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Next generation distributed computing for cancer research.

Pankaj Agarwal1, Kouros Owzar2

  • 1Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA.

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Summary
This summary is machine-generated.

Next generation sequencing (NGS) and mass spectrometry (MS) generate Big Data challenges. Scalable computing, like Hadoop, offers solutions for translational cancer research data management and analysis.

Keywords:
NGSbig datacancercloud computingclusterdata managementdata storagegenomicsgpuhadoophigh performance computinginformaticsscalable computing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) and mass spectrometry (MS) generate vast datasets for translational cancer research.
  • Traditional computing models struggle with the scale and complexity of this Big Data.
  • New informatics approaches are essential for effective data management and analysis.

Purpose of the Study:

  • To review next-generation distributed computing technologies for Big Data challenges in cancer research.
  • To describe scalable computing infrastructure, focusing on Hadoop.
  • To compare Hadoop with other distributed computing technologies.

Main Methods:

  • Overview of scalable computing principles.
  • Detailed description of Hadoop architecture and components (computing, storage, networking).
  • Presentation of a proof-of-concept Hadoop cluster for NGS read alignment benchmarking.

Main Results:

  • Hadoop is a rapidly adopted technology for large-scale distributed computing.
  • Benchmarking demonstrates Hadoop's capability for NGS data analysis.
  • Comparison highlights Hadoop's position relative to other distributed computing solutions.

Conclusions:

  • Distributed computing, particularly Hadoop, addresses the informatics challenges posed by NGS and MS data.
  • Scalable computing platforms are crucial for advancing translational cancer research.
  • Hadoop offers a viable solution for managing and analyzing Big Data in genomics and proteomics.