Pan-Genome Research: Emerging Challenges and Prospective Strategies

In the process of life science research moving towards precision, Pan-Genome sequencing is becoming the core tool to solve the mystery of biological heredity by depicting the diversity of the whole genome of species. However, its development faces many challenges, from the assembly dilemma of the highly repetitive genome to the computational pressure of massive data, from the functional annotation problem of variable genes to the technical barriers of cross-scale research. Exploring the breakthrough path and future optimization direction of these bottlenecks is of key significance to promoting the deep application of this technology in agricultural breeding, medical diagnosis and treatment, and other fields.

This paper discusses the technical problems, computing and storage challenges, functional annotation and verification difficulties faced by pan-genome research, and looks forward to the future application direction.

Technical Problems and Solutions

With the vigorous development of pan-genome research, technical bottlenecks restrict its further application. Problems such as genome assembly problems caused by highly repetitive sequences and polyploidy, and the contradiction between the cost and accuracy of long reading and long sequencing need to be solved urgently, and technological innovation and strategy optimization are providing paths to break through these bottlenecks.

Sequencing performance metrics using sequins (Reis et al., 2022)

Complex Genome Assembly Problem

In pan-genome sequencing, the assembly of highly repetitive sequences and polyploid genomes are two significant problems. The existence of highly repetitive sequences makes it difficult to accurately compare sequencing reads, which easily leads to assembly errors or gaps. Polyploid organisms have doubled genomes and highly similar homologous chromosome sequences, which makes it more difficult to distinguish different haplotypes.

To solve this problem, long reading and long sequencing technologies such as PacBio SMRT and Oxford Nanopore Technologies (ONT) came into being. The technology of long read length can span the repeated sequence area and provide longer sequence fragments, thus reducing the ambiguity in the assembly process. At the same time, the combination of Hi-C technology to capture the chromosome conformation can help to mount the assembled contigs on the chromosome and improve the integrity and accuracy of genome assembly. Taking cotton polyploid genome assembly as an example, the complex genome structure was successfully analyzed by using PacBio long reading length combined with Hi-C technology, which laid the foundation for cotton pan-genome research.

Subgenome discrimination using genomic information from closely related species (Jeon et al., 2024)

Balance Between Cost and Accuracy of Long-read Sequencing

Although long read sequencing technology have advantages in solving complex genome assembly problems, its high cost and relatively low accuracy are still bottlenecks to be broken through. At present, the cost of PacBio HiFi sequencing is about 10-20 times that of short reading length sequencing, while the single base error rate of ONT sequencing is high, which needs to be corrected by multiple sequencing or combining with short read data.

In terms of cost control, on the one hand, with the progress of sequencing technology, the productivity of equipment increases and the cost of reagents decreases, the unit cost of long-read sequencing is gradually decreasing. On the other hand, optimizing sequencing strategies, such as adopting a hybrid sequencing method, taking long-read long data as the main task, and short-read data as the supplement for error correction and filling, can reduce the cost while ensuring certain accuracy.

As for accuracy improvement, PacBio HiFi sequencing reduces the single base error rate to below 0.1% through circular consensus sequencing (CCS) technology, which is close to the accuracy of short reading and long sequencing. ONT is also constantly optimizing the base recognition algorithm, and the error rate of its latest R10.4.1 chip has dropped below 1%.

Computing and Storage Challenges

In the research of pan-genome, computing, and storage are facing severe challenges. The data of marine measurement sequences is growing exponentially, which is difficult for traditional computing architecture and storage modes to cope with. From sequence alignment to genome construction, each step requires higher computing power and storage, and how to break through this bottleneck becomes the key.

Algorithm Optimization of Massive Data Processing

Pan-genome sequencing will produce massive data, such as sequencing the whole genome of 100 individuals, which can reach TB level. Traditional data processing algorithms and storage architecture are under great pressure. In the process of data processing, steps such as sequence alignment, mutation detection, and pan-genome map construction all need efficient algorithm support, otherwise, the calculation time will be too long, and even the analysis cannot be completed.

