Apan-genomeis the sum of all genomic information within a species. With the development of genomic technology, researchers have found that a singlereference genomecan no longer meet the needs of genomic data analysis, and more and more species, including thehuman genome, are choosing to construct a pan-genome instead of a single reference genome.

Pan-genomesreflect structural variation (SV) and polymorphisms in the genome, allowing in-depth comparisons of variation at the species level or at higher taxonomic levels.Pan-genomes have potential applications in crop improvement, evolution and biodiversity research. To fully exploit the value of pan-genomes, a broader range of information such as phenotypic, environmental and expression data needs to be integrated to provide insight into the role of variable regions in the genome.

How to build a pan-genome?

There is extensive genomic diversity within species, and apan-genomeneed to capture this diversity while removing redundancies to generate an integrated single genome.

Map to pan, which starts withde novoassembly, and matches the sequences of each individual assembled to thereference genometo find the unmatched sequences, then finds all the unmatched sequences and builds thepan-genome, or iterative mapping and assembly methods

Iterative assemblystarts with a singlereference genomeand then complements it with non-redundant sequences from other individuals or the iterative mapping and assembly method starts from a single reference genome and then complements it with non-redundant sequences from other individuals to build apan-genome.

De novoassemblyrequires the individual genomes to be assembled separately, followed by whole genome comparison.

What is the difference between pan and core genome?

Pan-genomic analysis clusters gene sets by co-occurrence in each individual and is usually divided into three categories: Core gene, genes present in all plant and animal strains; dispensable gene, genes present in one or more plant and animal strains; private, genes present in only one strain. The core part is present in all individuals, while the dispensable part is present in only one individual.

Application of pan-genome

Pan-genomic analysis helps to understand the characteristics of species, while the complex genomic variation provided bypan-genomemapping helps to resolve the diversity of crop phenotypes and agronomic traits.

• Selection of different subspecies forpan-genomesequencing allows the study of important biological questions such as the origin and evolution of species
• Selecting germplasm resources with different characteristics, such as wild species and cultivated species, forpangenomesequencing can uncover genetic resources related to important traits and provide guidance for scientific breeding
• Selecting germplasm resources of different ecogeographic types for pangenome sequencing can carry out popular scientific questions such as adaptive evolution of species and invasiveness of exotic species
• The use of crop pangenome advancement QTL mapping andGWAScan be used to identify genomic regions associated with desired phenotypes
• The use of pangenomes to advance genomic prediction. With SNPs as predictors, important agronomic traits such as grain yield, grain moisture, grain quality, biomass traits, and stem and root collapse can be predicted with reasonable accuracy

Application of pan-genome in crop improvement (Della Coletta Ret al., 2021)

Advances in pan-genome assembly technologies

The reduced cost of Illumina sequencing and improvements in assembly algorithms have facilitated the use of low-cost short-read data (e.g., maize genome, rice genome, soybean genome). While this approach has generated highly complete and contiguous assemblies of low-copy gene regions, the more repetitive, TE-rich regions of the genome have proven difficult to assemble with short reads, resulting in large gaps and partial assemblies in these regions. Recently, the maturation oflong-read sequencingtechnologies, especially PacBioHiFi Sequencing, has facilitated more contiguous and complete assemblies of crop genomes and, in some cases, long-read length-based assemblies within a single species. Advances in PacBiopangenomesequencing technologies are described below.

Impact of sequencing technology on polyploid assembly (Della Coletta Ret al., 2021)

PacBio HiFi sequencing, a good solution for pan-genome construction

Nowadays,pan-genomeconstruction generally uses three-generation long read-length sequencing to assemble multiple samples of a population from scratch. The two technology platforms now commonly used for triple sequencing are PacBio'sHiFi sequencingand ONT'sNanopore sequencing, of which HiFi sequencing takes into account long read length and ultra-high accuracy, and is extremely suitable for sequencing genomicde novo assembly.

