E-book: Phylogenetics in the Genomic Era (Scornavacca et al. 2021)

This book was not peer-reviewed by PCI Genomics. It has undergone an internal review by the editors.

Accurate reconstructions of the relationships amongst species and the genes encoded in their genomes are an essential foundation for almost all evolutionary inferences emerging from downstream analyses. Molecular phylogenetics has developed as a field over many decades to build suites of models and methods to reconstruct reliable trees that explain, support, or refute such inferences. The genomic era has brought new challenges and opportunities to the field, opening up new areas of research and algorithm development to take advantage of the accumulating large-scale data. Such ‘big-data’ phylogenetics has come to be known as phylogenomics, which broadly aims to connect molecular and evolutionary biology research to address questions centred on relationships amongst taxa, mechanisms of molecular evolution, and the biological functions of genes and other genomic elements. This book brings together experts in the field to present a comprehensive synthesis of Phylogenetics in the Genomic Era, covering key conceptual and methodological aspects of how to build accurate phylogenies and how to apply them in molecular and evolutionary research. The paragraphs below briefly summarise the five constituent parts of the book, highlighting the key concepts, methods, and applications that each part addresses. Being organised in an accessible style, while presenting details to provide depth where necessary, and including guides describing real-world examples of major phylogenomic tools, this collection represents an invaluable resource, particularly for students and newcomers to the field of phylogenomics.

Part 1: Phylogenetic analyses in the genomic era

Modelling how sequences evolve is a fundamental cornerstone of phylogenetic reconstructions. This part of the book introduces the reader to phylogenetic inference methods and algorithmic optimisations in the contexts of Markov, Maximum Likelihood, and Bayesian models of sequence evolution. The main concepts and theoretical considerations are mapped out for probabilistic Markov models, efficient tree building with Maximum Likelihood methods, and the flexibility and robustness of Bayesian approaches. These are supported with practical examples of phylogenomic applications using the popular tools RAxML and PhyloBayes. By considering theoretical, algorithmic, and practical aspects, these chapters provide readers with a holistic overview of the challenges and recent advances in developing scalable phylogenetic analyses in the genomic era.

Part 2: Data quality, model adequacy

This part focuses on the importance of considering the appropriateness of the evolutionary models used and the accuracy of the underlying molecular and genomic data. Both these aspects can profoundly affect the results when applying current phylogenomic methods to make inferences about complex biological and evolutionary processes. A clear example is presented for methods for building multiple sequence alignments and subsequent filtering approaches that can greatly impact phylogeny inference. The importance of error detection in (meta)barcode sequencing data is also highlighted, with solutions offered by the MACSE_BARCODE pipeline for accurate taxonomic assignments. Orthology datasets are essential markers for phylogenomic inferences, but the overview of concepts and methods presented shows that they too face challenges with respect to model selection and data quality. Finally, an innovative approach using ancestral gene order reconstructions provides new perspectives on how to assess gene tree accuracy for phylogenomic analyses. By emphasising through examples the importance of using appropriate evolutionary models and assessing input data quality, these chapters alert readers to key limitations that the field as a whole strives to address.

Part 3: Resolving phylogenomic conflicts

Conflicting phylogenetic signals are commonplace and may derive from statistical or systematic bias. This part of the book addresses possible causes of conflict, discordance between gene trees and species trees and how processes that lead to such conflicts can be described by phylogenetic models. Furthermore, it provides an overview of various models and methods with examples in phylogenomics including their pros and cons. Outlined in detail is the multispecies coalescent model (MSC) and its applications in phylogenomics. An interesting aspect is that different phylogenetic signals leading to conflict are in fact a key source of information rather than a problem that can – and should – be used to point to events like introgression or hybridisation, highlighting possible future trends in this research area. Last but not least, this part of the book also addresses inferring species trees by concatenating single multiple sequence alignments (gene alignments) versus inferring the species tree based on ensembles of single gene trees pointing out advantages and disadvantages of both approaches. As an important take home message from these chapters, it is recommended to be flexible and identify the most appropriate approach for each dataset to be analysed since this may tremendously differ depending on the dataset, setting, taxa, and phylogenetic level addressed by the researcher.

