Plant pathogenic fungi represent serious economic threats. These organisms are rapidly adaptable, with plastic genomes containing many variable regions and evolving rapidly. It is, therefore, useful to characterize their genetic regulation in order to improve their control. One of the steps to do this is to obtain omics data that link their DNA structure and gene expression. 
In this paper, Clairet et al. (2022) studied the nucleosome positioning and gene expression of four plant pathogenic ascomycete species (Leptosphaeria maculans, Leptosphaeria maculans 'lepidii', Fusarium graminearum, Botrytis cinerea). The genomes of these species contain different compositions of transposable elements (from 4 to 30%), and present an equally variable compartmentalization. The authors established MNAse-seq and RNA-seq maps of these genomes in axenic cultures. Thanks to an ad-hoc tool allowing the visualization of MNA-seq data in combination with other "omics" data, they were able to compare the maps of the different species between them and to study different types of correlation. This tool, called MSTS for "MNase-Seq Tool Suite", allows for example to perform limited analyses on certain genetic subsets in an ergonomic way. 
In the fungi studied, nucleosomes are positioned every 161 to 172 bp, with intra-genome variations such as AT-rich regions but, surprisingly, particularly dense nucleosomes in the Lmb genome. The authors discuss the differences between these organisms with respect to this nucleosome density, the expression profile, and the structure and transposon composition of the different genomes. These data and insights thus represent interesting resources for researchers interested in the evolution of ascomycete genomes and their adaptation. For this, and for the development of the MSTS tool, we recommend this preprint.

References

Clairet C, Lapalu N, Simon A, Soyer JL, Viaud M, Zehraoui E, Dalmais B, Fudal I, Ponts N (2022) Nucleosome patterns in four plant pathogenic fungi with contrasted genome structures. bioRxiv, 2021.04.16.439968, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.04.16.439968

Developing new sequencing and bioinformatic strategies for non-model species is of great interest in many applications, such as phylogenetic studies of diverse related species, but also for studies in population genomics, where a relatively large number of individuals is necessary. Different approaches have been developed and used in these last two decades, such as RAD-Seq (e.g., Miller et al. 2007), exome sequencing (e.g., Teer and Mullikin 2010) and other genome reduced representation methods that avoid the use of a good reference and well annotated genome (reviewed at Davey et al. 2011). However, population genomics studies require the analysis of numerous individuals, which makes the studies still expensive. Pooling samples was thought as an inexpensive strategy to obtain estimates of variability and other related to the frequency spectrum, thus allowing the study of variability at population level (e.g., Van Tassell et al. 2008), although the major drawback was the loss of information related to the linkage of the variants. In addition, population analysis using all these sequencing strategies require statistical and empirical validations that are not always fully performed. A number of studies aiming to obtain unbiased estimates of variability using reduced representation libraries and/or with pooled data have been performed (e.g., Futschik and Schlötterer 2010, Gautier et al. 2013, Ferretti et al. 2013, Lynch et al. 2014), as well as validation of new sequencing methods for population genetic analyses (e.g., Gautier et al. 2013, Nevado et al. 2014). Nevertheless, empirical validation using both pooled and individual experimental approaches combined with different bioinformatic methods has not been always performed.
Here, Deleury et al. (2020) proposed an efficient and elegant way of quantifying the single-nucleotide polymorphisms (SNPs) of exon-derived sequences in a non-model species (i.e. for which no reference genome sequence is available) at the population level scale. They also designed a new procedure to capture exon-derived sequences based on a reference transcriptome. In addition, they were able to make predictions of intron-exon boundaries for de novo transcripts based on the decay of read depth at the ends of the coding regions.
Based on theoretical predictions (Gautier et al. 2013), Deleury et al. (2020) designed a procedure to test the accuracy of variant allele frequencies (AFs) with pooled samples, in a reduced genome-sequence library made with transcriptome regions, and additionally testing the effects of new bioinformatic methods in contrast to standardized methods. They applied their strategy on the non-model species Asian ladybird (Harmonia axyridis), for which a draft genome is available, thereby allowing them to benchmark their method with regard to a traditional mapping-based approach. Based on species-specific de novo transcriptomes, they designed capture probes which are then used to call SNPx and then compared the resulting SNP AFs at the individual (multiplexed) versus population (pooled) levels. Interestingly, they showed that SNP AFs in the pool sequencing strategy nicely correlate with the individual ones but obviously in a cost-effective way. Studies of population genomics for non-model species have usually limited budgets. The number of individuals required for population genomics analysis multiply the costs of the project, making pooling samples an interesting option. Furthermore, the use of pool sequencing is not always a choice, as many organisms are too small and/or individuals are too sticked each other to be individually sequenced (e.g., Choquet et al. 2019, Kurland et al. 2019). In addition, the study of a reduced section of the genome is cheaper and often sufficient for a number of population genetic questions, such as the understanding of general demographic events, or the estimation of the effects of positive and/or negative selection at functional coding regions. Studies on population genomics of non-model species have many applications in related fields, such as conservation genetics, control of invasive species, etc. The work of Deleury et al. (2020) is an elegant contribution to the assessment and validation of new methodologies used for the analysis of genome variations at the intra-population variability level, highlighting straight bioinformatic and reliable sequencing methods for population genomics studies.

