Hail-Powered Science

The following is an incomplete list of scientific work enabled by Hail.

If you use Hail for published work, please cite the software. You can get a citation for the version of Hail you installed by executing:

import hail as hl
print(hl.citation())

Or you could include the following line in your bibliography:

Hail Team. Hail 0.2. https://github.com/hail-is/hail

Otherwise, we welcome you to add additional examples by editing this page directly, after which we will review the pull request to confirm the addition is valid. Please adhere to the existing formatting conventions.

Last updated on March 30th, 2020

2020

2019

2018

2017

2016

Footnote In addition to software development, the Hail team engages in theoretical, algorithmic, and empirical research inspired by scientific collaboration. Examples include Loss landscapes of regularized linear autoencoders, Secure multi-party linear regression at plaintext speed, and A synthetic-diploid benchmark for accurate variant-calling evaluation.