The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing


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Background and motivation

Page, G. Stormo, and L. Stein eds. Current Protocols in Bioinformatics Supplement 17 , Unit 6. Petzold, D. Merkle, M. Middendorf, A. Zomaya ed. Petzold, M. Vingron, and A. Parallel Distrib. It implements a fast tree search algorithm, quartet puzzling, that allows analysis of large data sets and automatically assigns estimations of support to each internal branch. Branch lengths can be calculated with and without the molecular-clock assumption. In addition, TREE-PUZZLE o ers likelihood mapping, a method to investigate the support of a hypothesized internal branch without computing an overall tree and to visualize the phylogenetic content of a sequence alignment.

Rate heterogeneity is modeled by a discrete Gamma distribution and by allowing invariable sites. The corresponding parameters except for GTR can be inferred from the data set. Although clearly, discoveries stemming from data mining, reports of new tools and databases and review papers are also desirable. Phylogenetic analyses of molecular sequences are an integral part of many modern molecular and evolutionary biology studies.


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With the increasing pace of methodological developments it becomes a challenge for those authors that merely apply statistical methods to make sufficiently educated choices of what models and methods are most suitable for their data and purposes. As editors, we regularly come across submissions in which outdated methods are used with no apparent reason, undermining the reliability of reported findings. For example, most of the time no justification is provided for the use of alignment methods, typically with default settings followed by subjective manual intervention.

Other common issues include the use of overly simplistic substitution models or reliance on basic pairwise comparisons when multiple homologous sequences are available. In particular, with no justification, some authors rely solely on distance-based tree reconstruction and, worryingly, statistical support for inferred clades is not properly evaluated. Further downstream, selection or dating analyses are common, but again, they often suffer from the use of outdated methods that are based on pairwise comparisons or make overly simplistic assumptions.

While researchers in the field are somewhat critical of outdated methods, in fact, many of them made and still make a profound contribution to the development of methodologies for computational molecular evolution, which explains their frequent usage. However, the field has since moved on and now boasts an overwhelming variety of more advanced models and methods, which were shown to be either better more accurate than previous methods in general, or to deal better with data-specific features.

Appropriate application of this existing variety, nevertheless, requires a better understanding of the fundamental principles of the various methods and models, their underlying assumptions, and how they are implemented in various programs and web-servers. Looking forward, methods and strategies that are currently the state-of-the-art are likely to become outdated as well, so it is equally important to think broadly about the analysis performed.

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The field of molecular evolution is extremely interdisciplinary, bridging mathematics and statistics, computer science, ecology, evolutionary biology and population genetics, molecular biology, biochemistry, and physical chemistry. Few researchers have expertise in all of these areas, yet an analysis in molecular evolution is ultimately interdisciplinary, making assumptions across several areas, which may be not fully comprehended by a researcher undertaking the analysis.

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We appreciate that often, model and method choice is not a trivial task even for method developers. As a consequence, there has been a lot of recent effort in evaluating methods and models. Phylogenetic analysis of a set of sequences typically commences with the identification of homologous sequences. Next, a multiple sequence alignment MSA is constructed. This is often followed by phylogeny inference, which usually requires a substitution model. Further analyses and inferences may use other more sophisticated methods and models, which then rely on the inferred phylogeny.

For protein-coding genes, a typical task involves estimating selective pressure on the protein. Ideally, all these steps should be performed simultaneously, since, for instance, an MSA provides crucial information to detect homologs and can only be correctly evaluated in the light of phylogeny. Due to computational complexity and burden, software allowing joint analyses such as the simultaneous inference of alignment and phylogeny are rare and will not be discussed here, although they clearly constitute an important avenue of research.

Assume that an evolutionary pipeline has been established, and all methodological aspects have been appropriately considered: does this merit publication? If these are the only results reported in the manuscript then the answer is clearly no. In order for the analysis to be meaningful, the authors must clearly demonstrate that novel insights into the taxonomy or biology of studied organisms or the biology or biochemistry of specific molecular sequences were gained as a result.

They should explain the choice of molecular data, list open questions that motivated the study, and define the hypotheses to be tested. For example, simply stating that an analyzed enzyme plays a central role in a given pathway is an insufficient justification. Likewise, plain inference of species relationships may be of little interest if the resulting tree does not help explain evolutionary processes along this tree or has no clear practical applications.

In general, a method should be selected because it was shown to be either superior or as good as its alternatives, with relevant studies cited. Another strong argument for method choice includes the ability of the chosen method to reflect the features of the data being analysed, and to address specific biological questions. Below we provide more specific advice regarding decisions to be made on the different stages of phylogenetic analyses. It has often been shown that phylogenetic conclusions might reflect bias in the methodology used.

