CharGer: clinical Characterization of Germline variants

Adam D Scott; Kuan-Lin Huang; Amila Weerasinghe; R Jay Mashl; Qingsong Gao; Fernanda Martins Rodrigues; Matthew A Wyczalkowski; Li Ding

doi:10.1093/bioinformatics/bty649

CharGer: clinical Characterization of Germline variants

Bioinformatics. 2019 Mar 1;35(5):865-867. doi: 10.1093/bioinformatics/bty649.

Authors

Adam D Scott^{1

2}, Kuan-Lin Huang^{1

2}, Amila Weerasinghe^{1

2}, R Jay Mashl^{1

2}, Qingsong Gao^{1

2}, Fernanda Martins Rodrigues^{1

2}, Matthew A Wyczalkowski^{1

2}, Li Ding^{1

2

3

4}

Affiliations

¹ Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.
² McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
³ Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
⁴ Siteman Cancer Center, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.

Abstract

Summary: CharGer (Characterization of Germline variants) is a software tool for interpreting and predicting clinical pathogenicity of germline variants. CharGer gathers evidence from databases and annotations, provided by local tools and files or via ReST APIs, and classifies variants according to ACMG guidelines for assessing variant pathogenicity. User-designed pathogenicity criteria can be incorporated into CharGer's flexible framework, thereby allowing users to create a customized classification protocol.

Availability and implementation: Source code is freely available at https://github.com/ding-lab/CharGer and is distributed under the GNU GPL-v3.0 license. Software is also distributed through the Python Package Index (PyPI) repository. CharGer is implemented in Python 2.7 and is supported on Unix-based operating systems.

Supplementary information: Supplementary data are available at Bioinformatics online.

CharGer: clinical Characterization of Germline variants

Authors

Affiliations

Abstract

Publication types

MeSH terms

Grants and funding