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

Any multicellular organism is a molecular mosaic with some somatic mutations accumulated between cell lineages. Big long-lived trees have nourished this imaginary of a somatic mosaic tree, from the observation of spectacular phenotypic mosaics and also because somatic mutations are expected to potentially be passed on to gametes in plants (review in Schoen and Schultz 2019). The lower cost of genome sequencing now offers the opportunity to tackle the issue and identify somatic mutations in trees.

However, when it comes to characterizing this somatic mosaic from genome sequences, things become much more difficult than one would think in the first place. What separates cell lineages ontogenetically, in cell division number, or in time? How to sample clonal cell populations? How do somatic mutations distribute in a population of cells in an organ or an organ sample? Should they be fixed heterozygotes in the sample of cells sequenced or be polymorphic? Do we indeed expect somatic mutations to be fixed? How should we identify and count somatic mutations?

To date, the detection of somatic mutations has mostly been done with a single variant caller in a given study, and we have little perspective on how different callers provide similar or different results. Some studies have used standard SNP callers that assumed a somatic mutation is fixed at the heterozygous state in the sample of cells, with an expected allele coverage ratio of 0.5, and less have used cancer callers, designed to detect mutations in a fraction of the cells in the sample. However, standard SNP callers detect mutations that deviate from a balanced allelic coverage, and different cancer callers can have different characteristics that should affect their outcomes.

In order to tackle these issues, Schmitt et al. (2022) conducted an extensive simulation analysis to compare different variant callers. Then, they reanalyzed two large published datasets on pedunculate oak, Quercus robur.  The analysis of in silico somatic mutations allowed the authors to evaluate the performance of different variant callers as a function of the allelic fraction of somatic mutations and the sequencing depth. They found one of the seven callers to provide better and more robust calls for a broad set of allelic fractions and sequencing depths. The reanalysis of published datasets in oaks with the most effective cancer caller of the in silico analysis allowed them to identify numerous low-frequency mutations that were missed in the original studies.

I recommend the study of Schmitt et al. (2022) first because it shows the benefit of using cancer callers in the study of somatic mutations, whatever the allelic fraction you are interested in at the end. You can select fixed heterozygotes if this is your ultimate target, but cancer callers allow you to have in addition a valuable overview of the allelic fractions of somatic mutations in your sample, and most do as well as SNP callers for fixed heterozygous mutations. In addition, Schmitt et al. (2022) provide the pipelines that allow investigating in silico data that should correspond to a given study design, encouraging to compare different variant callers rather than arbitrarily going with only one. We can anticipate that the study of somatic mutations in non-model species will increasingly attract attention now that multiple tissues of the same individual can be sequenced at low cost, and the study of Schmitt et al. (2022) paves the way for questioning and choosing the best variant caller for the question one wants to address.

References

Schoen DJ, Schultz ST (2019) Somatic Mutation and Evolution in Plants. Annual Review of Ecology, Evolution, and Systematics, 50, 49–73. https://doi.org/10.1146/annurev-ecolsys-110218-024955

Schmitt S, Leroy T, Heuertz M, Tysklind N (2022) Somatic mutation detection: a critical evaluation through simulations and reanalyses in oaks. bioRxiv, 2021.10.11.462798. ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.10.11.462798