MAC: Merging Assemblies by Using Adjacency Algebraic Model and Classification

With the generation of a large amount of sequencing data, different assemblers have emerged to perform de novo genome assembly. As a single strategy is hard to fit various biases of datasets, none of these tools outperforms the others on all species. The process of assembly reconciliation is to merge multiple assemblies and generate a high-quality consensus assembly. Several assembly reconciliation tools have been proposed. However, the existing reconciliation tools cannot produce a merged assembly which has better contiguity and contains less errors simultaneously, and the results of these tools usually depend on the ranking of input assemblies. In this study, we propose a novel assembly reconciliation tool MAC, which merges assemblies by using the adjacency algebraic model and classification. In order to solve the problem of uneven sequencing depth and sequencing errors, MAC identifies consensus blocks between contig sets to construct an adjacency graph. To solve the problem of repetitive region, MAC employs classification to optimize the adjacency algebraic model. What's more, MAC designs an overall scoring function to solve the problem of unknown ranking of input assembly sets. The experimental results from four species of GAGE-B demonstrate that MAC outperforms other assembly reconciliation tools.