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27 Apr 2021
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Uncovering transposable element variants and their potential adaptive impact in urban populations of the malaria vector Anopheles coluzzii

Anopheles coluzzii, a new system to study how transposable elements may foster adaptation to urban environments

Recommended by based on reviews by Yann Bourgeois and 1 anonymous reviewer

Transposable 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.  
In their study [7], Vargas et al. establish the foundation to investigate how TEs may help Anopheles coluzzii -  the primary vectors of human malaria in sub-Saharan Africa - adapt to urban environments. To cover natural breeding sites in major Central Africa cities, they made use of the previously available An. coluzzii genome from Yaoundé (Cameroon) and sequenced with long-read technology six additional ones originating from Douala (Cameroon) and Libreville (Gabon). The de novo annotation of TEs in these genomes revealed 64 new anopheline TE families and allowed to identify seven active families. As a first step towards characterizing the potential role of TEs in the adaptation of An. coluzzii to urban environments, they further analyzed the distribution of TEs across the seven genomes. By doing so, they identified a significant number of polymorphic or fixed TE insertions located in the vicinity of genes involved in insecticide resistance and immune response genes.  
The availability of seven An. coluzzii genomes allowed the authors to explore how TE diversity may affect genes functionally relevant for the adaptation to urban environments and provide ground for further functional validation studies. More and more studies have demonstrated the impact of TEs on adaptation and as such, the work of Vargas et al. contributes to fostering our understanding of the link between TEs and gain of function in a species facing strong anthropogenic pressures.  
 
References  
  
[1] Wicker T, Sabot F, Hua-Van A, Bennetzen JL, Capy P, Chalhoub B, Flavell A, Leroy P, Morgante M, Panaud O, Paux E, SanMiguel P, Schulman AH (2007) A unified classification system for eukaryotic transposable elements. Nature Reviews Genetics, 8, 973–982. https://doi.org/10.1038/nrg2165    
  
[2] van’t Hof AE, Campagne P, Rigden DJ, Yung CJ, Lingley J, Quail MA, Hall N, Darby AC, Saccheri IJ (2016) The industrial melanism mutation in British peppered moths is a transposable element. Nature, 534, 102–105. https://doi.org/10.1038/nature17951    
  
[3] González J, Karasov TL, Messer PW, Petrov DA (2010) Genome-wide patterns of adaptation to temperate environments associated with transposable elements in Drosophila. PLOS Genetics, 6, e1000905. https://doi.org/10.1371/journal.pgen.1000905  
  
[4] Lisch D (2013) How important are transposons for plant evolution? Nature Reviews Genetics, 14, 49–61. https://doi.org/10.1038/nrg3374    
  
[5] Bonchev G, Parisod C (2013) Transposable elements and microevolutionary changes in natural populations. Molecular Ecology Resources, 13, 765–775. https://doi.org/10.1111/1755-0998.12133  
  
[6] Casacuberta E, González J (2013) The impact of transposable elements in environmental adaptation. Molecular Ecology, 22, 1503–1517. https://doi.org/10.1111/mec.12170    
  
[7] Vargas-Chavez C, Pendy NML, Nsango SE, Aguilera L, Ayala D, González J (2021). Uncovering transposable element variants and their potential adaptive impact in urban populations of the malaria vector Anopheles coluzzii. bioRxiv, 2020.11.22.393231, ver. 3 peer-reviewed and recommended by Peer community in Genomics. https://doi.org/10.1101/2020.11.22.393231  

 

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<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 genomicsAnne Roulin2020-12-02 14:58:47 View
24 Sep 2020
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A rapid and simple method for assessing and representing genome sequence relatedness

A quick alternative method for resolving bacterial taxonomy using short identical DNA sequences in genomes or metagenomes

