Latest recommendations
Id | Title * | Authors * ▲ | Abstract * | Picture * | Thematic fields * | Recommender | Reviewers | Submission date | |
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10 Mar 2025
![]() hdmax2, an R package to perform high dimension mediation analysisFlorence Pittion, Basile Jumentier, Aurélie Nakamura, Johanna Lepeule, Olivier François, Magali Richard https://hal.science/hal-04658960High-dimensional mediation analysis: Unraveling pathways linking external exposures to health outcomesRecommended by Guillaume Laval based on reviews by Pierre Neuvial and Gaspard KernerPittion et al. (2025) introduce an R package called hdmax2, which implements an enhanced version of the “High-Dimensional Mediation Analysis using the Max-Squared” (HDMAX2) method originally proposed by Jumentier et al. (2023) for high-dimensional mediation analysis. The goal of mediation analysis is to quantify the indirect effect of a variable M in the causal relationship between exposure X and outcome Y. The fundamental concept behind HDMAX2 methods is to use a latent factor mixed model to estimate the effects of unobserved confounders and a max-squared test to identify significant mediators. The HDMAX2 method represents a significant advancement in the case of high-dimensional mediation, such as DNA methylation or gene expression analysis, where the number of mediators often far exceeds the sample size. The main contributions of this article are the implementation of the HDMAX2 method as an R package, and an extension of the original method to binary outcomes and to binary, categorical, and multivariate exposures, as opposed to only continuous variables. The package includes visualization tools, helper functions for mediator selection, and options for handling multivariate exposures. A key strength of the package lies in its versatility. The new package, hdmax2, accommodates a variety of data types. This flexibility makes it a valuable tool for researchers analyzing high-throughput molecular data. Finally to illustrate this flexibility, the authors present two case studies that were not described in the Jumentier et al. (2023) analysis. In the first case study, the authors employed mediation analysis to assess the potential causal role of DNA methylation in the pathway linking the HER2 status of breast cancer (a marker for an aggressive breast cancer subtype) to a survival risk score, which was derived from a six-gene expression signature and is inversely correlated with patient survival. In the second case study, the authors conducted mediation analysis to explore the role of gene expression in the pathway linking patient gender to the occurrence of multiple sclerosis specific subtypes: clinically isolated syndrome and relapsing-remitting multiple sclerosis. These illustrate the relevance of hdmax2 to study the transcriptome and the methylome. In conclusion, the hdmax2 R package will be invaluable for handling high-dimensional molecular data in the study of the intricate pathways through which exposures influence health outcomes.
References Jumentier B, Barrot C-C, Estavoyer M, Tost J, Heude B, François O, Lepeule J (2023) High-dimensional mediation analysis: A new method applied to maternal smoking, placental DNA methylation, and birth outcomes. Environmental Health Perspectives, 131, 047011. https://doi.org/10.1289/EHP11559 Pittion F, Jumentier B, Nakamura A, Lepeule J, Francois O, Richard M (2025) hdmax2, an R package to perform high dimension mediation analysis. HAL, ver. 4 peer-reviewed and recommended by PCI Genomics. https://hal.science/hal-04658960 | hdmax2, an R package to perform high dimension mediation analysis | Florence Pittion, Basile Jumentier, Aurélie Nakamura, Johanna Lepeule, Olivier François, Magali Richard | <p>Mediation analysis plays a crucial role in epidemiology, unraveling the intricate pathways through which exposures exert influence on health outcomes. Recent advances in high-throughput sequencing techniques have generated growing interest in a... | ![]() | Bioinformatics | Guillaume Laval | 2024-09-10 11:49:02 | View | |
