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VCF-Server: A web-based visualization tool for high-throughput variant data mining and management.

Jianping Jiang1, Jianlei Gu1,2, Tingting Zhao1

  • 1Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Molecular Genetics & Genomic Medicine
|May 26, 2019
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Summary
This summary is machine-generated.

VCF-Server is a new web-based tool that simplifies genetic variant analysis for researchers. It enables easy querying, filtering, and annotation of variant data without programming skills, improving accessibility to next-generation sequencing insights.

Keywords:
NGSVCF visualizationsoftwarevariant filteringvariant management

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) is crucial for diagnosing genetic disorders and understanding disease etiology.
  • Genetic variants identified by NGS are stored in variant call format (VCF) files.
  • Querying and managing large VCF datasets pose significant challenges for researchers lacking programming expertise.

Purpose of the Study:

  • To develop an accessible web-based tool for interactive genetic variant data analysis.
  • To empower researchers and medical geneticists to manage, annotate, filter, and query VCF files efficiently.
  • To address the growing need for centralized management of exponentially increasing mutation data.

Main Methods:

  • Development of VCF-Server, a web-based visualization and analysis platform.
  • Implementation of user-friendly interface for querying, annotating, and filtering variants without coding.
  • Integration of annotation capabilities with common databases and user-defined lists.
  • Adoption of a web file system for centralized data storage and management.

Main Results:

  • VCF-Server provides an intuitive graphical interface for exploring genetic variant data.
  • Users can annotate VCF files with various databases and filter variants using flexible rules.
  • The tool supports centralized data management and analysis via web or local deployment.
  • Prioritized variants can be exported for further downstream analysis.

Conclusions:

  • VCF-Server democratizes variant analysis, enabling researchers with limited bioinformatics backgrounds to utilize NGS data.
  • The tool enhances the exploration and mining of mutation data, potentially expanding NGS applications in clinical and research settings.
  • VCF-Server is freely available, promoting wider adoption and accessibility of advanced genetic analysis techniques.