White paper: standards for handling and analyzing plant pan-genomes

2.1 Accession and assembly identifiers

Accession naming should adhere to MIAPPE (Minimum Information About Plant Phenotyping Experiments) standards. The Biological Material ID should incorporate institutional identifiers, followed by the accession number from germplasm catalogue or common name of the plant source/variety (e.g., IPK-Gatersleben:HOR_13170 for barley accession “Barke”) to ensure traceability (MIAPPE v1.1, Papoutsoglou et al., 2020). When complementary data regarding a specific accession is also available at external sources (e.g. Biosamples), a link to a Biological material external ID should be provided in the metadata.

Genome assembly identifiers should contain at least 4 fields—species, variety/line, project group, assembly version — separated by period (‘.’), with an optional fifth field for additional information (Cannon et al., 2025). For example, drOrySati.Nipponbare.RicePan.1.0, which refers to the assembly of Oryza sativa, Nipponbare cultivar, RicePan project, version 1.0 (ToLID identifier, https://id.tol.sanger.ac.uk/, Darwin Tree of Life Consortium, 2023).

2.2 Gene identifiers

Gene identifiers must balance stability with biological relevance, as outlined by Cannon et al. (2025), keeping track of the annotation version, chromosome and gene ID. Their framework proposes human- and machine-readable identifiers, including the assembly names (e.g. drOrySati.Nipponbare.RicePan.1.0) with the addition of gene models like drOrySati.Nipponbare.RicePan.1.0.1.01.g000100 (assembly version 1.0, annotation version 1, chromosome 01, gene 100). To enhance this for pan-genomics, the “group” field can denote pan-genome projects (e.g., RicePan), linking multiple assemblies, while optional fields like “Hap1” or metadata tags distinguish haplotypes or accession types (e.g., wild vs. cultivated). Pangenes, representing orthologous gene clusters, can be assigned identifiers like drOrySati.RicePan.pan00001, with metadata linking to specific gene models across assemblies. Cannon et al. (2025) advocate preserving legacy identifiers via cross-references to ensure stability, avoiding disruptive renaming as new accessions are added.

2.3 Metadata and ontologies

A core metadata schema is critical for interoperability. Required fields to properly annotate pan-genome studies include species details such as name (TaxonID), pedigree, geographic origin, ploidy and chromosome number, as well as sequencing technology used (e.g. PacBio HiFi, Oxford Nanopore, Hi-C, Illumina), assembly pipelines (e.g., Flye (Kolmogorov et al., 2020), hifiasm (Cheng et al., 2024), Canu (Koren et al., 2017), …), and assembly QC metrics (e.g., BUSCO scores (Manni et al., 2021)). Existing ontologies such as the Plant Ontology (PO) and Gene Ontology (GO) or probably more suited the Sequence Ontology (SO) should be extended to include pan-genome-specific terms that describe the layouts and structures of pan-genomes (Plant Ontology Consortium, 2023, The Gene Ontology Consortium, 2023; Eilbeck et al., 2005). These can be categorized as core, shell and cloud genome genes, but these terms may depend on the number of genomes and genotypes selected (Jayakodi et al., 2024). Collaboration with the AgBioData Nomenclature Working Group and the Genomics Standards Consortium (https://www.gensc.org/) ensures alignment with broader genomic standards (Cannon et al., 2025).

3.1 Sequencing and assembly QC

Quality control in genome assembly workflows begins with sequencing QC, where tools like FASTQC assess raw read integrity and adapter content, while k-mer plots generated via Jellyfish (Marçais et al., 2011) paired with GenomeScope (Ranallo-Benavidez et al., 2020) estimations reveal genome complexity, including ploidy, heterozygosity, and repetitive element profiles. For QC of individual assemblies, metrics such as contiguity, completeness, and consensus accuracy are evaluated using QUAST (Mikheenko et al., 2023) and CRAQ (Li et al., 2023b), with Merqury validating haplotype resolution in polyploid or heterozygous genomes (e.g., wheat, potato) by comparing k-mer spectra between raw reads and assemblies (Rhie et al., 2020). Finally, repeat QC ensures assembly integrity via the LTR Assembly Index (LAI, Ou et al., 2018) to evaluate retrotransposon completeness and tidk (Brown et al., 2025) for telomere motif identification, safeguarding chromosomal end-to-end accuracy.