07 Feb 2023
RAREFAN: A webservice to identify REPINs and RAYTs in bacterial genomesFrederic Bertels, Julia von Irmer, Carsten Fortmann-Grote https://doi.org/10.1101/2022.05.22.493013A workflow for studying enigmatic non-autonomous transposable elements across bacteriaRecommended by Gavin DouglasRepetitive extragenic palindromic sequences (REPs) are common repetitive elements in bacterial genomes (Gilson et al., 1984; Stern et al., 1984). In 2011, Bertels and Rainey identified that REPs are overrepresented in pairs of inverted repeats, which likely form hairpin structures, that they referred to as “REP doublets forming hairpins” (REPINs). Based on bioinformatics analyses, they argued that REPINs are likely selfish elements that evolved from REPs flanking particular transposes (Bertels and Rainey, 2011). These transposases, so-called REP-associated tyrosine transposases (RAYTs), were known to be highly associated with the REP content in a genome and to have characteristic upstream and downstream flanking REPs (Nunvar et al., 2010). The flanking REPs likely enable RAYT transposition, and their horizontal replication is physically linked to this process. In contrast, Bertels and Rainey hypothesized that REPINs are selfish elements that are highly replicated due to the similarity in arrangement to these RAYT-flanking REPs, but independent of RAYT transposition and generally with no impact on bacterial fitness (Bertels and Rainey, 2011). This last point was especially contentious, as REPINs are highly conserved within species (Bertels and Rainey, 2023), which is unusual for non-beneficial bacterial DNA (Mira et al., 2001). Bertels and Rainey have since refined their argument to be that REPINs must provide benefits to host cells, but that there are nonetheless signatures of intragenomic conflict in genomes associated with these elements (Bertels and Rainey, 2023). These signatures reflect the divergent levels of selections driving REPIN distribution: selection at the level of each DNA element and selection on each individual bacterium. I found this observation particularly interesting as I and my colleague recently argued that these divergent levels of selection, and the interaction between them, is key to understanding bacterial pangenome diversity (Douglas and Shapiro, 2021). REPINs could be an excellent system for investigating these levels of selection across bacteria more generally. The problem is that REPINs have not been widely characterized in bacterial genomes, partially because no bioinformatic workflow has been available for this purpose. To address this problem, Fortmann-Grote et al. (2023) developed RAREFAN, which is a web server for identifying RAYTs and associated REPINs in a set of input genomes. The authors showcase their tool by applying it to 49 Stenotrophomonas maltophilia genomes and providing examples of how to identify and assess RAYT-REPIN hits. The workflow requires several manual steps, but nonetheless represents a straightforward and standardized approach. Overall, this workflow should enable RAYTs and REPINs to be identified across diverse bacterial species, which will facilitate further investigation into the mechanisms driving their maintenance and spread. References Bertels F, Rainey PB (2023) Ancient Darwinian replicators nested within eubacterial genomes. BioEssays, 45, 2200085. https://doi.org/10.1002/bies.202200085 Bertels F, Rainey PB (2011) Within-Genome Evolution of REPINs: a New Family of Miniature Mobile DNA in Bacteria. PLOS Genetics, 7, e1002132. https://doi.org/10.1371/journal.pgen.1002132 Douglas GM, Shapiro BJ (2021) Genic Selection Within Prokaryotic Pangenomes. Genome Biology and Evolution, 