Whole genome sequences are revolutionizing our understanding across various biological fields. They not only shed light on the evolution of genetic material but also uncover the genetic basis of phenotypic diversity. The sequencing of underrepresented lineages, such as the one presented in this study, is of critical importance. It is crucial in filling significant gaps in our understanding of Metazoa evolution. Despite the wealth of genome sequences in public databases, it is crucial to acknowledge that some lineages across the Tree of Life are underrepresented or absent. This research represents a significant step towards addressing this imbalance, contributing to the collective knowledge of the global scientific community.

In this genome note, as part of the European Reference Genome Atlas pilot effort to generate reference genomes for European biodiversity (Mc Cartney et al. 2023), Klara Eleftheriadi and colleagues (Eleftheriadi et al. 2023) make a significant effort to add a genome sequence of an unrepresented group in the animal Tree of Life. More specifically, they present a taxonomic description and chromosome-level genome assembly of a newly described species of horsehair worm (Gordionus montsenyensis). Their sequence methodology gave rise to an assembly of 396 scaffolds totaling 288 Mb, with an N50 value of 64.4 Mb, where 97% of this assembly is grouped into five pseudochromosomes. The nuclear genome annotation predicted 10,320 protein-coding genes, and they also assembled the circular mitochondrial genome into a 15-kilobase sequence.

The selection of a species representing the phylum Nematomorpha, a group of parasitic organisms belonging to the Ecdysozoa lineage, is good, since today, there is only one publicly available genome for this animal phylum (Cunha et al. 2023). Interestingly, this article shows, among other things, that the species analyzed has lost ∼30% of the universal Metazoan genes. Efforts, like the one performed by Eleftheriadi and colleagues, are necessary to gain more insights, for example, on the evolution of this massive gene lost in this group of animals.

References

Cunha, T. J., de Medeiros, B. A. S, Lord, A., Sørensen, M. V., and Giribet, G. (2023). Rampant Loss of Universal Metazoan Genes Revealed by a Chromosome-Level Genome Assembly of the Parasitic Nematomorpha. Current Biology, 33 (16): 3514–21.e4. https://doi.org/10.1016/j.cub.2023.07.003

Eleftheriadi, K., Guiglielmoni, N., Salces-Ortiz, J., Vargas-Chavez, C., Martínez-Redondo, G. I., Gut, M., Flot, J.-F., Schmidt-Rhaesa, A., and Fernández, R. (2023). The Genome Sequence of the Montseny Horsehair worm, Gordionus montsenyensis sp. Nov., a Key Resource to Investigate Ecdysozoa Evolution. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.06.26.546503

Mc Cartney, A. M., Formenti, G., Mouton, A., De Panis, D., Marins, L. S., Leitão, H. G., Diedericks, G., et al. (2023). The European Reference Genome Atlas: Piloting a Decentralised Approach to Equitable Biodiversity Genomics. bioRxiv. https://doi.org/10.1101/2023.09.25.559365

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

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

Contamination, the presence of foreign DNA sequences in a sample of interest, is currently a major problem in genomics. Because contamination is often unavoidable at the experimental stage, it is increasingly recognized that the processing of high-throughput sequencing data must include a decontamination step. This is usually performed after the many sequence reads have been assembled into a relatively small number of contigs. Dubious contigs are then discarded based on their composition (e.g. GC-content) or because they are highly similar to a known piece of DNA from a foreign species.

Here [1], Mathieu Gautier explores a novel strategy consisting in decontaminating reads, not contigs. Why is this promising? Assembly programs and algorithms are complex, and it is not easy to predict, or monitor, how they handle contaminant reads. Ideally, contaminant reads will be assembled into obvious contaminant contigs. However, there might be more complex situations, such as chimeric contigs with alternating genuine and contaminant segments. Decontaminating at the read level, if possible, should eliminate such unfavorable situations where sequence information from contaminant and target samples are intimately intertwined by an assembler.

To achieve this aim, Gautier proposes to use methods initially designed for the analysis of metagenomic data. This is pertinent since the decontamination process involves considering a sample as a mixture of different sources of DNA. The programs used here, CLARK and CLARK-L, are based on so-called k-mer analysis, meaning that the similarity between a read to annotate and a reference sequence is measured by how many sub-sequences (of length 31 base pairs for CLARK and 27 base pairs for CLARK-L) they share. This is notoriously more efficient than traditional sequence alignment algorithms when it comes to comparing a very large number of (most often unrelated) sequences. This is, therefore, a reference-based approach, in which the reads from a sample are assigned to previously sequenced genomes based on k-mer content.

