Hail is an open-source, general-purpose, Python-based data analysis tool with additional data types and methods for working with genomic data.
Hail is built to scale and has first-class support for multi-dimensional structured data, like the genomic data in a genome-wide association study (GWAS).
Hail is exposed as a Python library, using primitives for distributed queries and linear algebra implemented in Scala, Spark, and increasingly C++.
See the documentation for more info on using Hail.
If you'd like to discuss or contribute to the development of methods or infrastructure, please:
Hail uses a continuous deployment approach to software development, which means we frequently add new features. We update users about changes to Hail via the Discussion Forum. We recommend creating an account on the Discussion Forum so that you can subscribe to these updates as well.
Hail is maintained by a team in the Neale lab at the Stanley Center for Psychiatric Research of the Broad Institute of MIT and Harvard and the Analytic and Translational Genetics Unit of Massachusetts General Hospital.
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())
Which will look like:
Hail Team. Hail 0.2.13-81ab564db2b4. https://github.com/hail-is/hail/releases/tag/0.2.13.
The Hail team has several sources of funding at the Broad Institute:
We are grateful for generous support from: