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GitHub release PyPI PyPI - Downloads GitHub


PLOS Computational Biology Article:

bioRxiv Preprint:

Getting started


DART-ID requires Python >= 3.7 (64-bit - miniconda distribution recommended), and has been tested on Windows 8 / OSX Mojave 10.14 / Centos 7 / Ubuntu 14.04.


DART-ID is available on PyPI and can be installed with pip.

pip install dart-id


DART-ID requires a YAML-formatted configuration file to run. An example annotated config file can be found in example/config_annotated.yaml. You can specify input files and the output folder on the command line, if that's what you prefer.

View the command-line arguments anytime by running: dart_id -h.

usage: dart_id [-h] [-i INPUT [INPUT ...]] [-o OUTPUT] [-v] [--version] -c

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT [INPUT ...], --input INPUT [INPUT ...]
                        Input file(s) from search engine output (e.g.,
                        MaxQuant evidence.txt). Not required if input files
                        are specified in the config file
  -o OUTPUT, --output OUTPUT
                        Path to output folder
  -v, --verbose
  --version             Display the program's version
  -c CONFIG_FILE, --config-file CONFIG_FILE
                        Path to config file (required). See

Example runs

An example configuration file can be downloaded from GitHub:

The first few lines of the above configuration file specify the path to the input file:

## Input
## ==========================

  - /path/to/SQC_67_95_Varied/evidence.txt

You can download the evidence.txt file from MassIVE:

Then edit the path to the file downloaded, and run the following command:

dart_id -c config_files/example_sqc_67_95_varied.yaml -o ~/DART_ID/SQC_67_95_varied_20181206

The -o parameter points to the output folder for DART-ID. You can also specify this path in the config file.

An example analysis of the data and configuration file specified above is available publicly at

About the project

DART-ID is a project developed in the Slavov Laboratory at Northeastern University Bioengineering, and was authored by Albert Tian Chen, Alexander Franks (of UCSB Statistics and Applied Probability), and Nikolai Slavov.

The article for DART-ID is freely available on PLOS Computational Biology:

The preprint is also available on bioRxiv:

Contact the authors by email: nslavov{at}


DART-ID is distributed by an MIT license.


Please feel free to contribute to this project by opening an issue or pull request in the GitHub repository.


All data used for the manuscript is available on UCSD's MassIVE Repository


Scripts for the figures in the DART-ID manuscript are available in a separate GitHub repository,