LazyB: fast and cheap genome assembly.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Genome sequencing costs have decreased, shifting focus to computational challenges in genome assembly.
• Large and complex genomes (e.g., plant, animal) require more efficient and accurate assembly methods.
• Hybrid assembly methods integrating short and long reads offer a promising solution.

Purpose of the Study:

• To develop a novel hybrid genome assembler, LazyB, optimized for low-coverage short and long reads.
• To improve the accuracy and efficiency of genome assembly for complex genomes.

Main Methods:

• LazyB utilizes a bipartite overlap graph between long reads and filtered short-read unitigs.
• It employs a novel subgraph extraction strategy to identify paths, avoiding traditional local feature removal.
• The method involves creating a consistently oriented subgraph, reducing it to a directed acyclic graph, and extracting contigs as maximum weight paths.

Main Results:

• LazyB achieves significantly more accurate genome assemblies for yeast and fruit fly compared to existing pipelines.
• The Python-implemented prototype demonstrates substantially lower computational effort.
• The assembler effectively utilizes low-coverage short and long reads.

Conclusions:

• LazyB is a cost-effective genome assembler adept at handling large genomes and low-coverage data.
• Its innovative path-reduction approach to overlap graphs presents new opportunities for advancing genome assembly.
• The tool is available as an open-source prototype.