Exploring gene content with pangene graphs

A survey of sequence-to-graph mapping algorithms in the pangenome era

A pangenome can reveal the genetic diversity across different individuals simultaneously. It offers a more comprehensive reference for genome analysis compared to a single linear genome that may introduce allele bias. Pangenomes are often represented as genome graphs, making sequence-to-graph mapping a fundamental task for pangenome construction and analysis. Numerous sequence-to-graph mapping algorithms have been developed over the past few years. Here, we provide a review of the advancements in sequence-to-graph mapping algorithms in the pangenome era. We also discuss the challenges and opportunities that arise in the context of pangenome graphs.

Pitfalls of bacterial pan-genome analysis approaches: a case study of Mycobacterium tuberculosis and two less clonal bacterial species

SUMMARY

Pan-genome analysis is a fundamental tool for studying bacterial genome evolution; however, the variety in methods used to define and measure the pan-genome poses challenges to the interpretation and reliability of results. Using Mycobacterium tuberculosis, a clonally evolving bacterium with a small accessory genome, as a model system, we systematically evaluated sources of variability in pan-genome estimates. Our analysis revealed that differences in assembly type (short-read versus hybrid), annotation pipeline, and pan-genome software, significantly impact predictions of core and accessory genome size. Extending our analysis to two additional bacterial species, Escherichia coli and Staphylococcus aureus, we observed consistent tool-dependent biases but species-specific patterns in pan-genome variability. Our findings highlight the importance of integrating nucleotide- and protein-level analyses to improve the reliability and reproducibility of pan-genome studies across diverse bacterial populations.

AVAILABILITY AND IMPLEMENTATION

Panqc is freely available under an MIT license at https://github.com/maxgmarin/panqc.

A genomic variation map provides insights into potato evolution and key agronomic traits

Hybrid potato breeding based on diploid inbred lines is transforming the way of genetic improvement of this staple food crop, which requires a deep understanding of potato domestication and differentiation. In the present study, we resequenced 314 diploid wild and landrace accessions to generate a variome map of 47,203,407 variants. Using the variome map, we discovered the reshaping of tuber transcriptome during potato domestication, characterized genome-wide differentiation between landrace groups Stenotomum and Phureja. We identified a jasmonic acid biosynthetic gene possibly affecting the tuber dormancy period. Genome-wide association studies revealed a UDP-glycosyltransferase gene for the biosynthesis of anti-nutritional steroidal glycoalkaloids (SGAs), and a Dehydration Responsive Element Binding (DREB) transcription factor conferring increased average tuber weight. In addition, genome similarity and group-specific SNP analyses indicated that tetraploid potatoes originated from the diploid Solanum tuberosum group Stenotomum. These findings shed light on the evolutionary trajectory of potato domestication and improvement, providing a solid foundation for advancing hybrid potato-breeding practices.

Genomic investigation of plant secondary metabolism: insights from synteny network analysis of oxidosqualene cyclase flanking genes

The clustered distribution of genes involved in metabolic pathways within the plant genome has garnered significant attention from researchers. By comparing and analyzing changes in the flanking regions of metabolic genes across a diverse array of species, we can enhance our understanding of the formation and distribution of biosynthetic gene clusters (BGCs). In this study, we have designed a workflow that uncovers and assesses conserved positional relationships between genes in various species by using synteny neighborhood networks (SNN). This workflow is then applied to the analysis of flanking genes associated with oxidosqualene cyclases (OSCs). The method allows for the recognition and comparison of homologous blocks with unique flanking genes accompanying different subfamilies of OSCs. The examination of the flanking genes of OSCs in 122 plant species revealed multiple genes with conserved positional relationships with OSCs in angiosperms. Specifically, the earliest adjacency of OSC genes and CYP716 genes first appeared in basal eudicots, and the nonrandom occurrence of CYP716 genes in the flanking region of OSC persists across different lineages of eudicots. Our study showed the substitution of genes in the flanking region of the OSC varies across different plant lineages, and our approach facilitates the investigation of flanking gene rearrangements in the formation of OSC-related BGCs.

Comparative population pangenomes reveal unexpected complexity and fitness effects of structural variants

Structural variants (SVs) are widespread in vertebrate genomes, yet their evolutionary dynamics remain poorly understood. Using 45 long-read de novo genome assemblies and pangenome tools, we analyze SVs within three closely related species of North American jays (Aphelocoma, scrub-jays) displaying a 60-fold range in effective population size. We find rapid evolution of genome architecture, including ~100 Mb variation in genome size driven by dynamic satellite landscapes with unexpectedly long (> 10 kb) repeat units and widespread variation in gene content, influencing gene expression. SVs exhibit slightly deleterious dynamics modulated by variant length and population size, with strong evidence of adaptive fixation only in large populations. Our results demonstrate how population size shapes the distribution of SVs and the importance of pangenomes to characterizing genomic diversity.