Recommended by based on reviews by Gavin Douglas and 1 anonymous reviewer

The bacterial species problem can be summarized as follows: bacteria recombine too little, and yet too much (Shapiro 2019).
Too little in the sense that recombination is not obligately coupled with reproduction, as in sexual eukaryotes. So the Biological Species Concept (BSC) of reproductive isolation does not strictly apply to clonally reproducing organisms like bacteria. Too much in the sense that genetic exchange can occur promiscuously across species (or even Domains), potentially obscuring species boundaries.
In parallel to such theoretical considerations, several research groups have taken more pragmatic approaches to defining bacterial species based on sequence similarity cutoffs, such as genome-wide average nucleotide identity (ANI). At a cutoff above 95% ANI, genomes are considered to come from the same species. While this cutoff may appear arbitrary, a discontinuity around 95% in the distribution of ANI values has been argued to provide a 'natural' cutoff (Jain et al. 2018). This discontinuity has been criticized as being an artefact of various biases in genome databases (Murray, Gao, and Wu 2020), but appears to be a general feature of relatively unbiased metagenome-assembled genomes as well (Olm et al. 2020). The 95% cutoff has been suggested to represent a barrier to homologous recombination (Olm et al. 2020), although clusters of genetic exchange consistent with BSC-like species are observed at much finer identity cutoffs (Shapiro 2019; Arevalo et al. 2019).
Although 95% ANI is the most widely used genomic standard for species delimitation, it is by no means the only plausible approach. In particular, tracts of identical DNA provide evidence for recent genetic exchange, which in turn helps define BSC-like clusters of genomes (Arevalo et al. 2019). In this spirit, Briand et al. (2020) introduce a genome-clustering method based on the number of shared identical DNA sequences of length k (or k-mers). Using a test dataset of Pseudomonas genomes, they find that 95% ANI corresponds to approximately 50% of shared 15-mers. Applying this cutoff yields 350 Pseudomonas species, whereas the current taxonomy only includes 207 recognized species. To determine whether splitting the genus into a greater number of species is at all useful, they compare their new classification scheme to the traditional one in terms of the ability to taxonomically classify metagenomic sequencing reads from three Pseudomonas-rich environments. In all cases, the new scheme (termed K-IS for "Kinship relationships Identification with Shared k-mers") yielded a higher number of classified reads, with an average improvement of 1.4-fold. This is important because increasing the number of genome sequences in a reference database – without consistent taxonomic annotation of these genomes – paradoxically leads to fewer classified metagenomic reads. Thus a rapid, automated taxonomy such as the one proposed here offers an opportunity to more fully harness the information from metagenomes.
KI-S is also fast to run, so it is feasible to test several values of k and quickly visualize the clustering using an interactive, zoomable circle-packing display (that resembles a cross-section of densely packed, three-dimensional dendrogram). This interface allows the rapid flagging of misidentified species, or understudied species with few sequenced representatives as targets for future study. Hopefully these initial Pseudomonas results will inspire future studies to apply the method to additional taxa, and to further characterize the relationship between ANI and shared identical k-mers. Ultimately, I hope that such investigations will resolve the issue of whether or not there is a 'natural' discontinuity for bacterial species, and what evolutionary forces maintain this cutoff.

References

Arevalo P, VanInsberghe D, Elsherbini J, Gore J, Polz MF (2019) A Reverse Ecology Approach Based on a Biological Definition of Microbial Populations. Cell, 178, 820-834.e14. https://doi.org/10.1016/j.cell.2019.06.033
 
Briand M, Bouzid M, Hunault G, Legeay M, Saux MF-L, Barret M (2020) A rapid and simple method for assessing and representing genome sequence relatedness. bioRxiv, 569640, ver. 5 peer-reveiwed and recommended by PCI Genomics. https://doi.org/10.1101/569640
 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S (2018) High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nature Communications, 9, 5114. https://doi.org/10.1038/s41467-018-07641-9
 
Murray CS, Gao Y, Wu M (2020) There is no evidence of a universal genetic boundary among microbial species. bioRxiv, 2020.07.27.223511. https://doi.org/10.1101/2020.07.27.223511
 
Olm MR, Crits-Christoph A, Diamond S, Lavy A, Carnevali PBM, Banfield JF (2020) Consistent Metagenome-Derived Metrics Verify and Delineate Bacterial Species Boundaries. mSystems, 5. https://doi.org/10.1128/mSystems.00731-19
 
Shapiro BJ (2019) What Microbial Population Genomics Has Taught Us About Speciation. In: Population Genomics: Microorganisms Population Genomics. (eds Polz MF, Rajora OP), pp. 31–47. Springer International Publishing, Cham. https://doi.org/10.1007/13836201810

A rapid and simple method for assessing and representing genome sequence relatednessM Briand, M Bouzid, G Hunault, M Legeay, M Fischer-Le Saux, M Barret<p>Coherent genomic groups are frequently used as a proxy for bacterial species delineation through computation of overall genome relatedness indices (OGRI). Average nucleotide identity (ANI) is a widely employed method for estimating relatedness ...Bioinformatics, MetagenomicsB. Jesse Shapiro Gavin Douglas2019-11-07 16:37:56 View
07 Oct 2021
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Fine-scale quantification of GC-biased gene conversion intensity in mammals

A systematic approach to the study of GC-biased gene conversion in mammals

Recommended by based on reviews by Fanny Pouyet , David Castellano and 1 anonymous reviewer

The role of GC-biased gene conversion (gBGC) in molecular evolution has interested scientists for the last two decades since its discovery in 1999 (Eyre-Walker 1999; Galtier et al. 2001). gBGC is a process that is associated with meiotic recombination, and is characterized by a transmission distortion in favor of G and C over A and T alleles at GC/AT heterozygous sites that occur in the vicinity of recombination-inducing double-strand breaks (Duret and Galtier 2009; Mugal et al. 2015). This transmission distortion results in a fixation bias of G and C alleles, equivalent to directional selection for G and C (Nagylaki 1983). The fixation bias subsequently leads to a correlation between recombination rate and GC content across the genome, which has served as indirect evidence for the prevalence of gBGC in many organisms. The fixation bias also produces shifts in the allele frequency spectrum (AFS) towards higher frequencies of G and C alleles.