To solve this problem, researchers have developed a series of optimization algorithms:

• In the aspect of sequence compression, the compression algorithm based on Burrows-Wheeler Transform (BWT), such as BGZIP, can compress the FASTQ file to 1/3-1/5 of the original size.
• In indexing technology, data structures such as MinHash and Bloom Filter are widely used in fast comparison and similarity search.
• In addition, the introduction of distributed computing frameworks such as Hadoop and Spark makes the parallel processing of massive data possible.

Overview and performance of the GRG inference algorithm (DeHaas et al., 2025)

Improvement of Computational Efficiency of Graph Genome

The traditional linear reference genome has limitations in expressing the pan-genome, while the Graph Genome can reflect the genetic diversity of species more comprehensively. However, the construction and query of graph genomes are highly complex and require harsh computing resources.

In order to improve the computational efficiency of graph genomes, researchers have proposed various optimization methods:

• In the aspect of graph compression, using shared subsequence and repeated structure to compress graphs can reduce storage space and computation.
• In the query algorithm, fast query technology based on hash table and index tree, such as GraphHash, Vg, and other tools, can reduce the query time by 1-2 orders of magnitude.
• In addition, the application of hardware acceleration technologies, such as GPU and FPGA, provides a new way for high-performance computing of graph genomes.

Studies have shown that using GPU to accelerate the mutation detection of map genomes can achieve a 10-20 times speed increase.

Comparison of compression rates of de Bruijn graphs of a genomes of model organisms and b bacterial pan-genomes, using unitigs, simplitigs, assemblies, and BOSS (Břinda et al., 2021)

Functional Annotation and Verification Dilemma

In the research of pan-genome, functional annotation, and verification are facing a significant dilemma. Variable genes are missing in the reference genome, and the traditional annotation method is limited, so it is difficult to predict the function. Experimental verification is difficult to advance efficiently because of the large number of genes and the low flux of traditional methods, which has become an important obstacle to the in-depth development of pan-genome research.

Function Prediction Method of Variable Genes

The variable genes in the pan-genome are often missing in the reference genome, and their functional annotation lacks a reference basis, so the traditional annotation method based on homologous alignment has limited effect. This dilemma stems from the limitation of reference genomes single human reference genome can only cover about 92% of pan-genome sequences, and the gaps in plant and microbial genomes are more obvious. Taking the study of the rice pan-genome as an example, about 40% of gene sequences are not included in the existing reference genome, which makes it difficult for traditional annotation methods to cross the gap of gene diversity within species.

The development of artificial intelligence (AI) technology provides a new idea for the function prediction of variable genes.

• The method based on deep learning, such as AlphaFold2, can predict its three-dimensional structure through protein sequence, and then infer its function.
• Natural language processing (NLP) technology can mine information related to gene function from massive literature and assist annotation.
• The integration of functional genomics data such as transcriptome, protein genome, and metabolomics data can improve the accuracy of functional prediction.

Overview of gene function prediction methods (Kasif et al., 2010)

Qualcomm Strategy Verified by Experiments

The accuracy of functional annotation needs to be verified by experiments, but the traditional methods of gene function verification, such as gene knockout and over-expression, have low flux and long cycles, which make it difficult to meet the verification requirements of a large number of variable genes in pan-genome. These traditional methods usually rely on manual operation, from constructing vectors and transforming cells to phenotypic observation, each link needs a lot of manpower and time, and a single experiment can only verify a single gene.

In addition, the traditional verification method still has the problem of an unstable success rate in the operation process, and some experiments may need to be repeated due to the failure of vector construction and low transformation efficiency, which further prolongs the research cycle and seriously restricts the transformation process of pan-genome research results to practical application.

