VRPG: an interactive visualization framework for reference-projected pangenome graph

Miao, Zepu

doi:10.1101/2023.01.20.524991

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…Besides scalability, visualization is another important aspect of pangenomic analysis. While graph genomes have associated visualization tools [69][70][71][72] , and PanMAN can be converted to these structures via panmanUtils, graph genomes are quite challenging to interpret and analyze. This is another aspect where PanMANs could prove valuable -our earlier work on visualizing UShER-MATs using Taxonium and the Treenome Browser 73,74 could be easily adapted to PanMANs in the future.…”

Section: Discussionmentioning

confidence: 99%

Compressive Pangenomics Using Mutation-Annotated Networks

Walia,

Motwani,

Smith

et al. 2024

Preprint

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Pangenomics is an emerging field that uses a collection of genomes of a species instead of a single reference genome to overcome reference bias and study the within-species genetic diversity. Future pangenomics applications will require analyzing large and ever-growing collections of genomes. Therefore, the choice of data representation is a key determinant of the scope, as well as the computational and memory performance of pangenomic analyses. Current pangenome formats, while capable of storing genetic variations across multiple genomes, fail to capture the shared evolutionary and mutational histories among them, thereby limiting their applications. They are also inefficient for storage, and therefore face significant scaling challenges. In this manuscript, we propose PanMAN, a novel data structure that is information-wise richer than all existing pangenome formats – in addition to representing the alignment and genetic variation in a collection of genomes, PanMAN represents the shared mutational and evolutionary histories inferred between those genomes. By using “evolutionary compression”, PanMAN achieves 5.2 to 680-fold compression over other variation-preserving pangenomic formats. PanMAN’s relative performance generally improves with larger datasets and it is compatible with any method for inferring phylogenies and ancestral nucleotide states. Using SARS-CoV-2 as a case study, we show that PanMAN offers a detailed and accurate portrayal of the pathogen’s evolutionary and mutational history, facilitating the discovery of new biological insights. We also presentpanmanUtils, a software toolkit that supports common pangenomic analyses and makes PanMANs interoperable with existing tools and formats. PanMANs are poised to enhance the scale, speed, resolution, and overall scope of pangenomic analyses and data sharing.

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