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SeqHBase: a big data toolset for family based sequencing data analysis.

Min He1, Thomas N Person2, Scott J Hebbring3

  • 1Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA Department of Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Journal of Medical Genetics
|January 15, 2015
PubMed
Summary
This summary is machine-generated.

SeqHBase efficiently analyzes large-scale genomic sequencing data for families. This big data toolset enables rapid identification of disease-causing mutations from whole-genome sequencing and whole-exome sequencing data.

Keywords:
big datade novo mutationsinherited homozygous or compound heterozygous mutationswhole-exome sequencingwhole-genome sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Whole-genome sequencing (WGS) and whole-exome sequencing (WES) are vital for identifying disease-causing mutations.
  • Processing large-scale family or cohort sequencing data presents significant computational challenges.
  • Efficient manipulation of genomic variants, annotations, and coverage is crucial for family-based analysis.

Purpose of the Study:

  • To develop a big data toolset for efficient analysis of family-based sequencing data.
  • To enable the manipulation of genome-wide variants, functional annotations, and coverage.
  • To facilitate the detection of disease-contributing mutations in familial studies.

Main Methods:

  • Developed SeqHBase, a big data toolset leveraging Hadoop and HBase.
  • SeqHBase processes BAM (coverage), VCF (variants), and functional annotation files.
  • The toolset is designed for analyzing family-based sequencing data to detect various mutation types.

Main Results:

  • SeqHBase was applied to WGS and WES data from multiple families (4-10 members).
  • Analysis time demonstrated near-linear scalability with the number of data nodes.
  • With 20 data nodes, WES data analysis took ~5 seconds, and WGS data analysis took ~1 minute.

Conclusions:

  • SeqHBase exhibits high efficiency and scalability for familial sequencing data analysis.
  • The toolset addresses the growing need for processing large genomic datasets in genetic research.
  • Its performance is critical as WGS and WES become standard for studying familial disorders.