MaSuRCA

MaSuRCA assembles whole genomes by transforming paired-end reads into super-reads and combining de Bruijn graph and Overlap-Layout-Consensus (OLC) methods to integrate Illumina, 454, and Sanger sequencing data.

Key Features:

• Hybrid Assembly Approach: Merges computational speed of de Bruijn graph methods with adaptability of overlap-based Overlap-Layout-Consensus (OLC) strategies.
• Super-Reads Transformation: Transforms large numbers of paired-end reads into a smaller set of longer super-reads to enable integration of short reads with longer reads.
• Versatility in Data Handling: Assembles datasets composed of only short reads or combinations of short and long reads from technologies including Illumina, 454, and Sanger.
• Performance Evaluation: Performed on par or better than Allpaths-LG and outperformed SOAPdenovo2 when evaluated against high-quality reference sequences of Rhodobacter sphaeroides and mouse chromosome 16.
• Enhanced Assembly with Long Reads: Improves assembly quality by augmenting short-read data with long reads.

Scientific Applications:

• Microbial Genomics: Reconstruction of microbial genomes from mixed read-length datasets.
• Model Organism Genomics: Assembly of chromosomes and genomes such as mouse chromosome 16 using mixed sequencing technologies.
• General Genome Reconstruction Projects: Precise whole-genome assembly tasks that require integration of Illumina, 454, Sanger, short-read, and long-read data.

Methodology:

Transforms paired-end reads into super-reads and uses those super-reads to construct a de Bruijn graph while integrating de Bruijn graph and Overlap-Layout-Consensus (OLC) approaches.