Abstract
BackgroundHigh throughput sequencing technologies have been increasingly used in basic genetic research as well as in clinical applications. More and more variants underlying Mendelian and complex diseases are being discovered and documented using these technologies. However, identifying and obtaining a short list of candidate disease-causing variants remains challenging for most of the users after variant calling, especially for people without computational skills.ResultsWe developed GenESysV (Genome Exploration System for Variants) as a scalable, intuitive and user-friendly open source tool. It can be used in any high throughput sequencing or genotyping project for storing, managing, prioritizing and efficient retrieval of variants of interest. GenESysV is designed for use by researchers from a wide range of disciplines and computational skills, including wet-lab scientists, clinicians, and bioinformaticians.ConclusionsGenESysV is the first tool to be able to handle genomic variant dataset ranging in size from a few to thousands of samples and still maintain fast data importation and good query performance. It has a very intuitive graphical user interface and can also be used in studies where secured data access is an important concern. We believe this tool will benefit the human disease research community to speed up discoveries for genetic variants underlying human genetic disorders.
Highlights
High throughput sequencing technologies have been increasingly used in basic genetic research as well as in clinical applications
The full 1000 Genomes Project phase 3 dataset containing 85 million variants from 2504 samples can be loaded in less than 34 h using GenESysV under our test system, as compared to nearly 91 h needed by GEMINI (Fig. 3)
We found that loading these Annovar annotated Variant Calling Format (VCF) files took a similar amount of time as the same set of VCF files annotated with Variant Effect Predictor (VEP), albeit the Annovar annotated files are two times larger in size due to more annotation fields they contain (Additional file 2: Figure S3)
Summary
High throughput sequencing technologies have been increasingly used in basic genetic research as well as in clinical applications. Variants are subsequently filtered by their minor allele frequencies and other criteria such as the functional consequences to the genes and transcripts they affect, conservation scores [10, 11], predicted pathogenicity scores [12, 13], known associations with disease phenotypes [14,15,16], etc. After these filtering steps, a short list of candidate
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