https://github.com/combat-tb/tb2neo

A tool to aggregate and load TB data to Neo4j
https://github.com/combat-tb/tb2neo

genome-annotation gff3 neo4j py2neo tuberculosis

Last synced: about 2 months ago
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A tool to aggregate and load TB data to Neo4j

• Host: GitHub
• URL: https://github.com/combat-tb/tb2neo
• Owner: COMBAT-TB
• License: gpl-3.0
• Created: 2017-07-27T10:12:42.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T04:49:37.000Z (over 3 years ago)
• Last Synced: 2025-05-25T20:45:48.144Z (about 1 year ago)
• Topics: genome-annotation, gff3, neo4j, py2neo, tuberculosis
• Language: Python
• Homepage:
• Size: 6.94 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# tb2neo

[![Build Status](https://travis-ci.org/COMBAT-TB/tb2neo.svg?branch=master)](https://travis-ci.org/COMBAT-TB/tb2neo) [![Coverage Status](https://coveralls.io/repos/github/COMBAT-TB/tb2neo/badge.svg?branch=master)](https://coveralls.io/github/COMBAT-TB/tb2neo?branch=master)

Parses _M.tuberculosis_ annotation (GFF file) and builds a Neo4j graph

database storing the annotated features. _tb2neo_ also aggregates and maps

these annotated features to external services such as UniProt, CheMBL,

DrugBank, KEGG, Reactome, QuickGo, STRING-DB etc.

## Usage

### Neo4j Installation

- Instructions for a standalone Neo4j installation can be found [here](https://neo4j.com/docs/operations-manual/current/installation/).

- Using [Docker](https://docs.docker.com/):

    ```sh

    $ docker run -d -p 7474:7474 -p 7687:7687 --name neo -e NEO4J_AUTH=none -v=$HOME/neo4j/data:/data neo4j:3.5

    ...

    ```

### `tb2neo` Installation

- Using `pip`:

    ```sh

    $ pip install -i https://test.pypi.org/simple/ tb2neo

    ...

    ```

- Using `setup`:

    Clone repository and create a virtual environment:

    ```sh

    $ git clone https://github.com/COMBAT-TB/tb2neo.git

    ...

    $ cd tb2neo

    $ virtualenv envname

    $ source envname/bin/activate

    $ pip install -r requirements.txt

    $ python setup.py install

    ```

### Loading TB Data

To load TB data, we use the H37Rv GFF3 file from [EnsemblBacteria](https://bacteria.ensembl.org/Mycobacterium_tuberculosis_h37rv/Info/Index/).

Run the following command to see available options

```sh

$ tb2neo --help

...

```

Run the following command to load GFF features to Neo4j

```sh

$ tb2neo load_gff --gff_files PATH/TO/TB_GFF3_FILES

...

```

### Exploring TB data

Point your browser to [localhost:7474](http://localhost:7474]).

```cql

db.schema()

```

![DB_MODEL](./images/neodbschema.png)