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

Repetitive extragenic palindromic sequences (REPs) are common repetitive elements in bacterial genomes (Gilson et al., 1984; Stern et al., 1984). In 2011, Bertels and Rainey identified that REPs are overrepresented in pairs of inverted repeats, which likely form hairpin structures, that they referred to as “REP doublets forming hairpins” (REPINs). Based on bioinformatics analyses, they argued that REPINs are likely selfish elements that evolved from REPs flanking particular transposes (Bertels and Rainey, 2011). These transposases, so-called REP-associated tyrosine transposases (RAYTs), were known to be highly associated with the REP content in a genome and to have characteristic upstream and downstream flanking REPs (Nunvar et al., 2010). The flanking REPs likely enable RAYT transposition, and their horizontal replication is physically linked to this process. In contrast, Bertels and Rainey hypothesized that REPINs are selfish elements that are highly replicated due to the similarity in arrangement to these RAYT-flanking REPs, but independent of RAYT transposition and generally with no impact on bacterial fitness (Bertels and Rainey, 2011).

This last point was especially contentious, as REPINs are highly conserved within species (Bertels and Rainey, 2023), which is unusual for non-beneficial bacterial DNA (Mira et al., 2001). Bertels and Rainey have since refined their argument to be that REPINs must provide benefits to host cells, but that there are nonetheless signatures of intragenomic conflict in genomes associated with these elements (Bertels and Rainey, 2023). These signatures reflect the divergent levels of selections driving REPIN distribution: selection at the level of each DNA element and selection on each individual bacterium. I found this observation particularly interesting as I and my colleague recently argued that these divergent levels of selection, and the interaction between them, is key to understanding bacterial pangenome diversity (Douglas and Shapiro, 2021). REPINs could be an excellent system for investigating these levels of selection across bacteria more generally.

The problem is that REPINs have not been widely characterized in bacterial genomes, partially because no bioinformatic workflow has been available for this purpose. To address this problem, Fortmann-Grote et al. (2023) developed RAREFAN, which is a web server for identifying RAYTs and associated REPINs in a set of input genomes. The authors showcase their tool by applying it to 49 Stenotrophomonas maltophilia genomes and providing examples of how to identify and assess RAYT-REPIN hits. The workflow requires several manual steps, but nonetheless represents a straightforward and standardized approach. Overall, this workflow should enable RAYTs and REPINs to be identified across diverse bacterial species, which will facilitate further investigation into the mechanisms driving their maintenance and spread.

References

Bertels F, Rainey PB (2023) Ancient Darwinian replicators nested within eubacterial genomes. BioEssays, 45, 2200085. https://doi.org/10.1002/bies.202200085

Bertels F, Rainey PB (2011) Within-Genome Evolution of REPINs: a New Family of Miniature Mobile DNA in Bacteria. PLOS Genetics, 7, e1002132. https://doi.org/10.1371/journal.pgen.1002132

Douglas GM, Shapiro BJ (2021) Genic Selection Within Prokaryotic Pangenomes. Genome Biology and Evolution, 13, evab234. https://doi.org/10.1093/gbe/evab234

Fortmann-Grote C, Irmer J von, Bertels F (2023) RAREFAN: A webservice to identify REPINs and RAYTs in bacterial genomes. bioRxiv, 2022.05.22.493013, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.05.22.493013

Gilson E, Clément J m., Brutlag D, Hofnung M (1984) A family of dispersed repetitive extragenic palindromic DNA sequences in E. coli. The EMBO Journal, 3, 1417–1421. https://doi.org/10.1002/j.1460-2075.1984.tb01986.x

Mira A, Ochman H, Moran NA (2001) Deletional bias and the evolution of bacterial genomes. Trends in Genetics, 17, 589–596. https://doi.org/10.1016/S0168-9525(01)02447-7

Nunvar J, Huckova T, Licha I (2010) Identification and characterization of repetitive extragenic palindromes (REP)-associated tyrosine transposases: implications for REP evolution and dynamics in bacterial genomes. BMC Genomics, 11, 44. https://doi.org/10.1186/1471-2164-11-44

Stern MJ, Ames GF-L, Smith NH, Clare Robinson E, Higgins CF (1984) Repetitive extragenic palindromic sequences: A major component of the bacterial genome. Cell, 37, 1015–1026. https://doi.org/10.1016/0092-8674(84)90436-7