• HiFi readsare more accurate

The higher accuracy ofHiFi reads allows the assembly algorithm to extend contigs to flanking mitotic regions with high confidence through more repeat assemblies at shorter read lengths, enhancing the integrity of the mitotic and telomeric regions

• Simplify the complexity of polyploidgenome assembly

High-quality genome assembly in polyploid species has been difficult to achieve due to the inclusion of multiple closely related subgenomes and the associated challenges in distinguishing homologous motifs and creating non-mosaic subgenomic scaffolds.Long-read sequencingwith low error rates (e.g., PacBioHiFi readlength) has enabled high-quality polyploid genome assembly, with recent assemblies containing fewer gaps and resolved homologous scaffolds. As polyploidpangenomes of more species are revealed, more novelstructural variantsand markers are likely to be observed.

Overview of population-scale studies usinglong-read sequencing.(De Costeret al., 2021)

Overview

Sequencing the deoxyribonucleic acid (DNA) or messenger ribonucleic acid (mRNA) of different individuals in single or multispecies populations (known as population-scale sequencing) is fundamentally aimed at revealing allelic variation in macroscopic population profiles. This approach provides a critical scaffold for addressing multifaceted queries spanning the research fields of evolutionary biology, agronomic biotechnology, and translational medicine. Historical precedents of population-centric genomic studies, especiallygenome-wide association studies (GWAS), have always faced challenges in capturing the full range of genetic determinants of human phenotypic expression and pathological manifestations. This gap in understanding can largely be attributed to the intricate network ofstructural variation (SV). These SVs include inversions, deletions, and other complex chromosomal rearrangements that often remain elusive in the face of traditional sequencing methods.

High-throughputshort-read sequencingplatforms are characterized by read lengths that fluctuate between 25 base pairs (bp) and an upper limit of 400 bp. Their abilities are often hampered when they are tasked with deciphering variations hidden within the "dark matter" regions of the genome. Furthermore, they do not perform well in accurately resolving broad or complex variants. These obstacles not only compromise the integrity of genetic inferences derived from ancestry cohort datasets, but ultimately lead to a weakened, if not fragmented, understanding of the intricate interplay between genetic markers and disease etiology.

Emerging on this horizon is the promising field oflong-read sequencing. This format enables the interrogation of genomic fragments spanning considerable contiguous lengths. The resulting capability is a holistic characterization of SVs across the human genomic landscape, setting the stage for an era dominated by population-scale long-read sequencing. By leveraging this cutting-edge technique, researchers are poised to unearth previously mysterious SVs with important links to phenotypic expression in humans, crops, fruit flies, and even birds such as songbirds. This paradigm shift is not just a technological advance, but marks a transformative leap inmetagenomicresearch, heralding unprecedented insights and breakthroughs.

Project Strategies for Population-scale Sequencing

At the start of a population-scale sequencing project, there are multiple strategies with specific budget requirements to consider, as shown below. These strategies allow for different sizes and budgets, which can have an impact on the level of resolution at which genetic variants are detected.

Full Coverage Approach

This strategy is designed to sequence every sample from a population with moderate to high coverage, allowing for the highest level of resolution. The main criterion for determining the coverage required for each sample is whether it is assembled from scratch (requiring >40-fold coverage) or a reference-based comparison method (requiring >12-fold coverage42 ). The advantages of this strategy are its comprehensiveness, simplicity of study design, and relatively simple computational workflow. In addition, the samples are similarly covered and therefore equally well-studied, and rare variations in each sample can be easily detected.