The development of CRISPR-Cas9 technology provides a revolutionary breakthrough for Qualcomm quantitative function verification. By designing specific single-stranded guide RNA (sgRNA), this technology guides Cas9 nuclease to accurately cut the target DNA sequence, thus realizing gene knockout, knock-in, or editing.

In practical application, researchers can construct a library containing thousands of sgRNA, and knock out multiple genes in cells or organisms at the same time. Combined with high-throughput sequencing technology, gene editing efficiency can be quickly detected; Through phenotypic analysis, gene function can be systematically analyzed.

dCas9-mediated transcriptional modulation (Shalem et al., 2015)

Interdisciplinary Integration Trend

Pan-genome research is no longer limited to a single subject category but presents a significant interdisciplinary integration trend. Its cross-integration with the fields of multi-omics, single-cell technology, and three-dimensional genomics is opening up a new path for revealing biological genetic diversity and the regulation mechanism of complex traits.

Combined Analysis of Single-Cell Sequencing and Pan-Genome

With its unique advantages, single-cell sequencing technology can finely analyze multi-dimensional biological information such as genome, transcriptome, and epigenome at the level of a single cell, and capture the subtle differences between cells with extremely high resolution. Pan-genome sequencing, on the other hand, constructs a comprehensive genetic diversity map from the species level, covering rich genetic variation information in the population. The joint analysis of the two forms a strong technical force, which realizes the multi-level and cross-scale research from species groups to single-celled individuals, and greatly expands the research boundary of life sciences.

In the field of tumor research, this joint analysis shows great application potential. Cancer is essentially a genetic disease. There are a large number of cell subsets with different genetic characteristics in tumor tissues, and their clonal evolution process and heterogeneity are the key factors leading to the occurrence, development, drug resistance, and recurrence of tumors. By integrating the pan-genome data of cancer patients, we can fully understand the variation distribution of tumor-related genes in the patient population. Combined with single-cell sequencing data, we can further analyze the gene expression dynamics, chromosome copy number variation, and the cloning relationship between cells in tumor cells.

Study on Correlation Between Three-Dimensional and Epigenetic Modification

Three-dimensional genome structure and epigenetic modification play an important role in gene expression regulation. Combining it with the pan-genome, we can deeply explore how genetic variation regulates phenotype by affecting the spatial structure and epigenetic state of the genome.

In the three-dimensional genome structure, chromatin forms advanced structures such as chromatin rings, topological-related domains (TADs), and Compartments through hierarchical folding, which affect the interaction between genes and regulatory elements. After combining them with pan-genome research, the researchers found that genetic variation not only changes the DNA sequence, but also regulates gene expression by reshaping the three-dimensional genome structure and apparent state, and finally affects the phenotype.

In plants, the correlation between the three-dimensional genome and pan-genome can be used to analyze the regulation mechanism of stress resistance. Studies have shown that under drought stress, the three-dimensional structure of the plant genome will be remolded, and some structural variations in the pan-genome may affect this remolded process, thus regulating the expression of stress-resistant genes. Combining Hi-C technology with epigenetic modification sequencing (such as ChIP-seq and ATAC-seq), we can systematically study the interaction between genetic variation, three-dimensional genome, and epigenetic modification, and provide new targets for crop stress resistance improvement.

Epigenome-3D Genome coupling (Abdulla et al., 2023)

Future Prospects of Pan-Genome

Driven by technical iteration and interdisciplinary integration, pan-genome research is moving towards a more forward-looking development stage. Its future application will continue to expand, from dynamically tracking biological evolution trajectory to empowering precision medical practice, from innovating crop breeding paradigms to revealing ecosystem gene networks.

Real-Time Dynamic Pan-Genome Monitoring

With the real-time and portable sequencing technology, it is expected to realize the dynamic monitoring of pathogens, agricultural pests, and other organisms in the future. Portable sequencing equipment, represented by ONT, breaks through the dependence of traditional sequencing on laboratory sites and large instruments through the principle of single-molecule real-time sequencing.