13, evab234. https://doi.org/10.1093/gbe/evab234 Fortmann-Grote C, Irmer J von, Bertels F (2023) RAREFAN: A webservice to identify REPINs and RAYTs in bacterial genomes. bioRxiv, 2022.05.22.493013, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.05.22.493013 Gilson E, Clément J m., Brutlag D, Hofnung M (1984) A family of dispersed repetitive extragenic palindromic DNA sequences in E. coli. The EMBO Journal, 3, 1417–1421. https://doi.org/10.1002/j.1460-2075.1984.tb01986.x Mira A, Ochman H, Moran NA (2001) Deletional bias and the evolution of bacterial genomes. Trends in Genetics, 17, 589–596. https://doi.org/10.1016/S0168-9525(01)02447-7 Nunvar J, Huckova T, Licha I (2010) Identification and characterization of repetitive extragenic palindromes (REP)-associated tyrosine transposases: implications for REP evolution and dynamics in bacterial genomes. BMC Genomics, 11, 44. https://doi.org/10.1186/1471-2164-11-44 Stern MJ, Ames GF-L, Smith NH, Clare Robinson E, Higgins CF (1984) Repetitive extragenic palindromic sequences: A major component of the bacterial genome. Cell, 37, 1015–1026. https://doi.org/10.1016/0092-8674(84)90436-7 | RAREFAN: A webservice to identify REPINs and RAYTs in bacterial genomes | Frederic Bertels, Julia von Irmer, Carsten Fortmann-Grote | <p style="text-align: justify;">Compared to eukaryotes, repetitive sequences are rare in bacterial genomes and usually do not persist for long. Yet, there is at least one class of persistent prokaryotic mobile genetic elements: REPINs. REPINs are ... | Bacteria and archaea, Bioinformatics, Evolutionary genomics, Viruses and transposable elements | Gavin Douglas | 2022-06-07 08:21:34 | View | ||
12 Aug 2024
![]() A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and DatabasesF. Tesson, R. Planel, A. Egorov, H. Georjon, H. Vaysset, B. Brancotte, B. Néron, E. Mordret, A Bernheim, G. Atkinson, J. Cury https://doi.org/10.1101/2024.01.25.577194DefenseFinder update advances prokaryotic antiviral system researchRecommended by Sishuo WangProkaryotic antiviral systems, such as CRISPR-Cas and restriction-modification systems, provide defense against viruses through diverse mechanisms including intracellular signaling, chemical defense, and nucleotide depletion. However, bioinformatic tools and resources for identifying and cataloging these systems are still in development. The work by Tesson and colleagues (2024) presents a significant advancement in understanding the defense systems of prokaryotes. The authors have provided an update of their previously developed online service DefenseFinder, which helps to detect known antiviral systems in prokaryotes genomes (Tesson et al. 2022), plus three new databases: one serving as a wiki for defense systems, one housing experimentally determined and AlphaFold2-predicted structures, and a third one consisting of precomputed results from DefenseFinder. Users can analyze their own data through the user-friendly interface. This initiative will help promote a community-driven approach to sharing knowledge on antiphage systems, which is very useful given their complexity and diversity. The authors' commitment to maintaining an up-to-date platform and encouraging community contributions makes this resource accessible to both newcomers and experienced researchers in the rapidly growing field of defense system research. Experienced researchers will find that there are ways to contribute to the future expansion of these databases, while new users can easily access and use the platform. Overall, the updated DefenseFinder, as well as the other databases introduced in the manuscript, are well-suited for researchers (both dry- and wet-lab ones) interested in antiphage defense. I am hopeful that the efforts by the authors will collectively create valuable online resources for researchers in this field and will foster an environment of open science and accessible bioinformatics tools.
