Transposable elements (TEs) are mobile DNA sequences that can increase their copy number and move from one location to another within the genome [1]. Because of their transposition dynamics, TEs constitute a significant fraction of eukaryotic genomes. TEs are also known to play an important functional role and a wealth of studies has now reported how TEs may influence single host traits [e.g. 2–4]. Given that TEs are more likely than classical point mutations to cause extreme changes in gene expression and phenotypes, they might therefore be especially prone to produce the raw diversity necessary for individuals to respond to challenging environments [5,6] such as the ones found in urban area.  
In their study [7], Vargas et al. establish the foundation to investigate how TEs may help Anopheles coluzzii -  the primary vectors of human malaria in sub-Saharan Africa - adapt to urban environments. To cover natural breeding sites in major Central Africa cities, they made use of the previously available An. coluzzii genome from Yaoundé (Cameroon) and sequenced with long-read technology six additional ones originating from Douala (Cameroon) and Libreville (Gabon). The de novo annotation of TEs in these genomes revealed 64 new anopheline TE families and allowed to identify seven active families. As a first step towards characterizing the potential role of TEs in the adaptation of An. coluzzii to urban environments, they further analyzed the distribution of TEs across the seven genomes. By doing so, they identified a significant number of polymorphic or fixed TE insertions located in the vicinity of genes involved in insecticide resistance and immune response genes.  
The availability of seven An. coluzzii genomes allowed the authors to explore how TE diversity may affect genes functionally relevant for the adaptation to urban environments and provide ground for further functional validation studies. More and more studies have demonstrated the impact of TEs on adaptation and as such, the work of Vargas et al. contributes to fostering our understanding of the link between TEs and gain of function in a species facing strong anthropogenic pressures.  
 
References  
  
[1] Wicker T, Sabot F, Hua-Van A, Bennetzen JL, Capy P, Chalhoub B, Flavell A, Leroy P, Morgante M, Panaud O, Paux E, SanMiguel P, Schulman AH (2007) A unified classification system for eukaryotic transposable elements. Nature Reviews Genetics, 8, 973–982. https://doi.org/10.1038/nrg2165    
  
[2] van’t Hof AE, Campagne P, Rigden DJ, Yung CJ, Lingley J, Quail MA, Hall N, Darby AC, Saccheri IJ (2016) The industrial melanism mutation in British peppered moths is a transposable element. Nature, 534, 102–105. https://doi.org/10.1038/nature17951    
  
[3] González J, Karasov TL, Messer PW, Petrov DA (2010) Genome-wide patterns of adaptation to temperate environments associated with transposable elements in Drosophila. PLOS Genetics, 6, e1000905. https://doi.org/10.1371/journal.pgen.1000905  
  
[4] Lisch D (2013) How important are transposons for plant evolution? Nature Reviews Genetics, 14, 49–61. https://doi.org/10.1038/nrg3374    
  
[5] Bonchev G, Parisod C (2013) Transposable elements and microevolutionary changes in natural populations. Molecular Ecology Resources, 13, 765–775. https://doi.org/10.1111/1755-0998.12133  
  
[6] Casacuberta E, González J (2013) The impact of transposable elements in environmental adaptation. Molecular Ecology, 22, 1503–1517. https://doi.org/10.1111/mec.12170    
  
[7] Vargas-Chavez C, Pendy NML, Nsango SE, Aguilera L, Ayala D, González J (2021). Uncovering transposable element variants and their potential adaptive impact in urban populations of the malaria vector Anopheles coluzzii. bioRxiv, 2020.11.22.393231, ver. 3 peer-reviewed and recommended by Peer community in Genomics. https://doi.org/10.1101/2020.11.22.393231  

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

Transposable elements are the raw material of the dark matter of the genome, the foundation of the next generation of genes and regulation networks". This sentence could be the essence of the paper of Baud et al. (2021). Transposable elements (TEs) are endogenous mobile genetic elements found in almost all genomes, which were discovered in 1948 by Barbara McClintock (awarded in 1983 the only unshared Medicine Nobel Prize so far). TEs are present everywhere, from a single isolated copy for some elements to more than millions for others, such as Alu. They are founders of major gene lineages (HET-A, TART and telomerases, RAG1/RAG2 proteins from mammals immune system; Diwash et al, 2017), and even of retroviruses (Xiong & Eickbush, 1988). However, most TEs appear as selfish elements that replicate, land in a new genomic region, then start to decay and finally disappear in the midst of the genome, turning into genomic ‘dark matter’ (Vitte et al, 2007). The mutations (single point, deletion, recombination, and so on) that occur during this slow death erase some of their most notable features and signature sequences, rendering them completely unrecognizable after a few million years. Numerous TE detection tools have tried to optimize their detection (Goerner-Potvin & Bourque, 2018), but further improvement is definitely challenging. This is what Baud et al. (2021) accomplished in their paper. They used a simple, elegant and efficient k-mer based approach to find small signatures that, when accumulated, allow identifying very old TEs. Using this method, called Duster, they improved the amount of annotated TEs in the model plant Arabidopsis thaliana by 20%, pushing the part of this genome occupied by TEs up from 40 to almost 50%. They further observed that these very old Duster-specific TEs (i.e., TEs that are only detected by Duster) are, among other properties, close to genes (much more than recent TEs), not targeted by small RNA pathways, and highly associated with conserved regions across the rosid family. In addition, they are highly associated with flowering or stress response genes, and may be involved through exaptation in the evolution of responses to environmental changes. TEs are not just selfish elements: more and more studies have shown their key role in the evolution of their hosts, and tools such as Duster will help us better understand their impact.
 

References

Baud, A., Wan, M., Nouaud, D., Francillonne, N., Anxolabéhère, D. and Quesneville, H. (2021). Traces of transposable elements in genome dark matter co-opted by flowering gene regulation networks. bioRxiv, 547877, ver. 5 peer-reviewed and recommended by PCI Genomics.doi: https://doi.org/10.1101/547877
 
Bourque, G., Burns, K.H., Gehring, M. et al. (2018) Ten things you should know about transposable elements. Genome Biology 19:199. doi: https://doi.org/10.1186/s13059-018-1577-z
 
Goerner-Potvin, P., Bourque, G. Computational tools to unmask transposable elements. Nature Reviews Genetics 19:688–704 (2018) https://doi.org/10.1038/s41576-018-0050-x
 
Jangam, D., Feschotte, C. and Betrán, E. (2017) Transposable element domestication as an adaptation to evolutionary conflicts. Trends in Genetics 33:817-831. doi: https://doi.org/10.1016/j.tig.2017.07.011
 
Vitte, C., Panaud, O. and Quesneville, H. (2007) LTR retrotransposons in rice (Oryza sativa, L.): recent burst amplifications followed by rapid DNA loss. BMC Genomics 8:218. doi: https://doi.org/10.1186/1471-2164-8-218
 
Xiong, Y. and Eickbush, T. H. (1988) Similarity of reverse transcriptase-like sequences of viruses, transposable elements, and mitochondrial introns. Molecular Biology and Evolution 5: 675–690. doi: https://doi.org/10.1093/oxfordjournals.molbev.a040521

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