References

[1] Choquet et al. (2019). Towards population genomics in non-model species with large genomes: a case study of the marine zooplankton Calanus finmarchicus. Royal Society open science, 6(2), 180608. doi: https://doi.org/10.1098/rsos.180608
[2] Davey, J. W., Hohenlohe, P. A., Etter, P. D., Boone, J. Q., Catchen, J. M. and Blaxter, M. L. (2011). Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nature Reviews Genetics, 12(7), 499-510. doi: https://doi.org/10.1038/nrg3012
[3] Deleury, E., Guillemaud, T., Blin, A. and Lombaert, E. (2020) An evaluation of pool-sequencing transcriptome-based exon capture for population genomics in non-model species. bioRxiv, 10.1101/583534, ver. 7 peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/583534
[4] Ferretti, L., Ramos‐Onsins, S. E. and Pérez‐Enciso, M. (2013). Population genomics from pool sequencing. Molecular ecology, 22(22), 5561-5576. doi: https://doi.org/10.1111/mec.12522
[5] Futschik, A. and Schlötterer, C. (2010). Massively parallel sequencing of pooled DNA samples—the next generation of molecular markers. Genetics, 186 (1), 207-218. doi: https://doi.org/10.1534/genetics.110.114397
[6] Gautier et al. (2013). Estimation of population allele frequencies from next‐generation sequencing data: pool‐versus individual‐based genotyping. Molecular Ecology, 22(14), 3766-3779. doi: https://doi.org/10.1111/mec.12360
[7] Kurland et al. (2019). Exploring a Pool‐seq‐only approach for gaining population genomic insights in nonmodel species. Ecology and evolution, 9(19), 11448-11463. doi: https://doi.org/10.1002/ece3.5646
[8] Lynch, M., Bost, D., Wilson, S., Maruki, T. and Harrison, S. (2014). Population-genetic inference from pooled-sequencing data. Genome biology and evolution, 6(5), 1210-1218. doi: https://doi.org/10.1093/gbe/evu085
[9] Miller, M. R., Dunham, J. P., Amores, A., Cresko, W. A. and Johnson, E. A. (2007). Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome research, 17(2), 240-248. doi: https://doi.org/10.1101%2Fgr.5681207
[10] Nevado, B., Ramos‐Onsins, S. E. and Perez‐Enciso, M. (2014). Resequencing studies of nonmodel organisms using closely related reference genomes: optimal experimental designs and bioinformatics approaches for population genomics. Molecular ecology, 23(7), 1764-1779. doi: https://doi.org/10.1111/mec.12693
[11] Teer, J. K. and Mullikin, J. C. (2010). Exome sequencing: the sweet spot before whole genomes. Human molecular genetics, 19(R2), R145-R151. doi: https://doi.org/10.1093/hmg/ddq333
[12] Van Tassell et al. (2008). SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nature methods, 5(3), 247-252. doi: https://doi.org/10.1038/nmeth.1185