References Tesson F, Hervé A, Mordret E, Touchon M, d’Humières C, Cury J, Bernheim A (2022) Systematic and quantitative view of the antiviral arsenal of prokaryotes. Nature Communications, 13, 2561. https://doi.org/10.1038/s41467-022-30269-9 Tesson F, Planel R, Egorov A, Georjon H, Vaysset H, Brancotte B, Néron B, Mordret E, Atkinson G, Bernheim A, Cury J (2024) A comprehensive resource for exploring antiphage defense: DefenseFinder webservice, wiki and databases. bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2024.01.25.577194 | A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases | F. Tesson, R. Planel, A. Egorov, H. Georjon, H. Vaysset, B. Brancotte, B. Néron, E. Mordret, A Bernheim, G. Atkinson, J. Cury | <p>In recent years, a vast number of novel antiphage defense mechanisms were uncovered. To<br>facilitate the exploration of mechanistic, ecological, and evolutionary aspects related to antiphage defense systems, we released DefenseFinder in 2021 (... | ![]() | Bacteria and archaea, Bioinformatics, Evolutionary genomics, Viruses and transposable elements | Sishuo Wang | 2024-04-17 18:30:32 | View | |
10 Jul 2023
![]() SNP discovery by exome capture and resequencing in a pea genetic resource collectionG. Aubert, J. Kreplak, M. Leveugle, H. Duborjal, A. Klein, K. Boucherot, E. Vieille, M. Chabert-Martinello, C. Cruaud, V. Bourion, I. Lejeune-Hénaut, M.L. Pilet-Nayel, Y. Bouchenak-Khelladi, N. Francillonne, N. Tayeh, J.P. Pichon, N. Rivière, J. Burstin https://doi.org/10.1101/2022.08.03.502586The value of a large Pisum SNP datasetRecommended by Wanapinun Nawae based on reviews by Rui Borges and 1 anonymous reviewerOne important goal of modern genetics is to establish functional associations between genotype and phenotype. Single nucleotide polymorphisms (SNPs) are numerous and widely distributed in the genome and can be obtained from nucleic acid sequencing (1). SNPs allow for the investigation of genetic diversity, which is critical for increasing crop resilience to the challenges posed by global climate change. The associations between SNPs and phenotypes can be captured in genome-wide association studies. SNPs can also be used in combination with machine learning, which is becoming more popular for predicting complex phenotypic traits like yield and biotic and abiotic stress tolerance from genotypic data (2). The availability of many SNP datasets is important in machine learning predictions because this approach requires big data to build a comprehensive model of the association between genotype and phenotype. Aubert and colleagues have studied, as part of the PeaMUST project, the genetic diversity of 240 Pisum accessions (3). They sequenced exome-enriched genomic libraries, a technique that enables the identification of high-density, high-quality SNPs at a low cost (4). This technique involves capturing and sequencing only the exonic regions of the genome, which are the protein-coding regions. A total of 2,285,342 SNPs were obtained in this study. The analysis of these SNPs with the annotations of the genome sequence of one of the studied pea accessions (5) identified a number of SNPs that could have an impact on gene activity. Additional analyses revealed 647,220 SNPs that were unique to individual pea accessions, which might contribute to the fitness and diversity of accessions in different habitats. Phylogenetic and clustering analyses demonstrated that the SNPs could distinguish Pisum germplasms based on their agronomic and evolutionary histories. These results point out the power of selected SNPs as markers for identifying Pisum individuals. Overall, this study found high-quality SNPs that are meaningful in a biological context. This dataset was derived from a large set of germplasm and is thus particularly useful for studying genotype-phenotype associations, as well as the diversity within Pisum species. These SNPs could also be used in breeding programs to develop new pea varieties that are resilient to abiotic and biotic stressors. References
https://doi.org/10.1139/gen-2021-005
https://doi.org/10.1186/s12870-022-03559-z
https://doi.org/10.1101/2022.08.03.502586
https://doi.org/10.1534/g3.115.018564
| SNP discovery by exome capture and resequencing in a pea genetic resource collection | G. Aubert, J. Kreplak, M. Leveugle, H. Duborjal, A. Klein, K. Boucherot, E. Vieille, M. Chabert-Martinello, C. Cruaud, V. Bourion, I. Lejeune-Hénaut, M.L. Pilet-Nayel, Y. Bouchenak-Khelladi, N. Francillonne, N. Tayeh, J.P. Pichon, N. Rivière, J. B... | <p style="text-align: justify;"><strong>Background & Summary</strong></p> <p style="text-align: justify;">In addition to being the model plant used by Mendel to establish genetic laws, pea (<em>Pisum sativum</em> L., 2n=14) is a major pulse c... | ![]() | Plants, Population genomics | Wanapinun Nawae | 2022-11-29 09:29:06 | View | |
