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An Efficient, Nonphylogenetic Method for Detecting Genes Sharing Evolutionary Signals in Phylogenomic Data Sets.

ABSTRACT: Assessing the compatibility between gene family phylogenies is a crucial and often computationally demanding step in many phylogenomic analyses. Here, we describe the Evolutionary Similarity Index (IES), a means to assess shared evolution between gene families using a weighted orthogonal distance regression model applied to sequence distances. The utilization of pairwise distance matrices circumvents comparisons between gene tree topologies, which are inherently uncertain and sensitive to evolutionary model choice, phylogenetic reconstruction artifacts, and other sources of error. Furthermore, IES enables the many-to-many pairing of multiple copies between similarly evolving gene families. This is done by selecting non-overlapping pairs of copies, one from each assessed family, and yielding the least sum of squared residuals. Analyses of simulated gene family data sets show that IES's accuracy is on par with popular tree-based methods while also less susceptible to noise introduced by sequence alignment and evolutionary model fitting. Applying IES to an empirical data set of 1,322 genes from 42 archaeal genomes identified eight major clusters of gene families with compatible evolutionary trends. The most cohesive cluster consisted of 62 genes with compatible evolutionary signal, which occur as both single-copy and multiple homologs per genome; phylogenetic analysis of concatenated alignments from this cluster produced a tree closely matching previously published species trees for Archaea. Four other clusters are mainly composed of accessory genes with limited distribution among Archaea and enriched toward specific metabolic functions. Pairwise evolutionary distances obtained from these accessory gene clusters suggest patterns of interphyla horizontal gene transfer. An IES implementation is available at https://github.com/lthiberiol/evolSimIndex.

SUBMITTER: Rangel LT

PROVIDER: S-EPMC8483891 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A statistical framework of model comparison and model validation is essential to resolving the debates over concatenation and coalescent models in phylogenomic data analysis. A set of statistical tests are here applied and developed to evaluate and compare the adequacy of substitution, concatenation, and multispecies coalescent (MSC) models across 47 phylogenomic data sets collected across tree of life. Tests for substitution models and the concatenation assumption of topologically congruent gene trees suggest that a poor fit of substitution models, rejected by 44% of loci, and concatenation models, rejected by 38% of loci, is widespread. Logistic regression shows that the proportions of GC content and informative sites are both negatively correlated with the fit of substitution models across loci. Moreover, a substantial violation of the concatenation assumption of congruent gene trees is consistently observed across six major groups (birds, mammals, fish, insects, reptiles, and others, including other invertebrates). In contrast, among those loci adequately described by a given substitution model, the proportion of loci rejecting the MSC model is 11%, significantly lower than those rejecting the substitution and concatenation models. Although conducted on reduced data sets due to computational constraints, Bayesian model validation and comparison both strongly favor the MSC over concatenation across all data sets; the concatenation assumption of congruent gene trees rarely holds for phylogenomic data sets with more than 10 loci. Thus, for large phylogenomic data sets, model comparisons are expected to consistently and more strongly favor the coalescent model over the concatenation model. We also found that loci rejecting the MSC have little effect on species tree estimation. Our study reveals the value of model validation and comparison in phylogenomic data analysis, as well as the need for further improvements of multilocus models and computational tools for phylogenetic inference. [Bayes factor; Bayesian model validation; coalescent prior; congruent gene trees; independent prior; Metazoa; posterior predictive simulation.].

Project description:There are considerable phylogenetic incongruencies between morphological and phylogenomic data for the deep evolution of animals. This has contributed to a heated debate over the earliest-branching lineage of the animal kingdom: the sister to all other Metazoa (SOM). Here, we use published phylogenomic data sets ($\sim $45,000-400,000 characters in size with $\sim $15-100 taxa) that focus on early metazoan phylogeny to evaluate the impact of incorporating morphological data sets ($\sim $15-275 characters). We additionally use small exemplar data sets to quantify how increased taxon sampling can help stabilize phylogenetic inferences. We apply a plethora of common methods, that is, likelihood models and their "equivalent" under parsimony: character weighting schemes. Our results are at odds with the typical view of phylogenomics, that is, that genomic-scale data sets will swamp out inferences from morphological data. Instead, weighting morphological data 2-10$\times $ in both likelihood and parsimony can in some cases "flip" which phylum is inferred to be the SOM. This typically results in the molecular hypothesis of Ctenophora as the SOM flipping to Porifera (or occasionally Placozoa). However, greater taxon sampling improves phylogenetic stability, with some of the larger molecular data sets ($>$200,000 characters and up to $\sim $100 taxa) showing node stability even with $\geqq100\times $ upweighting of morphological data. Accordingly, our analyses have three strong messages. 1) The assumption that genomic data will automatically "swamp out" morphological data is not always true for the SOM question. Morphological data have a strong influence in our analyses of combined data sets, even when outnumbered thousands of times by molecular data. Morphology therefore should not be counted out a priori. 2) We here quantify for the first time how the stability of the SOM node improves for several genomic data sets when the taxon sampling is increased. 3) The patterns of "flipping points" (i.e., the weighting of morphological data it takes to change the inferred SOM) carry information about the phylogenetic stability of matrices. The weighting space is an innovative way to assess comparability of data sets that could be developed into a new sensitivity analysis tool. [Metazoa; Morphology; Phylogenomics; Weighting.].

Project description:BackgroundIdentification of differentially expressed genes (DEGs) under different experimental conditions is an important task in many microarray studies. However, choosing which method to use for a particular application is problematic because its performance depends on the evaluation metric, the dataset, and so on. In addition, when using the Affymetrix GeneChip(R) system, researchers must select a preprocessing algorithm from a number of competing algorithms such as MAS, RMA, and DFW, for obtaining expression-level measurements. To achieve optimal performance for detecting DEGs, a suitable combination of gene selection method and preprocessing algorithm needs to be selected for a given probe-level dataset.ResultsWe introduce a new fold-change (FC)-based method, the weighted average difference method (WAD), for ranking DEGs. It uses the average difference and relative average signal intensity so that highly expressed genes are highly ranked on the average for the different conditions. The idea is based on our observation that known or potential marker genes (or proteins) tend to have high expression levels. We compared WAD with seven other methods; average difference (AD), FC, rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT). The evaluation was performed using a total of 38 different binary (two-class) probe-level datasets: two artificial "spike-in" datasets and 36 real experimental datasets. The results indicate that WAD outperforms the other methods when sensitivity and specificity are considered simultaneously: the area under the receiver operating characteristic curve for WAD was the highest on average for the 38 datasets. The gene ranking for WAD was also the most consistent when subsets of top-ranked genes produced from three different preprocessed data (MAS, RMA, and DFW) were compared. Overall, WAD performed the best for MAS-preprocessed data and the FC-based methods (AD, WAD, FC, or RP) performed well for RMA and DFW-preprocessed data.ConclusionWAD is a promising alternative to existing methods for ranking DEGs with two classes. Its high performance should increase researchers' confidence in microarray analyses.

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An Efficient, Nonphylogenetic Method for Detecting Genes Sharing Evolutionary Signals in Phylogenomic Data Sets.

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