These molecular signatures of gBGC provide a means to quantify the strength of gBGC and study its variation among species and across the genome. Following this idea, first Lartillot (2013) and Capra et al. (2013) developed phylogenetic methodology to quantify gBGC based on substitutions, and De Maio et al. (2013) combined information on polymorphism into a phylogenetic setting. Complementary to the phylogenetic methods, later Glemin et al. (2015) developed a method that draws information solely from polymorphism data and the shape of the AFS. Application of these methods to primates (Capra et al. 2013; De Maio et al. 2013; Glemin et al. 2015) and mammals (Lartillot 2013) supported the notion that variation in the strength of gBGC across the genome reflects the dynamics of the recombination landscape, while variation among species correlates with proxies of the effective population size. However, application of the polymorphism-based method by Glemin et al. (2015) to distantly related Metazoa did not confirm the correlation with effective population size (Galtier et al. 2018).

Here, Galtier (2021) introduces a novel phylogenetic approach applicable to the study of closely related species. Specifically, Galtier introduces a statistical framework that enables the systematic study of variation in the strength of gBGC among species and among genes. In addition, Galtier assesses fine-scale variation of gBGC across the genome by means of spatial autocorrelation analysis. This puts Galtier in a position to study variation in the strength of gBGC at three different scales, i) among species, ii) among genes, and iii) within genes. Galtier applies his method to four families of mammals, Hominidae, Cercopithecidae, Bovidae, and Muridae and provides a thorough discussion of his findings and methodology.

Galtier found that the strength of gBGC correlates with proxies of the effective population size (Ne), but that the slope of the relationship differs among the four families of mammals. Given the relationship between the population-scaled strength of gBGC B = 4Neb, this finding suggests that the conversion bias (b) could vary among mammalian species. Variation in b could either result from differences in the strength of the transmission distortion (Galtier et al. 2018) or evolutionary changes in the rate of recombination (Boman et al. 2021). Alternatively, Galtier suggests that also systematic variation in proxies of Ne could lead to similar observations. Finally, the present study reports intriguing inter-species differences between the extent of variation in the strength of gBGC among and within genes, which are interpreted in consideration of the recombination dynamics in mammals.

References

Boman J, Mugal CF, Backström N (2021) The Effects of GC-Biased Gene Conversion on Patterns of Genetic Diversity among and across Butterfly Genomes. Genome Biology and Evolution, 13. https://doi.org/10.1093/gbe/evab064

Capra JA, Hubisz MJ, Kostka D, Pollard KS, Siepel A (2013) A Model-Based Analysis of GC-Biased Gene Conversion in the Human and Chimpanzee Genomes. PLOS Genetics, 9, e1003684. https://doi.org/10.1371/journal.pgen.1003684

De Maio N, Schlötterer C, Kosiol C (2013) Linking Great Apes Genome Evolution across Time Scales Using Polymorphism-Aware Phylogenetic Models. Molecular Biology and Evolution, 30, 2249–2262. https://doi.org/10.1093/molbev/mst131

Duret L, Galtier N (2009) Biased Gene Conversion and the Evolution of Mammalian Genomic Landscapes. Annual Review of Genomics and Human Genetics, 10, 285–311. https://doi.org/10.1146/annurev-genom-082908-150001

Eyre-Walker A (1999) Evidence of Selection on Silent Site Base Composition in Mammals: Potential Implications for the Evolution of Isochores and Junk DNA. Genetics, 152, 675–683. https://doi.org/10.1093/genetics/152.2.675

Galtier N (2021) Fine-scale quantification of GC-biased gene conversion intensity in mammals. bioRxiv, 2021.05.05.442789, ver. 5 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.05.05.442789

Galtier N, Piganeau G, Mouchiroud D, Duret L (2001) GC-Content Evolution in Mammalian Genomes: The Biased Gene Conversion Hypothesis. Genetics, 159, 907–911. https://doi.org/10.1093/genetics/159.2.907

Galtier N, Roux C, Rousselle M, Romiguier J, Figuet E, Glémin S, Bierne N, Duret L (2018) Codon Usage Bias in Animals: Disentangling the Effects of Natural Selection, Effective Population Size, and GC-Biased Gene Conversion. Molecular Biology and Evolution, 35, 1092–1103. https://doi.org/10.1093/molbev/msy015

Glémin S, Arndt PF, Messer PW, Petrov D, Galtier N, Duret L (2015) Quantification of GC-biased gene conversion in the human genome. Genome Research, 25, 1215–1228. https://doi.org/10.1101/gr.185488.114

Lartillot N (2013) Phylogenetic Patterns of GC-Biased Gene Conversion in Placental Mammals and the Evolutionary Dynamics of Recombination Landscapes. Molecular Biology and Evolution, 30, 489–502. https://doi.org/10.1093/molbev/mss239

