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

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...

Full description

Saved in:
Bibliographic Details
Published in:BMC bioinformatics 2016-10, Vol.17 (1), p.435-435, Article 435
Main Authors: Lam, Ka-Kit, Hall, Richard, Clum, Alicia, Rao, Satish
Format: Article
Language:English
Subjects:
Algorithms
Colleges & universities
Computer Simulation
Datasets
Genomes
Metagenome
Metagenomics - methods
Quality
Sensors
Software
Statistics as Topic
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-016-1288-y