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

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

This manuscript describes the ongoing work and plans of Biogenome Portugal: a new network of researchers in the Portuguese biodiversity genomics community. The aims of this network are to jointly train scientists in ecology and evolution, generate new knowledge and understanding of Portuguese biodiversity, and better engage with the public and with international researchers, so as to advance conservation efforts in the region. In collaboration across disciplines and institutions, they are also contributing to the European Reference Genome Atlas (ERGA): a massive scientific effort, seeking to eventually produce reference-quality genomes for all species in the European continent (Mc Cartney et al. 2023).

The manuscript centers around six iconic and/or severely threatened species, whose range extends across parts of what is today considered Portuguese territory. Via the Portugal chapter of ERGA (ERGA-Portugal), the researchers will generate high-quality genome sequences from these species. The species are the Iberian hare, the Azores laurel, the Black wheatear, the Portuguese crowberry, the Cave ground beetle and the Iberian minnowcarp. In ignorance of human-made political borders, some of these species also occupy large parts of the rest of the Iberian peninsula, highlighting the importance of transnational collaboration in biodiversity efforts. The researchers extracted samples from members of each of these species, and are building reference genome sequences from them. In some cases, these sequences will also be co-analyzed with additional population genomic data from the same species or genetic data from cohabiting species. The researchers aim to answer a variety of ecological and evolutionary questions using this information, including how genetic diversity is being affected by the destruction of their habitat, and how they are being forced to adapt as a consequence of the climate emergency.

The authors did a very good job in providing a justification for the choice of pilot species, a thorough methodological overview of current work, and well thought-out plans for future analyses once the genome sequences are available for study. The authors also describe plans for networking and training activities to foster a well-connected Portuguese biodiversity genomics community.

Applying a genomic analysis lens is important for understanding the ever faster process of devastation of our natural world. Governments and corporations around the globe are destroying nature at ever larger scales (Diaz et al. 2019). They are also destabilizing the climatic conditions on which life has existed for thousands of years (Trisos et al. 2020). Thus, genetic diversity is decreasing faster than ever in human history, even when it comes to non-threatened species (Exposito-Alonso et al. 2022), and these decreases are disrupting ecological processes worldwide (Richardson et al. 2023). This, in turn, is threatening the conditions on which the stability of our societies rest (Gardner and Bullock 2021). The efforts of Biogenome Portal and ERGA-Portugal will go a long way in helping us understand in greater detail how this process is unfolding in Portuguese territories.

References

Díaz, Sandra, et al. "Pervasive human-driven decline of life on Earth points to the need for transformative change." Science 366.6471 (2019): eaax3100. https://doi.org/10.1126/science.aax3100

Exposito-Alonso, Moises, et al. "Genetic diversity loss in the Anthropocene." Science 377.6613 (2022): 1431-1435. https://doi.org/10.1126/science.abn5642

Gardner, Charlie J., and James M. Bullock. "In the climate emergency, conservation must become survival ecology." Frontiers in Conservation Science 2 (2021): 659912. https://doi.org/10.3389/fcosc.2021.659912

Mc Cartney, Ann M., et al. "The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics." bioRxiv (2023): 2023-09, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.32942/X20W3Q

Richardson, Katherine, et al. "Earth beyond six of nine planetary boundaries." Science Advances 9.37 (2023): eadh2458. https://doi.org/10.1126/sciadv.adh2458

Trisos, Christopher H., Cory Merow, and Alex L. Pigot. "The projected timing of abrupt ecological disruption from climate change." Nature 580.7804 (2020): 496-501. https://doi.org/10.1038/s41586-020-2189-9

Third-generation sequencing has revolutionised de novo genome assembly. Thanks to this technology, genome reference sequences have evolved from fragmented drafts to gapless, telomere-to-telomere genome assemblies. Long reads produced by Oxford Nanopore and PacBio technologies can span structural variants and resolve complex repetitive regions such as centromeres, unlocking previously inaccessible genomic information. Nowadays, many research groups can afford to sequence the genome of their working model using long reads. Nevertheless, genome assembly poses a significant computational challenge. Read length, quality, coverage and genomic features such as repeat content can affect assembly contiguity, accuracy, and completeness in almost unpredictable ways. Consequently, there is no best universal software or protocol for this task. Producing a high-quality assembly requires chaining several tools into pipelines and performing extensive comparisons between the assemblies obtained by different tool combinations to decide which one is the best. This task can be extremely challenging, as the number of tools available rises very rapidly, and thorough benchmarks cannot be updated and published at such a fast pace. 