Mugal CF, Weber CC, Ellegren H (2015) GC-biased gene conversion links the recombination landscape and demography to genomic base composition. BioEssays, 37, 1317–1326. https://doi.org/10.1002/bies.201500058

Nagylaki T (1983) Evolution of a finite population under gene conversion. Proceedings of the National Academy of Sciences, 80, 6278–6281. https://doi.org/10.1073/pnas.80.20.6278

Fine-scale quantification of GC-biased gene conversion intensity in mammalsNicolas Galtier<p style="text-align: justify;">GC-biased gene conversion (gBGC) is a molecular evolutionary force that favours GC over AT alleles irrespective of their fitness effect. Quantifying the variation in time and across genomes of its intensity is key t...Evolutionary genomics, Population genomics, VertebratesCarina Farah Mugal2021-05-25 09:25:52 View
05 May 2021
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A primer and discussion on DNA-based microbiome data and related bioinformatics analyses

A hitchhiker’s guide to DNA-based microbiome analysis

Recommended by based on reviews by Nicolas Pollet, Rafael Cuadrat and 1 anonymous reviewer

In the last two decades, microbial research in its different fields has been increasingly focusing on microbiome studies. These are defined as studies of complete assemblages of microorganisms in given environments and have been benefiting from increases in sequencing length, quality, and yield, coupled with ever-dropping prices per sequenced nucleotide. Alongside localized microbiome studies, several global collaborative efforts have emerged, including the Human Microbiome Project [1], the Earth Microbiome Project [2], the Extreme Microbiome Project, and MetaSUB [3].

Coupled with the development of sequencing technologies and the ever-increasing amount of data output, multiple standalone or online bioinformatic tools have been designed to analyze these data. Often these tools have been focusing on either of two main tasks: 1) Community analysis, providing information on the organisms present in the microbiome, or 2) Functionality, in the case of shotgun metagenomic data, providing information on the metabolic potential of the microbiome. Bridging between the two types of data, often extracted from the same dataset, is typically a daunting task that has been addressed by a handful of tools only.

The extent of tools and approaches to analyze microbiome data is great and may be overwhelming to researchers new to microbiome or bioinformatic studies. In their paper “A primer and discussion on DNA-based microbiome data and related bioinformatics analyses”, Douglas and Langille [4] guide us through the different sequencing approaches useful for microbiome studies. alongside their advantages and caveats and a selection of tools to analyze these data, coupled with examples from their own field of research.

Standing out in their primer-style review is the emphasis on the coupling between taxonomic/phylogenetic identification of the organisms and their functionality. This type of analysis, though highly important to understand the role of different microorganisms in an environment as well as to identify potential functional redundancy, is often not conducted. For this, the authors identify two approaches. The first, using shotgun metagenomics, has higher chances of attributing a function to the correct taxon. The second, using amplicon sequencing of marker genes, allows for a deeper coverage of the microbiome at a lower cost, and extrapolates the amplicon data to close relatives with a sequenced genome. As clearly stated, this approach makes the leap between taxonomy and functionality and has been shown to be erroneous in cases where the core genome of the bacterial genus or family does not encompass the functional diversity of the different included species. This practice was already common before the genomic era, but its accuracy is improving thanks to the increasing availability of sequenced reference genomes from cultures, environmentally picked single cells or metagenome-assembled genome.

In addition to their description of standalone tools useful for linking taxonomy and functionality, one should mention the existence of online tools that may appeal to researchers who do not have access to adequate bioinformatics infrastructure. Among these are the Integrated Microbial Genomes and Microbiomes (IMG) from the Joint Genome Institute [5], KBase [6] and MG-RAST [7].

A second important point arising from this review is the need for standardization in microbiome data analyses and the complexity of achieving this. As Douglas and Langille [4] state, this has been previously addressed, highlighting the variability in results obtained with different tools. It is often the case that papers describing new bioinformatic tools display their superiority relative to existing alternatives, potentially misleading newcomers to the field that the newest tool is the best and only one to be used. This is often not the case, and while benchmarking against well-defined datasets serves as a powerful testing tool, “real-life” samples are often not comparable. Thus, as done here, future primer-like reviews should highlight possible cross-field caveats, encouraging researchers to employ and test several approaches and validate their results whenever possible.

In summary, Douglas and Langille [4] offer both the novice and experienced researcher a detailed guide along the paths of microbiome data analysis, accompanied by informative background information, suggested tools with which analyses can be started, and an insightful view on where the field should be heading.

