Phylodynamic approaches enable viral genetic variation to be tracked over time, providing insight into pathogen phylogenetic relationships and epidemiological dynamics. These are important methods for monitoring viral spread, and identifying important parameters such as transmission rate, geographic origin and duration of infection [1]. This knowledge makes it possible to adjust public health measures in real-time and was important in the case of the COVID-19 pandemic [2]. However, these approaches can be complicated to use when combining a very large number of sequences. This was particularly true during the COVID-19 pandemic, when sequencing data representing millions of entire viral genomes was generated, with associated metadata enabling their precise identification.

Danesh et al. [3] present a bioinformatics pipeline, CovFlow, for selecting relevant sequences according to user-defined criteria to produce files that can be used directly for phylodynamic analyses. The selection of sequences first involves a quality filter on the size of the sequences and the absence of unresolved bases before being able to make choices based on the associated metadata. Once the sequences are selected, they are aligned and a time-scaled phylogenetic tree is inferred. An output file in a format directly usable by BEAST 2 [4] is finally generated.

To illustrate the use of the pipeline, Danesh et al. [3] present an analysis of the Delta variant in two regions of France. They observed a delay in the start of the epidemic depending on the region. In addition, they identified genetic variation linked to the start of the school year and the extension of vaccination, as well as the arrival of a new variant. This tool will be of major interest to researchers analysing SARS-CoV-2 sequencing data, and a number of future developments are planned by the authors.

References

[1] Baele G, Dellicour S, Suchard MA, Lemey P, Vrancken B. 2018. Recent advances in computational phylodynamics. Curr Opin Virol. 31:24-32. https://doi.org/10.1016/j.coviro.2018.08.009

[2] Attwood SW, Hill SC, Aanensen DM, Connor TR, Pybus OG. 2022. Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nat Rev Genet. 23:547-562. https://doi.org/10.1038/s41576-022-00483-8

[3] Danesh G, Boennec C, Verdurme L, Roussel M, Trombert-Paolantoni S, Visseaux B, Haim-Boukobza S, Alizon S. 2023. COVFlow: phylodynamics analyses of viruses from selected SARS-CoV-2 genome sequences. bioRxiv, ver. 7 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.06.17.496544

[4] Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu C-H et al. 2014. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol 10: e1003537. https://doi.org/10.1371/journal.pcbi.1003537

This manuscript describes the ongoing work and plans of Biogenome Portugal: a new network of researchers in the Portuguese biodiversity genomics community. The aims of this network are to jointly train scientists in ecology and evolution, generate new knowledge and understanding of Portuguese biodiversity, and better engage with the public and with international researchers, so as to advance conservation efforts in the region. In collaboration across disciplines and institutions, they are also contributing to the European Reference Genome Atlas (ERGA): a massive scientific effort, seeking to eventually produce reference-quality genomes for all species in the European continent (Mc Cartney et al. 2023).

The manuscript centers around six iconic and/or severely threatened species, whose range extends across parts of what is today considered Portuguese territory. Via the Portugal chapter of ERGA (ERGA-Portugal), the researchers will generate high-quality genome sequences from these species. The species are the Iberian hare, the Azores laurel, the Black wheatear, the Portuguese crowberry, the Cave ground beetle and the Iberian minnowcarp. In ignorance of human-made political borders, some of these species also occupy large parts of the rest of the Iberian peninsula, highlighting the importance of transnational collaboration in biodiversity efforts. The researchers extracted samples from members of each of these species, and are building reference genome sequences from them. In some cases, these sequences will also be co-analyzed with additional population genomic data from the same species or genetic data from cohabiting species. The researchers aim to answer a variety of ecological and evolutionary questions using this information, including how genetic diversity is being affected by the destruction of their habitat, and how they are being forced to adapt as a consequence of the climate emergency.

