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FMFilter: A fast model based variant filtering tool.

Mete Akgün1, Ö Faruk Gerdan1, Zeliha Görmez1

  • 1Advanced Genomics and Bioinformatics Research Center (İGBAM), Informatics and Information Security Research Center (BİLGEM), The Scientific and Technological Research Council of Turkey (TÜBİTAK), 41470 Gebze, Kocaeli, Turkey.

Journal of Biomedical Informatics
|March 2, 2016
PubMed
Summary
This summary is machine-generated.

FMFilter is a new, user-friendly software for analyzing genetic disease data from next-generation sequencing. It efficiently filters variants, supporting various inheritance models and handling large datasets on standard computers.

Keywords:
Next generation sequencingRare diseasesVariant filtering

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) has revolutionized genetic disease studies, enabling faster and more cost-effective identification of disease mechanisms.
  • Analyzing the vast amount of data generated by NGS presents challenges, with a need for efficient and user-friendly tools.
  • Existing tools often lack support for compound heterozygous and de novo models or require advanced computational expertise and resources.

Purpose of the Study:

  • To develop an efficient and user-friendly software tool, FMFilter, for analyzing next-generation sequencing data in genetic disease studies.
  • To address the limitations of current tools by supporting diverse inheritance models and simplifying data analysis for researchers without advanced IT skills.

Main Methods:

  • Developed FMFilter, a software with a graphical user interface (GUI) for analyzing NGS data.
  • Implemented support for multiple inheritance models: recessive, dominant, compound heterozygous, and de novo.
  • Integrated various filtering options to reduce false positives and optimized for low memory usage to handle large variant files.

Main Results:

  • FMFilter demonstrated significant variant reduction capabilities on public and in-house datasets across different inheritance models.
  • The software effectively handles large variant files, including multiple whole genomes, on ordinary computers due to its negligible memory requirement.
  • Comparative analysis showed FMFilter's effectiveness against existing filtering software.

Conclusions:

  • FMFilter provides an effective, efficient, and easy-to-use solution for analyzing next-generation sequencing data in Mendelian disease research.
  • The tool's GUI and low computational requirements make advanced genetic data analysis accessible to a broader range of researchers.
  • FMFilter facilitates faster and more accurate identification of disease-causing variants.