In their paper, Orjuela and collaborators present CulebrONT [1], a universal pipeline that greatly contributes to overcoming these challenges and facilitates long-read genome assembly for all taxonomic groups. CulebrONT incorporates six commonly used assemblers and allows to perform assembly, circularization (if needed), polishing, and evaluation in a simple framework. One important aspect of CulebrONT is its modularity, which allows the activation or deactivation of specific tools, giving great flexibility to the user. Nevertheless, possibly the best feature of CulebrONT is the opportunity to benchmark the selected tool combinations based on the excellent report generated by the pipeline. This HTML report aggregates the output of several tools for quality evaluation of the assemblies (e.g. BUSCO [2] or QUAST [3]) generated by the different assemblers, in addition to the running time and configuration parameters. Such information is of great help to identify the best-suited pipeline, as exemplified by the authors using four datasets of different taxonomic origins. Finally, CulebrONT can handle multiple samples in parallel, which makes it a good solution for laboratories looking for multiple assemblies on a large scale. 

References

1. Orjuela J, Comte A, Ravel S, Charriat F, Vi T, Sabot F, Cunnac S (2022) CulebrONT: a streamlined long reads multi-assembler pipeline for prokaryotic and eukaryotic genomes. bioRxiv, 2021.07.19.452922, ver. 5 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.07.19.452922

2. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM (2015) BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics, 31, 3210–3212. https://doi.org/10.1093/bioinformatics/btv351

3. Gurevich A, Saveliev V, Vyahhi N, Tesler G (2013) QUAST: quality assessment tool for genome assemblies. Bioinformatics, 29, 1072–1075. https://doi.org/10.1093/bioinformatics/btt086

The advent of High Throughput Sequencing (HTS) since the last decade has revealed previously unsuspected diversity of viruses as well as their (sometimes) unexpected presence in some healthy individuals. These results demonstrate that genomics offers a powerful tool for studying viruses at the individual level, allowing an in-depth inventory of those that are infecting an organism. Such approaches make it possible to study viromes with an unprecedented level of detail, both qualitative and quantitative, which opens new venues for analyses of viruses of humans, animals and plants. Consequently, the diagnostic field is using more and more HTS, fueling the need for efficient and reliable bioinformatics tools. 

Many such tools have already been developed, but in plant disease diagnostics, validation of the bioinformatics pipelines used for the detection of viruses in HTS datasets is still in its infancy. There is an urgent need for benchmarking the different tools and algorithms using well-designed reference datasets generated for this purpose. This is a crucial step to move forward and to improve existing solutions toward well-standardized bioinformatics protocols. This context has led to the creation of the Plant Health Bioinformatics Network (PHBN), a Euphresco network project aiming to build a bioinformatics community working on plant health. One of their objectives is to provide researchers with open-access reference datasets allowing to compare and validate virus detection pipelines. 

In this framework, Tamisier et al. [1] present real, semi-artificial, and completely artificial datasets, each aimed at addressing challenges that could affect virus detection. These datasets comprise real RNA-seq reads from virus-infected plants as well as simulated virus reads. Such a work, providing open-access datasets for benchmarking bioinformatics tools, should be encouraged as they are key to software improvement as demonstrated by the well-known success story of the protein structure prediction community: their pioneer community-wide effort, called Critical Assessment of protein Structure Prediction (CASP)[2], has been providing research groups since 1994 with an invaluable way to objectively test their structure prediction methods, thereby delivering an independent assessment of state-of-art protein-structure modelling tools. Following this success, many other bioinformatic community developed similar “competitions”, such as RNA-puzzles [3] to predict RNA structures, Critical Assessment of Function Annotation [4] to predict gene functions, Critical Assessment of Prediction of Interactions [5] to predict protein-protein interactions, Assemblathon [6] for genome assembly, etc. These are just a few examples from a long list of successful initiatives. Such efforts enable rigorous assessments of tools, stimulate the developers’ creativity, but also provide user communities with a state-of-art evaluation of available tools.

Inspired by these success stories, the authors propose a “VIROMOCK challenge” [7], asking researchers in the field to test their tools and to provide feedback on each dataset through a repository. This initiative, if well followed, will undoubtedly improve the field of virus detection in plants, but also probably in many other organisms. This will be a major contribution to the field of viruses, leading to better diagnostics and, consequently, a better understanding of viral diseases, thus participating in promoting human, animal and plant health.   

References

[1] Tamisier, L., Haegeman, A., Foucart, Y., Fouillien, N., Al Rwahnih, M., Buzkan, N., Candresse, T., Chiumenti, M., De Jonghe, K., Lefebvre, M., Margaria, P., Reynard, J.-S., Stevens, K., Kutnjak, D. and Massart, S. (2021) Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection. Zenodo, 4273791, version 4 peer-reviewed and recommended by Peer community in Genomics. doi: https://doi.org/10.5281/zenodo.4273791

[2] Critical Assessment of protein Structure Prediction” (CASP) - https://en.wikipedia.org/wiki/CASP

[3] RNA-puzzles - https://www.rnapuzzles.org

[4] Critical Assessment of Function Annotation (CAFA) - https://en.wikipedia.org/wiki/Critical_Assessment_of_Function_Annotation

[5] Critical Assessment of Prediction of Interactions (CAPI) - https://en.wikipedia.org/wiki/Critical_Assessment_of_Prediction_of_Interactions

[6] Assemblathon - https://assemblathon.org

[7] VIROMOCK challenge - https://gitlab.com/ilvo/VIROMOCKchallenge