09 Aug 2023
![]() Efficient k-mer based curation of raw sequence data: application in Drosophila suzukiiGautier Mathieu https://doi.org/10.1101/2023.04.18.537389Decontaminating reads, not contigsRecommended by Nicolas Galtier based on reviews by Marie Cariou and Denis BaurainContamination, the presence of foreign DNA sequences in a sample of interest, is currently a major problem in genomics. Because contamination is often unavoidable at the experimental stage, it is increasingly recognized that the processing of high-throughput sequencing data must include a decontamination step. This is usually performed after the many sequence reads have been assembled into a relatively small number of contigs. Dubious contigs are then discarded based on their composition (e.g. GC-content) or because they are highly similar to a known piece of DNA from a foreign species. Here [1], Mathieu Gautier explores a novel strategy consisting in decontaminating reads, not contigs. Why is this promising? Assembly programs and algorithms are complex, and it is not easy to predict, or monitor, how they handle contaminant reads. Ideally, contaminant reads will be assembled into obvious contaminant contigs. However, there might be more complex situations, such as chimeric contigs with alternating genuine and contaminant segments. Decontaminating at the read level, if possible, should eliminate such unfavorable situations where sequence information from contaminant and target samples are intimately intertwined by an assembler. To achieve this aim, Gautier proposes to use methods initially designed for the analysis of metagenomic data. This is pertinent since the decontamination process involves considering a sample as a mixture of different sources of DNA. The programs used here, CLARK and CLARK-L, are based on so-called k-mer analysis, meaning that the similarity between a read to annotate and a reference sequence is measured by how many sub-sequences (of length 31 base pairs for CLARK and 27 base pairs for CLARK-L) they share. This is notoriously more efficient than traditional sequence alignment algorithms when it comes to comparing a very large number of (most often unrelated) sequences. This is, therefore, a reference-based approach, in which the reads from a sample are assigned to previously sequenced genomes based on k-mer content. This original approach is here specifically applied to the case of Drosophila suzukii, an invasive pest damaging fruit production in Europe and America. Fortunately, Drosophila is a genus of insects with abundant genomic resources, including high-quality reference genomes in dozens of species. Having calibrated and validated his pipeline using data sets of known origins, Gautier quantifies in each of 258 presumed D. suzukii samples the proportion of reads that likely belong to other species of fruit flies, or to fruit fly-associated microbes. This proportion is close to one in 16 samples, which clearly correspond to mis-labelled individuals. It is non-negligible in another ~10 samples, which really correspond to D. suzukii individuals. Most of these reads of unexpected origin are contaminants and should be filtered out. Interestingly, one D. suzukii sample contains a substantial proportion of reads from the closely related D. subpulchera, which might instead reflect a recent episode of gene flow between these two species. The approach, therefore, not only serves as a crucial technical step, but also has the potential to reveal biological processes. Gautier's thorough, well-documented work will clearly benefit the ongoing and future research on D. suzuki, and Drosophila genomics in general. The author and reviewers rightfully note that, like any reference-based approach, this method is heavily dependent on the availability and quality of reference genomes - Drosophila being a favorable case. Building the reference database is a key step, and the interpretation of the output can only be made in the light of its content and gaps, as illustrated by Gautier's careful and detailed discussion of his numerous results. This pioneering study is a striking demonstration of the potential of metagenomic methods for the decontamination of high-throughput sequence data at the read level. The pipeline requires remarkably few computing resources, ensuring low carbon emission. I am looking forward to seeing it applied to a wide range of taxa and samples.
