LazyB

LazyB assembles genomes from low-coverage Illumina short reads and Nanopore long reads by constructing and reducing overlap graphs to extract contigs for large and complex genomes.

Key Features:

• Hybrid Assembly Approach: Integrates Illumina short reads and Nanopore long reads to leverage their complementary information for assembly accuracy.
• Bipartite Overlap Graph Construction: Builds a bipartite overlap graph between long reads and selectively filtered short-read unitigs and transforms it into a long-read overlap graph (G).
• Path Extraction Strategy: Extracts subgraphs with global properties resembling disjoint unions of paths rather than focusing on removing local features such as tips and bubbles.
• Graph Reduction and Contig Identification: Extracts a consistently oriented subgraph, reduces it into a directed acyclic graph (DAG), and identifies contigs as maximum weight paths using properties of proper interval graphs.
• Computational Efficiency: Reduces computational demands while enhancing assembly accuracy, targeting large genomes and low-coverage data scenarios.
• Implementation: Prototype implementation is provided in Python.

Scientific Applications:

• Model genome assembly: Demonstrated superior performance compared to state-of-the-art pipelines in assembling the yeast and fruit fly genomes, yielding more accurate assemblies with reduced computational effort.
• Large and complex genomes from low-coverage data: Applicable to assembly of large animal and plant genomes from low-coverage Illumina and Nanopore sequencing.

Methodology:

Construct a bipartite overlap graph between long reads and selectively filtered short-read unitigs, transform it into a long-read overlap graph (G), extract a consistently oriented subgraph, reduce this subgraph into a directed acyclic graph (DAG), and use properties of proper interval graphs to identify contigs as maximum weight paths.