References

[1]  Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI (2007) The Human Microbiome Project. Nature, 449, 804–810. https://doi.org/10.1038/nature06244

[2]  Gilbert JA, Jansson JK, Knight R (2014) The Earth Microbiome project: successes and aspirations. BMC Biology, 12, 69. https://doi.org/10.1186/s12915-014-0069-1

[3]  Mason C, Afshinnekoo E, Ahsannudin S, Ghedin E, Read T, Fraser C, Dudley J, Hernandez M, Bowler C, Stolovitzky G, Chernonetz A, Gray A, Darling A, Burke C, Łabaj PP, Graf A, Noushmehr H, Moraes  s., Dias-Neto E, Ugalde J, Guo Y, Zhou Y, Xie Z, Zheng D, Zhou H, Shi L, Zhu S, Tang A, Ivanković T, Siam R, Rascovan N, Richard H, Lafontaine I, Baron C, Nedunuri N, Prithiviraj B, Hyat S, Mehr S, Banihashemi K, Segata N, Suzuki H, Alpuche Aranda CM, Martinez J, Christopher Dada A, Osuolale O, Oguntoyinbo F, Dybwad M, Oliveira M, Fernandes A, Oliveira M, Fernandes A, Chatziefthimiou AD, Chaker S, Alexeev D, Chuvelev D, Kurilshikov A, Schuster S, Siwo GH, Jang S, Seo SC, Hwang SH, Ossowski S, Bezdan D, Udekwu K, Udekwu K, Lungjdahl PO, Nikolayeva O, Sezerman U, Kelly F, Metrustry S, Elhaik E, Gonnet G, Schriml L, Mongodin E, Huttenhower C, Gilbert J, Hernandez M, Vayndorf E, Blaser M, Schadt E, Eisen J, Beitel C, Hirschberg D, Schriml L, Mongodin E, The MetaSUB International Consortium (2016) The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium inaugural meeting report. Microbiome, 4, 24. https://doi.org/10.1186/s40168-016-0168-z

[4]  Douglas GM, Langille MGI (2021) A primer and discussion on DNA-based microbiome data and related bioinformatics analyses. OSF Preprints, ver. 4 peer-reviewed and recommended by Peer Community In Genomics. https://doi.org/10.31219/osf.io/3dybg

[5]  Chen I-MA, Markowitz VM, Chu K, Palaniappan K, Szeto E, Pillay M, Ratner A, Huang J, Andersen E, Huntemann M, Varghese N, Hadjithomas M, Tennessen K, Nielsen T, Ivanova NN, Kyrpides NC (2017) IMG/M: integrated genome and metagenome comparative data analysis system. Nucleic Acids Research, 45, D507–D516. https://doi.org/10.1093/nar/gkw929

[6]  Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, Dehal P, Ware D, Perez F, Canon S, Sneddon MW, Henderson ML, Riehl WJ, Murphy-Olson D, Chan SY, Kamimura RT, Kumari S, Drake MM, Brettin TS, Glass EM, Chivian D, Gunter D, Weston DJ, Allen BH, Baumohl J, Best AA, Bowen B, Brenner SE, Bun CC, Chandonia J-M, Chia J-M, Colasanti R, Conrad N, Davis JJ, Davison BH, DeJongh M, Devoid S, Dietrich E, Dubchak I, Edirisinghe JN, Fang G, Faria JP, Frybarger PM, Gerlach W, Gerstein M, Greiner A, Gurtowski J, Haun HL, He F, Jain R, Joachimiak MP, Keegan KP, Kondo S, Kumar V, Land ML, Meyer F, Mills M, Novichkov PS, Oh T, Olsen GJ, Olson R, Parrello B, Pasternak S, Pearson E, Poon SS, Price GA, Ramakrishnan S, Ranjan P, Ronald PC, Schatz MC, Seaver SMD, Shukla M, Sutormin RA, Syed MH, Thomason J, Tintle NL, Wang D, Xia F, Yoo H, Yoo S, Yu D (2018) KBase: The United States Department of Energy Systems Biology Knowledgebase. Nature Biotechnology, 36, 566–569. https://doi.org/10.1038/nbt.4163

[7]  Wilke A, Bischof J, Gerlach W, Glass E, Harrison T, Keegan KP, Paczian T, Trimble WL, Bagchi S, Grama A, Chaterji S, Meyer F (2016) The MG-RAST metagenomics database and portal in 2015. Nucleic Acids Research, 44, D590–D594. https://doi.org/10.1093/nar/gkv1322

A primer and discussion on DNA-based microbiome data and related bioinformatics analysesGavin M. Douglas and Morgan G. I. Langille<p style="text-align: justify;">The past decade has seen an eruption of interest in profiling microbiomes through DNA sequencing. The resulting investigations have revealed myriad insights and attracted an influx of researchers to the research are...Bioinformatics, MetagenomicsDanny Ionescu2021-02-17 00:26:46 View
06 Apr 2021
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Evidence for shared ancestry between Actinobacteria and Firmicutes bacteriophages

Viruses of bacteria: phages evolution across phylum boundaries

Recommended by based on reviews by 3 anonymous reviewers

Bacteria and phages have coexisted and coevolved for a long time. Phages are bacteria-infecting viruses, with a symbiotic status sensu lato, meaning they can be pathogenic, commensal or mutualistic. Thus, the association between bacteria phages has probably played a key role in the high adaptability of bacteria to most - if not all – of Earth’s ecosystems, including other living organisms (such as eukaryotes), and also regulate bacterial community size (for instance during bacterial blooms). 

