The assessment by Brézellec (2024) of the quality of HHpred-based SARS-CoV-2 protein annotations against the traditional Pfam annotations is highly justified and valuable. HHpred’s ability to detect remote homologies offers an expanded view of viral protein similarities, potentially uncovering subtle functional mimicries that Pfam may miss due to its sensitivity limitations when dealing with divergent sequences. However, the accuracy and specificity of HHpred results can be compromised by false positives, especially when dealing with complex viral proteins that feature transmembrane or low-complexity regions prone to spurious matches.

To address this, the author made a thoughtful decision to implement a multi-step validation protocol. This approach included establishing progressively lower probability thresholds to capture weaker but biologically plausible hits, and organizing hits into “families” of similarly located alignments to validate the robustness of matches. They also cross-verified results by running SARS-CoV-2 protein queries against non-human proteomes (plants, fruit flies, bacteria, and archaea), allowing them to discern between biologically meaningful matches and potentially random alignments. By adding manual verification with InterPro domain annotations, the authors took additional steps to ensure that identified similarities were not only statistically significant but also biologically relevant.

This rigorous validation strategy adds a layer of reliability to HHpred results, demonstrating an effective maximization of sensitivity while maintaining specificity. This approach yielded biologically intriguing and previously undocumented similarities, such as between the Spike-prominin and ORF3a-GPCR, underscoring the quality and depth of the annotation process. These findings highlight a pathway for further experimental validation and illustrate the potential of HHpred to contribute high-quality insights when applied with careful quality control measures.

In summary, the decision to adopt HHpred (Gabler et al. 2020) and enhance its outputs with a robust quality validation process not only improved the depth of SARS-CoV-2 protein annotations but also established a high standard for future viral annotation projects, striking an effective balance between discovery potential and annotation quality​. The authors have conducted a study that is methodologically rigorous, well-detailed, and highly pertinent to the field. This work stands as a significant contribution to the scientific community, providing resources and insights that are likely to guide future research in this area. 

              
References

Brézellec, P (2024) Re-annotation of SARS-CoV-2 proteins using an HHpred-based approach opens new opportunities for a better understanding of this virus. bioRxiv, ver. 3 peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/2023.06.06.543855

Gabler F, Nam S-Z, Till S, Mirdita M, Steinegger M, Söding J, Lupas AN, Alva V (2020) Protein Sequence Analysis Using the MPI Bioinformatics Toolkit. Current Protocols in Bioinformatics, 72, e108. https://doi.org/10.1002/cpbi.108

One way to identify all organisms at their various life stages is by their genetic signature. DNA-based taxonomy, gene tagging and barcoding are different shortcuts used to name such strategies (Lamb et al. 2019; Tautz et al. 2003). Reading and analyzing nucleic acid sequences to perform genetic inventories is now faster than ever, and the latest nucleic acid sequencing technologies reveal an impressive taxonomic, genetic, and functional diversity hidden in all ecosystems (Lamb et al. 2019; Sunagawa et al. 2015). This knowledge should enable us to evaluate biodiversity across its scales, from genetic to species to ecosystem and is sometimes referred to with the neologism of ecogenomics (Dicke et al. 2004).

The metabarcoding approach is a key workhorse of ecogenomics. At the core of metabarcoding strategies lies the sequencing of amplicons obtained from so-called multi-template PCR, a formidable and potent experiment with the potential to unravel hidden biosphere components from different samples obtained from organisms or the environment (Kalle et al. 2014; Rodríguez-Ezpeleta et al. 2021). Next to this core approach, and equally important, lies the bioinformatic analysis to convert the raw sequencing data into amplicon sequence variants or operational taxonomic units and interpretable abundance tables.

Methodologically, the analysis of sequences obtained from metabarcoding projects is replete with devilish details. This is why different pipelines and tools have been developed, starting with mothur (Schloss et al. 2009) and QIIME 2 (Bolyen et al. 2019), but including more user friendly tools such as FROGS (Escudié et al. 2018). Yet, across all available tools, scientists must choose the optimal algorithms and parameter values to filter raw reads, trim primers, identify chimeras and cluster reads into operational taxonomic units. In addition, the number of genetic markers used to characterize a sample using metabarcoding has increased as  sequencing methods are now less costly and more efficient. In such cases, results and interpretations may become limited or confounded. This is where the novel tools proposed by Barnabé and colleagues (2024), mbctools, will benefit researchers in this field.

