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24 Feb 2023
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MacSyFinder v2: Improved modelling and search engine to identify molecular systems in genomes

A unique and customizable approach for functionally annotating prokaryotic genomes

Recommended by ORCID_LOGO based on reviews by Kwee Boon Brandon Seah and Max Emil Schön

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

MacSyFinder v2: Improved modelling and search engine to identify molecular systems in genomesBertrand Néron, Rémi Denise, Charles Coluzzi, Marie Touchon, Eduardo P. C. Rocha, Sophie S. Abby<p style="text-align: justify;">Complex cellular functions are usually encoded by a set of genes in one or a few organized genetic loci in microbial genomes. Macromolecular System Finder (MacSyFinder) is a program that uses these properties to mod...Bacteria and archaea, Bioinformatics, Functional genomicsGavin Douglas Kwee Boon Brandon Seah, Max Emil Schön2022-09-09 10:30:31 View
23 Sep 2022
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MATEdb, a data repository of high-quality metazoan transcriptome assemblies to accelerate phylogenomic studies

MATEdb: a new phylogenomic-driven database for Metazoa

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

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

MATEdb, a data repository of high-quality metazoan transcriptome assemblies to accelerate phylogenomic studiesRosa Fernandez, Vanina Tonzo, Carolina Simon Guerrero, Jesus Lozano-Fernandez, Gemma I Martinez-Redondo, Pau Balart-Garcia, Leandro Aristide, Klara Eleftheriadi, Carlos Vargas-Chavez<p style="text-align: justify;">With the advent of high throughput sequencing, the amount of genomic data available for animals (Metazoa) species has bloomed over the last decade, especially from transcriptomes due to lower sequencing costs and ea...Bioinformatics, Evolutionary genomics, Functional genomicsSamuel Abalde2022-07-20 07:30:39 View
15 Sep 2022
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EukProt: A database of genome-scale predicted proteins across the diversity of eukaryotes

EukProt enables reproducible Eukaryota-wide protein sequence analyses

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

 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

EukProt: A database of genome-scale predicted proteins across the diversity of eukaryotesDaniel J. Richter, Cédric Berney, Jürgen F. H. Strassert, Yu-Ping Poh, Emily K. Herman, Sergio A. Muñoz-Gómez, Jeremy G. Wideman, Fabien Burki, Colomban de Vargas<p style="text-align: justify;">EukProt is a database of published and publicly available predicted protein sets selected to represent the breadth of eukaryotic diversity, currently including 993 species from all major supergroups as well as orpha...Bioinformatics, Evolutionary genomicsGavin Douglas2022-06-08 14:19:28 View
07 Feb 2023
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RAREFAN: A webservice to identify REPINs and RAYTs in bacterial genomes

A workflow for studying enigmatic non-autonomous transposable elements across bacteria

Recommended by ORCID_LOGO based on reviews by Sophie Abby and 1 anonymous reviewer

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

RAREFAN: A webservice to identify REPINs and RAYTs in bacterial genomesFrederic Bertels, Julia von Irmer, Carsten Fortmann-Grote<p style="text-align: justify;">Compared to eukaryotes, repetitive sequences are rare in bacterial genomes and usually do not persist for long. Yet, there is at least one class of persistent prokaryotic mobile genetic elements: REPINs. REPINs are ...Bacteria and archaea, Bioinformatics, Evolutionary genomics, Viruses and transposable elementsGavin Douglas2022-06-07 08:21:34 View
25 Nov 2022
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Phenotypic and transcriptomic analyses reveal major differences between apple and pear scab nonhost resistance

Apples and pears: two closely related species with differences in scab nonhost resistance

Recommended by based on reviews by 3 anonymous reviewers

Nonhost resistance is a common form of disease resistance exhibited by plants against microorganisms that are pathogenic to other plant species [1]. Apples and pears are two closely related species belonging to Rosaceae family, both affected by scab disease caused by fungal pathogens in the Venturia genus. These pathogens appear to be highly host-specific. While apples are nonhosts for Venturia pyrina, pears are nonhosts for Venturia inaequalis. To date, the molecular bases of scab nonhost resistance in apple and pear have not been elucidated.

