgnc.manager.models.RefSeq(**kwargs)[source]¶

RefSeq nucleotide accession(s). Found within the”NUCLEOTIDE SEQUENCES” section of the gene symbol report.

See also RefSeq database for more information.

Variables:	accession (str) – RefSeq accession number hgncs (list) – back populates to `HGNC`

RGD ¶

class pyhgnc.manager.models.RGD(**kwargs)[source]¶

Rat genome database gene ID. Found within the “HOMOLOGS” section of the gene symbol report

Variables:	rgdid (str) – Rat genome database gene ID hgncs – back populates to `HGNC`

OMIM ¶

class pyhgnc.manager.models.OMIM(**kwargs)[source]¶

Online Mendelian Inheritance in Man (OMIM) ID

Variables:	omimid (str) – OMIM ID hgnc – back populates to pyhgnc.manager.models.HGNC

MGD ¶

class pyhgnc.manager.models.MGD(**kwargs)[source]¶

Mouse genome informatics database ID. Found within the “HOMOLOGS” section of the gene symbol report

Variables:	mgdid (str) – Mouse genome informatics database ID hgncs (list) – back populates to `HGNC`

UniProt ¶

class pyhgnc.manager.models.UniProt(**kwargs)[source]¶

Universal Protein Resource (UniProt) protein accession. Found within the “PROTEIN RESOURCES” section of the gene symbol report.

See also UniProt webpage for more information.

Variables:	uniprotid (str) – UniProt identifier hgncs (list) – back populates to `HGNC`

CCDS ¶

class pyhgnc.manager.models.CCDS(**kwargs)[source]¶

Consensus CDS ID. Found within the “NUCLEOTIDE SEQUENCES” section of the gene symbol report.

PubMed ¶

class pyhgnc.manager.models.PubMed(**kwargs)[source]¶

PubMed and Europe PubMed Central PMID

Variables:	pubmedid (str) – Pubmed identifier hgncs (list) – back populates to `HGNC`

ENA ¶

class pyhgnc.manager.models.ENA(**kwargs)[source]¶

International Nucleotide Sequence Database Collaboration (GenBank, ENA and DDBJ) accession number(s). Found within the “NUCLEOTIDE SEQUENCES” section of the gene symbol report.

Variables:	enaid (str) – European Nucleotide Archive (ENA) identifier hgncs (list) – back populates to `HGNC`

Enzyme ¶

class pyhgnc.manager.models.Enzyme(**kwargs)[source]¶

Enzyme Commission number (EC number)

Variables:	ec_number (str) – EC number hgncs (list) – back populates to `HGNC`

LSDB ¶

class pyhgnc.manager.models.LSDB(**kwargs)[source]¶

The name of the Locus Specific Mutation Database and URL

Variables:	lsdb (str) – name of the Locus Specific Mutation Database url (str) – URL to database hgnc – back populates to `HGNC`

OrthologyPrediction ¶

class pyhgnc.manager.models.OrthologyPrediction(**kwargs)[source]¶

Orthology Predictions

Warning

OrthologyPrediction is still not correctly normalized and documented.

Variables:

ortholog_species (int) – NCBI taxonomy identifier
human_entrez_gene (int) – Human Entrey gene identifier
human_ensembl_gene (str) – Human Ensembl gene identifier
human_name (str) – Human gene name
human_symbol (str) – Human gene symbol
human_chr (str) – Human gene chromosome location
human_assert_ids (str) –
ortholog_species_entrez_gene (str) – Ortholog species Entrez gene identifier
ortholog_species_ensembl_gene (str) – Ortholog species Ensembl gene identifier
ortholog_species_db_id (str) – Ortholog species database identifier
ortholog_species_name (str) – Ortholog species gene name
ortholog_species_symbol (str) – Ortholog species gene symbol
ortholog_species_chr (str) – Ortholog species gene chromosome location
ortholog_species_assert_ids (str) –
support (str) –
hgnc – back populates to HGNC

Database functions ¶

set_connection ¶

pyhgnc.manager.database.set_connection(connection='sqlite:////home/docs/.pyhgnc/data/pyhgnc.db')[source]¶

Set the connection string for sqlalchemy and write it to the config file.

import pyhgnc
pyhgnc.set_connection('mysql+pymysql://{user}:{passwd}@{host}/{db}?charset={charset}')

Hint

valid connection strings

mysql+pymysql://user:passwd@localhost/database?charset=utf8
postgresql://scott:tiger@localhost/mydatabase
mssql+pyodbc://user:passwd@database
oracle://user:passwd@127.0.0.1:1521/database
Linux: sqlite:////absolute/path/to/database.db
Windows: sqlite:///C:path odatabase.db

Parameters:	connection (str) – sqlalchemy connection string

update ¶

pyhgnc.manager.database.update(connection=None, silent=False, hgnc_file_path=None, hcop_file_path=None, low_memory=False)[source]¶

