Dataset Information

Gene duplication, population genomics, and species-level differentiation within a tropical mountain shrub.

ABSTRACT: Gene duplication leads to paralogy, which complicates the de novo assembly of genotyping-by-sequencing (GBS) data. The issue of paralogous genes is exacerbated in plants, because they are particularly prone to gene duplication events. Paralogs are normally filtered from GBS data before undertaking population genomics or phylogenetic analyses. However, gene duplication plays an important role in the functional diversification of genes and it can also lead to the formation of postzygotic barriers. Using populations and closely related species of a tropical mountain shrub, we examine 1) the genomic differentiation produced by putative orthologs, and 2) the distribution of recent gene duplication among lineages and geography. We find high differentiation among populations from isolated mountain peaks and species-level differentiation within what is morphologically described as a single species. The inferred distribution of paralogs among populations is congruent with taxonomy and shows that GBS could be used to examine recent gene duplication as a source of genomic differentiation of nonmodel species.

SUBMITTER: Mastretta-Yanes A

PROVIDER: S-EPMC4224332 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Gene duplication, population genomics, and species-level differentiation within a tropical mountain shrub.

Mastretta-Yanes Alicia A Zamudio Sergio S Jorgensen Tove H TH Arrigo Nils N Alvarez Nadir N Piñero Daniel D Emerson Brent C BC

Genome biology and evolution 20140914 10

Gene duplication leads to paralogy, which complicates the de novo assembly of genotyping-by-sequencing (GBS) data. The issue of paralogous genes is exacerbated in plants, because they are particularly prone to gene duplication events. Paralogs are normally filtered from GBS data before undertaking population genomics or phylogenetic analyses. However, gene duplication plays an important role in the functional diversification of genes and it can also lead to the formation of postzygotic barriers. ...[more]

PMID: 25223767

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Project description:Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.

Dataset Information

Gene duplication, population genomics, and species-level differentiation within a tropical mountain shrub.

Publications

Gene duplication, population genomics, and species-level differentiation within a tropical mountain shrub.

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