This preprint by Vergne, et al (2022) [2] analyzed nonhost resistance symptoms in apple/V. pyrina and pear/V. inaequalis interactions as well as their transcriptomic responses. Interestingly, the author demonstrated that the nonhost apple/V. pyrina interaction was almost symptomless while hypersensitive reactions were observed for pear/V. inaequalis interaction. The transcriptomic analyses also revealed a number of differentially expressed genes (DEGs) that corresponded to the severity of the interactions, with very few DEGs observed during the apple/V. pyrina interaction and a much higher number of DEGs during the pear/V. inaequalis interaction.

This type of reciprocal host-pathogen interaction study is valuable in gaining new insights into how plants interact with microorganisms that are potential pathogens in related species. A few processes appeared to be involved in the pear resistance against the nonhost pathogen V. inaequalis at the transcriptomic level, such as stomata closure, modification of cell wall and production of secondary metabolites as well as phenylpropanoids. Based on the transcriptomics changes during the nonhost interaction, the author compared the responses to those of host-pathogen interactions and revealed some interesting findings. They proposed a series of cascading effects in pear induced by the presence of V. inaequalis, which I believe helps shed some light on the basic mechanism for nonhost resistance.

I am recommending this study because it provides valuable information that will strengthen our understanding of nonhost resistance in the Rosaceae family and other plant species. The knowledge gained here may be applied to genetically engineer plants for a broader resistance against a number of pathogens in the future.​

References

1. Senthil-Kumar M, Mysore KS (2013) Nonhost Resistance Against Bacterial Pathogens: Retrospectives and Prospects. Annual Review of Phytopathology, 51, 407–427. https://doi.org/10.1146/annurev-phyto-082712-102319

2. Vergne E, Chevreau E, Ravon E, Gaillard S, Pelletier S, Bahut M, Perchepied L (2022) Phenotypic and transcriptomic analyses reveal major differences between apple and pear scab nonhost resistance. bioRxiv, 2021.06.01.446506, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.06.01.446506

Phenotypic and transcriptomic analyses reveal major differences between apple and pear scab nonhost resistanceE. Vergne, E. Chevreau, E. Ravon, S. Gaillard, S. Pelletier, M. Bahut, L. Perchepied<p style="text-align: justify;"><strong>Background. </strong>Nonhost resistance is the outcome of most plant/pathogen interactions, but it has rarely been described in Rosaceous fruit species. Apple (<em>Malus x domestica</em> Borkh.) have a nonho...Functional genomics, PlantsWirulda Pootakham Jessica Soyer, Anonymous2022-05-13 15:06:08 View
13 Jul 2022
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Karyorelict ciliates use an ambiguous genetic code with context-dependent stop/sense codons

An accident frozen in time: the ambiguous stop/sense genetic code of karyorelict ciliates

Recommended by ORCID_LOGO based on reviews by Vittorio Boscaro and 2 anonymous reviewers

Several variations of the “universal” genetic code are known. Among the most striking are those where a codon can either encode for an amino acid or a stop signal depending on the context. Such ambiguous codes are known to have evolved in eukaryotes multiple times independently, particularly in ciliates – eight different codes have so far been discovered (1). We generally view such genetic codes are rare ‘variants’ of the standard code restricted to single species or strains, but this might as well reflect a lack of study of closely related species. In this study, Seah and co-authors (2) explore the possibility of codon reassignment in karyorelict ciliates closely related to Parduczia sp., which has been shown to contain an ambiguous genetic code (1). Here, single-cell transcriptomics are used, along with similar available data, to explore the possibility of codon reassignment across the diversity of Karyorelictea (four out of the six recognized families). Codon reassignments were inferred from their frequencies within conserved Pfam (3) protein domains, whereas stop codons were inferred from full-length transcripts with intact 3’-UTRs.

