💎 GeMS 💎

Geneset Management System

GeMS is an open source project that provides a lightweight framework for deploying a geneset management system locally using MongoDB and Flask.

Project Aims

• Central repository for storing geneset references
• Introduce new GMTx file format for storing geneset-related data
• Provide programmatic access to the database with a RESTful API

Group Members

• Albert Kang ([email protected])
• Laura Badi ([email protected])

Contents

• 1. For General Users
  • 1.1. Uploading Genesets
  • 1.2. Programmatic Access to Genesets
• 2. For Developers

1. For General Users

1.1. Uploading Genesets

What is a GMTx file?

• Like GMT files:

  • It is a tab-delimited text file
  • The trailing tail columns represent the membership genes

• Unlike GMT files:

  • GMTx files allow metadata on top of geneset names and geneset descriptions
  • GMTx files allow coefficients attached to a membership gene to be stored

• See the explanation of the GMT file format here: https://software.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats#GMT:_Gene_Matrix_Transposed_file_format_.28.2A.gmt.29

• GMTx file format on top of the GMT file format is as follows:

  • GMTx files require row 1 to be contain column headers:
    • ...accepted headers are: setName, genes, xref, setId, desc
    • ... any other header names will be considered as a 'meta-tag'
  • GMTx files MUST include the following headers: setName and genes
    • ... setName SHOULD be the first header column
    • ... genes MUST be the last header column
  • Membership gene can be written in the following formats:
    • ... GENE (as in regular GMT files)
    • ... GENE | VALUE (new to GMTx files)

Instructions for converting to the GMTx file format can be found here: ./GeMS/src/x_to_gmtx_converter

Dependencies

pip install gunicorn
pip install pymongo
pip install flask
pip install flask_restful
pip install xmltodict

How to upload

The main upload logic is here: \GeMS\src\api\upload.py

The CLI arguments are as follows:

Args	Name	Required	Examples
--fl	File location	O
--gf	Gene format	O	0, 1, 2, 3
--so	Source	O	Roche, MSigDB...
--ti	NCBI Taxonomy ID	O	9606, 10090...
--us	User	O	Public, badil...
--st	Subtype	X	C7, BP...
--do	Domain	X	pathway, cell marker...

Single GMTx file upload (Example: Reactome)

[\GeMS\src\api\] python upload.py --fl ../../data/Reactome/ReactomePathways.gmtx --gf 0 --so Reactome --ti 9606 --us Public --do pathway

Bulk upload on HPC (Reactome, CellMarker, CREEDS and MSigDB)

[\GeMS\] chmod +x upload.sh
[\GeMS\] sbatch -J bulkUpload -o bulkUpload.out -e bulkUpload.err --ntasks=1 --qos=normal --cpus-per-task=16 --wrap="./upload.sh"

1.2. Programmatic Access to Genesets

You can query the genesets stored in GeMS using our REST-API service. A detailed explanation of the supported tools and services can be found here: ./GeMS/src/api

1.2.1. R client in ribiosGSEA

From version 1.4.0, ribiosGSEA supports reading, inserting, and removing genesets from GeMS. See the vignette 'working-with-GeMS' of the ribiosGSEA package.

2. For Developers

src layout

.
├── ...
├── src
│   │
│   ├── api                    Flask REST-API and GMTx file loader
│   │   │
│   │   ├── db_utils.py        Database configuration
│   │   │   
│   │   ├── app.py             Main: Flask REST-API
│   │   ├── app_utils.py       Helper functions for quantifying geneset similarity
│   │   ├── wsgi.py            WSGI production server interface (for use with *gunicorn*)
│   │   │   
│   │   ├── upload.py          Main: GMTx upload + API upload
│   │   ├── db_utils.py        GeMS database initialisation logic
│   │   ├── map_utils.py       Use NCBI collections to infer gene IDs and symbols
│   │   ├── gmtx_utils.py      Helper functions for parsing GMTx files
│   │   │   
│   │   └── README.md          Documentation for the REST-API
│   │	
│   ├── ncbi_gene_mapper       Deprecated: NCBI Gene and Homologene to MongoDB 
│   └── x_to_gmtx_converter    Conversion to GMTx files
└── ...

Deploying RESTful-Flask application on a local machine - Docker required

cp start_podman.sh start_podman_localhost.sh

Edit start_podman_localhost.sh and change ENV variables to configure the connection to MongoDB instance

Then run:

bash start_podman_localhost.sh

Working with hierarchies

db.getCollection('GeMS_set2set').aggregate([{
   $graphLookup: {
      from: 'GeMS_set2set',
      startWith: '$setId',
      connectFromField: 'setId',
      connectToField: 'parentId',
      as: 'children',
      maxDepth: 10,
      depthField: 'depth'
   }
}])

GeMS