The advent of High Throughput Sequencing (HTS) since the last decade has revealed previously unsuspected diversity of viruses as well as their (sometimes) unexpected presence in some healthy individuals. These results demonstrate that genomics offers a powerful tool for studying viruses at the individual level, allowing an in-depth inventory of those that are infecting an organism. Such approaches make it possible to study viromes with an unprecedented level of detail, both qualitative and quantitative, which opens new venues for analyses of viruses of humans, animals and plants. Consequently, the diagnostic field is using more and more HTS, fueling the need for efficient and reliable bioinformatics tools. 

Many such tools have already been developed, but in plant disease diagnostics, validation of the bioinformatics pipelines used for the detection of viruses in HTS datasets is still in its infancy. There is an urgent need for benchmarking the different tools and algorithms using well-designed reference datasets generated for this purpose. This is a crucial step to move forward and to improve existing solutions toward well-standardized bioinformatics protocols. This context has led to the creation of the Plant Health Bioinformatics Network (PHBN), a Euphresco network project aiming to build a bioinformatics community working on plant health. One of their objectives is to provide researchers with open-access reference datasets allowing to compare and validate virus detection pipelines. 

In this framework, Tamisier et al. [1] present real, semi-artificial, and completely artificial datasets, each aimed at addressing challenges that could affect virus detection. These datasets comprise real RNA-seq reads from virus-infected plants as well as simulated virus reads. Such a work, providing open-access datasets for benchmarking bioinformatics tools, should be encouraged as they are key to software improvement as demonstrated by the well-known success story of the protein structure prediction community: their pioneer community-wide effort, called Critical Assessment of protein Structure Prediction (CASP)[2], has been providing research groups since 1994 with an invaluable way to objectively test their structure prediction methods, thereby delivering an independent assessment of state-of-art protein-structure modelling tools. Following this success, many other bioinformatic community developed similar “competitions”, such as RNA-puzzles [3] to predict RNA structures, Critical Assessment of Function Annotation [4] to predict gene functions, Critical Assessment of Prediction of Interactions [5] to predict protein-protein interactions, Assemblathon [6] for genome assembly, etc. These are just a few examples from a long list of successful initiatives. Such efforts enable rigorous assessments of tools, stimulate the developers’ creativity, but also provide user communities with a state-of-art evaluation of available tools.

Inspired by these success stories, the authors propose a “VIROMOCK challenge” [7], asking researchers in the field to test their tools and to provide feedback on each dataset through a repository. This initiative, if well followed, will undoubtedly improve the field of virus detection in plants, but also probably in many other organisms. This will be a major contribution to the field of viruses, leading to better diagnostics and, consequently, a better understanding of viral diseases, thus participating in promoting human, animal and plant health.   

References

[1] Tamisier, L., Haegeman, A., Foucart, Y., Fouillien, N., Al Rwahnih, M., Buzkan, N., Candresse, T., Chiumenti, M., De Jonghe, K., Lefebvre, M., Margaria, P., Reynard, J.-S., Stevens, K., Kutnjak, D. and Massart, S. (2021) Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection. Zenodo, 4273791, version 4 peer-reviewed and recommended by Peer community in Genomics. doi: https://doi.org/10.5281/zenodo.4273791

[2] Critical Assessment of protein Structure Prediction” (CASP) - https://en.wikipedia.org/wiki/CASP

[3] RNA-puzzles - https://www.rnapuzzles.org

[4] Critical Assessment of Function Annotation (CAFA) - https://en.wikipedia.org/wiki/Critical_Assessment_of_Function_Annotation

[5] Critical Assessment of Prediction of Interactions (CAPI) - https://en.wikipedia.org/wiki/Critical_Assessment_of_Prediction_of_Interactions