Results show the reassignment of UAA and UAG stop codons to code for glutamine (Q) and the reassignment of the UGA stop codon into tryptophan (W). This occurs only within the coding sequences, whereas the end of transcription is marked by UGA as the main stop codon, and to a lesser extent by UAA. In agreement with a previous model proposed that explains the functioning of ambiguous codes (1,4), the authors observe a depletion of in-frame UGAs before the UGA codon that indicates the stop, thus avoiding premature termination of transcription. The inferred codon reassignments occur in all studied karyorelicts, including the previously studied Parduczia sp. Despite the overall clear picture, some questions remain. Data for two out of six main karyorelict lineages are so far absent and the available data for Cryptopharyngidae was inconclusive; the phylogenetic affinities of Cryptopharyngidae have also been questioned (5). This indicates the need for further study of this interesting group of organisms. As nicely discussed by the authors, experimental evidence could further strengthen the conclusions of this paper, including ribosome profiling, mass spectrometry – as done for Condylostoma (1) – or even direct genetic manipulation. 

The uniformity of the ambiguous genetic code across karyorelicts might at first seem dull, but when viewed in a phylogenetic context character distribution strongly suggest that this genetic code has an ancient origin in the karyorelict ancestor ~455 Ma in the Proterozoic (6). This ambiguous code is also not a rarity of some obscure species, but it is shared by ciliates that are very diverse and ecologically important. The origin of the karyorelict code is also intriguing. Adaptive arguments suggest that it could confer robustness to mutations causing premature stop codons. However, we lack evidence for ambiguous codes being linked to specific habitats of lifestyles that could account for it. Instead, the authors favor the neutral view of an ancient “frozen accident”, fixed stochastically simply because it did not pose a significant selective disadvantage. Once a stop codon is reassigned to an amino acid, it is increasingly difficult to revert this without the deleterious effect of prematurely terminating translation. At the end, the origin of the genetic code itself is thought to be a frozen accident too (7).

References

1. Swart EC, Serra V, Petroni G, Nowacki M. Genetic codes with no dedicated stop codon: Context-dependent translation termination. Cell 2016;166: 691–702. https://doi.org/10.1016/j.cell.2016.06.020

2. Seah BKB, Singh A, Swart EC (2022) Karyorelict ciliates use an ambiguous genetic code with context-dependent stop/sense codons. bioRxiv, 2022.04.12.488043. ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.04.12.488043

3. Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, Tosatto SCE, Paladin L, Raj S, Richardson LJ, Finn RD, Bateman A. Pfam: The protein families database in 2021, Nuc Acids Res 2020;49: D412-D419. https://doi.org/10.1093/nar/gkaa913

4. Alkalaeva E, Mikhailova T. Reassigning stop codons via translation termination: How a few eukaryotes broke the dogma. Bioessays. 2017;39. https://doi.org/10.1002/bies.201600213

5. Xu Y, Li J, Song W, Warren A. Phylogeny and establishment of a new ciliate family, Wilbertomorphidae fam. nov. (Ciliophora, Karyorelictea), a highly specialized taxon represented by Wilbertomorpha colpoda gen. nov., spec. nov. J Eukaryot Microbiol. 2013;60: 480–489. https://doi.org/10.1111/jeu.12055

6. Fernandes NM, Schrago CG. A multigene timescale and diversification dynamics of Ciliophora evolution. Mol Phylogenet Evol. 2019;139: 106521. https://doi.org/10.1016/j.ympev.2019.106521

7. Crick FH. The origin of the genetic code. J Mol Biol. 1968;38: 367–379. https://doi.org/10.1016/0022-2836(68)90392-6

Karyorelict ciliates use an ambiguous genetic code with context-dependent stop/sense codonsBrandon Kwee Boon Seah, Aditi Singh, Estienne Carl Swart<p style="text-align: justify;">In ambiguous stop/sense genetic codes, the stop codon(s) not only terminate translation but can also encode amino acids. Such codes have evolved at least four times in eukaryotes, twice among ciliates (<em>Condylost...Bioinformatics, Evolutionary genomicsIker Irisarri2022-05-02 11:06:10 View
08 Nov 2022
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Somatic mutation detection: a critical evaluation through simulations and reanalyses in oaks

How to best call the somatic mosaic tree?