This original approach is here specifically applied to the case of Drosophila suzukii, an invasive pest damaging fruit production in Europe and America. Fortunately, Drosophila is a genus of insects with abundant genomic resources, including high-quality reference genomes in dozens of species. Having calibrated and validated his pipeline using data sets of known origins, Gautier quantifies in each of 258 presumed D. suzukii samples the proportion of reads that likely belong to other species of fruit flies, or to fruit fly-associated microbes. This proportion is close to one in 16 samples, which clearly correspond to mis-labelled individuals. It is non-negligible in another ~10 samples, which really correspond to D. suzukii individuals. Most of these reads of unexpected origin are contaminants and should be filtered out. Interestingly, one D. suzukii sample contains a substantial proportion of reads from the closely related D. subpulchera, which might instead reflect a recent episode of gene flow between these two species. The approach, therefore, not only serves as a crucial technical step, but also has the potential to reveal biological processes.

Gautier's thorough, well-documented work will clearly benefit the ongoing and future research on D. suzuki, and Drosophila genomics in general. The author and reviewers rightfully note that, like any reference-based approach, this method is heavily dependent on the availability and quality of reference genomes - Drosophila being a favorable case. Building the reference database is a key step, and the interpretation of the output can only be made in the light of its content and gaps, as illustrated by Gautier's careful and detailed discussion of his numerous results. 

This pioneering study is a striking demonstration of the potential of metagenomic methods for the decontamination of high-throughput sequence data at the read level. The pipeline requires remarkably few computing resources, ensuring low carbon emission. I am looking forward to seeing it applied to a wide range of taxa and samples.

Reference

[1] Gautier Mathieu. Efficient k-mer based curation of raw sequence data: application in Drosophila suzukii. bioRxiv, 2023.04.18.537389​, ver. 2, peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.04.18.537389​

One important goal of modern genetics is to establish functional associations between genotype and phenotype. Single nucleotide polymorphisms (SNPs) are numerous and widely distributed in the genome and can be obtained from nucleic acid sequencing (1). SNPs allow for the investigation of genetic diversity, which is critical for increasing crop resilience to the challenges posed by global climate change. The associations between SNPs and phenotypes can be captured in genome-wide association studies. SNPs can also be used in combination with machine learning, which is becoming more popular for predicting complex phenotypic traits like yield and biotic and abiotic stress tolerance from genotypic data (2). The availability of many SNP datasets is important in machine learning predictions because this approach requires big data to build a comprehensive model of the association between genotype and phenotype.

Aubert and colleagues have studied, as part of the PeaMUST project, the genetic diversity of 240 Pisum accessions (3). They sequenced exome-enriched genomic libraries, a technique that enables the identification of high-density, high-quality SNPs at a low cost (4). This technique involves capturing and sequencing only the exonic regions of the genome, which are the protein-coding regions. A total of 2,285,342 SNPs were obtained in this study. The analysis of these SNPs with the annotations of the genome sequence of one of the studied pea accessions (5) identified a number of SNPs that could have an impact on gene activity. Additional analyses revealed 647,220 SNPs that were unique to individual pea accessions, which might contribute to the fitness and diversity of accessions in different habitats. Phylogenetic and clustering analyses demonstrated that the SNPs could distinguish Pisum germplasms based on their agronomic and evolutionary histories. These results point out the power of selected SNPs as markers for identifying Pisum individuals.

Overall, this study found high-quality SNPs that are meaningful in a biological context. This dataset was derived from a large set of germplasm and is thus particularly useful for studying genotype-phenotype associations, as well as the diversity within Pisum species. These SNPs could also be used in breeding programs to develop new pea varieties that are resilient to abiotic and biotic stressors.  

References

1.         Fallah M, Jean M, Boucher St-Amour VT, O’Donoughue L, Belzile F. The construction of a high-density consensus genetic map for soybean based on SNP markers derived from genotyping-by-sequencing. Genome. 2022 Aug;65(8):413–25.

https://doi.org/10.1139/gen-2021-005

2.         Gill M, Anderson R, Hu H, Bennamoun M, Petereit J, Valliyodan B, et al. Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction. BMC Plant Biology. 2022 Apr 8;22(1):180.

https://doi.org/10.1186/s12870-022-03559-z

3.         Aubert G, Kreplak J, Leveugle M, Duborjal H, Klein A, Boucherot K, et al. SNP discovery by exome capture and resequencing in a pea genetic resource collection., biorxiv, ver. 4, peer-reviewed and recommended by Peer Community in Genomics.

https://doi.org/10.1101/2022.08.03.502586 

4.         Warr A, Robert C, Hume D, Archibald A, Deeb N, Watson M. Exome sequencing: current and future perspectives. G3 Genes|Genomes|Genetics. 2015 Aug 1;5(8):1543–50.

https://doi.org/10.1534/g3.115.018564

5.         Kreplak J, Madoui MA, Cápal P, Novák P, Labadie K, Aubert G, et al. A reference genome for pea provides insight into legume genome evolution. Nat Genet. 2019 Sep;51(9):1411–22.

https://doi.org/10.1038/s41588-019-0480-1