https://github.com/dridk/hpo2sqlite
convert hpo to sqlite database
https://github.com/dridk/hpo2sqlite
Last synced: about 1 month ago
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convert hpo to sqlite database
- Host: GitHub
- URL: https://github.com/dridk/hpo2sqlite
- Owner: dridk
- License: gpl-3.0
- Created: 2018-04-23T22:51:06.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2020-09-29T14:05:32.000Z (over 4 years ago)
- Last Synced: 2025-01-31T17:52:37.701Z (3 months ago)
- Language: Python
- Size: 3.6 MB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# hpo2sqlite
This script convert [HPO ontology](http://human-phenotype-ontology.github.io/) to a sqlite database as a tree according the [nested set model](https://en.wikipedia.org/wiki/Nested_set_model).
It takes [HPO obo file](http://purl.obolibrary.org/obo/hp.obo) as argument and construct a tree with [networkx](https://networkx.github.io/).
The Tree is visited to compute left and right index and the tree is stored into a sqlite database using [peewee](http://docs.peewee-orm.com/en/latest/) ORM model.
There are two main table: Terms and Nodes.
## Dependencies
You need python3 with the following modules
```
pip install networkx
pip install obonet
pip install peewee
pip install tqdm
```
## Run
Download hpo obo file from http://purl.obolibrary.org/obo/hp.obo and run the script as follow
```
python import_hpo_obo.py hp.obo
```
## Import gene and disease
```
wget http://compbio.charite.de/jenkins/job/hpo.annotations.current/lastSuccessfulBuild/artifact/current/phenotype.hpoa
python import_disease.py phenotype.hpoa
```
```
wget http://compbio.charite.de/jenkins/job/hpo.annotations/lastSuccessfulBuild/artifact/util/annotation/phenotype_to_genes.txt
python import_gene.py phenotype_to_genes.txt
```
### Table Terms
Terms contains each unique HPO term with name and definition.
| fields | description |
|------------|------------------------------|
| id | INTEGER "3" |
| hpo | TEXT "HP:0000012" |
| name | TEXT "Urinary urgency" |
| definition | TEXT "Urinary urgency is ..."|
| comment | TEXT |
### Table Nodes
Nodes table contains the HPO ontology as a Tree. Because hpo ontology is a Directed Acyclic Graph, the tree contains duplicate elements. Each node refere to his HPO terms from the term_id field.
| fields | description |
|------------|----------------------------------|
| id | INTEGER (primary key) |
| left | INTEGER (left index) |
| right | INTEGER (right index) |
| depth | INTEGER (depth of the node) |
| term_id | INTEGER (term_id) |
## Query example
### Children selection
Select all children of HPO term HP:0012632 (Abnormal intraocular pressure)
#### Step by step example
```
# Get the term id
SELECT id FROM terms WHERE hpo = "HP:0012632" // get 9124
# Get the node left and right
SELECT left,right FROM nodes WHERE term_id=9124 //get 99982 and 99987
# Select all children nodes
SELECT * FROM nodes WHERE left > 99982 AND right < 99987
```
All steps can be summarized in one query :
```
SELECT terms.hpo, terms.name FROM nodes
INNER JOIN (SELECT left, right FROM nodes WHERE term_id = (SELECT id FROM terms WHERE hpo = "HP:0012632")) as root ON nodes.left > root.left AND nodes.right < root.right
INNER JOIN terms ON terms.id = nodes.term_id
```
You can specify the depth if you only want to first level childs.
```
SELECT terms.hpo, terms.name FROM nodes
INNER JOIN (SELECT left, right FROM nodes WHERE term_id = (SELECT id FROM terms WHERE hpo = "HP:0012632")) as root ON nodes.left > root.left AND nodes.right < root.right AND (nodes.depth - root.depth) < 2
INNER JOIN terms ON terms.id = nodes.term_id
```