Latest recommendations
Id | Title * | Authors * ▲ | Abstract * | Picture * | Thematic fields * | Recommender | Reviewers | Submission date | |
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24 Feb 2023
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 https://doi.org/10.1101/2022.09.02.506364A unique and customizable approach for functionally annotating prokaryotic genomesRecommended by Gavin Douglas based on reviews by Kwee Boon Brandon Seah and Max Emil SchönMacromolecular 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 genomes | Bertrand 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 genomics | Gavin Douglas | Kwee Boon Brandon Seah, Max Emil Schön | 2022-09-09 10:30:31 | View | |
13 Jul 2022
Karyorelict ciliates use an ambiguous genetic code with context-dependent stop/sense codonsBrandon Kwee Boon Seah, Aditi Singh, Estienne Carl Swart https://doi.org/10.1101/2022.04.12.488043An accident frozen in time: the ambiguous stop/sense genetic code of karyorelict ciliatesRecommended by Iker Irisarri based on reviews by Vittorio Boscaro and 2 anonymous reviewersSeveral 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 codons | Brandon 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 genomics | Iker Irisarri | 2022-05-02 11:06:10 | View | ||
27 Apr 2021
Uncovering transposable element variants and their potential adaptive impact in urban populations of the malaria vector Anopheles coluzziiCarlos Vargas-Chavez, Neil Michel Longo Pendy, Sandrine E. Nsango, Laura Aguilera, Diego Ayala, and Josefa González https://doi.org/10.1101/2020.11.22.393231Anopheles coluzzii, a new system to study how transposable elements may foster adaptation to urban environmentsRecommended by Anne Roulin based on reviews by Yann Bourgeois and 1 anonymous reviewerTransposable elements (TEs) are mobile DNA sequences that can increase their copy number and move from one location to another within the genome [1]. Because of their transposition dynamics, TEs constitute a significant fraction of eukaryotic genomes. TEs are also known to play an important functional role and a wealth of studies has now reported how TEs may influence single host traits [e.g. 2–4]. Given that TEs are more likely than classical point mutations to cause extreme changes in gene expression and phenotypes, they might therefore be especially prone to produce the raw diversity necessary for individuals to respond to challenging environments [5,6] such as the ones found in urban area.
| Uncovering transposable element variants and their potential adaptive impact in urban populations of the malaria vector Anopheles coluzzii | Carlos Vargas-Chavez, Neil Michel Longo Pendy, Sandrine E. Nsango, Laura Aguilera, Diego Ayala, and Josefa González | <p style="text-align: justify;">Background</p> <p style="text-align: justify;">Anopheles coluzzii is one of the primary vectors of human malaria in sub-Saharan Africa. Recently, it has colonized the main cities of Central Africa threatening vecto... | Evolutionary genomics | Anne Roulin | 2020-12-02 14:58:47 | View | ||
06 Jul 2021
A pipeline to detect the relationship between transposable elements and adjacent genes in host genomesCaroline Meguerditchian, Ayse Ergun, Veronique Decroocq, Marie Lefebvre, Quynh-Trang Bui https://doi.org/10.1101/2021.02.25.432867A new tool to cross and analyze TE and gene annotationsRecommended by Emmanuelle Lerat based on reviews by 2 anonymous reviewersTransposable elements (TEs) are important components of genomes. Indeed, they are now recognized as having a major role in gene and genome evolution (Biémont 2010). In particular, several examples have shown that the presence of TEs near genes may influence their functioning, either by recruiting particular epigenetic modifications (Guio et al. 2018) or by directly providing new regulatory sequences allowing new expression patterns (Chung et al. 2007; Sundaram et al. 2014). Therefore, the study of the interaction between TEs and their host genome requires tools to easily cross-annotate both types of entities. In particular, one needs to be able to identify all TEs located in the close vicinity of genes or inside them. Such task may not always be obvious for many biologists, as it requires informatics knowledge to develop their own script codes. In their work, Meguerdichian et al. (2021) propose a command-line