Part 4: Functional evolutionary genomics

In this part of the book the focus shifts to functional considerations of phylogenomics approaches both in terms of molecular evolution and adaptation and with respect to gene expression. The utility of multi-species analysis is clearly presented in the context of annotating functional genomic elements through quantifying evolutionary constraint and protein-coding potential. An historical perspective on characterising rates of change highlights how phylogenomic datasets help to understand the modes of molecular evolution across the genome, over time, and between lineages. These are contextualised with respect to the specific aim of detecting signatures of adaptation from protein-coding DNA alignments using the example of the MutSelDP-ω∗ model. This is extended with the presentation of the generally rare case of adaptive sequence convergence, where consideration of appropriate models and knowledge of gene functions and phenotypic effects are needed. Constrained or relaxed, selection pressures on sequence or copy-number affect genomic elements in different ways, making the very concept of function difficult to pin down despite it being fundamental to relate the genome to the phenotype and organismal fitness. Here gene expression provides a measurable intermediate, for which the Expression Comparison tool from the Bgee suite allows exploration of expression patterns across multiple animal species taking into account anatomical homology. Overall, phylogenomics applications in functional evolutionary genomics build on a rich theoretical history from molecular analyses where integration with knowledge of gene functions is challenging but critical.

Part 5: Phylogenomic applications

Rather than attempting to review the full extent of applications linked to phylogenomics, this part of the book focuses on providing detailed specific insights into selected examples and methods concerning i) estimating divergence times, and ii) species delimitation in the era of ‘omics’ data. With respect to estimating divergence times, an exemplary overview is provided for fossil data recovered from geological records, either using fossil data as calibration points with an extant-species-inferred phylogeny, or using a fossilised birth-death process as a mechanistic model that accounts for lineage diversification. Included is a tutorial for a joint approach to infer phylogenies and estimate divergence times using the RevBayes software with various models implemented for different applications and datasets incorporating molecular and morphological data. An interesting excursion is outlined focusing on timescale estimates with respect to viral evolution introducing BEAGLE, a high-performance likelihood-calculation platform that can be used on multi-core systems. As a second major subject, species delimitation is addressed since currently the increasing amount of available genomic data enables extensive inferences, for instance about the degree of genetic isolation among species and ancient and recent introgression events. Describing the history of molecular species delimitation up to the current genomic era and presenting widely used computational methods incorporating single- and multi-locus genomic data, pros and cons are addressed. Finally, a proposal for a new method for delimiting species based on empirical criteria is outlined. In the closing chapter of this part of the book, BPP (Bayesian Markov chain Monte Carlo program) for analysing multi-locus sequence data under the multispecies coalescent (MSC) model with and without introgression is introduced, including a tutorial. These examples together provide accessible details on key conceptual and methodological aspects related to the application of phylogenetics in the genomic era.

References

Scornavacca C, Delsuc F, Galtier N (2021) Phylogenetics in the Genomic Era. https://hal.inria.fr/PGE/

Biological systems depend on finely tuned interactions of their components. Thus, regulating these components is critical for the system's functionality. In prokaryotic cells, riboswitches are regulatory elements controlling transcription or translation. Riboswitches are RNA molecules that are usually located in the 5′-untranslated region of protein-coding genes. They generate secondary structures leading to the regulation of the expression of the downstream protein-coding gene (Kavita and Breaker, 2022). Riboswitches are very versatile and can bind a wide range of small molecules; in many cases, these are metabolic byproducts from the gene’s enzymatic or signaling pathway. Their versatility and abundance in many species make them attractive for synthetic biological circuits. One class that has been drawing the attention of synthetic biologists is toehold switches (Ekdahl et al., 2022; Green et al., 2014). These are single-stranded RNA molecules harboring the necessary elements for translation initiation of the downstream gene: a ribosome-binding site and a start codon. Conformation change of toehold switches is triggered by an RNA molecule, which enables translation.