Although extensive research has detected and characterized biases in phylogenetic methods, there are likely to be many unknown biases, which may vary among methods. For example, one specific method or model can lead to biased results when too few taxa are analyzed, while another may be less accurate when sequences with high GC content prevail.

Moreover, many methods are uninformative for sequence homologs when their divergence is too low or too high. It is thus the responsibility of the researchers to show that their conclusion is general rather than reflecting the outcome of one possible methodology out of equally good alternatives.

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It should be emphasized that the need for using alternative methodologies cannot be used as a justification to use outdated methodologies. Instead, only when two or more well-performing methodologies exist, there is a benefit to evaluate the dependence of the results on the choice of alternative methodologies. We demonstrate this benefit in the case of MSA algorithms, below. It is reassuring when a result consistently holds for several relevant methods or models. However, if the result is sensitive to the choice of models that fit the data similarly or methods that are known to be similarly accurate, caution should be taken when interpreting the results or when the results are used for downstream analyses.

Ideally, one should aim to understand the underlying assumptions of the methodologies and discuss why they led to contrasting results. In a standard phylogenetic pipeline, the outcome of statistical inference at one step serves as input to the analysis at the next step. Pipelines should ideally be replaced by a probabilistic joint analysis of all relevant parameters. However, as this is almost never possible, pragmatic inference from pipelines should be conducted by methodologies that account for all uncertainties in all stages. While accounting for all possible scenarios is seldom feasible, many recent methodologies allow accounting for uncertainty when analyzing data.


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Bayesian methods allow integrating over uncertainty, for instance in phylogenetic inference. As a case in point, Bayesian tree search algorithms often integrate over the parameters of the assumed underlying model. When using Bayesian approaches, convergence diagnostics should be monitored and the influence of priors should be considered. While Bayesian approaches, when they exist, should clearly be considered, they may be extremely time consuming and so impractical for large datasets.

But even considering just a few main most probable competing outcomes, may help to validate the robustness of final conclusions.

Thus, instead of averaging over possible MSAs, one can filter out unreliable alignment regions that is, remove regions for which the methods used yield results with great uncertainty. Indeed, for some types of analyses, filtering out uncertainty was shown to be critical for accurate inference. For example, positive selection inference was found to be more accurate when unreliable MSA regions were filtered out. However, the use of filtering remains controversial; sometimes it can have detrimental impact on the accuracy of phylogeny inference, or introduce a systematic bias to the results by, for example, removing fast-evolving sites.

On the other hand, filtering is sometimes justified by avoiding the perception of long branches attraction in systematic analysis. Thus, the use of filtering and the choice of appropriate filtering strategies should be carefully considered and justified by including relevant citations, as there are conflicting perspectives on this in the scientific community.

To summarize, researchers should make an effort to demonstrate their results are reliable and do not represent a tiny fraction of all possible evolutionary scenarios that could have led to the generation of their analyzed data. If the results vary depending on the methodology used, this should be reported rather than ignored as it allows evaluation the uncertainty of the inference and may help understand how methodological choices affect the resulting inference and conclusions. For science to progress and build upon previous work, the reader of a paper should be able to evaluate and repeat the analyses reported in the manuscript.

Evolutionary studies are read by audiences with diverse sets of training. While standards in different fields may differ, authors should attempt to meet the standards of the different communities. To this end the methods should be detailed and ideally include, for example, the set of parameters and options used by the programs in the pipeline.

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If scripts or computer code were generated as part of the study, they should also be made available. In this way, intermediate results within a pipeline can be made readily available so that, if a researcher comes up with a better way to perform one step in the pipeline, there is no need to repeat the entire pipeline afresh. In this way data are discoverable, identifiable with DOI formatted for easy reuse, and updatable. Often, finding a set of homologous sequences is the first step in an evolutionary analysis. The first important point to consider is the goal of the search.

When the goal is to reconstruct a species tree, for most methods, only orthologs may be sought, because mis-identification of paralogs as orthologs can yield an incorrect result; however, a few methods reconstruct the species tree from the reconciliation of gene trees, overcoming the limitation of using orthologous sequences, and are therefore promising.

Such methods are dependent upon the model and assumptions made during the reconciliation process. For studies of gene families, orthologs, paralogs and xenologs are needed. Another point to consider is whether to search for all homologous sequences or to limit the search to a specific group for example only vertebrates or only mammals. Where the search is indeed limited to a specific group, it is necessary to explain the motivation behind such a decision.

The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing
The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing
The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing
The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing
The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing
The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing
The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing
The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing

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