A group of mammals, Xenathra, evolved and diversified in South America during its long period of isolation in the early to mid Cenozoic era. More recently, as a result of the Great Faunal Interchange between South America and North America, many xenarthran species went extinct. The thirty-one extant species belong to three groups: armadillos, sloths and anteaters. They share dental degeneration. However, the level of degeneration is variable. Anteaters entirely lack teeth, sloths have intermediately regressed teeth and most armadillos have a toothless premaxilla, as well as peg-like, single-rooted teeth that lack enamel in adult animals (Vizcaíno 2009). This diversity raises a number of questions about the evolution of dentition in these mammals. Unfortunately, the fossil record is too poor to provide refined information on the different stages of regressive evolution in these clades. In such cases, the identification of loss-of-function mutations and/or relaxed selection in genes related to a character regression can be very informative (Emerling and Springer 2014; Meredith et al. 2014; Policarpo et al. 2021). Indeed, shared and unique pseudogenes/relaxed selection can tell us to what extent regression has occurred in common ancestors and whether some changes are lineage-specific. In addition, the distribution of pseudogenes/relaxed selection on the branches of a phylogenetic tree is related to the evolutionary processes involved. A much higher density of pseudogenes in the most internal branches indicates that degeneration took place early and over a short period of time, consistent with selection against the presence of the morphological character with which they are associated, while pseudogenes distributed evenly in many internal and external branches suggest a more gradual process over many millions of years, in line with relaxed selection and fixation of loss-of-function mutations by genetic drift.

In this paper (Emerling et al. 2023), the authors examined the dynamics of decay of 11 dental genes that may parallel teeth regression. The analyses of the data reported in this paper clearly point to xenarthran teeth having repeatedly regressed in parallel in the three clades. In fact, no loss-of-function mutation is shared by all species examined. However, more genes should be studied to confirm the hypothesis that the common ancestor of extant xenarthrans had normal dentition. There are distinct patterns of gene loss in different lineages that are associated with the variation in dentition observed across the clades. These patterns of gene loss suggest that regressive evolution took place both gradually and in relatively rapid, discrete phases during the diversification of xenarthrans. This study underscores the utility of using pseudogenes to reconstruct evolutionary history of morphological characters when fossils are sparse.

References

Emerling CA, Gibb GC, Tilak M-K, Hughes JJ, Kuch M, Duggan AT, Poinar HN, Nachman MW, Delsuc F. 2023. Genomic data suggest parallel dental vestigialization within the xenarthran radiation. bioRxiv, 2022.12.09.519446, ver 2, peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/2022.12.09.519446

Emerling CA, Springer MS. 2014. Eyes underground: Regression of visual protein networks in subterranean mammals. Molecular Phylogenetics and Evolution 78: 260-270. https://doi.org/10.1016/j.ympev.2014.05.016

Meredith RW, Zhang G, Gilbert MTP, Jarvis ED, Springer MS. 2014. Evidence for a single loss of mineralized teeth in the common avian ancestor. Science 346: 1254390. https://doi.org/10.1126/science.1254390

Policarpo M, Fumey J, Lafargeas P, Naquin D, Thermes C, Naville M, Dechaud C, Volff J-N, Cabau C, Klopp C, et al. 2021. Contrasting gene decay in subterranean vertebrates: insights from cavefishes and fossorial mammals. Molecular Biology and Evolution 38: 589-605. https://doi.org/10.1093/molbev/msaa249

Vizcaíno SF. 2009. The teeth of the “toothless”: novelties and key innovations in the evolution of xenarthrans (Mammalia, Xenarthra). Paleobiology 35: 343-366. https://doi.org/10.1666/0094-8373-35.3.343

Transposable elements (TEs) are mobile genetic elements with an intrinsic mutagenic potential that influences the physiology of any cell type, whether somatic or germinal. Measuring TE expression is a fundamental prerequisite for analysing the processes leading to the activity of TE-derived sequences. This applies to both old and recent TEs, as even if they are deficient in mobilisation, transcription of TE sequences alone can impact neighbouring gene expression and other cellular activities.

In terms of TE physiology, transcription is crucial for mobilisation activity. The transcription of some TEs can be tissue-specific and associated with splicing events, as exemplified by the P-element isoforms in the fruit fly (Laski et al. 1986). Regarding host cell physiology, TE transcripts can include nearby exons, with or without splicing, and such chimeric transcripts can significantly alter gene activity. Thus, quantitative and qualitative analyses must be conducted to assess TE function and how they can modify genomic activities. Yet, due to the polymorphic, interspersed, and repetitive nature of TE sequences, the quantitative and qualitative analysis of TE transcript levels using short-read sequencing remains challenging (Lanciano and Cristofari 2020).