To exploit the most out of toehold switches, automation of their design would be highly advantageous. Cisneros and colleagues (Cisneros et al., 2022) developed a tool, “Toeholder”, that automates the design of toehold switches and performs in silico tests to select switch candidates for a target gene. Toeholder is an open-source tool that provides a comprehensive and automated workflow for the design of toehold switches. While web tools have been developed for designing toehold switches (To et al., 2018), Toeholder represents an intriguing approach to engineering biological systems by coupling synthetic biology with computational biology. Using molecular dynamics simulations, it identified the positions in the toehold switch where hydrogen bonds fluctuate the most. Identifying these regions holds great potential for modifications when refining the design of the riboswitches. To be effective, toehold switches should provide a strong ON signal and a weak OFF signal in the presence or the absence of a target, respectively. Toeholder nicely ranks the candidate toehold switches based on experimental evidence that correlates with toehold performance (based on good ON/OFF ratios).

Riboswitches are highly appealing for a broad range of applications, including pharmaceutical and medical purposes (Blount and Breaker, 2006; Giarimoglou et al., 2022; Tickner and Farzan, 2021), thanks to their adaptability and inexpensiveness. The Toeholder tool developed by Cisneros and colleagues is expected to promote the implementation of toehold switches into these various applications.

References

Blount KF, Breaker RR (2006) Riboswitches as antibacterial drug targets. Nature Biotechnology, 24, 1558–1564. https://doi.org/10.1038/nbt1268

Cisneros AF, Rouleau FD, Bautista C, Lemieux P, Dumont-Leblond N, ULaval 2019 T iGEM (2022) Toeholder: a Software for Automated Design and In Silico Validation of Toehold Riboswitches. bioRxiv, 2021.11.09.467922, ver. 3 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.11.09.467922

Ekdahl AM, Rojano-Nisimura AM, Contreras LM (2022) Engineering Toehold-Mediated Switches for Native RNA Detection and Regulation in Bacteria. Journal of Molecular Biology, 434, 167689. https://doi.org/10.1016/j.jmb.2022.167689

Giarimoglou N, Kouvela A, Maniatis A, Papakyriakou A, Zhang J, Stamatopoulou V, Stathopoulos C (2022) A Riboswitch-Driven Era of New Antibacterials. Antibiotics, 11, 1243. https://doi.org/10.3390/antibiotics11091243

Green AA, Silver PA, Collins JJ, Yin P (2014) Toehold Switches: De-Novo-Designed Regulators of Gene Expression. Cell, 159, 925–939. https://doi.org/10.1016/j.cell.2014.10.002

Kavita K, Breaker RR (2022) Discovering riboswitches: the past and the future. Trends in Biochemical Sciences. https://doi.org/10.1016/j.tibs.2022.08.009

Tickner ZJ, Farzan M (2021) Riboswitches for Controlled Expression of Therapeutic Transgenes Delivered by Adeno-Associated Viral Vectors. Pharmaceuticals, 14, 554. https://doi.org/10.3390/ph14060554

To AC-Y, Chu DH-T, Wang AR, Li FC-Y, Chiu AW-O, Gao DY, Choi CHJ, Kong S-K, Chan T-F, Chan K-M, Yip KY (2018) A comprehensive web tool for toehold switch design. Bioinformatics, 34, 2862–2864. https://doi.org/10.1093/bioinformatics/bty216

Lampreys are the focus of intense research. Together with hagfishes, they form the Cyclostomata, the sister group of jawed vertebrates, and hence they are a key group for disentangling the early evolution of many vertebrate features (Shimel and Donoghue 2012; McCauley et al. 2015). Ecologically, lamprey species show a diverse array of life modes, including parasitic and non-feeding species, and inhabit freshwater and marine habitats or both (i.e. anadromous species; Docker and Potter 2019). One of these anadromous species, the sea lamprey (Petromyzon marinus), took advantage of man-made canals to invade the North American Great Lakes in the early 20th century, decimating many fish populations. Today, the control of these invasive populations is paramount for the survival of the region’s fishing industry (Ferreira-Martins et al. 2021). All these research avenues will benefit from the generation of new genomic data, an invaluable resource in evolutionary and conservation biology.

