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Input data for inferring species distributions in Kyphosidae world-wide.

ABSTRACT: Input data files for inferring the relationship among the family Kyphosidae, as presented in (Knudsen and Clements, 2016) [1], is here provided together with resulting topologies, to allow the reader to explore the topologies in detail. The input data files comprise seven nexus-files with sequence alignments of mtDNA and nDNA markers for performing Bayesian analysis. A matrix of recoded character states inferred from the morphology examined in museum specimens representing Dichistiidae, Girellidae, Kyphosidae, Microcanthidae and Scorpididae, is also provided, and can be used for performing a parsimonious analysis to infer the relationship among these perciform families. The nucleotide input data files comprise both multiple and single representatives of the various species to allow for inference of the relationship among the species in Kyphosidae and between the families closely related to Kyphosidae. The '.xml'-files with various constrained relationships among the families potentially closely related to Kyphosidae are also provided to allow the reader to rerun and explore the results from the stepping-stone analysis. The resulting topologies are supplied in newick-file formats together with input data files for Bayesian analysis, together with '.xml'-files. Re-running the input data files in the appropriate software, will enable the reader to examine log-files and tree-files themselves.

SUBMITTER: Knudsen SW

PROVIDER: S-EPMC4969239 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Input data for inferring species distributions in Kyphosidae world-wide.

Knudsen Steen Wilhelm SW Clements Kendall D KD

Data in brief 20160705

Input data files for inferring the relationship among the family Kyphosidae, as presented in (Knudsen and Clements, 2016) [1], is here provided together with resulting topologies, to allow the reader to explore the topologies in detail. The input data files comprise seven nexus-files with sequence alignments of mtDNA and nDNA markers for performing Bayesian analysis. A matrix of recoded character states inferred from the morphology examined in museum specimens representing Dichistiidae, Girellid ...[more]

PMID: 27508256

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Project description:BACKGROUND:Several methods have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured intracellular fluxes. RESULTS:We present a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data coupled with genome-scale metabolic reconstructions. It consists of two different template models called DC (determined carbon source model) and AC (all possible carbon sources model) and two different new methods called E-Flux2 (E-Flux method combined with minimization of l2 norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen and combined depending on the availability of knowledge on carbon source or objective function. This enables us to simulate a broad range of experimental conditions. We examined E. coli and S. cerevisiae as representative prokaryotic and eukaryotic microorganisms respectively. The predictive accuracy of our algorithm was validated by calculating the uncentered Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements determined by 13C metabolic flux analysis (13C-MFA), which is the largest dataset assembled to date for the purpose of validating inference methods for predicting intracellular fluxes. In both organisms, our method achieves an average correlation coefficient ranging from 0.59 to 0.87, outperforming a representative sample of competing methods. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (http://most.ccib.rutgers.edu/). CONCLUSION:Our method represents a significant advance over existing methods for inferring intracellular metabolic flux from transcriptomic data. It not only achieves higher accuracy, but it also combines into a single method a number of other desirable characteristics including applicability to a wide range of experimental conditions, production of a unique solution, fast running time, and the availability of a user-friendly implementation.

Dataset Information

Input data for inferring species distributions in Kyphosidae world-wide.

Publications

Input data for inferring species distributions in Kyphosidae world-wide.

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