tmtk - TranSMART data curation toolkit

Author:Jochem Bijlard
Source Code:
Generated:Jul 31, 2020


A toolkit for ETL curation for the tranSMART data warehouse for translational research.

The TranSMART curation toolkit (tmtk) aims to provide a language and set of classes for describing data to be uploaded to tranSMART. The toolkit can be used to edit and validate studies prior to loading them with transmart-batch.

Functionality currently available:
  • create a transmart-batch ready study from clinical data files.
  • load an existing study and validate its contents.
  • edit the transmart concept tree in The Arborist graphical editor.
  • create chromosomal region annotation files.
  • map HGNC gene symbols to corresponding Entrez gene IDs using


tmtk is a python3 package meant to be run in Jupyter notebooks. Results for other setups may vary.

Basic Usage

Step 1: Opening a notebook

First open a Jupyter Notebook, open a shell and change directory to some place where your data is. Then start the notebook server:

cd /path/to/studies/
jupyter notebook

This should open your browser to Jupyters file browser, create a new notebook for here.

Step 2: Using tmtk

# First import the toolkit into your environment
import tmtk

# Then create a <tmtk.Study> object by pointing to study.params of a transmart-batch study
study = tmtk.Study('~/studies/a_tm_batch_ready_study/study.params')
# Or, by using the study wizard on a directory with correctly structured, clinical data files.
# (Visit the transmart-batch documentation to find out what is expected.)
study = tmtk.wizard.create_study('~/studies/dir_with_some_clinical_data_files/')

Now we have loaded the study as a tmtk.Study object we have some interesting functions available:

# Check whether transmart-batch will find any issues with the way your study is setup

# Graphically manipulate the concept tree in this study by using The Arborist


  • Wibo Pipping
  • Stefan Payrable
  • Ward Weistra