In this manuscript, Tørresen‬ et al. (2025) present phased, chromosome-level assemblies from two lamprey species: the European river lamprey (Lampetra fluviatilis) and the brook lamprey (Lampetra planeri). These two genome assemblies are of high quality and will undoubtedly become a key resource in lamprey research. In particular, the authors showcase the potential of such genomes from two perspectives. First, comparing their assemblies to the already published genomes from P. marinus and another specimen of L. fluviatilis, they propose that lamprey genomes are highly conserved and display large syntenic blocks shared among species. Second, phylogenetic analyses and the annotation of SNPs suggest that L. fluviatilis and L. planeri should be considered two ecotypes of the same species complex, instead of two separate species. This might not be new for anyone knowledgeable in lamprey biology (Rougemont et al. 2017), but it is surprising given the distinct ecology of the two lampreys: L. fluviatilis is a parasitic, anadromous species, whereas L. planeri is a non-feeding, freshwater species. 

In addition to the biological significance of this manuscript, I would like to acknowledge the robustness of the analytical approaches. These genomes were assembled and annotated following two pipelines recently developed at EBP-Nor, the Norwegian initiative of the Earth BioGenome Project (EBP). These pipelines are designed to be an easy-to-use, end-to-end solution for genomic analyses and are likely to become a standard for the EBP and European Reference Genome Atlas initiatives. There can be no better evidence of their effectiveness than these two phased, chromosome-level, highly complete genome assemblies.

References

Docker MF, Potter IC (2019) Life history evolution in lampreys: Alternative migratory and feeding types. In: Docker M (ed) Lampreys: Biology, Conservation and Control. Fish & Fisheries Series, vol 38. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1684-8_4

Ferreira-Martins D, Champer J, McCauley DW, Zhang Z, Docker MF (2021) Genetic control of invasive sea lamprey in the Great Lakes. Journal of Great Lakes Research, 47, S764-S775. https://doi.org/10.1016/j.jglr.2021.10.018

McCauley DW, Docker MF, Whyard S, Li W (2015) Lampreys as diverse model organisms in the genomics era. BioScience, 65(11), 1046-1056. https://doi.org/10.1093/biosci/biv139

Rougemont Q, Gagnaire PA, Perrier C, Genthon C, Besnard AL, Launey S, Evanno G (2017) Inferring the demographic history underlying parallel genomic divergence among pairs of parasitic and nonparasitic lamprey ecotypes. Molecular Ecology, 26(1), 142-162. https://doi.org/10.1111/mec.13664

Shimeld SM, Donoghue PC (2012) Evolutionary crossroads in developmental biology: cyclostomes (lamprey and hagfish). Development, 139(12), 2091-2099. https://doi.org/10.1242/dev.074716

Tørresen OK, Garmann-Aarhus B, Hoff SNK, Jentoft S, Svensson M, Schartum E, Tooming-Klunderud A, Skage M, Krabberød‬​ A, ‭Vøllestad‬​ LA, Jakobsen KS (2025) Comparison of whole-genome assemblies of European river lamprey (Lampetra fluviatilis) and brook lamprey (Lampetra planeri). bioRxiv, ver. 5 peer-reviewed and recommended by PCI Genomics https://doi.org/10.1101/2024.12.06.627158​