Macromolecular System Finder (MacSyFinder) v2 (Néron et al., 2023) is a newly updated approach for performing functional annotation of prokaryotic genomes (Abby et al., 2014). This tool parses an input file of protein sequences from a single genome (either ordered by genome location or unordered) and identifies the presence of specific cellular functions (referred to as “systems”). These systems are called based on two criteria: (1) that the "quorum" of a minimum set of core proteins involved is reached the “quorum” of a minimum set of core proteins being involved that are present, and (2) that the genes encoding these proteins are in the expected genomic organization (e.g., within the same order in an operon), when ordered data is provided. I believe the MacSyFinder approach represents an improvement over more commonly used methods exactly because it can incorporate such information on genomic organization, and also because it is more customizable.

Before properly appreciating these points, it is worth noting the norms and key challenges surrounding high-throughput functional annotation of prokaryotic genomes. Genome sequences are being added to online repositories at increasing rates, which has led to an enormous amount of bacterial genome diversity available to investigate (Altermann et al., 2022). A key aspect of understanding this diversity is the functional annotation step, which enables genes to be grouped into more biologically interpretable categories. For instance, gene calls can be mapped against existing Clusters of Orthologous Genes, which are themselves grouped into general categories such as ‘Transcription’ and ‘Lipid metabolism’ (Galperin et al., 2021).

This approach is valuable but is primarily used for global summaries of functional annotations within a genome: for example, it could be useful to know that a genome is particularly enriched for genes involved in lipid metabolism. However, knowing that a particular gene is involved in the general process of lipid metabolism is less likely to be actionable. In other words, the desired specificity of a gene’s functional annotation will depend on the exact question being investigated. There is no shortage of functional ontologies in genomics that can be applied for this purpose (Douglas and Langille, 2021), and researchers are often overwhelmed by the choice of which functional ontology to use. In this context, giving researchers the ability to precisely specify the gene families and operon structures they are interested in identifying across genomes provides useful control over what precise functions they are profiling. Of course, most researchers will lack the information and/or expertise to fully take advantage of MacSyFinder’s customizable features, but having this option for specialized purposes is valuable.

The other MacSyFinder feature that I find especially noteworthy is that it can incorporate genomic organization (e.g., of genes ordered in operons) when calling systems. This is a rare feature among commonly used tools for functional annotation and likely results in much higher specificity. As the authors note, this capability makes the co-occurrence of paralogs, and other divergent genes that share sequence similarity, to contribute less noise (i.e., they result in fewer false positive calls).

It is important to emphasize that these features are not new additions in MacSyFinder v2, but there are many other valuable changes. Most practically, this release is written in Python 3, rather than the obsolete Python 2.7, and was made more computationally efficient, which will enable MacSyFinder to be more widely used and more easily maintained moving forward. In addition, the search algorithm for analyzing individual proteins was fundamentally updated as well. The authors show that their improvements to the search algorithm result in an 8% and 20% increase in the number of identified calls for single and multi-locus secretion systems, respectively. Taken together, MacSyFinder v2 represents both practical and scientific improvements over the previous version, which will be of great value to the field. 

References

Abby SS, Néron B, Ménager H, Touchon M, Rocha EPC (2014) MacSyFinder: A Program to Mine Genomes for Molecular Systems with an Application to CRISPR-Cas Systems. PLOS ONE, 9, e110726. https://doi.org/10.1371/journal.pone.0110726

Altermann E, Tegetmeyer HE, Chanyi RM (2022) The evolution of bacterial genome assemblies - where do we need to go next? Microbiome Research Reports, 1, 15. https://doi.org/10.20517/mrr.2022.02

Douglas GM, Langille MGI (2021) A primer and discussion on DNA-based microbiome data and related bioinformatics analyses. Peer Community Journal, 1. https://doi.org/10.24072/pcjournal.2

Galperin MY, Wolf YI, Makarova KS, Vera Alvarez R, Landsman D, Koonin EV (2021) COG database update: focus on microbial diversity, model organisms, and widespread pathogens. Nucleic Acids Research, 49, D274–D281. https://doi.org/10.1093/nar/gkaa1018

Néron B, Denise R, Coluzzi C, Touchon M, Rocha EPC, Abby SS (2023) MacSyFinder v2: Improved modelling and search engine to identify molecular systems in genomes. bioRxiv, 2022.09.02.506364, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.09.02.506364