Recommended by based on reviews by 2 anonymous reviewers

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

Somatic mutation detection: a critical evaluation through simulations and reanalyses in oaksSylvain Schmitt, Thibault Leroy, Myriam Heuertz, Niklas Tysklind<p style="text-align: justify;">1. Mutation, the source of genetic diversity, is the raw material of evolution; however, the mutation process remains understudied, especially in plants. Using both a simulation and reanalysis framework, we set out ...Bioinformatics, PlantsNicolas BierneAnonymous, Anonymous2022-04-28 13:24:19 View
23 Aug 2022
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A novel lineage of the Capra genus discovered in the Taurus Mountains of Turkey using ancient genomics

Goat ancient DNA analysis unveils a new lineage that may have hybridized with domestic goats

Recommended by based on reviews by Torsten Günther and 1 anonymous reviewer

The genomic analysis of ancient remains has revolutionized the study of the past over the last decade. On top of the discoveries related to human evolution, plant and animal archaeogenomics has been used to gain new insights into the domestication process and the dispersal of domestic forms.

In this study, Daly and colleagues analyse the genomic data from seven goat specimens from the Epipalaeolithic recovered from the Direkli Cave in the Taurus Mountains in southern Turkey. They also generate new genomic data from Capra lineages across the phylogeny, contributing to the availability of genomic resources for this genus. Analysis of the ancient remains is compared to modern genomic variability and sheds light on the complexity of the Tur wild Capra lineages and their relationship with domestic goats and their wild ancestors.

Authors find that during the Late Pleistocene in the Taurus Mountains wild goats from the Tur lineage, today restricted to the Caucasus region, were not rare and cohabited with Bezoar, the wild goats that are the ancestors of domestic goats. They identify the Direkli Cave specimens as a lineage separate from the 
West and East Caucasus Tur modern lineages. Also, analysis of the genomic data and mitochondrial haplotypes reveals hybridization between the Tur and the Bezoar wild lineages. Interestingly, authors also find an uneven amount of Tur ancestry among Neolithic domestic goats, with European domestic goats showing evidence of this ancient Tur ancestry, whereas Neolithic Iranian domestic goats do not, a pattern that is also observed in some modern European domestic goats.

A modified D statistic, Dex, is developed to examine the contribution of the ancient Tur lineage in domestic goats through time and space. Dex measures the relative degree of allele sharing, derived specifically in a selected genome or group of genomes, and may have some utility in genera with complex admixture histories or admixture from ghost lineages. Results confirm that Neolithic European goat had an excess of allele sharing with this ancient Tur lineage, something that is absent in contemporary goats eastwards or in modern goats.

Interspecific gene flow is not uncommon among mammals, but the case of Capra has the additional motivation of understanding the origins of the domestic species. This work uncovers an ancient Tur lineage that is different from the modern ones and is additionally found in another geographic area. Furthermore, evidence shows that this ancient lineage exhibits substantial amounts of allele sharing with the wild ancestor of the domestic goat, but also with the Neolithic Eurasian domestic goats, highlighting the complexity of the domestication process.

This work has also important implications in understanding the effect of over-hunting and habitat disruption during the Anthropocene on the evolution of the Capra genus. The availability of more ancient specimens and better coverage of the modern genomic variability can help quantifying the lineages that went lost and identify the causes of their extinction.

This work is limited by the current availability of whole genomes from modern Capra specimens, but pieces of evidence as well that an effort is needed to obtain more genomic data from ancient goats from different geographic ranges to determine to what extent these lineages contributed to goat domestication.

References

Daly KG, Arbuckle BS, Rossi C, Mattiangeli V, Lawlor PA, Mashkour M, Sauer E, Lesur J, Atici L, Cevdet CM and Bradley DG (2022) A novel lineage of the Capra genus discovered in the Taurus Mountains of Turkey using ancient genomics. bioRxiv, 2022.04.08.487619, ver. 5 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.04.08.487619

A novel lineage of the Capra genus discovered in the Taurus Mountains of Turkey using ancient genomicsKevin G. Daly, Benjamin S. Arbuckle, Conor Rossi, Valeria Mattiangeli, Phoebe A. Lawlor, Marjan Mashkour, Eberhard Sauer, Joséphine Lesur, Levent Atici, Cevdet Merih Erek, Daniel G. Bradley<p>Direkli Cave, located in the Taurus Mountains of southern Turkey, was occupied by Late Epipaleolithic hunters-gatherers for the seasonal hunting and processing of game including large numbers of wild goats. We report genomic data from new and p...Evolutionary genomics, Population genomics, VertebratesLaura Botigué2022-04-15 12:05:47 View
24 Feb 2023
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Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation study

How to interpret the inference of recombination landscapes on methods based on linkage disequilibrium?