Reference [1] Gautier Mathieu. Efficient k-mer based curation of raw sequence data: application in Drosophila suzukii. bioRxiv, 2023.04.18.537389, ver. 2, peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.04.18.537389 | Efficient k-mer based curation of raw sequence data: application in *Drosophila suzukii* | Gautier Mathieu | <p>Several studies have highlighted the presence of contaminated entries in public sequence repositories, calling for special attention to the associated metadata. Here, we propose and evaluate a fast and efficient kmer-based approach to assess th... | ![]() | Bioinformatics, Population genomics | Nicolas Galtier | 2023-04-20 22:05:13 | View | |
05 May 2021
![]() A primer and discussion on DNA-based microbiome data and related bioinformatics analysesGavin M. Douglas and Morgan G. I. Langille https://doi.org/10.31219/osf.io/3dybgA hitchhiker’s guide to DNA-based microbiome analysisRecommended by Danny IonescuIn 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 analyses | Gavin 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, Metagenomics | Danny Ionescu | 2021-02-17 00:26:46 | View | |
21 Aug 2024
![]() MATEdb2, a collection of high-quality metazoan proteomes across the Animal Tree of Life to speed up phylogenomic studiesGemma I. Martínez-Redondo, Carlos Vargas-Chávez, Klara Eleftheriadi, Lisandra Benítez-Álvarez, Marçal Vázquez-Valls, Rosa Fernández https://doi.org/10.1101/2024.02.21.581367MATEdb2 is a valuable phylogenomics resource across MetazoaRecommended by Philipp SchifferMartínez-Redondo and colleagues (2024) present MATEdb2, which provides the scientific community with Metazoa proteomes that have been predicted and annotated in a standardised way. The authors improved the taxon representation from the earlier MATEdb and their current database has a strong focus on Arthropoda, Annelida, and Mollusca. In particular, for the latter two groups not many high-quality reference genomes are available. Standardisation of the prediction and annotation process in a reproducible pipeline, as integrated in MATEdb2, is of great value, in particular to infer phylogenies as correctly as possible. Thus, I am sure that MATEdb2 will be an excellent go-to resource for phylogenomic studies, as well as for probing the biology of new, obscure species, especially marine ones.
| MATEdb2, a collection of high-quality metazoan proteomes across the Animal Tree of Life to speed up phylogenomic studies | Gemma I. Martínez-Redondo, Carlos Vargas-Chávez, Klara Eleftheriadi, Lisandra Benítez-Álvarez, Marçal Vázquez-Valls, Rosa Fernández | <p>Recent advances in high throughput sequencing have exponentially increased the number of genomic data available for animals (Metazoa) in the last decades, with high-quality chromosome-level genomes being published almost daily. Nevertheless, ge... | ![]() | Arthropods, Bioinformatics, Evolutionary genomics, Marine invertebrates, Terrestrial invertebrates | Philipp Schiffer | 2024-03-04 11:37:21 | View | |
11 Sep 2023
![]() 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 https://doi.org/10.1101/2022.06.17.496544A pipeline to select SARS-CoV-2 sequences for reliable phylodynamic analysesRecommended by Emmanuelle LeratPhylodynamic 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 sequences | Gonché 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 genomics | Emmanuelle Lerat | 2022-12-12 09:04:01 | View | |
28 Nov 2024
![]() Factors influencing the accuracy and precision in dating single gene treesGuillaume Louvel and Hugues Roest Crollius https://doi.org/10.1101/2020.08.24.264671Dating single gene trees in the age of phylogenomicsRecommended by Federico Hoffmann based on reviews by Sishuo Wang, David Duchêne and 1 anonymous reviewerDating evolutionary trees is a critical task that allows us to connect biological history to ecological and geological events, helping us explore connections between environmental change and genetic innovations. The central idea behind these techniques is to link changes at the sequence level to divergence times, under the general assumption that substitutions accumulate steadily over time. So, sequences that diverged earlier are expected to be more different than sequences that diverged more recently. For a number