Computational Strategies for Eukaryotic Pangenome Analyses

Abstract

Over the last few years, pangenome analyses have been applied to eukaryotes, especially to important crops. A handful of eukaryotic pangenome studies have demonstrated widespread variation in gene presence/absence among plant species and its implications on agronomically important traits. In this chapter, we focus on the methodology of pangenome analysis, which can generally be classified into two different types of approaches, a homolog-based strategy and a “map-to-pan” strategy. In a homolog-based strategy, the genomes of individuals are independently assembled, and the presence/absence of a gene family is determined by clustering protein sequences into homologs. Alternatively, in a “map-to-pan” strategy, pangenome sequences are constructed by combining a well-annotated reference genome with newly identified non-reference representative sequences, from which the presence/absence of a gene is then determined based on read coverage after individual reads are mapped to the pangenome. We highlight the advantages and limitations of the homolog-based strategy and several variant approaches to the “map-to-pan” strategy. We conclude that the “map-to-pan” strategy is highly recommended for eukaryotic pangenome analysis. However, programs and parameters for pangenome analysis need to be carefully selected for eukaryotes with different genome sizes.

WGS-Based Lineage and Antimicrobial Resistance Pattern of Salmonella Typhimurium Isolated during 2000–2017 in Peru

“…For further analysis, the annotation was performed using PROKKA v1.11 [ 34 ], and only the genome sequences with >20× depth coverage were considered. We considered 20× to be the minimum coverage to include the maximum number of our isolates ( n = 90 out of a total n = 109) for core genome analysis, variant determination, and monitoring of infectious outbreaks in other previous studies [ 35 , 36 , 37 ]. Plasmids were predicted and reconstructed from the assembled genomes using MOB-suite [ 38 ] and classified as conjugative, mobilisable, and non-mobilisable plasmids.…”

How the pan-genome is changing crop genomics and improvement

“…Several methods exist for summarizing SV information in a pan-genome context. One approach is to map resequencing reads to a reference genome, de novo assemble unmapped reads, and add the assembled contigs to the reference assembly (known as the map-to-pan approach) [ 48 , 86 ]. This strategy can minimize errors by exploiting the information already available from a high-quality reference genome and limit the coordinate consolidation issue, but the genomic locations of newly assembled contigs remain unknown without further analysis.…”

Comparative pangenomic insights into the distinct evolution of virulence factors among grapevine trunk pathogens

“…Genome completeness was 97.98 ± 0.80% for P. minimum , 98.58 ± 0.05% for N. parvum , and 97.50 ± 0.03% for E. lata , as evaluated by BUSCO v.5.4.2 (Figure 2). Because genome completeness is essential for pangenome construction (31, 32), we further verified the assembly completeness by comparing the genome assemblies based on short-reads with chromosome-scale assemblies produced through long-read sequencing (Supplementary table 1). We have previously reported the chromosome-scale assemblies of the genomes of P. minimum isolate Pm1119 (5) and N. parvum isolate UCD646So (6).…”