Novel Gene Discovery in the Human Malaria Parasite using Nucleosome Positioning Data.

N Pokhriyal, N Ponts, E Y Harris, K G Le Roch, S Lonardi
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Abstract

Recent genome-wide studies on nucleosome positioning in model organisms have shown strong evidence that nucleosome landscapes in the proximity of protein-coding genes exhibit regular characteristic patterns. Here, we propose a computational framework to discover novel genes in the human malaria parasite genome P. falciparum using nucleosome positioning inferred from MAINE-seq data. We rely on a classifier trained on the nucleosome landscape profiles of experimentally verified genes, and then used to discover new genes (without considering the primary DNA sequence). Cross-validation experiments show that our classifier is very accurate. About two thirds of the locations reported by the classifier match experimentally determined expressed sequence tags in GenBank, for which no gene has been annotated in the human malaria parasite.

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利用核糖体定位数据发现人类疟疾寄生虫中的新基因
最近关于模式生物核糖体定位的全基因组研究有力地证明,蛋白编码基因附近的核糖体景观表现出规律性的特征模式。在这里,我们提出了一个计算框架,利用从 MAINE-seq 数据中推断出的核糖体定位发现人类疟原虫基因组恶性疟原虫中的新基因。我们依靠的是根据实验验证基因的核糖体分布图训练的分类器,然后用来发现新基因(不考虑主 DNA 序列)。交叉验证实验表明,我们的分类器非常准确。分类器报告的位置中约有三分之二与 GenBank 中经实验确定的表达序列标签相匹配,而人类疟原虫中还没有基因被注释。
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Novel Gene Discovery in the Human Malaria Parasite using Nucleosome Positioning Data. Estimating support for protein-protein interaction data with applications to function prediction. On the accurate construction of consensus genetic maps. Efficient haplotype inference from pedigrees with missing data using linear systems with disjoint-set data structures. Knowledge representation and data mining for biological imaging.
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