The authors did a very good job in providing a justification for the choice of pilot species, a thorough methodological overview of current work, and well thought-out plans for future analyses once the genome sequences are available for study. The authors also describe plans for networking and training activities to foster a well-connected Portuguese biodiversity genomics community.

Applying a genomic analysis lens is important for understanding the ever faster process of devastation of our natural world. Governments and corporations around the globe are destroying nature at ever larger scales (Diaz et al. 2019). They are also destabilizing the climatic conditions on which life has existed for thousands of years (Trisos et al. 2020). Thus, genetic diversity is decreasing faster than ever in human history, even when it comes to non-threatened species (Exposito-Alonso et al. 2022), and these decreases are disrupting ecological processes worldwide (Richardson et al. 2023). This, in turn, is threatening the conditions on which the stability of our societies rest (Gardner and Bullock 2021). The efforts of Biogenome Portal and ERGA-Portugal will go a long way in helping us understand in greater detail how this process is unfolding in Portuguese territories.

References

Díaz, Sandra, et al. "Pervasive human-driven decline of life on Earth points to the need for transformative change." Science 366.6471 (2019): eaax3100. https://doi.org/10.1126/science.aax3100

Exposito-Alonso, Moises, et al. "Genetic diversity loss in the Anthropocene." Science 377.6613 (2022): 1431-1435. https://doi.org/10.1126/science.abn5642

Gardner, Charlie J., and James M. Bullock. "In the climate emergency, conservation must become survival ecology." Frontiers in Conservation Science 2 (2021): 659912. https://doi.org/10.3389/fcosc.2021.659912

Mc Cartney, Ann M., et al. "The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics." bioRxiv (2023): 2023-09, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.32942/X20W3Q

Richardson, Katherine, et al. "Earth beyond six of nine planetary boundaries." Science Advances 9.37 (2023): eadh2458. https://doi.org/10.1126/sciadv.adh2458

Trisos, Christopher H., Cory Merow, and Alex L. Pigot. "The projected timing of abrupt ecological disruption from climate change." Nature 580.7804 (2020): 496-501. https://doi.org/10.1038/s41586-020-2189-9

In their paper, Guiglielmoni et al. propose we pick up our snorkels and palms and take "A deep dive into genome assemblies of non-vertebrate animals" (1). Indeed, while numerous assembly-related tools were developed and tested for human genomes (or at least vertebrates such as mice), very few were tested on non-vertebrate animals so far. Moreover, most of the benchmarks are aimed at raw assembly tools, and very few offer a guide from raw reads to an almost finished assembly, including quality control and phasing.

This huge and exhaustive review starts with an overview of the current sequencing technologies, followed by the theory of the different approaches for assembly and their implementation. For each approach, the authors present some of the most representative tools, as well as the limits of the approach.

The authors additionally present all the steps required to obtain an almost complete assembly at a chromosome-scale, with all the different technologies currently available for scaffolding, QC, and phasing, and the way these tools can be applied to non-vertebrates animals. Finally, they propose some useful advice on the choice of the different approaches (but not always tools, see below), and advocate for a robust genome database with all information on the way the assembly was obtained.

This review is a very complete one for now and is a very good starting point for any student or scientist interested to start working on genome assembly, from either model or non-model organisms. However, the authors do not provide a list of tools or a benchmark of them as a recommendation. Why? Because such a proposal may be obsolete in less than a year.... Indeed, with the explosion of the 3rd generation of sequencing technology, assembly tools (from different steps) are constantly evolving, and their relative performance increases on a monthly basis. In addition, some tools are really efficient at the time of a review or of an article, but are not further developed later on, and thus will not evolve with the technology. We have all seen it with wonderful tools such as Chiron (2) or TopHat (3), which were very promising ones, but cannot be developed further due to the stop of the project, the end of the contract of the post-doc in charge of the development, or the decision of the developer to switch to another paradigm. Such advice would, therefore, need to be constantly updated.

