title: BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  BIGMAC demonstrates the effectiveness of the post-processing approach in improving the quality of metagenomic assemblies.
authors: Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, USA.
authors: Ka-Kit
authors: http://orcid.org/0000-0002-9183-0223
authors: KK
authors: Lam
authors: Pacific Biosciences, Menlo Park, USA.
authors: Richard
authors: R
authors: Hall
authors: Joint Genome Institute, Walnut Creek, USA.
authors: Alicia
authors: A
authors: Clum
authors: Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, USA. satishr@cs.berkeley.edu.
authors: Satish
authors: S
authors: Rao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D054892:Metagenome; D056186:Metagenomics / Q000379:methods*; D012984:Software; D013223:Statistics as Topic
keywords: Genome assembly; Metagenomics; Next generation sequencing