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Translational genomics for achieving higher genetic gains in groundnut.
Pandey, MK, Pandey, AK, Kumar, R, Nwosu, CV, Guo, B, Wright, GC, Bhat, RS, Chen, X, Bera, SK, Yuan, M, et al
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik. 2020;(5):1679-1702
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Abstract
Groundnut has entered now in post-genome era enriched with optimum genomic and genetic resources to facilitate faster trait dissection, gene discovery and accelerated genetic improvement for developing climate-smart varieties. Cultivated groundnut or peanut (Arachis hypogaea), an allopolyploid oilseed crop with a large and complex genome, is one of the most nutritious food. This crop is grown in more than 100 countries, and the low productivity has remained the biggest challenge in the semiarid tropics. Recently, the groundnut research community has witnessed fast progress and achieved several key milestones in genomics research including genome sequence assemblies of wild diploid progenitors, wild tetraploid and both the subspecies of cultivated tetraploids, resequencing of diverse germplasm lines, genome-wide transcriptome atlas and cost-effective high and low-density genotyping assays. These genomic resources have enabled high-resolution trait mapping by using germplasm diversity panels and multi-parent genetic populations leading to precise gene discovery and diagnostic marker development. Furthermore, development and deployment of diagnostic markers have facilitated screening early generation populations as well as marker-assisted backcrossing breeding leading to development and commercialization of some molecular breeding products in groundnut. Several new genomics applications/technologies such as genomic selection, speed breeding, mid-density genotyping assay and genome editing are in pipeline. The integration of these new technologies hold great promise for developing climate-smart, high yielding and more nutritious groundnut varieties in the post-genome era.
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An improved Gibbs sampling method for motif discovery via sequence weighting.
Chen, X, Jiang, T
Computational systems bioinformatics. Computational Systems Bioinformatics Conference. 2006;:239-47
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Abstract
The discovery of motifs in DNA sequences remains a fundamental and challenging problem in computational molecular biology and regulatory genomics, although a large number of computational methods have been proposed in the past decade. Among these methods, the Gibbs sampling strategy has shown great promise and is routinely used for finding regulatory motif elements in the promoter regions of co-expressed genes. In this paper, we present an enhancement to the Gibbs sampling method when the expression data of the concerned genes is given. A sequence weighting scheme is proposed by explicitly taking gene expression variation into account in Gibbs sampling. That is, every putative motif element is assigned a weight proportional to the fold change in the expression level of its downstream gene under a single experimental condition, and a position specific scoring matrix (PSSM) is estimated from these weighted putative motif elements. Such an estimated PSSM might represent a more accurate motif model since motif elements with dramatic fold changes in gene expression are more likely to represent true motifs. This weighted Gibbs sampling method has been implemented and successfully tested on both simulated and biological sequence data. Our experimental results demonstrate that the use of sequence weighting has a profound impact on the performance of a Gibbs motif sampling algorithm.