In this context, Rebollo et al. (2024) employed nanopore long-read sequencing to analyse cDNAs derived from Drosophila melanogaster germline RNAs. The authors constructed two long-read cDNA libraries from pooled ovaries and testes using a protocol to obtain full-length cDNAs and sequenced them separately. They carefully compared their results with their short-read datasets. Overall, their observations corroborate known patterns of germline-specific expression of certain TEs and provide initial evidence of novel spliced TE transcript isoforms in Drosophila.

Rebollo and colleagues have provided a well-documented and detailed analysis of their results, which will undoubtedly benefit the scientific community. They presented the challenges and limitations of their approach, such as the length of the transcripts, and provided a reproducible analysis workflow that will enable better characterisation of TE expression using long-read technology.

Despite the small number of samples and limited sequencing depth, this pioneering study strikingly demonstrates the potential of long-read sequencing for the quantitative and qualitative analysis of TE transcription, a technology that will facilitate a better understanding of the transposon landscape.

              
References

Lanciano S, Cristofari G (2020) Measuring and interpreting transposable element expression. Nature Reviews Genetics, 21, 721–736. https://doi.org/10.1038/s41576-020-0251-y

Laski FA, Rio DC, Rubin GM (1986) Tissue specificity of Drosophila P element transposition is regulated at the level of mRNA splicing. Cell, 44, 7–19. https://doi.org/10.1016/0092-8674(86)90480-0

Rebollo R, Gerenton P, Cumunel E, Mary A, Sabot F, Burlet N, Gillet B, Hughes S, Oliveira DS, Goubert C, Fablet M, Vieira C, Lacroix V (2024) Identification and quantification of transposable element transcripts using Long-Read RNA-seq in Drosophila germline tissues. bioRxiv, ver.4 peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/2023.05.27.542554

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

Böhne et al. (2024) do not present a classical scientific paper per se but a report on how the European Reference Genome Atlas (ERGA) aims to deal with sampling and sample information, i.e. metadata.

As the goal of ERGA is to provide an almost fully representative set of reference genomes representative of European biodiversity to serve many research areas in biology, they have to be really exhaustive. In this regard, in addition to providing sample metadata recording guidelines, they also discuss the biases existing in sampling and sequencing projects.

The first task for such a project is to be sure that the data they generate will be usable and available in the future (“[in] perpetuity", Böhne et al. 2024). The authors deployed a very efficient pipeline for conserving information on sampling: location, physical information, copies of tissues and of DNA, shipping, legal/ethical aspects regarding the Nagoya Protocol, etc., alongside a best-practice manual. This effort is linked to practical guides for the DNA extraction of specific taxa. More generally, these details enable “Findable, Accessible, Interoperable, and Reusable” (FAIR) principles (Wilkinson et al. 2016) to be followed.

An important aspect of this paper, in addition to practical points, is the reflection upon the different biases inherent to the choice of sequenced samples. Acknowledging their own biases with regards to DNA extraction protocol efficiency, small genome size choice, as well as the availability of material (Nagoya Protocol aspects) and material transfer efficiency, the authors recommend in the future to not survey biodiversity by selecting one’s favorite samples or species, but also considering "orphan" taxa. Some of these "orphan" taxonomic groups belong to non-arthropod invertebrates but internal disparities are also prominent within other taxa. Finally, the implementation of the "Collective benefit, Authority to control, Responsibility, and Ethics" (CARE) principles (Carroll et al. 2021) will allow Indigenous rights to be considered when prioritizing samples, and to enable their "knowledge systems to permeate throughout the process of reference genome production and beyond" (Böhne et al. 2024).

Last, but not least, as ERGA, including its Sampling and Sample Processing committee, is a large collective effort, it is very refreshing to read a paper starting with the acknowledgements and the roles of each member.

References

Böhne A, Fernández R, Leonard JA, McCartney AM, McTaggart S, Melo-Ferreira J, Monteiro R, Oomen RA, Pettersson OV, Struck TH (2024) Contextualising samples: Supporting reference genomes of European biodiversity through sample and associated metadata collection. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.06.28.546652

Carroll SR, Herczog E, Hudson M, Russell K, Stall S (2021) Operationalizing the CARE and FAIR Principles for Indigenous data futures. Scientific Data, 8, 108. https://doi.org/10.1038/s41597-021-00892-0

Wilkinson MD, Dumontier M, Aalbersberg IjJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, ’t Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S-A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016) The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18

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