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MAC: Merging Assemblies by Using Adjacency Algebraic Model and Classification.

Li Tang1, Min Li1, Fang-Xiang Wu1,2

  • 1School of Computer Science and Engineering, Central South University, Changsha, China.

Frontiers in Genetics
|February 22, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces MAC, a novel tool for genome assembly reconciliation. MAC merges multiple genome assemblies to create a superior consensus assembly, outperforming existing methods in contiguity and accuracy.

Keywords:
adjacency algebraic modelcontig classificationcontig reconciliationde novo assemblynext-generation sequencing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • De novo genome assembly generates large sequencing datasets, necessitating various assemblers.
  • No single assembler is optimal for all species due to dataset biases.
  • Assembly reconciliation merges multiple assemblies for improved quality.

Purpose of the Study:

  • To develop a novel assembly reconciliation tool, MAC.
  • To address limitations of existing tools, which struggle with contiguity, error rates, and input assembly ranking.

Main Methods:

  • MAC utilizes an adjacency algebraic model and classification for merging assemblies.
  • Identifies consensus blocks to build an adjacency graph, addressing uneven sequencing depth and errors.
  • Employs classification to optimize the model for repetitive regions.
  • Incorporates a scoring function to overcome unknown input assembly rankings.

Main Results:

  • MAC successfully merges multiple genome assemblies.
  • Demonstrates superior performance compared to other assembly reconciliation tools.
  • Experimental results on four GAGE-B species validate MAC's effectiveness.

Conclusions:

  • MAC offers an improved approach to genome assembly reconciliation.
  • The tool enhances both contiguity and accuracy in consensus assemblies.
  • MAC provides a robust solution independent of input assembly ranking.