As genetic entities, phages are submitted to mutations and natural selection, which changes their DNA sequence. Therefore, comparative genomic analyses of contemporary phages can be useful to understand their evolutionary dynamics. International initiatives such as SEA-PHAGES have started to tackle the issue of history of phage-bacteria interactions and to describe the dynamics of the co-evolution between bacterial hosts and their associated viruses. Indeed, the understanding of this cross-talk has many potential implications in terms of health and agriculture, among others.

The work of Koert et al. (2021) deals with one of the largest groups of bacteria (Actinobacteria), which are Gram-positive bacteria mainly found in soil and water. Some soil-born Actinobacteria develop filamentous structures reminiscent of the mycelium of eukaryotic fungi. In this study, the authors focused on the Streptomyces clade, a large genus of Actinobacteria colonized by phages known for their high level of genetic diversity.

The authors tested the hypothesis that large exchanges of genetic material occurred between Streptomyces and diverse phages associated with bacterial hosts. Using public datasets, their comparative phylogenomic analyses identified a new cluster among Actinobacteria–infecting phages closely related to phages of Firmicutes. Moreover, the GC content and codon-usage biases of this group of phages of Actinobacteria are similar to those of Firmicutes. 

This work demonstrates for the first time the transfer of a bacteriophage lineage from one bacterial phylum to another one. The results presented here suggest that the age of the described transfer is probably recent since several genomic characteristics of the phage are not fully adapted to their new hosts. However, the frequency of such transfer events remains an open question. If frequent, such exchanges would mean that pools of bacteriophages are regularly fueled by genetic material coming from external sources, which would have important implications for the co-evolutionary dynamics of phages and bacteria.

References

Koert, M., López-Pérez, J., Courtney Mattson, C., Caruso, S. and Erill, I. (2021) Evidence for shared ancestry between Actinobacteria and Firmicutes bacteriophages. bioRxiv, 842583, version 5 peer-reviewed and recommended by Peer community in Genomics. doi: https://doi.org/10.1101/842583 

Evidence for shared ancestry between Actinobacteria and Firmicutes bacteriophagesMatthew Koert, Júlia López-Pérez, Courtney Mattson, Steven M. Caruso, Ivan Erill<p>Bacteriophages typically infect a small set of related bacterial strains. The transfer of bacteriophages between more distant clades of bacteria has often been postulated, but remains mostly unaddressed. In this work we leverage the sequencing ...Evolutionary genomicsDenis Tagu2019-12-10 15:26:31 View
07 Aug 2023
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Genomic data suggest parallel dental vestigialization within the xenarthran radiation

What does dental gene decay tell us about the regressive evolution of teeth in South American mammals?

Recommended by based on reviews by Juan Opazo, Régis Debruyne and Nicolas Pollet

A 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 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<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, VertebratesDidier Casane2022-12-12 16:01:57 View
06 Jul 2021
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A pipeline to detect the relationship between transposable elements and adjacent genes in host genomes

A new tool to cross and analyze TE and gene annotations

Recommended by based on reviews by 2 anonymous reviewers

Transposable 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 genomesCaroline 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 elementsEmmanuelle Lerat2021-03-03 15:08:34 View
11 Sep 2023
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COVFlow: phylodynamics analyses of viruses from selected SARS-CoV-2 genome sequences

A pipeline to select SARS-CoV-2 sequences for reliable phylodynamic analyses

Recommended by based on reviews by Gabriel Wallau and Bastien Boussau

Phylodynamic approaches enable viral genetic variation to be tracked over time, providing insight into pathogen phylogenetic relationships and epidemiological dynamics. These are important methods for monitoring viral spread, and identifying important parameters such as transmission rate, geographic origin and duration of infection [1]. This knowledge makes it possible to adjust public health measures in real-time and was important in the case of the COVID-19 pandemic [2]. However, these approaches can be complicated to use when combining a very large number of sequences. This was particularly true during the COVID-19 pandemic, when sequencing data representing millions of entire viral genomes was generated, with associated metadata enabling their precise identification.

Danesh et al. [3] present a bioinformatics pipeline, CovFlow, for selecting relevant sequences according to user-defined criteria to produce files that can be used directly for phylodynamic analyses. The selection of sequences first involves a quality filter on the size of the sequences and the absence of unresolved bases before being able to make choices based on the associated metadata. Once the sequences are selected, they are aligned and a time-scaled phylogenetic tree is inferred. An output file in a format directly usable by BEAST 2 [4] is finally generated.

