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Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
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Complete assembly of circular and chloroplast genomes based on global optimization
Rumen Andonov1, Hristo Djidjev2, Sebastien François1
1* Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France.
Journal of Bioinformatics and Computational Biology
|July 11, 2019
Summary
This study unifies genome assembly scaffolding and gap-filling into one optimization problem. It uses mixed-integer linear programming to find exact solutions, addressing challenges from repetitive DNA sequences.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Genome assembly is crucial for understanding genetic information.
- Scaffolding and gap-filling are critical but challenging final stages.
- Existing methods may struggle with complex genomic structures.
Purpose of the Study:
- To integrate scaffolding and gap-filling into a single optimization framework.
- To model genome assembly as a pathfinding problem with distance constraints.
- To develop an exact solver for these genome assembly stages.
Main Methods:
- Formulating genome assembly as a mixed-integer linear programming (MILP) problem.
- Utilizing graph theory to represent assembly as a simple path problem.
- Applying optimization solvers to chloroplast genome data.
Main Results:
- Demonstrated that scaffolding and gap-filling can be solved as a unified optimization problem.
- Identified repetitive sequences as a source of multiple equivalent assembly solutions.
- Developed algorithms to efficiently identify alternative subpaths.
Conclusions:
- The proposed MILP approach provides an exact solution for genome assembly scaffolding and gap-filling.
- Repetitions necessitate methods for handling alternative subpaths.
- The developed tool shows competitive performance compared to existing assemblers.
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