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

 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

The development (and standardization) of high-throughput sequencing techniques has revolutionized evolutionary biology, to the point that we almost see as normal fine-detail studies of genome architecture evolution (Robert et al., 2022), adaptation to new habitats (Rahi et al., 2019), or the development of key evolutionary novelties (Hilgers et al., 2018), to name three examples. One of the fields that has benefited the most is phylogenomics, i.e. the use of genome-wide data for inferring the evolutionary relationships among organisms. Dealing with such amount of data, however, has come with important analytical and computational challenges. Likewise, although the steady generation of genomic data from virtually any organism opens exciting opportunities for comparative analyses, it also creates a sort of “information fog”, where it is hard to find the most appropriate and/or the higher quality data. I have personally experienced this not so long ago, when I had to spend several weeks selecting the most complete transcriptomes from several phyla, moving back and forth between the NCBI SRA repository and the relevant literature.

In an attempt to deal with this issue, some research labs have committed their time and resources to the generation of taxa- and topic-specific databases (Lathe et al., 2008), such as MolluscDB (Liu et al., 2021), focused on mollusk genomics, or EukProt (Richter et al., 2022), a protein repository representing the diversity of eukaryotes. A new database that promises to become an important resource in the near future is MATEdb (Fernández et al., 2022), a repository of high-quality genomic data from Metazoa. MATEdb has been developed from publicly available and newly generated transcriptomes and genomes, prioritizing quality over quantity. Upon download, the user has access to both raw data and the related datasets: assemblies, several quality metrics, the set of inferred protein-coding genes, and their annotation. Although it is clear to me that this repository has been created with phylogenomic analyses in mind, I see how it could be generalized to other related problems such as analyses of gene content or evolution of specific gene families. In my opinion, the main strengths of MATEdb are threefold:

1. Rosa Fernández and her team have carefully scrutinized the genomic data available in several repositories to retrieve only the most complete transcriptomes and genomes, saving a lot of time in data mining to the user.
2. These data have been analyzed to provide both the assembly and the set of protein-coding genes, easing the computational burden that usually accompanies these pipelines. Interestingly, all the data have been analyzed with the same software and parameters, facilitating comparisons among taxa.
3. Genomic analysis can be intimidating, and even more for inexperienced users. That is particularly important when it comes to transcriptome and genome assembly because it has an effect in all downstream analyses. I believe that having access to already analyzed data softens this transition. The users can move forward on their research while they learn how to generate and analyze their data at their own pace.

On a negative note, I see two main drawbacks. First, as of today (September 16th, 2022) this database is in an early stage and it still needs to incorporate a lot of animal groups. This has been discussed during the revision process and the authors are already working on it, so it is only a matter of time until all major taxa are represented. Second, there is a scalability issue. In its current format it is not possible to select the taxa of interest and the full database has to be downloaded, which will become more and more difficult as it grows. Nonetheless, with the appropriate resources it would be easy to find a better solution. There are plenty of examples that could serve as inspiration, so I hope this does not become a big problem in the future.

Altogether, I and the researchers that participated in the revision process believe that MATEdb has the potential to become an important and valuable addition to the metazoan phylogenomics community. Personally, I wish it was available just a few months ago, it would have saved me so much time.

References

Fernández R, Tonzo V, Guerrero CS, Lozano-Fernandez J, Martínez-Redondo GI, Balart-García P, Aristide L, Eleftheriadi K, Vargas-Chávez C (2022) MATEdb, a data repository of high-quality metazoan transcriptome assemblies to accelerate phylogenomic studies. bioRxiv, 2022.07.18.500182, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.07.18.500182

Hilgers L, Hartmann S, Hofreiter M, von Rintelen T (2018) Novel Genes, Ancient Genes, and Gene Co-Option Contributed to the Genetic Basis of the Radula, a Molluscan Innovation. Molecular Biology and Evolution, 35, 1638–1652. https://doi.org/10.1093/molbev/msy052

Lathe W, Williams J, Mangan M, Karolchik, D (2008). Genomic data resources: challenges and promises. Nature Education, 1(3), 2.

Liu F, Li Y, Yu H, Zhang L, Hu J, Bao Z, Wang S (2021) MolluscDB: an integrated functional and evolutionary genomics database for the hyper-diverse animal phylum Mollusca. Nucleic Acids Research, 49, D988–D997. https://doi.org/10.1093/nar/gkaa918

Rahi ML, Mather PB, Ezaz T, Hurwood DA (2019) The Molecular Basis of Freshwater Adaptation in Prawns: Insights from Comparative Transcriptomics of Three Macrobrachium Species. Genome Biology and Evolution, 11, 1002–1018. https://doi.org/10.1093/gbe/evz045

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

Robert NSM, Sarigol F, Zimmermann B, Meyer A, Voolstra CR, Simakov O (2022) Emergence of distinct syntenic density regimes is associated with early metazoan genomic transitions. BMC Genomics, 23, 143. https://doi.org/10.1186/s12864-022-08304-2

Sobala (2024) introduces a new, comprehensive, and curated eukaryotic database. It consolidates information from EukProt (Richter et al. 2022) and various other resources to enhance Metazoa representation in existing protein databases. The preprint is of significant interest to the phylogenomics and comparative genomics communities, and I commend the author for their work.

