Dataset Information

Impact of the partitioning scheme on divergence times inferred from Mammalian genomic data sets.

ABSTRACT: Data partitioning has long been regarded as an important parameter for phylogenetic inference. The division of heterogeneous multigene data sets into partitions with similar substitution patterns is known to increase the performance of probabilistic phylogenetic methods. However, the effect of the partitioning scheme on divergence time estimates has generally been ignored. To investigate the impact of data partitioning on the estimation of divergence times, we have constructed two genomic data sets. The first one with 15 nuclear genes comprising 50,928 bp were selected from the OrthoMam database; the second set was composed of complete mitochondrial genomes. We studied two partitioning schemes: concatenated supermatrices and partitioned gene analysis. We have also measured the impact of taxonomic sampling on the estimates. After drawing divergence time inferences using the uncorrelated relaxed clock in BEAST, we have compared the age estimates between the partitioning schemes. Our results show that, in general, both schemes resulted in similar chronological estimates, however the concatenated data sets were more efficient than the partitioned ones in attaining suitable effective sample sizes.

SUBMITTER: Voloch CM

PROVIDER: S-EPMC3362329 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Impact of the partitioning scheme on divergence times inferred from Mammalian genomic data sets.

Voloch Carolina M CM Schrago Carlos G CG

Evolutionary bioinformatics online 20120514

Data partitioning has long been regarded as an important parameter for phylogenetic inference. The division of heterogeneous multigene data sets into partitions with similar substitution patterns is known to increase the performance of probabilistic phylogenetic methods. However, the effect of the partitioning scheme on divergence time estimates has generally been ignored. To investigate the impact of data partitioning on the estimation of divergence times, we have constructed two genomic data s ...[more]

PMID: 22654486

Similar Datasets

Project description:BACKGROUND:Malagasy tenrecs belong to the Afrotherian clade of placental mammals and comprise three subfamilies divided in eight genera (Tenrecinae: Tenrec, Echinops, Setifer and Hemicentetes; Oryzorictinae: Oryzorictes, Limnogale and Microgale; Geogalinae:Geogale). The diversity of their morphology and incomplete taxon sampling made it difficult until now to resolve phylogenies based on either morphology or molecular data for this group. Therefore, in order to delineate the evolutionary history of this family, phylogenetic and dating analyses were performed on a four nuclear genes dataset (ADRA2B, AR, GHR and vWF) including all Malagasy tenrec genera. Moreover, the influence of both taxon sampling and data partitioning on the accuracy of the estimated ages were assessed. RESULTS:Within Afrotheria the vast majority of the nodes received a high support, including the grouping of hyrax with sea cow and the monophyly of both Afroinsectivora (Macroscelidea + Afrosoricida) and Afroinsectiphillia (Tubulidentata + Afroinsectivora). Strongly supported relationships were also recovered among all tenrec genera, allowing us to firmly establish the grouping of Geogale with Oryzorictinae, and to confirm the previously hypothesized nesting of Limnogale within the genus Microgale. The timeline of Malagasy tenrec diversification does not reflect a fast adaptive radiation after the arrival on Madagascar, indicating that morphological specializations have appeared over the whole evolutionary history of the family, and not just in a short period after colonization. In our analysis, age estimates at the root of a clade became older with increased taxon sampling of that clade. Moreover an augmentation of data partitions resulted in older age estimates as well, whereas standard deviations increased when more extreme partition schemes were used. CONCLUSION:Our results provide as yet the best resolved gene tree comprising all Malagasy tenrec genera, and may lead to a revision of tenrec taxonomy. A timeframe of tenrec evolution built on the basis of this solid phylogenetic framework showed that morphological specializations of the tenrecs may have been affected by environmental changes caused by climatic and/or subsequent colonization events. Analyses including various taxon sampling and data partitions allow us to point out some possible pitfalls that may lead to biased results in molecular dating; however, further analyses are needed to corroborate these observations.

Project description:The explosive growth of molecular sequence data has made it possible to estimate species divergence times under relaxed-clock models using genome-scale data sets with many gene loci. In order to improve both model realism and to best extract information about relative divergence times in the sequence data, it is important to account for the heterogeneity in the evolutionary process across genes or genomic regions. Partitioning is a commonly used approach to achieve those goals. We group sites that have similar evolutionary characteristics into the same partition and those with different characteristics into different partitions, and then use different models or different values of model parameters for different partitions to account for the among-partition heterogeneity. However, how to partition data in practical phylogenetic analysis, and in particular in relaxed-clock dating analysis, is more art than science. Here, we use computer simulation and real data analysis to study the impact of the partition scheme on divergence time estimation. The partition schemes had relatively minor effects on the accuracy of posterior time estimates when the prior assumptions were correct and the clock was not seriously violated, but showed large differences when the clock was seriously violated, when the fossil calibrations were in conflict or incorrect, or when the rate prior was mis-specified. Concatenation produced the widest posterior intervals with the least precision. Use of many partitions increased the precision, as predicted by the infinite-sites theory, but the posterior intervals might fail to include the true ages because of the conflicting fossil calibrations or mis-specified rate priors. We analyzed a data set of 78 plastid genes from 15 plant species with serious clock violation and showed that time estimates differed significantly among partition schemes, irrespective of the rate drift model used. Multiple and precise fossil calibrations reduced the differences among partition schemes and were important to improving the precision of divergence time estimates. While the use of many partitions is an important approach to reducing the uncertainty in posterior time estimates, we do not recommend its general use for the present, given the limitations of current models of rate drift for partitioned data and the challenges of interpreting the fossil evidence to construct accurate and informative calibrations.

Project description:BackgroundMulti-dimensional scaling (MDS) is aimed to represent high dimensional data in a low dimensional space with preservation of the similarities between data points. This reduction in dimensionality is crucial for analyzing and revealing the genuine structure hidden in the data. For noisy data, dimension reduction can effectively reduce the effect of noise on the embedded structure. For large data set, dimension reduction can effectively reduce information retrieval complexity. Thus, MDS techniques are used in many applications of data mining and gene network research. However, although there have been a number of studies that applied MDS techniques to genomics research, the number of analyzed data points was restricted by the high computational complexity of MDS. In general, a non-metric MDS method is faster than a metric MDS, but it does not preserve the true relationships. The computational complexity of most metric MDS methods is over O(N2), so that it is difficult to process a data set of a large number of genes N, such as in the case of whole genome microarray data.ResultsWe developed a new rapid metric MDS method with a low computational complexity, making metric MDS applicable for large data sets. Computer simulation showed that the new method of split-and-combine MDS (SC-MDS) is fast, accurate and efficient. Our empirical studies using microarray data on the yeast cell cycle showed that the performance of K-means in the reduced dimensional space is similar to or slightly better than that of K-means in the original space, but about three times faster to obtain the clustering results. Our clustering results using SC-MDS are more stable than those in the original space. Hence, the proposed SC-MDS is useful for analyzing whole genome data.ConclusionOur new method reduces the computational complexity from O(N3) to O(N) when the dimension of the feature space is far less than the number of genes N, and it successfully reconstructs the low dimensional representation as does the classical MDS. Its performance depends on the grouping method and the minimal number of the intersection points between groups. Feasible methods for grouping methods are suggested; each group must contain both neighboring and far apart data points. Our method can represent high dimensional large data set in a low dimensional space not only efficiently but also effectively.

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.].

Dataset Information

Impact of the partitioning scheme on divergence times inferred from Mammalian genomic data sets.

Publications

Impact of the partitioning scheme on divergence times inferred from Mammalian genomic data sets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets