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 brown hare, or European hare, Lupus europaeus, is a widespread mammal whose natural range spans western Eurasia. At the northern limit of its range, it hybridises with the mountain hare (L. timidis), and humans have introduced it into other continents. It represents a particularly interesting mammal to study for its population genetics, extensive hybridisation zones, and as an invasive species.

This study (Michell et al. 2024) has generated a high-quality assembly of a genome from a brown hare from Finland using long PacBio HiFi sequencing reads and Hi-C scaffolding. The contig N50 of this new genome is 43 Mb, and completeness, assessed using BUSCO, is 96.1%. The assembly comprises 23 autosomes, and an X chromosome and Y chromosome, with many chromosomes including telomeric repeats, indicating the high level of completeness of this assembly.

While the genome of the mountain hare has previously been assembled, its assembly was based on a short-read shotgun assembly, with the rabbit as a reference genome. The new high-quality brown hare genome assembly allows a direct comparison with the rabbit genome assembly. For example, the assembly addresses the karyotype difference between the hare (n=24) and the rabbit (n=22). Chromosomes 12 and 17 of the hare are equivalent to chromosome 1 of the rabbit, and chromosomes 13 and 16 of the hare are equivalent to chromosome 2 of the rabbit. The new assembly also provides a hare Y-chromosome, as the previous mountain hare genome was from a female.

This new genome assembly provides an important foundation for population genetics and evolutionary studies of lagomorphs.

References

Michell, C., Collins, J., Laine, P. K., Fekete, Z., Tapanainen, R., Wood, J. M. D., Goffart, S., Pohjoismäki, J. L. O. (2024). High quality genome assembly of the brown hare (Lepus europaeus) with chromosome-level scaffolding. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.08.29.555262

Developing new sequencing and bioinformatic strategies for non-model species is of great interest in many applications, such as phylogenetic studies of diverse related species, but also for studies in population genomics, where a relatively large number of individuals is necessary. Different approaches have been developed and used in these last two decades, such as RAD-Seq (e.g., Miller et al. 2007), exome sequencing (e.g., Teer and Mullikin 2010) and other genome reduced representation methods that avoid the use of a good reference and well annotated genome (reviewed at Davey et al. 2011). However, population genomics studies require the analysis of numerous individuals, which makes the studies still expensive. Pooling samples was thought as an inexpensive strategy to obtain estimates of variability and other related to the frequency spectrum, thus allowing the study of variability at population level (e.g., Van Tassell et al. 2008), although the major drawback was the loss of information related to the linkage of the variants. In addition, population analysis using all these sequencing strategies require statistical and empirical validations that are not always fully performed. A number of studies aiming to obtain unbiased estimates of variability using reduced representation libraries and/or with pooled data have been performed (e.g., Futschik and Schlötterer 2010, Gautier et al. 2013, Ferretti et al. 2013, Lynch et al. 2014), as well as validation of new sequencing methods for population genetic analyses (e.g., Gautier et al. 2013, Nevado et al. 2014). Nevertheless, empirical validation using both pooled and individual experimental approaches combined with different bioinformatic methods has not been always performed.
Here, Deleury et al. (2020) proposed an efficient and elegant way of quantifying the single-nucleotide polymorphisms (SNPs) of exon-derived sequences in a non-model species (i.e. for which no reference genome sequence is available) at the population level scale. They also designed a new procedure to capture exon-derived sequences based on a reference transcriptome. In addition, they were able to make predictions of intron-exon boundaries for de novo transcripts based on the decay of read depth at the ends of the coding regions.
Based on theoretical predictions (Gautier et al. 2013), Deleury et al. (2020) designed a procedure to test the accuracy of variant allele frequencies (AFs) with pooled samples, in a reduced genome-sequence library made with transcriptome regions, and additionally testing the effects of new bioinformatic methods in contrast to standardized methods. They applied their strategy on the non-model species Asian ladybird (Harmonia axyridis), for which a draft genome is available, thereby allowing them to benchmark their method with regard to a traditional mapping-based approach. Based on species-specific de novo transcriptomes, they designed capture probes which are then used to call SNPx and then compared the resulting SNP AFs at the individual (multiplexed) versus population (pooled) levels. Interestingly, they showed that SNP AFs in the pool sequencing strategy nicely correlate with the individual ones but obviously in a cost-effective way. Studies of population genomics for non-model species have usually limited budgets. The number of individuals required for population genomics analysis multiply the costs of the project, making pooling samples an interesting option. Furthermore, the use of pool sequencing is not always a choice, as many organisms are too small and/or individuals are too sticked each other to be individually sequenced (e.g., Choquet et al. 2019, Kurland et al. 2019). In addition, the study of a reduced section of the genome is cheaper and often sufficient for a number of population genetic questions, such as the understanding of general demographic events, or the estimation of the effects of positive and/or negative selection at functional coding regions. Studies on population genomics of non-model species have many applications in related fields, such as conservation genetics, control of invasive species, etc. The work of Deleury et al. (2020) is an elegant contribution to the assessment and validation of new methodologies used for the analysis of genome variations at the intra-population variability level, highlighting straight bioinformatic and reliable sequencing methods for population genomics studies.