3.2 Annotation QC

In addition to the assembly strategy used, the corresponding annotation pipelines should also be documented. These may include gene model integration pipelines based on MAKER2 (Holt & Yandell, 2011), PASA (Haas et al., 2003) or BRAKER3 (Gabriel et al., 2024), or more advanced tools integrating deep learning methods like Helixer (Stiehler et al., 2020) for ab initio prediction. Additionally, Liftoff (Shumate & Salzberg, 2021) for annotation transfer should be integrated into versioned workflows (e.g., Snakemake (Köster & Rahmann, 2012), Nextflow (Di Tommaso et al., 2017)). Transcriptomic data (RNA-Seq) may be used to validate gene models, particularly for accessory genes lacking orthologs (Qin et al., 2021). It is important to ensure that multiple tissues, such as roots and shoots, are represented, and that sufficient replication is included to capture transcriptome diversity.

QC of genome structural annotation may leverage lineage-specific BUSCO analyses to assess gene space completeness (adjusted for polyploidy; Manni et al., 2021), complemented by Mercator4 (Bolger et al., 2021) for automated gene family classification, PSAURON for structural annotation validation (Sommer et al., 2025), and OMArk for contamination detection through evolutionary consistency checks (Nevers et al., 2025).

3.3 Pan-genome-specific QC

Pan-genome completeness requires saturation analysis, where gene accumulation curves determine whether new accessions yield novel genes (Tettelin et al., 2005). For species like barley, benchmark datasets of 100+ conserved genes enable orthology tool validation (Jayakodi et al., 2024). Additionally, gene family expansion and contraction analyses, facilitated by tools such as OrthoFinder (Emms & Kelly, 2019) and CAFE5 (Mendes et al., 2020), provide insights into evolutionary dynamics across accessions. Structural variant detection (e.g., using Sniffles2 (Smolka et al., 2024) or SVIM (Heller & Vingron, 2019)) must quantify representation of indels and inversions (Qin et al., 2021). Similarly, presence-absence variation (PAV) detection, enabled by tools like Panaroo or PAV-specific pipelines, is critical for identifying variable gene content that contributes to phenotypic diversity (Tonkin-Hill et al., 2020).

4.1 File formats

4.2 Repositories

A centralized E-PAN repository hosted by ELIXIR will archive versioned datasets (e.g., Barley v2, Rice v1.5) with DOI-based identifiers (DataCite). Public deposition in INSDC (raw reads and assembly) and Ensembl Plants (annotations, see documentation of Ensembl, 2025) ensures global accessibility (ENA Documentation, 2025).

4.3 Metadata requirements

Mandatory metadata fields include sequencing technology and coverage (e.g., PacBio HiFi, Oxford Nanopore), assembly method (e.g., Flye (Kolmogorov et al., 2020), Canu (Koren et al., 2017)), and accession provenance (BioSample IDs). Missing metadata, as observed in early barley submissions, must be addressed via enforced submission guidelines (Jayakodi et al., 2024).

5.1 Visualization tools

Plant pan-genomes capture a species’ full genomic diversity, constructed using linear-based or graph-based methods, each with distinct strengths and limitations.

Linear-based approaches align genomes to a single reference or consensus sequence, identifying SNPs, indels, and presence/absence variations (PAVs) using tools like Ensembl Compara (Dyer et al., 2025) or OrthoFinder (Emms & Kelly, 2019). These methods are simple, computationally efficient, and compatible with tools like JBrowse2 or IGV for synteny and PAV visualization (Diesh et al., 2023; Robinson et al., 2023). However, reference bias limits their ability to capture complex structural variations, particularly in repetitive or polyploid plant genomes.

Graph-based approaches model genomes as interconnected nodes (shared regions) and edges (SNPs, indels, structural variants) using tools like VG Toolkit (Hickey et al., 2020), PGGB (Garrison et al., 2024), PanTools (Jonkheer et al., 2022), and wfmash (Guarracino et al., 2021). They provide an unbiased, comprehensive view of genomic diversity, ideal for complex genomes like tomato (Zhou et al., 2022). Visualization tools like Bandage (Wick et al., 2015) or Cytoscape (Shannon et al., 2003) address structural complexity but are computationally intensive and require specialized expertise.

In essence, linear methods suit rapid, basic analyses, while graph-based approaches excel for complex structural variations despite greater computational demands. As tools and computational resources advance, graph-based methods are becoming more accessible, enhancing plant pan-genome studies. A multitude of tools are summarized in Naithani et al. (2023).

5.2 Analysis best practices

Orthology inference tools (e.g., OrthoFinder, OMA) must be benchmarked using inflation value sweeps to minimize false positives (Emms & Kelly, 2019). Trait association studies should integrate pan-genomes with GWAS/QTL data, as demonstrated in rice (Qin et al., 2021), and deliver such integrated knowledge graphs to assist scientists and breeders in evidence-based gene discovery as being developed at KnetMiner (Hassani-Pak et al., 2021).