pipeline that takes as input the annotations of both genes and TEs for a given genome, then detects and reports the positional relationships between each TE insertion and their closest genes. The results are processed into an R script to provide tables displaying some statistics and graphs to visualize these relationships. This tool has the potential to be very useful for performing preliminary analyses before studying the impact of TEs on gene functioning, especially for biologists. Indeed, it makes it possible to identify genes close to TE insertions. These identified genes could then be specifically considered in order to study in more detail the link between the presence of TEs and their functioning. For example, the identification of TEs close to genes may allow to determine their potential role on gene expression. References Biémont C (2010). A brief history of the status of transposable elements: from junk DNA to major players in evolution. Genetics, 186, 1085–1093. https://doi.org/10.1534/genetics.110.124180 Chung H, Bogwitz MR, McCart C, Andrianopoulos A, ffrench-Constant RH, Batterham P, Daborn PJ (2007). Cis-regulatory elements in the Accord retrotransposon result in tissue-specific expression of the Drosophila melanogaster insecticide resistance gene Cyp6g1. Genetics, 175, 1071–1077. https://doi.org/10.1534/genetics.106.066597 Guio L, Vieira C, González J (2018). Stress affects the epigenetic marks added by natural transposable element insertions in Drosophila melanogaster. Scientific Reports, 8, 12197. https://doi.org/10.1038/s41598-018-30491-w Meguerditchian C, Ergun A, Decroocq V, Lefebvre M, Bui Q-T (2021). A pipeline to detect the relationship between transposable elements and adjacent genes in host genomes. bioRxiv, 2021.02.25.432867, ver. 4 peer-reviewed and recommended by Peer Community In Genomics. https://doi.org/10.1101/2021.02.25.432867 Sundaram V, Cheng Y, Ma Z, Li D, Xing X, Edge P, Snyder MP, Wang T (2014). Widespread contribution of transposable elements to the innovation of gene regulatory networks. Genome Research, 24, 1963–1976. https://doi.org/10.1101/gr.168872.113 | A pipeline to detect the relationship between transposable elements and adjacent genes in host genomes | Caroline Meguerditchian, Ayse Ergun, Veronique Decroocq, Marie Lefebvre, Quynh-Trang Bui | <p>Understanding the relationship between transposable elements (TEs) and their closest positional genes in the host genome is a key point to explore their potential role in genome evolution. Transposable elements can regulate and affect gene expr... | Bioinformatics, Viruses and transposable elements | Emmanuelle Lerat | 2021-03-03 15:08:34 | View | ||
08 Apr 2022
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Phylogenetics in the Genomic EraCéline Scornavacca, Frédéric Delsuc, Nicolas Galtier https://hal.inria.fr/PGE/“Phylogenetics in the Genomic Era” brings together experts in the field to present a comprehensive synthesisRecommended by Robert Waterhouse and Karen MeusemannE-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 Era | Céline 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 elements | Robert Waterhouse | 2022-03-15 17:43:52 | View | ||
19 Sep 2024
Trends in genome diversity of small populations under a conservation program: a case study of two French chicken breedsChiara Bortoluzzi, Gwendal Restoux, Romuald Rouger, Benoit Desnoues, Florence Petitjean, Mirte Bosse, Michele Tixier-Boichard https://doi.org/10.1101/2024.02.22.581528Professionalising conservation programmes for local chicken breedsRecommended by Claudia Kasper based on reviews by Markus Neuditschko and Claudia Fontsere AlemanyWhile 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. 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 | Trends in genome diversity of small populations under a conservation program: a case study of two French chicken breeds | Chiara Bortoluzzi, Gwendal Restoux, Romuald Rouger, Benoit Desnoues, Florence Petitjean, Mirte Bosse, Michele Tixier-Boichard | <p>Livestock biodiversity is declining globally at rates unprecedented in human history. Of all avian species, chickens are among the most affected ones because many local breeds have a small effective population size that makes them more suscepti... | Bioinformatics, Evolutionary genomics, Population genomics, Vertebrates | Claudia Kasper | 2024-02-26 13:01:08 | View | ||
