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- Adam D Scott
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA
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- Kuan-Lin Huang
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA
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- Amila Weerasinghe
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA
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- R Jay Mashl
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA
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- Qingsong Gao
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA
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- Fernanda Martins Rodrigues
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA
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- Matthew A Wyczalkowski
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA
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- Li Ding
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA
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- John Hancock
- editor
抄録
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Summary</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>
収録刊行物
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- Bioinformatics
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Bioinformatics 35 (5), 865-867, 2018-08-09
Oxford University Press (OUP)
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キーワード
詳細情報 詳細情報について
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- CRID
- 1362825894141091328
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- ISSN
- 13674811
- 13674803
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- データソース種別
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- Crossref