NovoGraph: Human genome graph construction from multiple long-read de novo assemblies.

Evan Biederstedt,Nathan Dunn,Andrew Olson,Jeffrey C Oliver,Alexander T Dilthey,Aarti Jajoo,Ben Busby,Nancy F Hansen

doi:10.12688/f1000research.15895.2

Abstract

Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

Highlights

Since the release of the first complete version of the human reference genome in 2003, large-scale genomic sequencing has been established as a key tool for both fundamental research and personalized medicine
We have presented NovoGraph, a pipeline for the construction of genome graphs from de novo assemblies and applied the pipeline to construct a genome graph from seven high-quality, ethnically diverse human assemblies (Biederstedt, 2018). 19
Human Leukocyte Antigen (HLA)-B is the most polymorphic gene of the human genome and sequence polymorphisms are known to cluster around the peptide-binding-site encoding exons 2 and 3 (Marsh et al, n.d.); consistent with this, high rates of polymorphism are observed in our multiple sequence alignment around these loci

Summary

Introduction

Since the release of the first complete version of the human reference genome in 2003, large-scale genomic sequencing has been established as a key tool for both fundamental research and personalized medicine. As the first step of data analysis, these reads are typically mapped to the human reference genome to determine their genomic locations. This approach works well for the large majority of reads; critically, it fails for reads that come from regions in the sequenced genome that are strongly divergent from the reference genome. Important examples include immunogenetic regions known to harbor important disease-associated variants like the major histocompatibility complex (MHC) and the killer-cell immunoglobulin-like receptor (KIR) genes (Kuśnierczyk, 2013; Trowsdale & Knight, 2013), as well as regions affected by large or complex structural variants, which together account for more than 50% of total base pair differences between individuals (Sudmant et al, 2015). The total proportion of the human genome inaccessible to classical reference-based analysis is estimated to be greater than 1% (Dilthey et al, 2015)

Methods

Results

Conclusion