23 Oct 2024
mbctools: A User-Friendly Metabarcoding and Cross-Platform Pipeline for Analyzing Multiple Amplicon Sequencing Data across a Large Diversity of OrganismsChristian Barnabé, Guilhem Sempéré, Vincent Manzanilla, Joel Moo Millan, Antoine Amblard-Rambert, Etienne Waleckx https://doi.org/10.1101/2024.02.08.579441One tool to metabarcode them allRecommended by Nicolas Pollet based on reviews by Ali Hakimzadeh and Sourakhata TireraOne way to identify all organisms at their various life stages is by their genetic signature. DNA-based taxonomy, gene tagging and barcoding are different shortcuts used to name such strategies (Lamb et al. 2019; Tautz et al. 2003). Reading and analyzing nucleic acid sequences to perform genetic inventories is now faster than ever, and the latest nucleic acid sequencing technologies reveal an impressive taxonomic, genetic, and functional diversity hidden in all ecosystems (Lamb et al. 2019; Sunagawa et al. 2015). This knowledge should enable us to evaluate biodiversity across its scales, from genetic to species to ecosystem and is sometimes referred to with the neologism of ecogenomics (Dicke et al. 2004). The metabarcoding approach is a key workhorse of ecogenomics. At the core of metabarcoding strategies lies the sequencing of amplicons obtained from so-called multi-template PCR, a formidable and potent experiment with the potential to unravel hidden biosphere components from different samples obtained from organisms or the environment (Kalle et al. 2014; Rodríguez-Ezpeleta et al. 2021). Next to this core approach, and equally important, lies the bioinformatic analysis to convert the raw sequencing data into amplicon sequence variants or operational taxonomic units and interpretable abundance tables. Methodologically, the analysis of sequences obtained from metabarcoding projects is replete with devilish details. This is why different pipelines and tools have been developed, starting with mothur (Schloss et al. 2009) and QIIME 2 (Bolyen et al. 2019), but including more user friendly tools such as FROGS (Escudié et al. 2018). Yet, across all available tools, scientists must choose the optimal algorithms and parameter values to filter raw reads, trim primers, identify chimeras and cluster reads into operational taxonomic units. In addition, the number of genetic markers used to characterize a sample using metabarcoding has increased as sequencing methods are now less costly and more efficient. In such cases, results and interpretations may become limited or confounded. This is where the novel tools proposed by Barnabé and colleagues (2024), mbctools, will benefit researchers in this field. The authors provide a detailed description with a walk-through of the mbctools pipeline to analyse raw reads obtained in a metabarcoding project. The mbctools pipeline can be installed under different computing environments, requires only VSEARCH and a few Python dependencies, and is easy to use with a menu-driven interface. Users need to prepare their data following simple rules, providing single or paired-end reads, primer and target database sequences. An interesting feature of mbctools output is the possibility of integration with the metaXplor visualization tool developed by the authors (Sempéré et al. 2021). As it stands, mbctools should be used for short-read sequences. The taxonomy assignment module has the advantage to enable parameters exploration in an easy way, but it may be oversimplistic for specific taxa. The lightweight aspect of mbctools and its overall simplicity are appealing. These features will make it a useful pipeline for training workshops and to help disseminate the use of metabarcoding. It also holds the potential for further improvement, by the developers or by others. In the end, mbctools will support study reproducibility by enabling a streamlined analysis of raw reads, and like many useful tools, only time will tell whether it is widely adopted. Barnabé C, Sempéré G, Manzanilla V, Millan JM, Amblard-Rambert A, Waleckx E (2024) mbctools: A user-friendly metabarcoding and cross-platform pipeline for analyzing multiple amplicon sequencing data across a large diversity of organisms. bioRxiv, ver. 2 peer-reviewed and recommended by PCI Genomics https://doi.org/10.1101/2024.02.08.579441 Bolyen E, Rideout JR, Dillon MR, Bokulich