The study of genomic variability within and between populations, as well as among species, relies on comparative analyses of homologous positions—sites that share a common evolutionary origin. Homology is inferred through sequence similarity (Reeck et al. 1987). However, the ability to detect homologous regions can be compromised when sequence mismatches accumulate due to mutations, especially when analyzing short DNA fragments, as in short-read sequencing (Li et al. 2008). In the genomic era, accurately mapping homologous DNA fragments to a reference genome is essential for obtaining precise estimates of genetic variability and evolutionary inferences (e.g., Li et al. 2008; Ellegren 2014). However, short-read, high-throughput sequencing often introduces mapping bias, disproportionately favoring the reference allele. This bias distorts allele frequency estimates, ancestry proportions, and genotype likelihoods, impacting downstream analyses (e.g., Günther & Nettelblad 2019; Martiniano et al. 2020).

Mapping bias is particularly problematic in ancient DNA studies, where post-mortem damage exacerbates sequencing errors. DNA fragmentation limits read length, while deamination, causing G to A and C to U transitions, increases mismatches and further complicates homology identification (Dabney & Pääbo 2013). These degradation processes contribute to the misidentification of true variants, confounding evolutionary inferences. Various strategies have been developed to mitigate mapping bias, including the commonly used approach, called pseudo-haploid data, that randomly picks a single read at each analyzed position for each  individual, thereby retaining a single allele at each polymorphic site (Günther & Nettelblad 2019; Barlow et al. 2020). 

Günther et al. (2025) introduce a novel method to correct mapping bias using a genotype likelihood-based approach, incorporating a mapping bias ratio to adjust for reference allele overrepresentation. The method specifically targets known single nucleotide polymorphisms (SNPs) because in population genomic analysis of ancient DNA data, low coverage and post-mortem damage often hinder the ability to identify novel SNPs in most individuals. The analysis focuses on DNA fragmentation, assuming that deamination effects are minimal when considering ascertained SNPs. The proposed method was compared against existing approaches, including pseudo-haploid data and standard genotype likelihood-based probabilistic methods. The evaluation was performed using both empirical and simulated data. For empirical data, low-coverage sequencing data from the 1000 Genomes Project (Finnish in Finland, Japanese in Tokyo, Yoruba in Ibadan, Nigeria populations) was analyzed, while for simulated data, ancient DNA-like datasets were generated using ms-prime (Kelleher et al. 2016), modeling different sequencing depths, divergence times, and reference genome choices. The study assesses the impact of mapping bias on the ratio of reference versus non-reference allele mapping, the accuracy of SNP allele frequency estimates relative to true frequencies, the deviation and variance between estimated and true allele frequencies, population differentiation and the estimation of admixture proportions using supervised and unsupervised methods, considering both genotype likelihoods and genotype calls.

Günther et al. (2025) bring to light that all methods analyzed exhibit minor but systematic reference allele bias. The new corrected genotype likelihood method outperforms the standard genotype likelihood approach in correlating with true allele frequencies, although the pseudo-haploid method still provides the most accurate estimates. Mapping bias also affects ancestry estimation, leading to admixture proportion errors of up to 4%, though this effect is smaller than the 10% discrepancy observed across different inference methods.

The work performed by Günther et al. (2025) provides a rigorous and innovative evaluation of mapping bias in the context of ascertained SNPs, introducing a probabilistic approach that improves bias correction. Unlike non-probabilistic methods such as pseudo-haploid data, the genotype likelihood framework leverages all sequencing reads for each analyzed SNP, and can incorporate additional bias corrections, enhancing its applicability across different sequencing conditions. While probabilistic approaches offer clear advantages in bias correction, they can be less intuitive to interpret compared to traditional genotype calling methods. This study highlights that mapping bias is pervasive across all methods, influencing evolutionary inferences such as selection signals and population differentiation. Although the improvements in allele frequency recovery may seem modest, the genome-wide impact of mapping bias is significant, especially in ancient DNA studies, making bias correction essential for robust evolutionary analyses.

