BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly

Ka-Kit Lam, Richard Hall, Alicia Clum, Satish Rao

The problem of de-novo assembly for metagenomes using only long reads is gaining attention. We study whether post-processing metagenomic assemblies with the original input long reads can result in quality improvement. Previous approaches have focused on pre-processing reads and optimizing assemblers. BIGMAC takes an alternative perspective to focus on the post-processing step. Using both the assembled contigs and original long reads as input, BIGMAC first breaks the contigs at potentially mis-assembled locations and subsequently scaffolds contigs. Our experiments on metagenomes assembled from long reads show that BIGMAC can improve assembly quality by reducing the number of mis-assemblies while maintaining or increasing N50 and N75. Moreover, BIGMAC shows the largest N75 to number of mis-assemblies ratio on all tested datasets when compared to other post-processing tools. BIGMAC demonstrates the effectiveness of the post-processing approach in improving the quality of metagenomic assemblies.