of biological and statistical reasons, the relationship between sequence divergence and time is not linear, so it is not always the case that more divergent sequences have accumulated more substitutions than less divergent ones. In the case of organismal-level divergences, a natural approach to mitigate these challenges is to incorporate as many genes as possible into the analyses. However, this route is not available when we are focusing our interest on a single gene or a gene family. Thus, exploring how different features of single gene trees impact the accuracy and precision of divergence time estimates is of interest. In this study, Louvel and Roest Crollius (2024), select a well-studied group of mammals, primates, extract single copy genes from their genomes, and explore how different factors such as alignment size, evolutionary rate variation and discordance between the gene and species trees impact divergence time estimates. There are many strengths of this study. The central ones are the number of factors considered and the transparent discussion of the limitations. In this regard, the study is an elegant combination of empirical and simulated data. Some of the results match intuitive expectations. For example, the authors find that longer alignments are more informative than shorter ones, that differences in evolutionary rate among branches lead to loss in precision, and that slow-evolving genes perform worse. Intriguingly, they also find differences in performance among genes with different ontologies. The empirical data used in this study is limited to a single group, and generally considers genes that have apparently remained as single copies. Accordingly, the conclusions that can be drawn are somewhat limited, calling for future studies building on and expanding the concepts of the study by Louvel and colleagues. For example, including genes that have been lost or duplicated would be of interest because changes in gene complement are a prevalent source of variation at the genome level in mammals in general (Demuth et al. 2006), and particularly in primates (Hahn et al. 2007).
References Demuth JP, De Bie T, Stajich JE, Cristianini N, Hahn MW (2006) The evolution of mammalian gene families. PLoS One, e85. https://doi.org/10.1371/journal.pone.0000085 Hahn MW, Demuth JP, Han SG (2007) Accelerated rate of gene gain and loss in primates. Genetics, 177,1941-1949. https://doi.org/10.1534/genetics.107.080077 Louvel, G and Roest Crollius, H (2024) Factors influencing the accuracy and precision in dating single gene trees. bioRxiv, ver. 6 peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/2020.08.24.264671
| Factors influencing the accuracy and precision in dating single gene trees | Guillaume Louvel and Hugues Roest Crollius | <p>Molecular dating is the inference of divergence time from genetic sequences. Knowing the time of appearance of a taxon sets the evolutionary context by connecting it with past ecosystems and species. Knowing the divergence times of gene lineage... | ![]() | Bioinformatics, Evolutionary genomics, Vertebrates | Federico Hoffmann | 2023-08-15 12:06:09 | View | |
22 Jan 2025
![]() Spatio-temporal diversity and genetic architecture of pyrantel resistance in Cylicocyclus nassatus, the most abundant horse parasiteGuillaume Sallé, Élise Courtot, Cédric Cabau, Hugues Parrinello, Delphine Serreau, Fabrice Reigner, Amandine Gesbert, Lauriane Jacquinot, Océane Lenhof, Annabelle Aimé, Valérie Picandet, Tetiana Kuzmina, Oleksandr Holovachov, Jennifer Bellaw, Martin K. Nielsen, Georg von Samson-Himmelstjerna, Sophie Valière, Marie Gislard, Jérôme Lluch, Claire Kuchly, Christophe Klopp https://doi.org/10.1101/2023.07.19.549683Genomic and transcriptomic insights into the genetic basis of anthelmintic resistance in a cyathostomin parasitic nematodeRecommended by Nicolas Pollet based on reviews by 2 anonymous reviewersParasitic worms infect billions of animals worldwide. While parasitism is now considered a context-dependent relation along a symbiosis continuum, most of these parasitic worms, also known as helminths, can cause diseases that have a significant impact (Hopkins et al. 2017; Selzer, Epe 2021). When considering livestock animals, these impacts have a high economic cost, and