Recommended by based on reviews by 2 anonymous reviewers

Data interpretation depends on previously established and validated tools, designed for a specific type of data. These methods, however, are usually based on simple models with validity subject to a set of theoretical parameterized conditions and data types. Accordingly, the tool developers provide the potential users with guidelines for data interpretations within the tools’ limitation. Nevertheless, once the methodology is accepted by the community, it is employed in a large variety of empirical studies outside of the method’s original scope or that typically depart from the standard models used for its design, thus potentially leading to the wrong interpretation of the results.

Numerous empirical studies inferred recombination rates across genomes, detecting hotspots of recombination and comparing related species (e.g., Shanfelter et al. 2019, Spence and Song 2019). These studies used indirect methodologies based on the signals that recombination left in the genome, such as linkage disequilibrium and the patterns of haplotype segregation (e.g.,Chan et al. 2012). The conclusions from these analyses have been used, for example, to interpret the evolution of the chromosomal structure or the evolution of recombination among closely related species.

Indirect methods have the advantage of collecting a large quantity of recombination events, and thus have a better resolution than direct methods (which only detect the few recombination events occurring at that time). On the other hand, indirect methods are affected by many different evolutionary events, such as demographic changes and selection. Indeed, the inference of recombination levels across the genome has not been studied accurately in non-standard conditions. Linkage disequilibrium is affected by several factors that can modify the recombination inference, such as demographic history, events of selection, population size, and mutation rate, but is also related to the size of the studied sample, and other technical parameters defined for each specific methodology.

Raynaud et al (2023) analyzed the reliability of the recombination rate inference when considering the violation of several standard assumptions (evolutionary and methodological) in one of the most popular families of methods based on LDhat (McVean et al. 2004), specifically its improved version, LDhelmet (Chan et al. 2012). These methods cover around 70 % of the studies that infer recombination rates. The authors used recombination maps, obtained from empirical studies on humans, and included hotspots, to perform a detailed simulation study of the capacity of this methodology to correctly infer the pattern of recombination and the location of these hotspots. Correlations between the real, and inferred values from simulations were obtained, as well as several rates, such as the true positive and false discovery rate to detect hotspots.

The authors of this work send a message of caution to researchers that are applying this methodology to interpret data from the inference of recombination landscapes and the location of hotspots. The inference of recombination landscapes and hotspots can differ considerably even in standard model conditions. In addition, demographic processes, like bottleneck or admixture, but also the level of population size and mutation rates, can substantially affect the estimation accuracy of the level of recombination and the location of hotspots. Indeed, the inference of the location of hotspots in simulated data with the same landscape, can be very imprecise when standard assumptions are violated or not considered. These effects may lead to incorrect interpretations, for example about the conservation of recombination maps between closely related species. Finally, Raynaud et al (2023) included a useful guide with advice on how to obtain accurate recombination estimations with methods based on linkage disequilibrium, also emphasizing the limitations of such approaches.

REFERENCES

Chan AH, Jenkins PA, Song YS (2012) Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster. PLOS Genetics, 8, e1003090. https://doi.org/10.1371/journal.pgen.1003090

McVean GAT, Myers SR, Hunt S, Deloukas P, Bentley DR, Donnelly P (2004) The Fine-Scale Structure of Recombination Rate Variation in the Human Genome. Science, 304, 581–584. https://doi.org/10.1126/science.1092500

Raynaud M, Gagnaire P-A, Galtier N (2023) Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation study. bioRxiv, 2022.03.30.486352, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.03.30.486352

Spence JP, Song YS (2019) Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations. Science Advances, 5, eaaw9206. https://doi.org/10.1126/sciadv.aaw9206

Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation studyMarie Raynaud, Pierre-Alexandre Gagnaire, Nicolas Galtier<p style="text-align: justify;">Knowledge of recombination rate variation along the genome provides important insights into genome and phenotypic evolution. Population genomic approaches offer an attractive way to infer the population-scaled recom...Bioinformatics, Evolutionary genomics, Population genomicsSebastian E. Ramos-Onsins2022-04-05 14:59:14 View
08 Apr 2022
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POSTPRINT

Phylogenetics in the Genomic Era

“Phylogenetics in the Genomic Era” brings together experts in the field to present a comprehensive synthesis

Recommended by and

E-book: Phylogenetics in the Genomic Era (Scornavacca et al. 2021)

This book was not peer-reviewed by PCI Genomics. It has undergone an internal review by the editors.

Accurate reconstructions of the relationships amongst species and the genes encoded in their genomes are an essential foundation for almost all evolutionary inferences emerging from downstream analyses. Molecular phylogenetics has developed as a field over many decades to build suites of models and methods to reconstruct reliable trees that explain, support, or refute such inferences. The genomic era has brought new challenges and opportunities to the field, opening up new areas of research and algorithm development to take advantage of the accumulating large-scale data. Such ‘big-data’ phylogenetics has come to be known as phylogenomics, which broadly aims to connect molecular and evolutionary biology research to address questions centred on relationships amongst taxa, mechanisms of molecular evolution, and the biological functions of genes and other genomic elements. This book brings together experts in the field to present a comprehensive synthesis of Phylogenetics in the Genomic Era, covering key conceptual and methodological aspects of how to build accurate phylogenies and how to apply them in molecular and evolutionary research. The paragraphs below briefly summarise the five constituent parts of the book, highlighting the key concepts, methods, and applications that each part addresses. Being organised in an accessible style, while presenting details to provide depth where necessary, and including guides describing real-world examples of major phylogenomic tools, this collection represents an invaluable resource, particularly for students and newcomers to the field of phylogenomics.

Part 1: Phylogenetic analyses in the genomic era

Modelling how sequences evolve is a fundamental cornerstone of phylogenetic reconstructions. This part of the book introduces the reader to phylogenetic inference methods and algorithmic optimisations in the contexts of Markov, Maximum Likelihood, and Bayesian models of sequence evolution. The main concepts and theoretical considerations are mapped out for probabilistic Markov models, efficient tree building with Maximum Likelihood methods, and the flexibility and robustness of Bayesian approaches. These are supported with practical examples of phylogenomic applications using the popular tools RAxML and PhyloBayes. By considering theoretical, algorithmic, and practical aspects, these chapters provide readers with a holistic overview of the challenges and recent advances in developing scalable phylogenetic analyses in the genomic era.

Part 2: Data quality, model adequacy

This part focuses on the importance of considering the appropriateness of the evolutionary models used and the accuracy of the underlying molecular and genomic data. Both these aspects can profoundly affect the results when applying current phylogenomic methods to make inferences about complex biological and evolutionary processes. A clear example is presented for methods for building multiple sequence alignments and subsequent filtering approaches that can greatly impact phylogeny inference. The importance of error detection in (meta)barcode sequencing data is also highlighted, with solutions offered by the MACSE_BARCODE pipeline for accurate taxonomic assignments. Orthology datasets are essential markers for phylogenomic inferences, but the overview of concepts and methods presented shows that they too face challenges with respect to model selection and data quality. Finally, an innovative approach using ancestral gene order reconstructions provides new perspectives on how to assess gene tree accuracy for phylogenomic analyses. By emphasising through examples the importance of using appropriate evolutionary models and assessing input data quality, these chapters alert readers to key limitations that the field as a whole strives to address.