Third-generation sequencing has revolutionised de novo genome assembly. Thanks to this technology, genome reference sequences have evolved from fragmented drafts to gapless, telomere-to-telomere genome assemblies. Long reads produced by Oxford Nanopore and PacBio technologies can span structural variants and resolve complex repetitive regions such as centromeres, unlocking previously inaccessible genomic information. Nowadays, many research groups can afford to sequence the genome of their working model using long reads. Nevertheless, genome assembly poses a significant computational challenge. Read length, quality, coverage and genomic features such as repeat content can affect assembly contiguity, accuracy, and completeness in almost unpredictable ways. Consequently, there is no best universal software or protocol for this task. Producing a high-quality assembly requires chaining several tools into pipelines and performing extensive comparisons between the assemblies obtained by different tool combinations to decide which one is the best. This task can be extremely challenging, as the number of tools available rises very rapidly, and thorough benchmarks cannot be updated and published at such a fast pace. 

In their paper, Orjuela and collaborators present CulebrONT [1], a universal pipeline that greatly contributes to overcoming these challenges and facilitates long-read genome assembly for all taxonomic groups. CulebrONT incorporates six commonly used assemblers and allows to perform assembly, circularization (if needed), polishing, and evaluation in a simple framework. One important aspect of CulebrONT is its modularity, which allows the activation or deactivation of specific tools, giving great flexibility to the user. Nevertheless, possibly the best feature of CulebrONT is the opportunity to benchmark the selected tool combinations based on the excellent report generated by the pipeline. This HTML report aggregates the output of several tools for quality evaluation of the assemblies (e.g. BUSCO [2] or QUAST [3]) generated by the different assemblers, in addition to the running time and configuration parameters. Such information is of great help to identify the best-suited pipeline, as exemplified by the authors using four datasets of different taxonomic origins. Finally, CulebrONT can handle multiple samples in parallel, which makes it a good solution for laboratories looking for multiple assemblies on a large scale. 

References

1. Orjuela J, Comte A, Ravel S, Charriat F, Vi T, Sabot F, Cunnac S (2022) CulebrONT: a streamlined long reads multi-assembler pipeline for prokaryotic and eukaryotic genomes. bioRxiv, 2021.07.19.452922, ver. 5 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.07.19.452922

2. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM (2015) BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics, 31, 3210–3212. https://doi.org/10.1093/bioinformatics/btv351

3. Gurevich A, Saveliev V, Vyahhi N, Tesler G (2013) QUAST: quality assessment tool for genome assemblies. Bioinformatics, 29, 1072–1075. https://doi.org/10.1093/bioinformatics/btt086

The European Reference Genome Atlas (ERGA) (Mc Cartney et al, 2024, Mazzoni et al, 2023) demonstrates the collaborative spirit and intellectual abilities of researchers from 33 European countries. This ambitious project, which is part of the Earth BioGenome Project (Lewin et al., 2018) Phase II, has embarked on an unprecedented mission: to decipher the genetic makeup of 150,000 species over a span of four years. At the heart of ERGA is a decentralized pilot infrastructure specifically built to assist the production of high-quality reference genomes. This structure acts as a scaffold for the massive task of genome sequencing, giving the necessary framework to manage the complexity of genomic research. The research paper under consideration offers a comprehensive narrative of ERGA's evolution, outlining both successes and challenges encountered along the road. 

One of the most significant issues addressed in the manuscript is the equitable distribution of resources and expertise among participating laboratories and countries. In a project of this magnitude, it is critical to leverage the pooled talents and capacities of researchers from across Europe. ERGA's pan-European network promotes communications and collaboration, creating an environment in which knowledge flows freely and barriers are overcome. This adoption of strong coordination and communication tactics will be essential to ERGA's success. Scientific collaboration depends on efficient communication channels because they allow researchers to share resources, collaborate on new initiatives, and exchange ideas. Through a diverse range of gatherings, courses, and virtual discussion boards, ERGA fosters an environment of transparency and cooperation among members, enabling scientists to overcome challenges and make significant discoveries. The importance ERGA places on training and information transfer programmes is a pillar of its strategy. Understanding the importance of capacity development, ERGA invests in providing researchers with the knowledge and abilities necessary for effectively navigating the complicated terrain of genomic research. A wide range of subjects are covered in training programmes (Larivière et al. 2023), from sample preparation and collection to data processing methods and sequencing technology. Through the development of a group of highly qualified experts, ERGA creates the foundation for continued advancement and creativity in the genomics sector.