During the outbreak, the ONT portable sequencer can track the genome variation of the virus in real time, draw its evolutionary map, and provide timely genetic information for the early warning of the epidemic and the adjustment of prevention and control strategies. In the agricultural field, real-time pan-genome monitoring of crop pathogens can help predict the epidemic trend of diseases, guide accurate drug use, and reduce pesticide use.

Application Potential of Pan-Genome in Personalized Medicine

Pan-genome can fully reflect the genetic variation of individuals, including structural variation, copy number variation, etc. These variations are closely related to disease susceptibility and drug response. In the future, with the further reduction of sequencing costs and the maturity of analytical technology, the pan-genome is expected to play an important role in personalized medicine.

By constructing the patient's pan-genome map, combined with clinical data, the accurate diagnosis of the disease and the personalized formulation of the treatment plan can be realized. In cancer treatment, pan-genome analysis can help identify tumor-specific driving variation and provide more accurate targets for targeted therapy and immunotherapy.

In addition, pan-genome also has potential in drug research and development. By analyzing the pan-genomic differences of different populations, the efficacy and side effects of drugs can be predicted, which can guide the optimization of drugs and the design of individualized drug delivery schemes. In the research and development of antibiotics, pan-genome research can help identify the distribution of drug-resistant genes of pathogens and provide a direction for developing new antibiotics.

Illustration of operations to be supported by a pan-genome data structure (Computational Pan-Genomics Consortium., 2018)

Conclusion

The development of Pan-Genome sequencing technology has brought revolutionary changes to life science research, but at the same time, it also faces many challenges such as technology, calculation, and functional annotation. Through technological innovation, algorithm optimization, interdisciplinary integration, and other means, these challenges are gradually being overcome.

In the future, with the continuous progress of technology and the expansion of application scenarios, Pan-Genome sequencing will play an increasingly important role in agriculture, medicine, ecology, and other fields, providing strong technical support for solving major problems such as health, food security and environment faced by human beings.

References

1. Reis ALM, Deveson IW., et al. "Using synthetic chromosome controls to evaluate the sequencing of difficult regions within the human genome." Genome Biol. 2022 23(1): 19 https://doi.org/10.1186/s13059-021-02579-6
2. Jeon D, Kim C. "Polyploids of Brassicaceae: Genomic Insights and Assembly Strategies." Plants (Basel). 2024 13(15): 2087 https://doi.org/10.3390/plants13152087
3. DeHaas D, Pan Z, Wei X. "Genotype Representation Graphs: Enabling Efficient Analysis of Biobank-Scale Data." bioRxiv [Preprint]. 2024 2024.04.23.590800 https://doi.org/10.1101/2024.04.23.590800
4. Břinda K, Baym M, Kucherov G. "Simplitigs as an efficient and scalable representation of de Bruijn graphs." Genome Biol. 2021 22(1): 96 https://doi.org/10.1186/s13059-021-02297-z
5. Kasif, S., Steffen, M. "Biochemical networks: The evolution of gene annotation." Nat Chem Biol. 6 4–5 (2010) https://doi.org/10.1038/nchembio.288
6. Shalem, O., Sanjana, N. Zhang, F. "High-throughput functional genomics using CRISPR-Cas9." Nat Rev Genet. 16 299–311 (2015) https://doi.org/10.1038/nrg3899
7. Abdulla AZ, Salari H., et al. "4D epigenomics: deciphering the coupling between genome folding and epigenomic regulation with biophysical modeling." Curr Opin Genet Dev. 2023 79: 102033 https://doi.org/10.1016/j.gde.2023.102033
8. Computational Pan-Genomics Consortium. "Computational pan-genomics: status, promises and challenges." Brief Bioinform. 2018 19(1): 118-135 https://doi.org/10.1093/bib/bbw089