A hitchhiker’s guide to DNA-based microbiome analysis
A primer and discussion on DNA-based microbiome data and related bioinformatics analyses
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 , the Earth Microbiome Project , the Extreme Microbiome Project, and MetaSUB .
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  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 , KBase  and MG-RAST .
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  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  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.
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 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
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Danny Ionescu (2021) A hitchhiker’s guide to DNA-based microbiome analysis. Peer Community in Genomics, 100008. 10.24072/pci.genomics.100008
Evaluation round #212 Apr 2021
DOI or URL of the preprint: 10.31219/osf.io/3dybg
Version of the preprint: 2
Decision by Danny Ionescu
Dear Drs. Douglas and Langille,
Thank you for revising your manuscript according to the reviewer's and my suggestions.
I would like to ask for several minor changes prior to recommending your paper.
1) On line 73 you write "First..." but there is never "Second". Probably this should come on line 83. Please add "Second" or rephrase "First".
2) In line 1163 you have "hereafter 16S". I think this can be replaced by the "hereafter 16S sequencing" in line 305. As also there it seems you mean to replace the 16S rRNA gene with the shorter 16S.
The following requests were made by the PCI management board with regards to the original version and I could not see these amendments in the revised version:
1) Authors must have no financial conflict of interest relating to the article. The article must contain a "Conflict of interest disclosure" paragraph before the reference section containing this sentence: "The authors of this article declare that they have no financial conflict of interest with the content of this article.";
2) This disclosure has to be completed by a sentence indicating that some of the authors are PCI recommenders: “XY is one of the PCI Genomics recommenders.”
I believe that other requests made by the board regarding data or code availability are not relevant for a review-type manuscript.
Following these minor changes/additions, I am looking forward to recommending your manuscript.
Evaluation round #122 Mar 2021
DOI or URL of the preprint: 10.31219/osf.io/3dybg
Version of the preprint:
Decision by Danny Ionescu
Dear Dr. Douglas and Langille,
Thank you for submitting your manuscript to be reviewed by PCI members.
I have obtained 3 independent reviews for your manuscript and have further reviewed the manuscript myself. The attached file contains all comments and suggestions.
Generally, the reviewers and I found the manuscript relevant and interesting. I do agree with the first reviewer that occasionally there are distracting facts, of added value, that reduce the usability of the manuscript as a "guide for the novice". I do not suggest removing these but rather relocating them to a box. For example - the necessary traits of a marker gene are good to know, but realistically, most people embarking on the metabarcoding adventure will initially embrace known markers.
With this respect, I feel that the paper can be made somewhat more concise.
As a primer - I suggest adding a glossary and to minimize abbreviations as much as possible.
Last, it is evident that the authors come from the field of human microbiome and so are most of the examples. I suggest adding a paragraph where this is specified clearly, explaining how the provided guidelines can be applied to microbial ecology in other types of environments (e.g. water, soil, biofilms, etc).
I hope the provided suggestions are useful,
looking forward to reading your revised version,
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