This manuscript also covers in detail the technological workflows and sequencing techniques used in ERGA's pilot infrastructure. With the aid of cutting-edge sequencing technologies based on both long-read and short-read sequencing, they are working to unravel the complex structure of the genetic code with a level of accuracy and precision never before possible. To guarantee the accuracy of genetic data and prevent mistakes and flaws that can jeopardize the findings' integrity, quality control methods are put in place. Despite having a focus on genome sequencing due to its technological complexities, ERGA also remains firm in its dedication to metadata collection and sample validation. Metadata serves as a critical link between raw genetic data and useful scientific insights, giving necessary context and allowing researchers to draw practical findings from their investigations. Sample validation approaches improve the reliability and reproducibility of the results, providing users confidence in the quality of the genetic data provided by ERGA.​

Looking ahead, ERGA envisions its decentralized infrastructure serving as a model for global collaborative research efforts. By embracing diversity, encouraging cooperation, and pushing for open access to data and resources, ERGA hopes to catalyze scientific discovery and generate positive change in the field of biodiversity genomics. ERGA aims to promote a more equitable and sustainable future for all by ongoing interaction with stakeholders, intensive outreach and education activities, and policy change advocacy. In addition to its immediate goals, ERGA considers the long-term implications of its work. As genomic technology progresses, the potential application of high-quality reference genomes will continue to grow. From informing conservation efforts and illuminating evolutionary histories to revolutionizing healthcare and agriculture, it is likely that ERGA's contributions will have far-reaching consequences for people and the planet as a whole.​

Furthermore, ERGA understands the importance of interdisciplinary collaboration in addressing the difficult challenges of the twenty-first century. ERGA aims to integrate genetic research into larger initiatives to promote sustainability and biodiversity conservation by forming relationships with stakeholders from other areas, such as policymakers, conservationists, and indigenous groups. Through shared knowledge and community action, ERGA seeks to create a future in which mankind coexists peacefully with the natural world, guided by a thorough grasp of its genetic legacy and ecological interconnectivity.

Finally, the manuscript exemplifies ERGA's collaborative ambitions and achievements, capturing the spirit of creativity and collaboration that defines this ground-breaking effort. As ERGA continues to push the boundaries of genetic research, it remains dedicated to scientific excellence, inclusivity, and the quest of knowledge for the benefit of society. I wholeheartedly recommend the publication of this groundbreaking initiative, offering my enthusiastic endorsement for its valuable contribution to the scientific community.​​

References
Larivière, D., Abueg, L., Brajuka, N. et al. (2024). Scalable, accessible and reproducible reference genome assembly and evaluation in Galaxy. Nature Biotechnology 42, 367-370. https://doi.org/10.1038/s41587-023-02100-3

Lewin, H. A., Robinson, G. E., Kress, W. J., Baker, W. J., Coddington, J., Crandall, K. A., Durbin, R., Edwards, S. V., Forest, F., Gilbert, M. T. P., Goldstein, M. M., Grigoriev, I. V., Hackett, K. J., Haussler, D., Jarvis, E. D., Johnson, W. E., Patrinos, A., Richards, S., Castilla-Rubio, J. C., … Zhang, G. (2018). Earth BioGenome Project: Sequencing life for the future of life. Proceedings of the National Academy of Sciences, 115(17), 4325–4333. https://doi.org/10.1073/pnas.1720115115

Mazzoni, C. J., Claudio, C.i, Waterhouse, R. M. (2023). Biodiversity: an atlas of European reference genomes. Nature 619 : 252-252. https://doi.org/10.1038/d41586-023-02229-w

Mc Cartney, A. M., Formenti, G., Mouton, A., Panis, D. de, Marins, L. S., Leitão, H. G., Diedericks, G., Kirangwa, J., Morselli, M., Salces-Ortiz, J., Escudero, N., Iannucci, A., Natali, C., Svardal, H., Fernández, R., Pooter, T. de, Joris, G., Strazisar, M., Wood, J., … Mazzoni, C. J. (2024). The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics. bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.09.25.559365​