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      Exon-primed intron-crossing (EPIC) markers for non-model teleost fishes

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      1 , , 1 , 2 , 3 ,
      BMC Evolutionary Biology
      BioMed Central

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          Abstract

          Background

          Exon-primed intron-crossing (EPIC) markers have three advantages over anonymous genomic sequences in studying evolution of natural populations. First, the universal primers designed in exon regions can be applied across a broad taxonomic range. Second, the homology of EPIC-amplified sequences can be easily determined by comparing either their exon or intron portion depending on the genetic distance between the taxa. Third, having both the exon and intron fragments could help in examining genetic variation at the intraspecific and interspecific level simultaneously, particularly helpful when studying species complex. However, the paucity of EPIC markers has hindered multilocus studies using nuclear gene sequences, particularly in teleost fishes.

          Results

          We introduce a bioinformatics pipeline for developing EPIC markers by comparing the whole genome sequences between two or more species. By applying this approach on five teleost fishes whose genomes were available in the Ensembl database http://www.ensembl.org, we identified 210 EPIC markers that have single-copy and conserved exon regions with identity greater than 85% among the five teleost fishes. We tested 12 randomly chosen EPIC markers in nine teleost species having a wide phylogenetic range. The success rate of amplifying and sequencing those markers varied from 44% to 100% in different species. We analyzed the exon sequences of the 12 EPIC markers from 13 teleosts. The resulting phylogeny contains many traditionally well-supported clades, indicating the usefulness of the exon portion of EPIC markers in reconstructing species phylogeny, in addition to the value of the intron portion of EPIC markers in interrogating the population history.

          Conclusions

          This study illustrated an effective approach to develop EPIC markers in a taxonomic group, where two or more genome sequences are available. The markers identified could be amplified across a broad taxonomic range of teleost fishes. The phylogenetic utility of individual markers varied according to intron size and amplifiability. The bioinformatics pipelines developed are readily adapted to other taxonomic groups.

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          Most cited references27

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          Bayesian phylogenetic analysis of combined data.

          The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.
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            LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters.

            We present a Markov chain Monte Carlo coalescent genealogy sampler, LAMARC 2.0, which estimates population genetic parameters from genetic data. LAMARC can co-estimate subpopulation Theta = 4N(e)mu, immigration rates, subpopulation exponential growth rates and overall recombination rate, or a user-specified subset of these parameters. It can perform either maximum-likelihood or Bayesian analysis, and accomodates nucleotide sequence, SNP, microsatellite or elecrophoretic data, with resolved or unresolved haplotypes. It is available as portable source code and executables for all three major platforms. LAMARC 2.0 is freely available at http://evolution.gs.washington.edu/lamarc
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              Bayesian identification of admixture events using multilocus molecular markers.

              Bayesian statistical methods for the estimation of hidden genetic structure of populations have gained considerable popularity in the recent years. Utilizing molecular marker data, Bayesian mixture models attempt to identify a hidden population structure by clustering individuals into genetically divergent groups, whereas admixture models target at separating the ancestral sources of the alleles observed in different individuals. We discuss the difficulties involved in the simultaneous estimation of the number of ancestral populations and the levels of admixture in studied individuals' genomes. To resolve this issue, we introduce a computationally efficient method for the identification of admixture events in the population history. Our approach is illustrated by analyses of several challenging real and simulated data sets. The software (baps), implementing the methods introduced here, is freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.
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                Author and article information

                Journal
                BMC Evol Biol
                BMC Evolutionary Biology
                BioMed Central
                1471-2148
                2010
                31 March 2010
                : 10
                : 90
                Affiliations
                [1 ]School of Biological Sciences, University of Nebraska - Lincoln, NE 68588-0118, USA
                [2 ]Bioinformatics Core Research Facility, University of Nebraska - Lincoln, NE 68588-0665, USA
                [3 ]East China Sea Fisheries Research Institute, Chinese Academy of Fisheries Sciences, Shanghai, 200090, PR China
                Article
                1471-2148-10-90
                10.1186/1471-2148-10-90
                2853542
                20353608
                aa11456a-8af0-4610-9772-ed158b413568
                Copyright ©2010 Li et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 3 December 2009
                : 31 March 2010
                Categories
                Methodology article

                Evolutionary Biology
                Evolutionary Biology

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