References
 
Barlow A, Hartmann S, Gonzalez J, Hofreiter M, Paijmans JLA. (2020) Consensify: A method for generating pseudohaploid genome sequences from palaeogenomic datasets with reduced error rates. Genes;11(1):50. https://doi.org/10.3390/genes11010050 
 
Dabney J, Meyer M, Pääbo S. (2013) Ancient DNA damage. Cold Spring Harb Perspect Biol. 5(7):a012567. https://doi.org/10.1101/cshperspect.a012567 

Ellegren H. (2014) Genome sequencing and population genomics in non-model organisms. Trends Ecol Evol. 29(1):51-63. https://doi.org/10.1016/j.tree.2013.09.008 

Günther T, Nettelblad C. (2019) The presence and impact of reference bias on population genomic studies of prehistoric human populations. PLoS Genet.15(7):e1008302. https://doi.org/10.1371/journal.pgen.1008302 

Günther T., Goldberg A., Schraiber J. G.  (2025) Estimating allele frequencies, ancestry proportions and genotype likelihoods in the presence of mapping bias. bioRxiv, ver. 5 peer-reviewed and recommended by PCI Genomics https://doi.org/10.1101/2024.07.01.601500 

Kelleher J., Etheridge A. M., McVean G. (2016) Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS computational biology, 12(5):e1004842. https://doi.org/10.1371/journal.pcbi.1004842

Li H, Ruan J, Durbin R. (2008) Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 18(11):1851-8. https://doi.org/10.1101/gr.078212.108 

Reeck GR, de Haën C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE, et al. (1987) "Homology" in proteins and nucleic acids: a terminology muddle and a way out of it. Cell. 50 (5): 667. https://doi.org/10.1016/0092-8674(87)90322-9 

About twenty-five years after the sequencing of the first fungal genome and a dozen years after the first plant pathogenic fungi genomes were sequenced, unprecedented international efforts have led to an impressive collection of genomes available for the community of mycologists in international databases (Goffeau et al. 1996, Dean et al. 2005; Spatafora et al. 2017). For instance, to date, the Joint Genome Institute Mycocosm database has collected more than 2,100 fungal genomes over the fungal tree of life (https://mycocosm.jgi.doe.gov). Such resources are paving the way for comparative genomics, population genomics and phylogenomics to address a large panel of questions regarding the biology and the ecology of fungal species. Early on, population genomics applied to pathogenic fungi revealed a great diversity of genome content and organization and a wide variety of variants and rearrangements (Raffaele and Kamoun 2012, Hartmann 2022). Such plasticity raises questions about how to choose a representative genome to serve as an ideal reference to address pertinent biological questions.

Cryphonectria parasitica is a fungal pathogen that is infamous for the devastation of chestnut forests in North America after its accidental introduction more than a century ago (Anagnostakis 1987). Since then, it has been a quarantine species under surveillance in various parts of the world. As for other fungi causing diseases on forest trees, the study of adaptation to its host in the forest ecosystem and of its reproduction and dissemination modes is more complex than for crop-targeting pathogens. A first reference genome was published in 2020 for the chestnut blight fungus C. parasitica strain EP155 in the frame of an international project with the DOE JGI (Crouch et al. 2020). Another genome was then sequenced from the French isolate YVO003, which showed a few differences in the assembly suggesting possible rearrangements (Demené et al. 2019). Here the sequencing of a third isolate ESM015 from the native area of C. parasitica in Japan allows to draw broader comparative analysis and particularly to compare between native and introduced isolates (Demené et al. 2022).