LukProt, the expanded database, significantly increases the taxon sampling within holozoans. It integrates data from the previously assembled EukProt and AniProtDB (Barreira et al. 2021) databases, with additional datasets from early-diverging animal lineages such as ctenophores, sponges, and cnidarians. This effort will undoubtedly be useful for researchers investigating these clades and their origins, as well as for the broader field of comparative genomics.

The author provides both web-portal and command-line versions of the database, making it accessible to users with varying degrees of bioinformatic proficiency. The curation effort is commendable, and I believe the comparative genomics community, especially those interested in animal origins, will find LukProt to be a valuable resource.

References

Barreira SN, Nguyen A-D, Fredriksen MT, Wolfsberg TG, Moreland RT, Baxevanis AD (2021) AniProtDB: A collection of consistently generated metazoan proteomes for comparative genomics studies. Molecular Biology and Evolution 38, 4628–4633. https://doi.org/10.1093/molbev/msab165

Richter DJ, Berney C, Strassert JFH, Poh Y-P, Herman EK, Muñoz-Gómez SA, Wideman JG, Burki F, de Vargas C (2022) EukProt: A database of genome-scale predicted proteins across the diversity of eukaryotes. Peer Community Journal 2, e56. https://doi.org/10.24072/pcjournal.173

Sobala ŁF (2024) LukProt: A database of eukaryotic predicted proteins designed for investigations of animal origins. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2024.01.30.577650

While it is widely agreed that the conservation of local breeds is key to the maintenance of livestock biodiversity, the implementation of such programmes is often carried out by amateur breeders and may be inadequate due to a lack of knowledge and financial resources. Bortoluzzi et al. (2024) clearly demonstrate the utility of whole-genome sequencing (WGS) data for this purpose, compare two scenarios that differ in the consistency of conservation efforts, and provide valuable recommendations for conservation programmes.

Genetic diversity in livestock is generally considered to be crucial to maintaining food security and ensuring the provision of necessary nutrients to humans (Godde et al. 2021). It is also important to recognise that the preservation of local breeds is a matter of cultural identity for certain regions, and that the products of these breeds are niche products which are in high demand. Especially today, as we face extreme weather conditions, drought and other consequences of global warming, modern breeds selected to perform under constant and temperate conditions are being challenged. The possibility of tapping into the reservoir of genetic variation held by traditional, locally adapted breeds offers an important option for breeding robust livestock. The best way to characterise genetic diversity is through modern molecular methods, based on whole genome sequencing and subsequent advanced population analyses, which has been demonstrated for domesticated and wild chicken (Qanbari et al. 2019).

But are local breed conservation programmes up to the task? In their article, Bortoluzzi and colleagues show that well-designed and professionally managed conservation programmes for local chicken breeds are effective in maintaining genetic diversity. Their article is based on a comparison of two examples of conservation programmes for local chicken breeds: the Barbezieux and the Gasconne, which originated from comparably sized founder populations and for which WGS data were available in a biobank at two timepoints, 2003 and 2013, representing 10 generations. While the conservation programme for the former was continuous, that for the latter was interrupted and later started from scratch with a small number of sires and dams.

The greater loss of genomic diversity in the Gasconne than in the Barbezieux shown in this article may therefore be unsurprising, but the authors provide a range of evidence for this using their population genomics toolbox. The less well-managed breed, Gasconne, shows a lower genome-wide heterozygosity, higher lengths of runs of homozygosity, higher levels of genomic inbreeding, a smaller effective population size and a higher genetic load in terms of predicted deleterious mutations. 

The sample sizes available for population genetic analyses are typically small for local breeds, which is difficult to change as the populations are very small at any given time. It is therefore all the more important to make the most out of it, and Bortoluzzi and co-authors approach the issue from several angles that help support their claim, using WGS data and the latest genomic resources.

In addition to their analyses, the authors provide clear and valuable advice for the management of such conservation programmes. Their analysis of signatures of selection suggests that, apart from adult fertility, not much selection has been taking place. However, the authors emphasise that clear selection objectives other than maintaining the breed, such as production and product quality, can help conservation efforts by providing better guidelines for managing the programme and increasing the availability of resources for conservation programmes when the products of these local breeds become successful. 

In summary, Bortoluzzi et al. (2024) have provided a clear, well-written account of the impact of conservation programme management on the genetic diversity of local chicken breeds, using the most up-to-date genomic resources and analysis methods. As such, this article makes a significant and valuable contribution to the maintenance of genomic resources in livestock, providing approaches and lessons that will hopefully be adopted by other such initiatives.

            
References

Bortoluzzi C, Restoux G, Rouger R, Desnoues B, Petitjean F, Bosse M, Tixier-Boichard M (2024) Trends in genome diversity of small populations under a conservation program: a case study of two French chicken breeds. bioRxiv, ver. 2 peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/2024.02.22.581528

Godde CM, Mason-D’Croz D, Mayberry DE, Thornton PK, Herrero M (2021) Impacts of climate change on the livestock food supply chain; a review of the evidence. Global Food Security 28:100488. https://doi.org/10.1016/j.gfs.2020.100488

Qanbari S, Rubin C-J, Maqbool K, Weigend S, Weigend A, Geibel J, Kerje S, Wurmser C, Peterson AT, IL Brisbin Jr., Preisinger R, Fries R, Simianer H, Andersson L (2019) Genetics of adaptation in modern chicken. PLOS Genetics, 15, e1007989. https://doi.org/10.1371/journal.pgen.1007989