Open AccessImproved high-molecular-weight DNA extraction, nanopore sequencing and metagenomic assembly from the human gut microbiome
Author(s) -
Dylan G. Maghini,
Eli L. Moss,
Summer E. Vance,
Ami S. Bhatt
Publication year - 2020
Publication title -
nature protocols
Language(s) - English
Resource type - Journals
eISSN - 1754-2189
pISSN - 1750-2799
DOI - 10.1038/s41596-020-00424-x
Subject(s) - metagenomics , genome , nanopore sequencing , biology , computational biology , dna sequencing , microbiome , dna extraction , bacterial genome size , sequence assembly , hybrid genome assembly , dna , human microbiome project , genomic dna , shotgun sequencing , human genome , genetics , gene , polymerase chain reaction , gene expression , transcriptome
Short-read metagenomic sequencing and de novo genome assembly of the human gut microbiome can yield draft bacterial genomes without isolation and culture. However, bacterial genomes assembled from short-read sequencing are often fragmented. Furthermore, these metagenome-assembled genomes often exclude repeated genomic elements, such as mobile genetic elements, compromising our understanding of the contribution of these elements to important bacterial phenotypes. Although long-read sequencing has been applied successfully to the assembly of contiguous bacterial isolate genomes, extraction of DNA of sufficient molecular weight, purity and quantity for metagenomic sequencing from stool samples can be challenging. Here, we present a protocol for the extraction of microgram quantities of high-molecular-weight DNA from human stool samples that are suitable for downstream long-read sequencing applications. We also present Lathe ( www.github.com/bhattlab/lathe ), a computational workflow for long-read basecalling, assembly, consensus refinement with long reads or Illumina short reads and genome circularization. Altogether, this protocol can yield high-quality contiguous or circular bacterial genomes from a complex human gut sample in approximately 10 d, with 2 d of hands-on bench and computational effort.