Thus, the manuscript from Guiglielmoni et al will be an almost intemporal one (up to the next sequencing revolution at last), and as they advocated for a more informed genome database, I think we should consider a rolling benchmarking system (tools, genome and sequence dataset) allowing to keep the performance of the tools up-to-date, and to propose the best set of assembly tools for a given type of genome.

References

1. Guiglielmoni N, Rivera-Vicéns R, Koszul R, Flot J-F (2022) A Deep Dive into Genome Assemblies of Non-vertebrate Animals. Preprints, 2021110170, ver. 3 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.20944/preprints202111.0170

2. Teng H, Cao MD, Hall MB, Duarte T, Wang S, Coin LJM (2018) Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience, 7, giy037. https://doi.org/10.1093/gigascience/giy037

3. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25, 1105–1111. https://doi.org/10.1093/bioinformatics/btp120

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

 Comparative genomics is a general approach for understanding how genomes differ, which can be considered from many angles. For instance, this approach can delineate how gene content varies across organisms, which can lead to novel hypotheses regarding what those organisms do. It also enables investigations into the sequence-level divergence of orthologous DNA, which can provide insight into how evolutionary forces differentially shape genome content and structure across lineages. 
 
Such comparisons are often restricted to protein-coding genes, as these are sensible units for assessing putative function and for identifying homologous matches in divergent genomes. Although information is lost by focusing only on the protein-coding portion of genomes, this simplifies analyses and has led to crucial findings in recent years. Perhaps most dramatically, analyses based on hundreds of orthologous proteins across microbial eukaryotes are fundamentally changing our understanding of the eukaryotic tree of life (Burki et al. 2020).
 
These and other topics are highlighted in a new pre-print from Dr. Daniel Richter and colleagues, which describes EukProt (Richter et al. 2022): a database containing protein sets from 993 eukaryotic species. The authors provide a BLAST portal for matching custom sequences against this database (https://evocellbio.com/eukprot/) and the entire database is available for download (https://doi.org/10.6084/m9.figshare.12417881.v3). They also provide a subset of their overall dataset, ‘The Comparative Set’, which contains only high-quality proteomes and is meant to maximize phylogenetic diversity.
 
There are two major advantages of EukProt:
 
   1. It will enable researchers to quickly compare proteomes and perform phylogenomic analyses, without needing the skills or the time commitment to aggregate and process these data. The authors make it clear that acquiring the raw protein sets was non-trivial, as they were distributed across a wide variety of online repositories (some of which are no longer accessible!).
 
    2. Analyses based on this database will be more reproducible and easily compared across studies than those based on custom-made databases for individual studies. This is because the EukProt authors followed FAIR principles (Wilkinson et al. 2016) when building their database, which is a set of guidelines for enhancing data reusability. So, for instance, each proteome has a unique identifier in EukProt, and all species are annotated in a unified taxonomic framework, which will aid in standardizing comparisons across studies.
 
The authors make it clear that there is still work to be done. For example, there is an uneven representation of proteomes across different eukaryotic lineages, which can only be addressed by further characterization of poorly studied lineages. In addition, the authors note that it would ultimately be best for the EukProt database to be integrated into an existing large-scale repository, like NCBI, which would help ensure that important eukaryotic diversity was not ignored. Nonetheless, EukProt represents an excellent example of how reproducible bioinformatics resources should be designed and should prove to be an extremely useful resource for the field.
 
References

Burki F, Roger AJ, Brown MW, Simpson AGB (2020) The New Tree of Eukaryotes. Trends in Ecology & Evolution, 35, 43–55. https://doi.org/10.1016/j.tree.2019.08.008

Richter DJ, Berney C, Strassert JFH, Poh Y-P, Herman EK, Muñoz-Gómez SA, Wideman JG, Burki F, Vargas C de (2022) EukProt: A database of genome-scale predicted proteins across the diversity of eukaryotes. bioRxiv, 2020.06.30.180687, ver. 5 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2020.06.30.180687

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