Update the database with current version of HGNC

Parameters:	connection (str) – conncetion string silent (bool) – silent while import hgnc_file_path (str) – import from path HGNC hcop_file_path (str) – import from path HCOP (orthologs) low_memory (bool) – set to True if you have low memory
Returns:

set_mysql_connection ¶

pyhgnc.manager.database.set_mysql_connection(host='localhost', user='pyhgnc_user', passwd='pyhgnc_passwd', db='pyhgnc', charset='utf8')[source]¶

Method to set a MySQL connection

Parameters:	host (str) – MySQL database host user (str) – MySQL database user passwd (str) – MySQL database password db (str) – MySQL database name charset (str) – MySQL database charater set
Returns:	connection string
Return type:	str

Roadmap¶

Next steps:

Export of query results to different formats
Tests for all query functions
Improve documentation and tutorials
Increase code coverage
Collections of Jupyter notebooks with examples

Technology¶

Warning

The following is in the moment not implemented! But already written here that a lot of things all still need to be done.

This page is meant to describe the development stack for PyHGNC, and should be a useful introduction for contributors.

Versioning¶

PyHGNC is kept under version control on GitHub and GitLab. This allows for changes in the software to be tracked over time, and for tight integration of the management aspect of software development. Code will be in future produced following the Git Flow philosophy, which means that new features are coded in branches off of the development branch and merged after they are triaged. Finally, develop is merged into master for releases. If there are bugs in releases that need to be fixed quickly, “hot fix” branches from master can be made, then merged back to master and develop after fixing the problem.

Testing in PyHGNC¶

PyHGNC is written with unit testing. Whenever possible, PyHGNC will prefer to practice test-driven development. This means that new ideas for functions and features are encoded as blank classes/functions and directly writing tests for the desired output. After tests have been written that define how the code should work, the functionality can be implemented.

Test-driven development requires us to think about design before making quick and dirty implementations. This results in better code. Additionally, thorough testing suites make it possible to catch when changes break existing functionality.

Tests are written with the standard unittest library.

Tox¶

While IDEs like PyCharm provide excellent testing tools, they are not programmatic. Tox is a python package that providesa CLI interface to run automated testing procedures (as well as other build functions, that aren’t important to explain here). In PyHGNC, it is used to run the unit tests in the tests folder with the py.test harness. It also runs check-manifest, builds the documentation with sphinx, and computes the code coverage of the tests. The entire procedure is defined in tox.ini. Tox also allows test to be done on many different versions of Python.

Continuous Integration¶

Continuous integration is a philosophy of automatically testing code as it changes. PyHGNC makes use of the Travis CI server to perform testing because of its tight integration with GitHub. Travis automatically installs git hooks inside GitHub so it knows when a new commit is made. Upon each commit, Travis downloads the newest commit from GitHub and runs the tests configured in the .travis.yml file in the top level of the PyHGNC repository. This file effectively instructs the Travis CI server to run Tox. It also allows for the modification of the environment variables. This is used in PyHGNC to test on different versions of python.

Code Coverage¶

Is not implemented in the moment, but will be added in the next months.

Distribution¶

Versioning¶

PyHGNC tries to fulfil the following philosophy in future:

PyHGNC uses semantic versioning. In general, the project’s version string will have a suffix -dev like in 0.3.4-dev throughout the development cycle. After code is merged from feature branches to develop and it is time to deploy, this suffix is removed and develop branch is merged into master.

The version string appears in multiple places throughout the project, so BumpVersion is used to automate the updating of these version strings. See .bumpversion.cfg for more information.

Deployment¶

Code for PyHGNC is an open-source project on GitHub, but it is also distributed on the PyPI (pronounced Py-Pee-Eye) server. Travis CI has a wonderful integration with PyPI, so any time a tag is made on the master branch (and also assuming the tests pass), a new distribution is packed and sent to PyPI. Refer to the “deploy” section at the bottom of the .travis.yml file for more information, or the Travis CI PyPI deployment documentation. As a side note, Travis CI has an encryption tool so the password for the PyPI account can be displayed publicly on GitHub. Travis decrypts it before performing the upload to PyPI.

Acknowledgment and contribution to scientific projects¶

Software development by:

Christian Ebeling

The software development of PyHGNC by Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) is supported and funded by the IMI (INNOVATIVE MEDICINES INITIATIVE) projects AETIONOMY and PHAGO. The aim of both projects is the identification of mechnisms in Alzhiemer’s and Parkinson’s disease in complex biological BEL networks for drug development.

PyHGNC Documentation¶

Installation¶

System requirements¶

Supported databases¶

Install software¶

MySQL/MariaDB setup¶

Updating¶

Changing database configuration¶

Tutorial¶

Setup¶

Update¶

Query¶

3. Return pandas.DataFrame as result¶

RESTful API¶

HGNC¶

About¶

Citation¶

Links¶

HCOP¶

About¶

Citation¶

Links¶

Benchmarks¶

MySQL/MariaDB¶

Update¶

Low memory option¶

Roadmap¶

Technology¶

Versioning¶

Testing in PyHGNC¶

Tox¶

Continuous Integration¶

Code Coverage¶

Distribution¶

Versioning¶

Deployment¶

Acknowledgment and contribution to scientific projects¶

Indices and Tables¶

3. Return `pandas.DataFrame` as result¶