Demené and collaborators report on a new genome sequence using up-to-date long-read sequencing technologies and they provide an improved genome assembly. Comparison with previously published C. parasitica genomes did not reveal dramatic changes in the overall chromosomal landscapes, but large rearrangements could be spotted. Despite these rearrangements, the genome content and organization – i.e. genes and repeats – remain stable, with a limited number of genes gains and losses. As in any fungal plant pathogen genome, the repertoire of candidate effectors predicted among secreted proteins was more particularly scrutinized. Such effector genes have previously been reported in other pathogens in repeat-enriched plastic genomic regions with accelerated evolutionary rates under the pressure of the host immune system (Raffaele and Kamoun 2012). Demené and collaborators established a list of priority candidate effectors in the C. parasitica gene catalog likely involved in the interaction with the host plant which will require more attention in future functional studies. Six major inter-chromosomal translocations were detected and are likely associated with double break strands repairs. The authors speculate on the possible effects that these translocations may have on gene organization and expression regulation leading to dramatic phenotypic changes in relation to introduction and invasion in new continents and the impact regarding sexual reproduction in this fungus (Demené et al. 2022).

I recommend this article not only because it is providing an improved assembly of a reference genome for C. parasitica, but also because it adds diversity in terms of genome references availability, with a third high-quality assembly. Such an effort in the tree pathology community for a pathogen under surveillance is of particular importance for future progress in post-genomic analysis, e.g. in further genomic population studies (Hartmann 2022). 

References

Anagnostakis SL (1987) Chestnut Blight: The Classical Problem of an Introduced Pathogen. Mycologia, 79, 23–37. https://doi.org/10.2307/3807741

Crouch JA, Dawe A, Aerts A, Barry K, Churchill ACL, Grimwood J, Hillman BI, Milgroom MG, Pangilinan J, Smith M, Salamov A, Schmutz J, Yadav JS, Grigoriev IV, Nuss DL (2020) Genome Sequence of the Chestnut Blight Fungus Cryphonectria parasitica EP155: A Fundamental Resource for an Archetypical Invasive Plant Pathogen. Phytopathology®, 110, 1180–1188. https://doi.org/10.1094/PHYTO-12-19-0478-A

Dean RA, Talbot NJ, Ebbole DJ, Farman ML, Mitchell TK, Orbach MJ, Thon M, Kulkarni R, Xu J-R, Pan H, Read ND, Lee Y-H, Carbone I, Brown D, Oh YY, Donofrio N, Jeong JS, Soanes DM, Djonovic S, Kolomiets E, Rehmeyer C, Li W, Harding M, Kim S, Lebrun M-H, Bohnert H, Coughlan S, Butler J, Calvo S, Ma L-J, Nicol R, Purcell S, Nusbaum C, Galagan JE, Birren BW (2005) The genome sequence of the rice blast fungus Magnaporthe grisea. Nature, 434, 980–986. https://doi.org/10.1038/nature03449

Demené A., Laurent B., Cros-Arteil S., Boury C. and Dutech C. 2022. Chromosomal rearrangements with stable repertoires of genes and transposable elements in an invasive forest-pathogenic fungus. bioRxiv, 2021.03.09.434572, ver.6 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.03.09.434572

Goffeau A, Barrell BG, Bussey H, Davis RW, Dujon B, Feldmann H, Galibert F, Hoheisel JD, Jacq C, Johnston M, Louis EJ, Mewes HW, Murakami Y, Philippsen P, Tettelin H, Oliver SG (1996) Life with 6000 Genes. Science, 274, 546–567. https://doi.org/10.1126/science.274.5287.546

Hartmann FE (2022) Using structural variants to understand the ecological and evolutionary dynamics of fungal plant pathogens. New Phytologist, 234, 43–49. https://doi.org/10.1111/nph.17907

Raffaele S, Kamoun S (2012) Genome evolution in filamentous plant pathogens: why bigger can be better. Nature Reviews Microbiology, 10, 417–430. https://doi.org/10.1038/nrmicro2790

Spatafora JW, Aime MC, Grigoriev IV, Martin F, Stajich JE, Blackwell M (2017) The Fungal Tree of Life: from Molecular Systematics to Genome-Scale Phylogenies. Microbiology Spectrum, 5, 5.5.03. https://doi.org/10.1128/microbiolspec.FUNK-0053-2016