NA, et al. (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9 Dicke M, van Loon JJA, de Jong PW (2004) Ecogenomics benefits community ecology. Science, 305, 618–619. https://doi.org/10.1126/science.1101788 Escudié F, Auer L, Bernard M, Mariadassou M, Cauquil L, Vidal K, Maman S, Hernandez-Raquet G, Combes S, Pascal G (2018) FROGS: Find, Rapidly, OTUs with Galaxy Solution. Bioinformatics, 34, 1287-1294. https://doi.org/10.1093/bioinformatics/btx791 Kalle E, Kubista M, Rensing C (2014) Multi-template polymerase chain reaction. Biomolecular Detection and Quantification, 2, 11–29. https://doi.org/10.1016/j.bdq.2014.11.002 Lamb CT, Ford AT, Proctor MF, Royle JA, Mowat G, Boutin S (2019) Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems. Ecological Applications, 29, e01876. https://doi.org/10.1002/eap.1876 Rodríguez-Ezpeleta N, Zinger L, Kinziger A, Bik HM, Bonin A, Coissac E, Emerson BC, Lopes CM, Pelletier TA, Taberlet P, Narum S (2021) Biodiversity monitoring using environmental DNA. Molecular Ecology Resources, 21, 1405–1409. https://doi.org/10.1111/1755-0998.13399 Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology 75, 7537-41. https://doi.org/10.1128/AEM.01541-09 Sempéré G, Pétel A, Abbé M, Lefeuvre P, Roumagnac P, Mahé F, Baurens G, Filloux D 2021 metaXplor: an interactive viral and microbial metagenomic data manager. Gigascience, 10, https://doi.org/10.1093/gigascience/giab001 Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, Djahanschiri B, Zeller G, Mende DR, Alberti A, Cornejo-Castillo FM, Costea PI, Cruaud C, d’Ovidio F, Engelen S, Ferrera I, Gasol JM, Guidi L, Hildebrand F, Kokoszka F, Lepoivre C, Lima-Mendez G, Poulain J, Poulos BT, Royo-Llonch M, Sarmento H, Vieira-Silva S, Dimier C, Picheral M, Searson S, Kandels-Lewis S, Tara Oceans coordinators, Bowler C, de Vargas C, Gorsky G, Grimsley N, Hingamp P, Iudicone D, Jaillon O, Not F, Ogata H, Pesant S, Speich S, Stemmann L, Sullivan MB, Weissenbach J, Wincker P, Karsenti E, Raes J, Acinas SG, Bork P (2015) Structure and function of the global ocean microbiome. Science, 348, 1261359. https://doi.org/10.1126/science.1261359 Tautz D, Arctander P, Minelli A, Thomas RH, Vogler AP (2003) A plea for DNA taxonomy. Trends in Ecology & Evolution, 18, 70–74. https://doi.org/10.1016/S0169-5347(02)00041-1
| mbctools: A User-Friendly Metabarcoding and Cross-Platform Pipeline for Analyzing Multiple Amplicon Sequencing Data across a Large Diversity of Organisms | Christian Barnabé, Guilhem Sempéré, Vincent Manzanilla, Joel Moo Millan, Antoine Amblard-Rambert, Etienne Waleckx | <p>We developed a python package called mbctools, designed to offer a cross-platform tool for processing amplicon data from various organisms in the context of metabarcoding studies. It can handle the most common tasks in metabarcoding pipelines s... | Bioinformatics, Metagenomics | Nicolas Pollet | 2024-02-27 11:22:41 | View | ||
07 Aug 2023
Genomic data suggest parallel dental vestigialization within the xenarthran radiationChristopher A Emerling, Gillian C Gibb, Marie-Ka Tilak, Jonathan J Hughes, Melanie Kuch, Ana T Duggan, Hendrik N Poinar, Michael W Nachman, Frederic Delsuc https://doi.org/10.1101/2022.12.09.519446What does dental gene decay tell us about the regressive evolution of teeth in South American mammals?Recommended by Didier Casane based on reviews by Juan C. Opazo, Régis Debruyne and Nicolas PolletA group of mammals, Xenathra, evolved and diversified in South America during its long period of isolation in the early to mid Cenozoic era. More recently, as a result of the Great Faunal Interchange between South America and North America, many xenarthran species went extinct. The thirty-one extant species belong to three groups: armadillos, sloths and anteaters. They share dental degeneration. However, the level of degeneration is variable. Anteaters entirely lack teeth, sloths have intermediately regressed teeth and most armadillos have a toothless premaxilla, as well as peg-like, single-rooted teeth that lack enamel in adult animals (Vizcaíno 2009). This diversity raises a number of questions about the evolution of dentition in these mammals. Unfortunately, the fossil record is too poor to provide refined information on the different stages of regressive evolution in these clades. In such cases, the identification of loss-of-function mutations