References

[1] Choquet et al. (2019). Towards population genomics in non-model species with large genomes: a case study of the marine zooplankton Calanus finmarchicus. Royal Society open science, 6(2), 180608. doi: https://doi.org/10.1098/rsos.180608
[2] Davey, J. W., Hohenlohe, P. A., Etter, P. D., Boone, J. Q., Catchen, J. M. and Blaxter, M. L. (2011). Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nature Reviews Genetics, 12(7), 499-510. doi: https://doi.org/10.1038/nrg3012
[3] Deleury, E., Guillemaud, T., Blin, A. and Lombaert, E. (2020) An evaluation of pool-sequencing transcriptome-based exon capture for population genomics in non-model species. bioRxiv, 10.1101/583534, ver. 7 peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/583534
[4] Ferretti, L., Ramos‐Onsins, S. E. and Pérez‐Enciso, M. (2013). Population genomics from pool sequencing. Molecular ecology, 22(22), 5561-5576. doi: https://doi.org/10.1111/mec.12522
[5] Futschik, A. and Schlötterer, C. (2010). Massively parallel sequencing of pooled DNA samples—the next generation of molecular markers. Genetics, 186 (1), 207-218. doi: https://doi.org/10.1534/genetics.110.114397
[6] Gautier et al. (2013). Estimation of population allele frequencies from next‐generation sequencing data: pool‐versus individual‐based genotyping. Molecular Ecology, 22(14), 3766-3779. doi: https://doi.org/10.1111/mec.12360
[7] Kurland et al. (2019). Exploring a Pool‐seq‐only approach for gaining population genomic insights in nonmodel species. Ecology and evolution, 9(19), 11448-11463. doi: https://doi.org/10.1002/ece3.5646
[8] Lynch, M., Bost, D., Wilson, S., Maruki, T. and Harrison, S. (2014). Population-genetic inference from pooled-sequencing data. Genome biology and evolution, 6(5), 1210-1218. doi: https://doi.org/10.1093/gbe/evu085
[9] Miller, M. R., Dunham, J. P., Amores, A., Cresko, W. A. and Johnson, E. A. (2007). Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome research, 17(2), 240-248. doi: https://doi.org/10.1101%2Fgr.5681207
[10] Nevado, B., Ramos‐Onsins, S. E. and Perez‐Enciso, M. (2014). Resequencing studies of nonmodel organisms using closely related reference genomes: optimal experimental designs and bioinformatics approaches for population genomics. Molecular ecology, 23(7), 1764-1779. doi: https://doi.org/10.1111/mec.12693
[11] Teer, J. K. and Mullikin, J. C. (2010). Exome sequencing: the sweet spot before whole genomes. Human molecular genetics, 19(R2), R145-R151. doi: https://doi.org/10.1093/hmg/ddq333
[12] Van Tassell et al. (2008). SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nature methods, 5(3), 247-252. doi: https://doi.org/10.1038/nmeth.1185

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

Contamination, 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​