To illustrate the use of the pipeline, Danesh et al. [3] present an analysis of the Delta variant in two regions of France. They observed a delay in the start of the epidemic depending on the region. In addition, they identified genetic variation linked to the start of the school year and the extension of vaccination, as well as the arrival of a new variant. This tool will be of major interest to researchers analysing SARS-CoV-2 sequencing data, and a number of future developments are planned by the authors.

References

[1] Baele G, Dellicour S, Suchard MA, Lemey P, Vrancken B. 2018. Recent advances in computational phylodynamics. Curr Opin Virol. 31:24-32. https://doi.org/10.1016/j.coviro.2018.08.009

[2] Attwood SW, Hill SC, Aanensen DM, Connor TR, Pybus OG. 2022. Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nat Rev Genet. 23:547-562. https://doi.org/10.1038/s41576-022-00483-8

[3] Danesh G, Boennec C, Verdurme L, Roussel M, Trombert-Paolantoni S, Visseaux B, Haim-Boukobza S, Alizon S. 2023. COVFlow: phylodynamics analyses of viruses from selected SARS-CoV-2 genome sequences. bioRxiv, ver. 7 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.06.17.496544

[4] Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu C-H et al. 2014. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol 10: e1003537. https://doi.org/10.1371/journal.pcbi.1003537

COVFlow: phylodynamics analyses of viruses from selected SARS-CoV-2 genome sequencesGonché Danesh, Corentin Boennec, Laura Verdurme, Mathilde Roussel, Sabine Trombert-Paolantoni, Benoit Visseaux, Stephanie Haim-Boukobza, Samuel Alizon<p style="text-align: justify;">Phylodynamic analyses generate important and timely data to optimise public health response to SARS-CoV-2 outbreaks and epidemics. However, their implementation is hampered by the massive amount of sequence data and...Bioinformatics, Evolutionary genomicsEmmanuelle Lerat2022-12-12 09:04:01 View
18 Feb 2021
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Traces of transposable element in genome dark matter co-opted by flowering gene regulation networks

Using small fragments to discover old TE remnants: the Duster approach empowers the TE detection

Recommended by based on reviews by Josep Casacuberta and 1 anonymous reviewer

Transposable elements are the raw material of the dark matter of the genome, the foundation of the next generation of genes and regulation networks". This sentence could be the essence of the paper of Baud et al. (2021). Transposable elements (TEs) are endogenous mobile genetic elements found in almost all genomes, which were discovered in 1948 by Barbara McClintock (awarded in 1983 the only unshared Medicine Nobel Prize so far). TEs are present everywhere, from a single isolated copy for some elements to more than millions for others, such as Alu. They are founders of major gene lineages (HET-A, TART and telomerases, RAG1/RAG2 proteins from mammals immune system; Diwash et al, 2017), and even of retroviruses (Xiong & Eickbush, 1988). However, most TEs appear as selfish elements that replicate, land in a new genomic region, then start to decay and finally disappear in the midst of the genome, turning into genomic ‘dark matter’ (Vitte et al, 2007). The mutations (single point, deletion, recombination, and so on) that occur during this slow death erase some of their most notable features and signature sequences, rendering them completely unrecognizable after a few million years. Numerous TE detection tools have tried to optimize their detection (Goerner-Potvin & Bourque, 2018), but further improvement is definitely challenging. This is what Baud et al. (2021) accomplished in their paper. They used a simple, elegant and efficient k-mer based approach to find small signatures that, when accumulated, allow identifying very old TEs. Using this method, called Duster, they improved the amount of annotated TEs in the model plant Arabidopsis thaliana by 20%, pushing the part of this genome occupied by TEs up from 40 to almost 50%. They further observed that these very old Duster-specific TEs (i.e., TEs that are only detected by Duster) are, among other properties, close to genes (much more than recent TEs), not targeted by small RNA pathways, and highly associated with conserved regions across the rosid family. In addition, they are highly associated with flowering or stress response genes, and may be involved through exaptation in the evolution of responses to environmental changes. TEs are not just selfish elements: more and more studies have shown their key role in the evolution of their hosts, and tools such as Duster will help us better understand their impact.
 

References

Baud, A., Wan, M., Nouaud, D., Francillonne, N., Anxolabéhère, D. and Quesneville, H. (2021). Traces of transposable elements in genome dark matter co-opted by flowering gene regulation networks. bioRxiv, 547877, ver. 5 peer-reviewed and recommended by PCI Genomics.doi: https://doi.org/10.1101/547877
 
Bourque, G., Burns, K.H., Gehring, M. et al. (2018) Ten things you should know about transposable elements. Genome Biology 19:199. doi: https://doi.org/10.1186/s13059-018-1577-z
 
Goerner-Potvin, P., Bourque, G. Computational tools to unmask transposable elements. Nature Reviews Genetics 19:688–704 (2018) https://doi.org/10.1038/s41576-018-0050-x
 
