Inference of Ancestral Recombination Graphs through Topological Data Analysis

The recent explosion of genomic data has underscored the need for interpretable and comprehensive analyses that can capture complex phylogenetic relationships within and across species. Recombination, reassortment and horizontal gene transfer constitute examples of pervasive biological phenomena that cannot be captured by tree-like representations. Starting from hundreds of genomes, we are interested in the reconstruction of potential evolutionary histories leading to the observed data. Ancestral recombination graphs represent potential histories that explicitly accommodate recombination and mutation events across orthologous genomes. However, they are computationally costly to reconstruct, usually being infeasible for more than few tens of genomes. Recently, Topological Data Analysis (TDA) methods have been proposed as robust and scalable methods that can capture the genetic scale and frequency of recombination. We build upon previous TDA developments for detecting and quantifying recombination, and present a novel framework that can be applied to hundreds of genomes and can be interpreted in terms of minimal histories of mutation and recombination events, quantifying the scales and identifying the genomic locations of recombinations. We implement this framework in a software package, called TARGet, and apply it to several examples, including small migration between different populations, human recombination, and horizontal evolution in finches inhabiting the Gal\'apagos Islands.Comment: 33 pages, 12 figures. The accompanying software, instructions and example files used in the manuscript can be obtained from https://github.com/RabadanLab/TARGe

Computational pan-genomics: status, promises and challenges

International audienceMany disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains

Einkorn genomics sheds light on history of the oldest domesticated wheat

Einkorn (Triticum monococcum) was the first domesticated wheat species, and was central to the birth of agriculture and the Neolithic Revolution in the Fertile Crescent around 10,000 years ago$^{1,2}$. Here we generate and analyse 5.2-Gb genome assemblies for wild and domesticated einkorn, including completely assembled centromeres. Einkorn centromeres are highly dynamic, showing evidence of ancient and recent centromere shifts caused by structural rearrangements. Whole-genome sequencing analysis of a diversity panel uncovered the population structure and evolutionary history of einkorn, revealing complex patterns of hybridizations and introgressions after the dispersal of domesticated einkorn from the Fertile Crescent. We also show that around 1% of the modern bread wheat (Triticum aestivum) A subgenome originates from einkorn. These resources and findings highlight the history of einkorn evolution and provide a basis to accelerate the genomics-assisted improvement of einkorn and bread wheat

New computational methods and plant models for evolutionary genomics

This thesis is in the service of a greater understanding of the genetic basis of adaptive traits. Chapter 1 introduces background literature relevant to this thesis. Chapters 2, 3, and 4 develop novel methods and software for the analysis of genetic sequencing data. Chapter 5 details a large collaborative project to establish genetic resources in the model cereal Brachypodium, and perform a genome-wide association study for several agriculturally-relevant traits under two climate change scenarios. Chapter 6 investigates the spatial genetic patterns in two species of woodland eucalypt, and determines the landscape process that could be driving these patterns. Finally, Chapter 7 summarises these works, and proposes some areas of further study. In Chapters 2 and 3, I develop methods that enable analysis of Genotyping-by-sequencing analysis. Axe, a short read sequence demultiplexer, demultiplexes samples from multiplexed GBS sequencing datasets. I show Axe has high accuracy, and outperforms previously published software. Axe also tolerates complex indexing schemes such as the variable-length combinatorial indexes used in GBS data. Trimit and libqcpp (Chapter 3) implements several low-level sequence read quality assessment and control methods as a C++ library, and as a command line tool. Both these works have been published in peer-reviewed journals, and are used by numerous groups internationally. In Chapter 4, I develop kWIP, a de novo estimator of genetic distance. kWIP enables rapid estimation of genetic distances directly from sequence reads. We first show kWIP outperforms a competing method at low coverage using simulations that mimic a population resequencing experiment. We propose and demonstrate several use cases for kWIP, including population resequencing, initial assessment of sample identity, and estimating metagenomic similarity. kWIP was published in PLoS Computational Biology. In Chapter 5, I present the results of a large, collaborative project which surveys the global genetic diversity of the model cereal Brachypodium. We amass a collection of over 2000 accessions from the Brachypodium species complex. Using GBS and whole genome sequencing we identify around 800 accessions of the diploid Brachypodium distachyon, within which we find extensive population structure and clonal families. Through population restructuring we create a core collection of 74 accessions containing the majority of genetic diversity in the "A genome" sub-population. Using this core collection, we assay several phenotypes of agricultural interest including early vigour, harvest index and energy use efficiency under two climates, and dissect the genetic basis of these traits using a genome-wide association study (GWAS). This work has been accepted for publication at Genetics; I am co-first author with Pip Wilson and Jared Streich, having lead many genomic analyses. In Chapter 6, I perform a study of landscape genomic variation in two woodland eucalypt species. Using whole genome sequencing of around 200 individuals from around 20 localities of both E. albens and E. sideroxylon, I find incredible genetic diversity and low genome-wide inter-species differentiation.I find no support for strong discrete population structure, but strong support for isolation by (geographic) distance (IBD). Using generalised dissimilarity modelling, I further examine the pattern of IBD, and establish additional isolation by environment (IBE). E. albens shows moderately strong IBD, explaining 26% of deviance in genetic distance using geographic distance, and an additional 6% deviance explained by incorporating environmental predictors (IBE). E. sideroxylon shows much stronger IBD, with 78% of deviance explained by geography, and stronger IBE (12% additional deviance explained). This work will soon be submitted for publication