Variant Mendelian Tools
From Center for Statistical Genetics
- Hang Dai, Baylor College of Medicine
- Gao T. Wang, Baylor College of Medicine
- Regie Lyn Pastor Santos-Cortez, Baylor College of Medicine
- Di Zhang, Baylor College of Medicine
- Bo Peng, The University of Texas MD Anderson Cancer Center
- Suzanne M. Leal, Baylor College of Medicine
Development of Variant Mendelian Tools (VMT) was supported by the Centers for Mendelian Genomics.
Variant Mendelian Tools (VMT) is a software implementing well-defined bioinformatics protocols for gene discovery for Mendelian and complex familial traits. VMT can be used to analyze NGS data to identify causal variants which are either de novo, underlie Mendelian phenotypes or complex traits with familial aggregation. Analysis can be performed using sequence data from a single study subject to many individuals from multiple families. Utility of VMT is supported by successful implementation in the identification of novel genes for traits ranging from thoracic aortic aneurysm and dissection to nonsyndromic hearing impairment.
Use of VMT involves simple steps to integrate data from different source files (e.g. VCF, SAM/BAM, genotype data from ExomeCytoChip, phenotype, genotype and variant information files of arbitrary formats), performing variant annotations, and selecting potentially causal variants from single or multiple exomes based on several parameters, including but not limited to mode of inheritance, variant sharing among pedigree members, population minor allele frequency (MAF), functional annotation and prediction, and if available results from linkage mapping studies. The design of VMT makes it possible for users to download our established protocol in VMT syntax which is compact and human-readable, to make adjustments to adapt the protocol to their research projects without having knowledge in programming, and to complete the analysis by simply submitting the protocol to the VMT environment. VMT also maintains a mechanism to organize and browse various protocol implementations, which greatly enhances speed and reproducibility of work and facilitates collaborations in data analysis. VMT will be highly beneficial to clinicians and researchers who aim to identify disease-causal variants from NGS data but have minimal knowledge and experience in the use of Linux and programming languages, variant annotations and/or variant discovery in family data.
VMT requires installing Anaconda for Python 2 as dependencies. After installation, open a terminal and type in 'python'. If an error is returned, it need to add python to system path.
To install VMT,
- Download source code
- Unarchive the files
tar -xf VMT-.tar.gz
- Installing the package
python setup.py install