Jangam, D., Feschotte, C. and Betrán, E. (2017) Transposable element domestication as an adaptation to evolutionary conflicts. Trends in Genetics 33:817-831. doi: https://doi.org/10.1016/j.tig.2017.07.011
 
Vitte, C., Panaud, O. and Quesneville, H. (2007) LTR retrotransposons in rice (Oryza sativa, L.): recent burst amplifications followed by rapid DNA loss. BMC Genomics 8:218. doi: https://doi.org/10.1186/1471-2164-8-218
 
Xiong, Y. and Eickbush, T. H. (1988) Similarity of reverse transcriptase-like sequences of viruses, transposable elements, and mitochondrial introns. Molecular Biology and Evolution 5: 675–690. doi: https://doi.org/10.1093/oxfordjournals.molbev.a040521

Traces of transposable element in genome dark matter co-opted by flowering gene regulation networksAgnes Baud, Mariene Wan, Danielle Nouaud, Nicolas Francillonne, Dominique Anxolabehere, Hadi Quesneville<p>Transposable elements (TEs) are mobile, repetitive DNA sequences that make the largest contribution to genome bulk. They thus contribute to the so-called 'dark matter of the genome', the part of the genome in which nothing is immediately recogn...Bioinformatics, Evolutionary genomics, Functional genomics, Plants, Structural genomics, Viruses and transposable elementsFrancois SabotAnonymous, Josep Casacuberta2020-04-07 17:12:12 View
06 May 2022
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A deep dive into genome assemblies of non-vertebrate animals

Diving, and even digging, into the wild jungle of annotation pathways for non-vertebrate animals

Recommended by based on reviews by Yann Bourgeois, Cécile Monat, Valentina Peona and Benjamin Istace

In their paper, Guiglielmoni et al. propose we pick up our snorkels and palms and take "A deep dive into genome assemblies of non-vertebrate animals" (1). Indeed, while numerous assembly-related tools were developed and tested for human genomes (or at least vertebrates such as mice), very few were tested on non-vertebrate animals so far. Moreover, most of the benchmarks are aimed at raw assembly tools, and very few offer a guide from raw reads to an almost finished assembly, including quality control and phasing.

This huge and exhaustive review starts with an overview of the current sequencing technologies, followed by the theory of the different approaches for assembly and their implementation. For each approach, the authors present some of the most representative tools, as well as the limits of the approach.

The authors additionally present all the steps required to obtain an almost complete assembly at a chromosome-scale, with all the different technologies currently available for scaffolding, QC, and phasing, and the way these tools can be applied to non-vertebrates animals. Finally, they propose some useful advice on the choice of the different approaches (but not always tools, see below), and advocate for a robust genome database with all information on the way the assembly was obtained.

This review is a very complete one for now and is a very good starting point for any student or scientist interested to start working on genome assembly, from either model or non-model organisms. However, the authors do not provide a list of tools or a benchmark of them as a recommendation. Why? Because such a proposal may be obsolete in less than a year.... Indeed, with the explosion of the 3rd generation of sequencing technology, assembly tools (from different steps) are constantly evolving, and their relative performance increases on a monthly basis. In addition, some tools are really efficient at the time of a review or of an article, but are not further developed later on, and thus will not evolve with the technology. We have all seen it with wonderful tools such as Chiron (2) or TopHat (3), which were very promising ones, but cannot be developed further due to the stop of the project, the end of the contract of the post-doc in charge of the development, or the decision of the developer to switch to another paradigm. Such advice would, therefore, need to be constantly updated.

Thus, the manuscript from Guiglielmoni et al will be an almost intemporal one (up to the next sequencing revolution at last), and as they advocated for a more informed genome database, I think we should consider a rolling benchmarking system (tools, genome and sequence dataset) allowing to keep the performance of the tools up-to-date, and to propose the best set of assembly tools for a given type of genome.

References

1. Guiglielmoni N, Rivera-Vicéns R, Koszul R, Flot J-F (2022) A Deep Dive into Genome Assemblies of Non-vertebrate Animals. Preprints, 2021110170, ver. 3 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.20944/preprints202111.0170

2. Teng H, Cao MD, Hall MB, Duarte T, Wang S, Coin LJM (2018) Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience, 7, giy037. https://doi.org/10.1093/gigascience/giy037

3. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25, 1105–1111. https://doi.org/10.1093/bioinformatics/btp120

A deep dive into genome assemblies of non-vertebrate animalsNadège Guiglielmoni, Ramón Rivera-Vicéns, Romain Koszul, Jean-François Flot<p style="text-align: justify;">Non-vertebrate species represent about ∼95% of known metazoan (animal) diversity. They remain to this day relatively unexplored genetically, but understanding their genome structure and function is pivotal for expan...Bioinformatics, Evolutionary genomicsFrancois Sabot Valentina Peona, Benjamin Istace, Cécile Monat, Yann Bourgeois2021-11-10 17:47:31 View