[6] Assemblathon - https://assemblathon.org

[7] VIROMOCK challenge - https://gitlab.com/ilvo/VIROMOCKchallenge

The bacterial species problem can be summarized as follows: bacteria recombine too little, and yet too much (Shapiro 2019).
Too little in the sense that recombination is not obligately coupled with reproduction, as in sexual eukaryotes. So the Biological Species Concept (BSC) of reproductive isolation does not strictly apply to clonally reproducing organisms like bacteria. Too much in the sense that genetic exchange can occur promiscuously across species (or even Domains), potentially obscuring species boundaries.
In parallel to such theoretical considerations, several research groups have taken more pragmatic approaches to defining bacterial species based on sequence similarity cutoffs, such as genome-wide average nucleotide identity (ANI). At a cutoff above 95% ANI, genomes are considered to come from the same species. While this cutoff may appear arbitrary, a discontinuity around 95% in the distribution of ANI values has been argued to provide a 'natural' cutoff (Jain et al. 2018). This discontinuity has been criticized as being an artefact of various biases in genome databases (Murray, Gao, and Wu 2020), but appears to be a general feature of relatively unbiased metagenome-assembled genomes as well (Olm et al. 2020). The 95% cutoff has been suggested to represent a barrier to homologous recombination (Olm et al. 2020), although clusters of genetic exchange consistent with BSC-like species are observed at much finer identity cutoffs (Shapiro 2019; Arevalo et al. 2019).
Although 95% ANI is the most widely used genomic standard for species delimitation, it is by no means the only plausible approach. In particular, tracts of identical DNA provide evidence for recent genetic exchange, which in turn helps define BSC-like clusters of genomes (Arevalo et al. 2019). In this spirit, Briand et al. (2020) introduce a genome-clustering method based on the number of shared identical DNA sequences of length k (or k-mers). Using a test dataset of Pseudomonas genomes, they find that 95% ANI corresponds to approximately 50% of shared 15-mers. Applying this cutoff yields 350 Pseudomonas species, whereas the current taxonomy only includes 207 recognized species. To determine whether splitting the genus into a greater number of species is at all useful, they compare their new classification scheme to the traditional one in terms of the ability to taxonomically classify metagenomic sequencing reads from three Pseudomonas-rich environments. In all cases, the new scheme (termed K-IS for "Kinship relationships Identification with Shared k-mers") yielded a higher number of classified reads, with an average improvement of 1.4-fold. This is important because increasing the number of genome sequences in a reference database – without consistent taxonomic annotation of these genomes – paradoxically leads to fewer classified metagenomic reads. Thus a rapid, automated taxonomy such as the one proposed here offers an opportunity to more fully harness the information from metagenomes.
KI-S is also fast to run, so it is feasible to test several values of k and quickly visualize the clustering using an interactive, zoomable circle-packing display (that resembles a cross-section of densely packed, three-dimensional dendrogram). This interface allows the rapid flagging of misidentified species, or understudied species with few sequenced representatives as targets for future study. Hopefully these initial Pseudomonas results will inspire future studies to apply the method to additional taxa, and to further characterize the relationship between ANI and shared identical k-mers. Ultimately, I hope that such investigations will resolve the issue of whether or not there is a 'natural' discontinuity for bacterial species, and what evolutionary forces maintain this cutoff.

References

Arevalo P, VanInsberghe D, Elsherbini J, Gore J, Polz MF (2019) A Reverse Ecology Approach Based on a Biological Definition of Microbial Populations. Cell, 178, 820-834.e14. https://doi.org/10.1016/j.cell.2019.06.033
 
Briand M, Bouzid M, Hunault G, Legeay M, Saux MF-L, Barret M (2020) A rapid and simple method for assessing and representing genome sequence relatedness. bioRxiv, 569640, ver. 5 peer-reveiwed and recommended by PCI Genomics. https://doi.org/10.1101/569640
 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S (2018) High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nature Communications, 9, 5114. https://doi.org/10.1038/s41467-018-07641-9
 
Murray CS, Gao Y, Wu M (2020) There is no evidence of a universal genetic boundary among microbial species. bioRxiv, 2020.07.27.223511. https://doi.org/10.1101/2020.07.27.223511
 