and/or relaxed selection in genes related to a character regression can be very informative (Emerling and Springer 2014; Meredith et al. 2014; Policarpo et al. 2021). Indeed, shared and unique pseudogenes/relaxed selection can tell us to what extent regression has occurred in common ancestors and whether some changes are lineage-specific. In addition, the distribution of pseudogenes/relaxed selection on the branches of a phylogenetic tree is related to the evolutionary processes involved. A much higher density of pseudogenes in the most internal branches indicates that degeneration took place early and over a short period of time, consistent with selection against the presence of the morphological character with which they are associated, while pseudogenes distributed evenly in many internal and external branches suggest a more gradual process over many millions of years, in line with relaxed selection and fixation of loss-of-function mutations by genetic drift. In this paper (Emerling et al. 2023), the authors examined the dynamics of decay of 11 dental genes that may parallel teeth regression. The analyses of the data reported in this paper clearly point to xenarthran teeth having repeatedly regressed in parallel in the three clades. In fact, no loss-of-function mutation is shared by all species examined. However, more genes should be studied to confirm the hypothesis that the common ancestor of extant xenarthrans had normal dentition. There are distinct patterns of gene loss in different lineages that are associated with the variation in dentition observed across the clades. These patterns of gene loss suggest that regressive evolution took place both gradually and in relatively rapid, discrete phases during the diversification of xenarthrans. This study underscores the utility of using pseudogenes to reconstruct evolutionary history of morphological characters when fossils are sparse. References Emerling CA, Gibb GC, Tilak M-K, Hughes JJ, Kuch M, Duggan AT, Poinar HN, Nachman MW, Delsuc F. 2023. Genomic data suggest parallel dental vestigialization within the xenarthran radiation. bioRxiv, 2022.12.09.519446, ver 2, peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/2022.12.09.519446 Emerling CA, Springer MS. 2014. Eyes underground: Regression of visual protein networks in subterranean mammals. Molecular Phylogenetics and Evolution 78: 260-270. https://doi.org/10.1016/j.ympev.2014.05.016 Meredith RW, Zhang G, Gilbert MTP, Jarvis ED, Springer MS. 2014. Evidence for a single loss of mineralized teeth in the common avian ancestor. Science 346: 1254390. https://doi.org/10.1126/science.1254390 Policarpo M, Fumey J, Lafargeas P, Naquin D, Thermes C, Naville M, Dechaud C, Volff J-N, Cabau C, Klopp C, et al. 2021. Contrasting gene decay in subterranean vertebrates: insights from cavefishes and fossorial mammals. Molecular Biology and Evolution 38: 589-605. https://doi.org/10.1093/molbev/msaa249 Vizcaíno SF. 2009. The teeth of the “toothless”: novelties and key innovations in the evolution of xenarthrans (Mammalia, Xenarthra). Paleobiology 35: 343-366. https://doi.org/10.1666/0094-8373-35.3.343 | Genomic data suggest parallel dental vestigialization within the xenarthran radiation | Christopher A Emerling, Gillian C Gibb, Marie-Ka Tilak, Jonathan J Hughes, Melanie Kuch, Ana T Duggan, Hendrik N Poinar, Michael W Nachman, Frederic Delsuc | <p style="text-align: justify;">The recent influx of genomic data has provided greater insights into the molecular basis for regressive evolution, or vestigialization, through gene loss and pseudogenization. As such, the analysis of gene degradati... | Evolutionary genomics, Vertebrates | Didier Casane | 2022-12-12 16:01:57 | View | ||
02 Jun 2023