therefore, prophylactic drugs are widely used (Selzer and Epe 2021). Consequently, drug resistance has become increasingly common across all parasites and concerns about drug effects on non-target organisms have been raised (de Souza and Guimarães 2022). This is why understanding the relationship between parasitic worms and their animal hosts and the diseases they cause at the genetic and molecular level is high on the agenda of parasitologists (Doyle 2022). The development of genomics resources plays a pivotal role in this agenda and is at the origin of Sallé and colleagues' article (2025). The most common intestinal parasites in equids are helminths of the cyathostomin nematode complex. These are the primary parasitic cause of death in young horses and also exhibit a reduced sensitivity to anthelmintic drugs. Therefore, Sallé and colleagues embarked on the arduous journey to build a reference annotated genome of the Cylicocylus nassatus nematode. They used cutting-edge molecular genetics methods to amplify and sequence the genome of a single individual and obtained chromosomal-level contiguity using Hi-C technology for six chromosomes and an assembly of 514.7 Mbp. Remarkably, transposable elements occupy more than half of the C. nassatus genome and may have led to an increase in genome size in this nematode. In parallel, the authors built a gene catalogue using transcriptomic data, reaching a BUSCO gene completion score of 94.1% with 22,718 protein-coding genes. They quantified allele frequencies based on the resequencing of nine populations, including an ancient Egyptian worm from the 19th century, indicating a recent loss of genetic diversity in European cyathostomin even if geographical sampling was limited. They also analysed transcriptomic differences between sexes and found differences linked with drug treatment. While there may be confounding effects due to global differences between sex that could explain this finding, these results will likely fuel future transcriptomic analyses investigating the response to antiparasitic drugs. The Cylicocylus nassatus genome assembly obtained will be invaluable for studying nematode genome evolution and analysing the genetic and molecular basis of drug resistance in these parasites.
References Doyle SR (2022) Improving helminth genome resources in the post-genomic era. Trends in Parasitology, 38, 831–840. https://doi.org/10.1016/j.pt.2022.06.002 Hopkins SR, Wojdak JM, Belden LK (2017) Defensive symbionts mediate host–parasite interactions at multiple scales. Trends in Parasitology, 33, 53–64. https://doi.org/10.1016/j.pt.2016.10.003 Sallé G, Courtot É, Cabau C, Parrinello H, Serreau D, Reigner F, Gesbert A, Jacquinot L, Lenhof O, Aimé A, Picandet V, Kuzmina T, Holovachov O, Bellaw J, Nielsen MK, Samson-Himmelstjerna G von, Valière S, Gislard M, Lluch J, Kuchly C, Klopp C (2024) Spatio-temporal diversity and genetic architecture of pyrantel resistance in Cylicocyclus nassatus, the most abundant horse parasite. bioRxiv, ver. 2 peer-reviewed and recommended by PCI Genomics https://doi.org/10.1101/2023.07.19.549683 Selzer PM, Epe C (2021) Antiparasitics in animal health: quo vadis? Trends in Parasitology, 37, 77–89. https://doi.org/10.1016/j.pt.2020.09.004 de Souza RB, Guimarães JR (2022) Effects of avermectins on the environment based on its toxicity to plants and soil invertebrates–a review. Water, Air, and Soil Pollution, 233, 259. https://doi.org/10.1007/s11270-022-05744-0
| Spatio-temporal diversity and genetic architecture of pyrantel resistance in *Cylicocyclus nassatus*, the most abundant horse parasite | Guillaume Sallé, Élise Courtot, Cédric Cabau, Hugues Parrinello, Delphine Serreau, Fabrice Reigner, Amandine Gesbert, Lauriane Jacquinot, Océane Lenhof, Annabelle Aimé, Valérie Picandet, Tetiana Kuzmina, Oleksandr Holovachov, Jennifer Bellaw, Mart... | <p>Cyathostomins are a complex of 50 intestinal parasite species infecting horses and wild equids. The massive administration of modern anthelmintic drugs has increased their relative abundance in horse helminth communities and selected drug-resis... | ![]() | Terrestrial invertebrates | Nicolas Pollet | Jane Hodgkinson, Anonymous | 2023-07-27 20:45:09 | View |
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