Olm MR, Crits-Christoph A, Diamond S, Lavy A, Carnevali PBM, Banfield JF (2020) Consistent Metagenome-Derived Metrics Verify and Delineate Bacterial Species Boundaries. mSystems, 5. https://doi.org/10.1128/mSystems.00731-19
 
Shapiro BJ (2019) What Microbial Population Genomics Has Taught Us About Speciation. In: Population Genomics: Microorganisms Population Genomics. (eds Polz MF, Rajora OP), pp. 31–47. Springer International Publishing, Cham. https://doi.org/10.1007/13836201810

Data interpretation depends on previously established and validated tools, designed for a specific type of data. These methods, however, are usually based on simple models with validity subject to a set of theoretical parameterized conditions and data types. Accordingly, the tool developers provide the potential users with guidelines for data interpretations within the tools’ limitation. Nevertheless, once the methodology is accepted by the community, it is employed in a large variety of empirical studies outside of the method’s original scope or that typically depart from the standard models used for its design, thus potentially leading to the wrong interpretation of the results.

Numerous empirical studies inferred recombination rates across genomes, detecting hotspots of recombination and comparing related species (e.g., Shanfelter et al. 2019, Spence and Song 2019). These studies used indirect methodologies based on the signals that recombination left in the genome, such as linkage disequilibrium and the patterns of haplotype segregation (e.g.,Chan et al. 2012). The conclusions from these analyses have been used, for example, to interpret the evolution of the chromosomal structure or the evolution of recombination among closely related species.

Indirect methods have the advantage of collecting a large quantity of recombination events, and thus have a better resolution than direct methods (which only detect the few recombination events occurring at that time). On the other hand, indirect methods are affected by many different evolutionary events, such as demographic changes and selection. Indeed, the inference of recombination levels across the genome has not been studied accurately in non-standard conditions. Linkage disequilibrium is affected by several factors that can modify the recombination inference, such as demographic history, events of selection, population size, and mutation rate, but is also related to the size of the studied sample, and other technical parameters defined for each specific methodology.

Raynaud et al (2023) analyzed the reliability of the recombination rate inference when considering the violation of several standard assumptions (evolutionary and methodological) in one of the most popular families of methods based on LDhat (McVean et al. 2004), specifically its improved version, LDhelmet (Chan et al. 2012). These methods cover around 70 % of the studies that infer recombination rates. The authors used recombination maps, obtained from empirical studies on humans, and included hotspots, to perform a detailed simulation study of the capacity of this methodology to correctly infer the pattern of recombination and the location of these hotspots. Correlations between the real, and inferred values from simulations were obtained, as well as several rates, such as the true positive and false discovery rate to detect hotspots.

The authors of this work send a message of caution to researchers that are applying this methodology to interpret data from the inference of recombination landscapes and the location of hotspots. The inference of recombination landscapes and hotspots can differ considerably even in standard model conditions. In addition, demographic processes, like bottleneck or admixture, but also the level of population size and mutation rates, can substantially affect the estimation accuracy of the level of recombination and the location of hotspots. Indeed, the inference of the location of hotspots in simulated data with the same landscape, can be very imprecise when standard assumptions are violated or not considered. These effects may lead to incorrect interpretations, for example about the conservation of recombination maps between closely related species. Finally, Raynaud et al (2023) included a useful guide with advice on how to obtain accurate recombination estimations with methods based on linkage disequilibrium, also emphasizing the limitations of such approaches.

REFERENCES

Chan AH, Jenkins PA, Song YS (2012) Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster. PLOS Genetics, 8, e1003090. https://doi.org/10.1371/journal.pgen.1003090

McVean GAT, Myers SR, Hunt S, Deloukas P, Bentley DR, Donnelly P (2004) The Fine-Scale Structure of Recombination Rate Variation in the Human Genome. Science, 304, 581–584. https://doi.org/10.1126/science.1092500

Raynaud M, Gagnaire P-A, Galtier N (2023) Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation study. bioRxiv, 2022.03.30.486352, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.03.30.486352

Spence JP, Song YS (2019) Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations. Science Advances, 5, eaaw9206. https://doi.org/10.1126/sciadv.aaw9206