The authors provide a detailed description with a walk-through of the mbctools pipeline to analyse raw reads obtained in a metabarcoding project. The mbctools pipeline can be installed under different computing environments, requires only VSEARCH and a few Python dependencies, and is easy to use with a menu-driven interface. Users need to prepare their data following simple rules, providing single or paired-end reads, primer and target database sequences. An interesting feature of mbctools output is the possibility of integration with the metaXplor visualization tool developed by the authors (Sempéré et al. 2021). As it stands, mbctools should be used for short-read sequences. The taxonomy assignment module has the advantage to enable parameters exploration in an easy way, but it may be oversimplistic for specific taxa.

The lightweight aspect of mbctools and its overall simplicity are appealing. These features will make it a useful pipeline for training workshops and to help disseminate the use of metabarcoding. It also holds the potential for further improvement, by the developers or by others. In the end, mbctools will support study reproducibility by enabling a streamlined analysis of raw reads, and like many useful tools, only time will tell whether it is widely adopted.

         
References

Barnabé C, Sempéré G, Manzanilla V, Millan JM, Amblard-Rambert A, Waleckx E (2024) mbctools: A user-friendly metabarcoding and cross-platform pipeline for analyzing multiple amplicon sequencing data across a large diversity of organisms. bioRxiv, ver. 2 peer-reviewed and recommended by PCI Genomics https://doi.org/10.1101/2024.02.08.579441

Bolyen E, Rideout JR, Dillon MR, Bokulich NA, et al. (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9

Dicke M, van Loon JJA, de Jong PW (2004) Ecogenomics benefits community ecology. Science, 305, 618–619. https://doi.org/10.1126/science.1101788

Escudié F, Auer L, Bernard M, Mariadassou M, Cauquil L, Vidal K, Maman S, Hernandez-Raquet G, Combes S, Pascal G (2018) FROGS: Find, Rapidly, OTUs with Galaxy Solution. Bioinformatics, 34, 1287-1294. https://doi.org/10.1093/bioinformatics/btx791

Kalle E, Kubista M, Rensing C (2014) Multi-template polymerase chain reaction. Biomolecular Detection and Quantification, 2, 11–29. https://doi.org/10.1016/j.bdq.2014.11.002

Lamb CT, Ford AT, Proctor MF, Royle JA, Mowat G, Boutin S (2019) Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems. Ecological Applications, 29, e01876. https://doi.org/10.1002/eap.1876

Rodríguez-Ezpeleta N, Zinger L, Kinziger A, Bik HM, Bonin A, Coissac E, Emerson BC, Lopes CM, Pelletier TA, Taberlet P, Narum S (2021) Biodiversity monitoring using environmental DNA. Molecular Ecology Resources, 21, 1405–1409. https://doi.org/10.1111/1755-0998.13399

Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology 75, 7537-41. https://doi.org/10.1128/AEM.01541-09

Sempéré G, Pétel A, Abbé M, Lefeuvre P, Roumagnac P, Mahé F, Baurens G, Filloux D 2021 metaXplor: an interactive viral and microbial metagenomic data manager. Gigascience, 10, https://doi.org/10.1093/gigascience/giab001

Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, Djahanschiri B, Zeller G, Mende DR, Alberti A, Cornejo-Castillo FM, Costea PI, Cruaud C, d’Ovidio F, Engelen S, Ferrera I, Gasol JM, Guidi L, Hildebrand F, Kokoszka F, Lepoivre C, Lima-Mendez G, Poulain J, Poulos BT, Royo-Llonch M, Sarmento H, Vieira-Silva S, Dimier C, Picheral M, Searson S, Kandels-Lewis S, Tara Oceans coordinators, Bowler C, de Vargas C, Gorsky G, Grimsley N, Hingamp P, Iudicone D, Jaillon O, Not F, Ogata H, Pesant S, Speich S, Stemmann L, Sullivan MB, Weissenbach J, Wincker P, Karsenti E, Raes J, Acinas SG, Bork P (2015) Structure and function of the global ocean microbiome. Science, 348, 1261359. https://doi.org/10.1126/science.1261359

Tautz D, Arctander P, Minelli A, Thomas RH, Vogler AP (2003) A plea for DNA taxonomy. Trends in Ecology & Evolution, 18, 70–74. https://doi.org/10.1016/S0169-5347(02)00041-1

Martínez-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.

                                    
The manuscript was evaluated by two experts in the field of orthology search and orthology databases, and computational biology. The authors diligently implemented the modifications suggested by both referees and I am gladly recommending the manuscript at this point.

                
References
Martínez-Redondo GI, Vargas-Chávez C, Eleftheriadi K, Benítez-Álvarez L, Vázquez-Valls M, Fernández R (2024) MATEdb2, a collection of high-quality metazoan proteomes across the Animal Tree of Life to speed up phylogenomic studies. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2024.02.21.581367

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

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