ODGI: understanding pangenome graphs

Building pangenome graphs

Pangenome graphs can represent all variation between multiple genomes, but existing methods for constructing them are biased due to reference-guided approaches. In response, we have developed PanGenome Graph Builder (PGGB), a reference-free pipeline for constructing unbi-ased pangenome graphs. PGGB uses all-to-all whole-genome alignments and learned graph embeddings to build and iteratively refine a model in which we can identify variation, measure conservation, detect recombination events, and infer phylogenetic relationships.

Wheat Panache: A pangenome graph database representing presence-absence variation across sixteen bread wheat genomes

Bread wheat (Triticum aestivum L.) is one of humanity's most important staple crops, characterized by a large and complex genome with a high level of gene presence-absence variation (PAV) between cultivars, hampering genomic approaches for crop improvement. With the growing global population and the increasing impact of climate change on crop yield, there is an urgent need to apply genomic approaches to accelerate wheat breeding. With recent advances in DNA sequencing technology, a growing number of high-quality reference genomes are becoming available, reflecting the genetic content of a diverse range of cultivars. However, information on the presence or absence of genomic regions has been hard to visualize and interrogate because of the size of these genomes and the lack of suitable bioinformatics tools. To address this limitation, we have produced a wheat pangenome graph maintained within an online database to facilitate interrogation and comparison of wheat cultivar genomes. The database allows users to visualize regions of the pangenome to assess PAV between bread wheat genomes.

Population-scale genotyping of structural variation in the era of long-read sequencing

Population-scale studies of structural variation (SV) are growing rapidly worldwide with the development of long-read sequencing technology, yielding a considerable number of novel SVs and complete gap-closed genome assemblies. Herein, we highlight recent studies using a hybrid sequencing strategy and present the challenges toward large-scale genotyping for SVs due to the reference bias. Genotyping SVs at a population scale remains challenging, which severely impacts genotype-based population genetic studies or genome-wide association studies of complex diseases. We summarize academic efforts to improve genotype quality through linear or graph representations of reference and alternative alleles. Graph-based genotypers capable of integrating diverse genetic information are effectively applied to large and diverse cohorts, contributing to unbiased downstream analysis. Meanwhile, there is still an urgent need in this field for efficient tools to construct complex graphs and perform sequence-to-graph alignments.

Computational graph pangenomics: a tutorial on data structures and their applications

Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations-thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.