Improved metagenome assemblies and taxonomic binning using long-read circular consensus sequence data

Jeremy A. Frank; Yao Pan; Ave Tooming-Klunderud; Vincent G.H. Eijsink; Alice C. McHardy; Alexander J. Nederbragt; Phillip B. Pope

doi:10.1101/026922

bioRxiv · 10.1101/026922

Improved metagenome assemblies and taxonomic binning using long-read circular consensus sequence data

Jeremy A. Frank·Yao Pan·Ave Tooming-Klunderud·Vincent G.H. Eijsink·Alice C. McHardy·Alexander J. Nederbragt·Phillip B. Pope

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Abstract

DNA assembly is a core methodological step in metagenomic pipelines used to study the structure and function within microbial communities. Here we investigate the utility of Pacific Biosciences long and high accuracy circular consensus sequencing (CCS) reads for metagenomics projects. We compared the application and performance of both PacBio CCS and Illumina HiSeq data with assembly and taxonomic binning algorithms using metagenomic samples representing a complex microbial community. Eight SMRT cells produced approximately 94 Mb of CCS reads from a biogas reactor microbiome sample, which averaged 1319 nt in length and 99.7 % accuracy. CCS data assembly generated a comparative number of large contigs greater than 1 kb, to those assembled from a [~]190x larger HiSeq dataset ([~]18 Gb) produced from the same sample (i.e approximately 62 % of total contigs). Hybrid assemblies using PacBio CCS and HiSeq contigs produced improvements in assembly statistics, including an increase in the average contig length and number of large contigs. The incorporation of CCS data produced significant enhancements in taxonomic binning and genome reconstruction of two dominant phylotypes, which assembled and binned poorly using HiSeq data alone. Collectively these results illustrate the value of PacBio CCS reads in certain metagenomics applications.

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Jeremy A. Frank, Yao Pan, Ave Tooming-Klunderud, Vincent G.H. Eijsink, Alice C. McHardy, Alexander J. Nederbragt, Phillip B. Pope. 2015-09-16. Improved metagenome assemblies and taxonomic binning using long-read circular consensus sequence data. https://doi.org/10.1101/026922

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