21 research outputs found

    Polymorphism at selected defence gene analogs (DGAs) of Musa accessions in Mauritius

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    One of the major diseases affecting banana is Sigatoka or leaf spot disease that comprises three species, Mycosphaerella fijiensis, Mycosphaerella musicola and Mycosphaerella eumusae. Plants have a large number of defence related genes which trigger a cascade of defense responses that halt the spread of pathogens. Knowledge of the diversity present in genes related to the defense against Sigatoka disease will be useful in developing disease resistant banana cultivars. The defence genes of all sterile commercial banana cultivars (AAA genomes) are considered to have arisen from a similar gene pool belonging to the Musa acuminata complex. The objectives of this study were, (i) to assess the disease response of twelve banana cultivars to M. eumusae, (ii) to assess the level of polymorphisms in selected genes associated with defence against Sigatoka in banana, and (iii) ascertain if this polymorphism was related to levels of resistance to M. eumusae. Defence genes reported to act in response to M. fijiensis were selected and related to the response of M. eumusae. The genetic diversity of selected defence gene analogs (DGA) was assessed using degenerate primers designed from conserved motifs in the aligned amino acid sequences from known resistance genes. Highly polymorphic amplicon profiles for DGAs were selected for comparison. Cluster analysis was used to differentiate to some extent, cultivars considered as resistant/susceptible to M. eumusae. Specific amplicons from the profiles of phenylalanine ammonia-lyase (PAL), iron superoxide dismutase (FeSOD) and ascorbate peroxidase (APX) were unique to a group of resistant cultivars and could act as markers for resistance to M. eumusae.Keywords: Banana, defence gene analogs, polymorphis

    Exploring the stability of feature selection methods across a palette of gene expression datasets

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    Gene expression data often need to be classified into classes or grouped into clusters for further analysis, using different machine learning techniques and an important pre-processing step is feature selection (FS). The aim of this study is to investigate the stability of some diverse FS methods on a plethora of microarray gene expression data. This experimental work is broken into three parts. Step 1 involves running some FS methods on one gene expression dataset to have a preliminary assessment on the similarity, or dissimilarity, of the resulting feature subsets across methods. Step 2 involves running two of these methods on a large number of different datasets to investigate whether the results produced by the methods are dependent on the features of the dataset: binary, multiclass, small or large dataset. The final step explores how the similarity of selected feature subsets between pairs of methods evolves as the size of the subsets are increased. Results show that the studied methods display a high amount of variability in terms of the resulting selected features. The feature subsets differed both inter- and intra- methods for different datasets. The reason behind this is not clear yet and is being further investigated. The final objective of the research, that is to define how to select a FS method, is an ongoing work whose initial findings are reported herein

    A meta-review of feature selection techniques in the context of microarray data

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    Microarray technologies produce very large amounts of data that need to be classified for interpretation. Large data coupled with small sample sizes make it challenging for researchers to get useful information and therefore a lot of effort goes into the design and testing of feature selection tools; literature abounds with description of numerous methods. In this paper we select five representative review papers in the field of feature selection for microarray data in order to understand their underlying classification of methods. Finally, on this base, we propose an extended taxonomy for categorizing feature selection techniques and use it to classify the main methods presented in the selected reviews

    A comparative study of feature selection methods for biomarker discovery

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    A major area of research is biomarker discovery using gene expression data. Such data is huge and often needs to be classified into classes or clustered, using different machine learning techniques, for further analysis. An important preprocessing step is feature selection (FS) and different such methods have been devised. However, applying different FS techniques to the same dataset do not always produce the same results. In this work, the robustness of FS methods will be looked into. Robustness is defined here as the stability of a given gene pool with respect to the data and the FS method used. Our approach is to investigate the resulting feature subset obtained when running diverse FS methods on different gene expression datasets. As a first step, 10 FS methods were executed using 2 different datasets. Based on the results obtained, 2 of these methods were further investigated using 10 different datasets. The effects of selecting an increasing number of features on the percentage similarity inter-methods were also studied. Our results show that the studied methods exhibit a high amount of variability in the resulting feature subset. The selected feature subsets differed both inter-methods and intra-methods for different datasets. The reason behind this is not clear and possible objective assessment on the ideal (best) subset should be further investigated

    H3ABioNet computational metagenomics workshop in Mauritius : training to analyse microbial diversity for Africa

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    In the context of recent international initiatives to bolster genomics research for Africa, and more specifically to develop bioinformatics expertise and networks across the continent, a workshop on computational metagenomics was organized during the end of 2014 at the University of Mauritius. The workshop offered background on various aspects of computational biology, including databases and algorithms, sequence analysis fundamentals, metagenomics concepts and tools, practical exercises, journal club activities and research seminars. We have discovered a strong interest in metagenomics research across Africa, to advance practical applications both for human health and the environment. We have also realized the great potential to develop genomics and bioinformatics through collaborative efforts across the continent, and the need for further reinforcing the untapped human potential and exploring the natural resources for stronger engagement of local scientific communities, with a view to contributing towards the improvement of human health and well-being for the citizens of Africa

    Polygenic risk scores for disease risk prediction in Africa: current challenges and future directions

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    Abstract Early identification of genetic risk factors for complex diseases can enable timely interventions and prevent serious outcomes, including mortality. While the genetics underlying many Mendelian diseases have been elucidated, it is harder to predict risk for complex diseases arising from the combined effects of many genetic variants with smaller individual effects on disease aetiology. Polygenic risk scores (PRS), which combine multiple contributing variants to predict disease risk, have the potential to influence the implementation for precision medicine. However, the majority of existing PRS were developed from European data with limited transferability to African populations. Notably, African populations have diverse genetic backgrounds, and a genomic architecture with smaller haplotype blocks compared to European genomes. Subsequently, growing evidence shows that using large-scale African ancestry cohorts as discovery for PRS development may generate more generalizable findings. Here, we (1) discuss the factors contributing to the poor transferability of PRS in African populations, (2) showcase the novel Africa genomic datasets for PRS development, (3) explore the potential clinical utility of PRS in African populations, and (4) provide insight into the future of PRS in Africa
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