Mixed Coverage Approach

In a "mixed-coverage" approach, a subset of samples representing subgroups (e.g., ethnicities or subgroups) of a cohort is sequenced at high coverage, and the remaining samples are sequenced at low coverage. Although this approach is generally less expensive than the full coverage approach, it still achieves higher overall detection sensitivity and is therefore particularly suitable for studies with a large number of individuals or a limited budget. However, some analytical challenges remain, especially in achieving high accuracy of genotypes across multiple samples or in distinguishing somatic versus heterozygous germline variants, which is further complicated by regions exhibiting recurrent mutations. In addition, this hybrid coverage approach will certainly bias against common alleles, as many rare alleles may be missed, especially when a locus is heterozygous and alternative alleles are therefore sparsely covered.

Hybrid Sequencing Methods

This approach involves sequencing only a small number of samples (e.g., 10-20% of all samples) with long reads, sequencing the remaining samples with short reads, and genotyping the variants found in the long reads. Once a subset of samples has been sequenced using the long read technique to produce a set of identified SVs, they can be genotyped for their breakpoint coordinates in the short read long sequence dataset. In this way, robust allele frequencies for the identified variants can be obtained. This strategy has been applied to diversity panels of human SVs to discover new expression quantitative trait loci (eQTL) and evolutionarily adapted traits.

Overview of long-read population study design. (De Costeret al., 2021)

The Importance of Long-Read Sequencing Technology in Population-Scale Study

One of the inherent challenges of population genetics is the accurate phasing of haplotypes-determining specific combinations of alleles located on a single chromosome.Long-read sequencingprovides an effective solution by capturing longer DNA fragments, which can directly determine haplotype structure without relying on computational prediction or family-based studies. This capability is transformative for population-scale studies, where understanding the distribution and combination of specific allele sets can help decipher population history, migration patterns, and shared inheritance patterns.

Structural variants, such as deletions, duplications, and inversions, can have profound effects on gene function and expression. Capturing these variants at high resolution is critical when studying large populations.Long-read sequencingcan identify structural variants that may be overlooked or inaccurately represented by short-read methods.

Population-scale Sequencing Downstream Analysis Methods

The choice of analytical tools is critical for downstream analysis at the population scale. Prior to downstream analysis, quality control must be performed on experimental factors that directly affect the performance of assembly,SV detection, and read-sequencing phases.There are several strategies for population-scale downstream analysis:

Read Alignment-based Analysis

Comparison-based methods are often the preferred approach for population-scale studies because they facilitate the comparison of all samples to a common coordinate system (i.e., thereference genome). In addition, these methods are usually less computationally demanding and require much less coverage than compilation-based methods. Comparison-based methods rely on matching sequencing reads to a reference genome, the overall correctness of which will affect the analysis of the read data.

Software for analyzing long-read sequence data, such as NGMLR and LAST methods, speeds up the matching process and improves the accuracy of long-read matching. In addition, a variety of tools for detecting genetic variation can eliminate the need for high sequencing coverage by enabling SV calling and genotyping at lower coverage.

Population-scaleDe NovoAssemblies

Traditionalreference genomes, often based onshort-read sequencing, can be fragmented and may miss key sequences. Such omissions may lead to significant differences, including false-positive or false-negative variant identifications. Therefore, there is an urgent need to construct and compare scratch assemblies.

The increased availability and affordability oflong-read sequencingdata haveled to an explosion of faster and more accurategenome assembly tools.De novoassembly-based methods are often more sensitive and better suited to reconstructing highly diverse regions of the genome than comparison-based methods. The increasing yield of long-read sequencing technologies will allow sufficient coverage of each sample to be sequenced for high-qualityde novo assembly.

Graph Genome Methods

Both read matching andde novoassembly methods can have systematic problems with complexstructural variants, missing insertion sequences in thereference genome, repetitive variants, and highly polymorphic loci. A major benefit of graph genomes is the use of short reads for genotyping SVs. In addition, with this graph-based approach, for population studies,the often discussed dichotomy of using an existing reference genome for alignment or constructing a new reference genome by assembling it from scratch can be avoidedsince downstream of this step all sequences have to be aligned with the backbone of the individual (reference) assembly orpan-genomemap for identification of variants, annotation,and statistical evaluation.