Part 3: Resolving phylogenomic conflicts

Conflicting phylogenetic signals are commonplace and may derive from statistical or systematic bias. This part of the book addresses possible causes of conflict, discordance between gene trees and species trees and how processes that lead to such conflicts can be described by phylogenetic models. Furthermore, it provides an overview of various models and methods with examples in phylogenomics including their pros and cons. Outlined in detail is the multispecies coalescent model (MSC) and its applications in phylogenomics. An interesting aspect is that different phylogenetic signals leading to conflict are in fact a key source of information rather than a problem that can – and should – be used to point to events like introgression or hybridisation, highlighting possible future trends in this research area. Last but not least, this part of the book also addresses inferring species trees by concatenating single multiple sequence alignments (gene alignments) versus inferring the species tree based on ensembles of single gene trees pointing out advantages and disadvantages of both approaches. As an important take home message from these chapters, it is recommended to be flexible and identify the most appropriate approach for each dataset to be analysed since this may tremendously differ depending on the dataset, setting, taxa, and phylogenetic level addressed by the researcher.

Part 4: Functional evolutionary genomics

In this part of the book the focus shifts to functional considerations of phylogenomics approaches both in terms of molecular evolution and adaptation and with respect to gene expression. The utility of multi-species analysis is clearly presented in the context of annotating functional genomic elements through quantifying evolutionary constraint and protein-coding potential. An historical perspective on characterising rates of change highlights how phylogenomic datasets help to understand the modes of molecular evolution across the genome, over time, and between lineages. These are contextualised with respect to the specific aim of detecting signatures of adaptation from protein-coding DNA alignments using the example of the MutSelDP-ω∗ model. This is extended with the presentation of the generally rare case of adaptive sequence convergence, where consideration of appropriate models and knowledge of gene functions and phenotypic effects are needed. Constrained or relaxed, selection pressures on sequence or copy-number affect genomic elements in different ways, making the very concept of function difficult to pin down despite it being fundamental to relate the genome to the phenotype and organismal fitness. Here gene expression provides a measurable intermediate, for which the Expression Comparison tool from the Bgee suite allows exploration of expression patterns across multiple animal species taking into account anatomical homology. Overall, phylogenomics applications in functional evolutionary genomics build on a rich theoretical history from molecular analyses where integration with knowledge of gene functions is challenging but critical.

Part 5: Phylogenomic applications

Rather than attempting to review the full extent of applications linked to phylogenomics, this part of the book focuses on providing detailed specific insights into selected examples and methods concerning i) estimating divergence times, and ii) species delimitation in the era of ‘omics’ data. With respect to estimating divergence times, an exemplary overview is provided for fossil data recovered from geological records, either using fossil data as calibration points with an extant-species-inferred phylogeny, or using a fossilised birth-death process as a mechanistic model that accounts for lineage diversification. Included is a tutorial for a joint approach to infer phylogenies and estimate divergence times using the RevBayes software with various models implemented for different applications and datasets incorporating molecular and morphological data. An interesting excursion is outlined focusing on timescale estimates with respect to viral evolution introducing BEAGLE, a high-performance likelihood-calculation platform that can be used on multi-core systems. As a second major subject, species delimitation is addressed since currently the increasing amount of available genomic data enables extensive inferences, for instance about the degree of genetic isolation among species and ancient and recent introgression events. Describing the history of molecular species delimitation up to the current genomic era and presenting widely used computational methods incorporating single- and multi-locus genomic data, pros and cons are addressed. Finally, a proposal for a new method for delimiting species based on empirical criteria is outlined. In the closing chapter of this part of the book, BPP (Bayesian Markov chain Monte Carlo program) for analysing multi-locus sequence data under the multispecies coalescent (MSC) model with and without introgression is introduced, including a tutorial. These examples together provide accessible details on key conceptual and methodological aspects related to the application of phylogenetics in the genomic era.

References

Scornavacca C, Delsuc F, Galtier N (2021) Phylogenetics in the Genomic Era. https://hal.inria.fr/PGE/

Phylogenetics in the Genomic EraCéline Scornavacca, Frédéric Delsuc, Nicolas Galtier<p style="text-align: justify;">Molecular phylogenetics was born in the middle of the 20th century, when the advent of protein and DNA sequencing offered a novel way to study the evolutionary relationships between living organisms. The first 50 ye...Bacteria and archaea, Bioinformatics, Evolutionary genomics, Functional genomics, Fungi, Plants, Population genomics, Vertebrates, Viruses and transposable elementsRobert Waterhouse2022-03-15 17:43:52 View