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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Reconstructing evolutionary trees in parallel for massive sequences.

Quan Zou1,2,3, Shixiang Wan1, Xiangxiang Zeng4

  • 1School of Computer Science and Technology, Tianjin University, Tianjin, People's Republic of China.

BMC Systems Biology
|January 4, 2018
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Summary
This summary is machine-generated.

This study introduces HPTree, a novel software tool for rapidly reconstructing evolutionary trees from massive DNA sequences using parallel processing. HPTree efficiently handles large datasets, improving upon existing methods for evolutionary biology research.

Keywords:
AlgorithmComputational biologyEvolutionary treeHadoopMultiple sequence alignmentSpark

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Genetics

Background:

  • Reconstructing evolutionary trees from massive, unaligned DNA sequences presents significant computational challenges.
  • Traditional methods for multiple sequence alignment and evolutionary tree building are time and space-intensive for large datasets.

Purpose of the Study:

  • To develop a parallelized software tool for efficient evolutionary tree reconstruction from large-scale, unaligned DNA sequences.
  • To leverage the Hadoop and Spark platforms for addressing computational bottlenecks in bioinformatics.

Main Methods:

  • Designed and implemented HPTree, a software tool utilizing Hadoop and Spark for parallel processing.
  • Employed clustering and multiple sequence alignment in parallel.
  • Utilized the Neighbour-Joining model for evolutionary tree construction.

Main Results:

  • HPTree demonstrates efficient processing of DNA sequence files exceeding 1GB.
  • The tool exhibits superior performance compared to existing evolutionary reconstruction software.
  • HPTree is suitable for deployment on computer clusters and cloud platforms like Amazon Cloud.

Conclusions:

  • HPTree offers a scalable and efficient solution for evolutionary tree reconstruction from massive DNA datasets.
  • The software, including source code, is publicly available, facilitating its adoption in population evolution research and metagenomics.
  • Parallel processing on Hadoop and Spark significantly enhances the speed and capability of handling large-scale bioinformatics problems.