Kinome-Focused CRISPR-Cas9 Screens in African Ancestry Patient-Derived Breast Cancer Organoids Identify Essential Kinases and Synergy of EGFR and FGFR1 Inhibition

Precision medicine approaches to cancer treatment aim to exploit genomic alterations that are specific to individual patients to tailor therapeutic strategies. Yet, some targetable genes and pathways are essential for tumor cell viability even in the absence of direct genomic alterations. In underrepresented populations, the mutational landscape and determinants of response to existing therapies are poorly characterized because of limited inclusion in clinical trials and studies. One way to reveal tumor essential genes is with genetic screens. Most screens are conducted on cell lines that bear little resemblance to patient tumors, after years of culture under nonphysiologic conditions. To address this problem, we aimed to develop a CRISPR screening pipeline in three-dimensionally grown patient-derived tumor organoid (PDTO) models. A breast cancer PDTO biobank that focused on underrepresented populations, including West African patients, was established and used to conduct a negative-selection kinome-focused CRISPR screen to identify kinases essential for organoid growth and potential targets for combination therapy with EGFR or MEK inhibitors. The screen identified several previously unidentified kinase targets, and the combination of FGFR1 and EGFR inhibitors synergized to block organoid proliferation. Together, these data demonstrate the feasibility of CRISPR-based genetic screens in patient-derived tumor models, including PDTOs from underrepresented patients with cancer, and identify targets for cancer therapy. Significance: Generation of a breast cancer patient-derived tumor organoid biobank focused on underrepresented populations enabled kinome-focused CRISPR screening that identified essential kinases and potential targets for combination therapy with EGFR or MEK inhibitors. See related commentary by Trembath and Spanheimer, p. 407.

Small variant benchmark from a complete assembly of X and Y chromosomes

The sex chromosomes contain complex, important genes impacting medical phenotypes, but differ from the autosomes in their ploidy and large repetitive regions. To enable technology developers along with research and clinical laboratories to evaluate variant detection on male sex chromosomes X and Y, we create a small variant benchmark set with 111,725 variants for the Genome in a Bottle HG002 reference material. We develop an active evaluation approach to demonstrate the benchmark set reliably identifies errors in challenging genomic regions and across short and long read callsets. We show how complete assemblies can expand benchmarks to difficult regions, but highlight remaining challenges benchmarking variants in long homopolymers and tandem repeats, complex gene conversions, copy number variable gene arrays, and human satellites.

BWT construction and search at the terabase scale

MOTIVATION

Burrows-Wheeler Transform (BWT) is a common component in full-text indices. Initially developed for data compression, it is particularly powerful for encoding redundant sequences such as pangenome data. However, BWT construction is resource intensive and hard to be parallelized, and many methods for querying large full-text indices only report exact matches or their simple extensions. These limitations have hampered the biological applications of full-text indices.

RESULTS

We developed ropebwt3 for efficient BWT construction and query. Ropebwt3 indexed 320 assembled human genomes in 65 h and indexed 7.3 terabases of commonly studied bacterial assemblies in 26 days. This was achieved using up to 170 gigabytes of memory at the peak without working disk space. Ropebwt3 can find maximal exact matches and inexact alignments under affine-gap penalties, and can retrieve similar local haplotypes matching a query sequence. It demonstrates the feasibility of full-text indexing at the terabase scale.

AVAILABILITY AND IMPLEMENTATION

https://github.com/lh3/ropebwt3.

Fitness consequences of structural variation inferred from a House Finch pangenome

Genomic structural variants (SVs) play a crucial role in adaptive evolution, yet their average fitness effects and characterization with pangenome tools are understudied in wild animal populations. We constructed a pangenome for House Finches (Haemorhous mexicanus), a model for studies of host-pathogen coevolution, using long-read sequence data on 16 individuals (32 de novo-assembled haplotypes) and one outgroup. We identified 887,118 SVs larger than 50 base pairs, mostly (60%) involving repetitive elements, with reduced SV diversity in the eastern US as a result of its introduction by humans. The distribution of fitness effects of genome-wide SVs was estimated using maximum likelihood approaches and revealed that SVs in both coding and noncoding regions were on average more deleterious than smaller indels or single nucleotide polymorphisms. The reference-free pangenome facilitated identification of a > 10-My-old, 11-megabase-long pericentric inversion on chromosome 1. We found that the genotype frequencies of the inversion, estimated from 135 birds widely sampled temporally and geographically, increased steadily over the 25 y since House Finches were first exposed to the bacterial pathogen Mycoplasma gallisepticum and showed signatures of balancing selection, capturing genes related to immunity and telomerase activity. We also observed shorter telomeres in populations with a greater number of years exposure to Mycoplasma. Our study illustrates the utility of long-read sequencing and pangenome methods for understanding wild animal populations, estimating fitness effects of genome-wide SVs, and advancing our understanding of adaptive evolution through structural variation.

Haplotype-aware sequence alignment to pangenome graphs

Modern pangenome graphs are built using haplotype-resolved genome assemblies. When mapping reads to a pangenome graph, prioritizing alignments that are consistent with the known haplotypes improves genotyping accuracy. However, the existing rigorous formulations for colinear chaining and alignment problems do not consider the haplotype paths in a pangenome graph. This often leads to spurious read alignments to those paths that are unlikely recombinations of the known haplotypes. In this paper, we develop novel formulations and algorithms for sequence-to-graph alignment and chaining problems. Inspired by the genotype imputation models, we assume that a query sequence is an imperfect mosaic of reference haplotypes. Accordingly, we introduce a recombination penalty in the scoring functions for each haplotype switch. First, we solve haplotype-aware sequence-to-graph alignment in [Formula: see text] time, where Q is the query sequence, E is the set of edges, and H is the set of haplotypes represented in the graph. To complement our solution, we prove that an algorithm significantly faster than [Formula: see text] is impossible under the strong exponential time hypothesis (SETH). Second, we propose a haplotype-aware chaining algorithm that runs in [Formula: see text] time after graph preprocessing, where N is the count of input anchors. We then establish that a chaining algorithm significantly faster than [Formula: see text] is impossible under SETH. As a proof-of-concept, we implemented our chaining algorithm in the Minichain aligner. By aligning sequences sampled from the human major histocompatibility complex (MHC) to a pangenome graph of 60 MHC haplotypes, we demonstrate that our algorithm achieves better consistency with ground-truth recombinations compared with a haplotype-agnostic algorithm.