Near-chromosome level genome assembly of devil firefish, Pterois milesChristos V. Kitsoulis, Vasileios Papadogiannis, Jon B. Kristoffersen, Elisavet Kaitetzidou, Aspasia Sterioti, Costas S. Tsigenopoulos, Tereza Manousaki https://doi.org/10.1101/2023.01.10.523469The genome of a dangerous invader (fish) beautyRecommended by Iker Irisarri based on reviews by Maria Recuerda and 1 anonymous reviewerHigh-quality genomes are currently being generated at an unprecedented speed powered by long-read sequencing technologies. However, sequencing effort is concentrated unequally across the tree of life and several key evolutionary and ecological groups remain largely unexplored. So is the case for fish species of the family Scorpaenidae (Perciformes). Kitsoulis et al. present the genome of the devil firefish, Pterois miles (1). Following current best practices, the assembly relies largely on Oxford Nanopore long reads, aided by Illumina short reads for polishing to increase the per-base accuracy. PacBio’s IsoSeq was used to sequence RNA from a variety of tissues as direct evidence for annotating genes. The reconstructed genome is 902 Mb in size and has high contiguity (N50=14.5 Mb; 660 scaffolds, 90% of the genome covered by the 83 longest scaffolds) and completeness (98% BUSCO completeness). The new genome is used to assess the phylogenetic position of P. miles, explore gene synteny against zebrafish, look at orthogroup expansion and contraction patterns in Perciformes, as well as to investigate the evolution of toxins in scorpaenid fish (2). In addition to its value for better understanding the evolution of scorpaenid and teleost fishes, this new genome is also an important resource for monitoring its invasiveness through the Mediterranean Sea (3) and the Atlantic Ocean, in the latter case forming the invasive lionfish complex with P. volitans (4). REFERENCES 1. Kitsoulis CV, Papadogiannis V, Kristoffersen JB, Kaitetzidou E, Sterioti E, Tsigenopoulos CS, Manousaki T. (2023) Near-chromosome level genome assembly of devil firefish, Pterois miles. BioRxiv, ver. 6 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.01.10.523469 2. Kiriake A, Shiomi K. (2011) Some properties and cDNA cloning of proteinaceous toxins from two species of lionfish (Pterois antennata and Pterois volitans). Toxicon, 58(6-7):494–501. https://doi.org/10.1016/j.toxicon.2011.08.010 3. Katsanevakis S, et al. (2020) Un- published Mediterranean records of marine alien and cryptogenic species. BioInvasions Records, 9:165–182. https://doi.org/10.3391/bir.2020.9.2.01 4. Lyons TJ, Tuckett QM, Hill JE. (2019) Data quality and quantity for invasive species: A case study of the lionfishes. Fish and Fisheries, 20:748–759. https://doi.org/10.1111/faf.12374 | Near-chromosome level genome assembly of devil firefish, *Pterois miles* | Christos V. Kitsoulis, Vasileios Papadogiannis, Jon B. Kristoffersen, Elisavet Kaitetzidou, Aspasia Sterioti, Costas S. Tsigenopoulos, Tereza Manousaki | <p style="text-align: justify;">Devil firefish (<em>Pterois miles</em>), a member of Scorpaenidae family, is one of the most successful marine non-native species, dominating around the world, that was rapidly spread into the Mediterranean Sea, thr... | Evolutionary genomics | Iker Irisarri | 2023-01-17 12:37:20 | View | ||
13 Jul 2022
Nucleosome patterns in four plant pathogenic fungi with contrasted genome structuresColin Clairet, Nicolas Lapalu, Adeline Simon, Jessica L. Soyer, Muriel Viaud, Enric Zehraoui, Berengere Dalmais, Isabelle Fudal, Nadia Ponts https://doi.org/10.1101/2021.04.16.439968Genome-wide chromatin and expression datasets of various pathogenic ascomycetesRecommended by Sébastien Bloyer and Romain Koszul based on reviews by Ricardo C. Rodríguez de la Vega and 1 anonymous reviewerPlant pathogenic fungi represent serious economic threats. These organisms are rapidly adaptable, with plastic genomes containing many variable regions and evolving rapidly. It is, therefore, useful to characterize their genetic regulation in order to improve their control. One of the steps to do this is to obtain omics data that link their DNA structure and gene expression. Clairet C, Lapalu N, Simon A, Soyer JL, Viaud M, Zehraoui E, Dalmais B, Fudal I, Ponts N (2022) Nucleosome patterns in four plant pathogenic fungi with contrasted genome structures. bioRxiv, 2021.04.16.439968, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.04.16.439968 | Nucleosome patterns in four plant pathogenic fungi with contrasted genome structures | Colin Clairet, Nicolas Lapalu, Adeline Simon, Jessica L. Soyer, Muriel Viaud, Enric Zehraoui, Berengere Dalmais, Isabelle Fudal, Nadia Ponts | <p style="text-align: justify;">Fungal pathogens represent a serious threat towards agriculture, health, and environment. Control of fungal diseases on crops necessitates a global understanding of fungal pathogenicity determinants and their expres... | Epigenomics, Fungi | Sébastien Bloyer | 2021-04-17 10:32:41 | View |