title: A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function.
abstract: The identification of genes associated with a given biological function in plants remains a challenge, although network-based gene prioritization algorithms have been developed forArabidopsis thalianaand many non-model plant species. Nevertheless, these network-based gene prioritization algorithms have encountered several problems; one in particular is that of unsatisfactory prediction accuracy due to limited network coverage, varying link quality, and/or uncertain network connectivity. Thus, a model that integrates complementary biological data may be expected to increase the prediction accuracy of gene prioritization. Toward this goal, we developed a novel gene prioritization method named RafSee, to rank candidate genes using a random forest algorithm that integrates sequence, evolutionary, and epigenetic features of plants. Subsequently, we proposed an integrative approach named RAP (Rank Aggregation-based data fusion for gene Prioritization), in which an order statistics-based meta-analysis was used to aggregate the rank of the network-based gene prioritization method and RafSee, for accurately prioritizing candidate genes involved in a pre-specific biological function. Finally, we showcased the utility of RAP by prioritizing 380 flowering-time genes inArabidopsis. The "leave-one-out" cross-validation experiment showed that RafSee could work as a complement to a current state-of-art network-based gene prioritization system (AraNet v2). Moreover, RAP ranked 53.68% (204/380) flowering-time genes higher than AraNet v2, resulting in an 39.46% improvement in term of the first quartile rank. Further evaluations also showed that RAP was effective in prioritizing genes-related to different abiotic stresses. To enhance the usability of RAP forArabidopsisand non-model plant species, an R package implementing the method is freely available at http://bioinfo.nwafu.edu.cn/software.
authors.affiliation: State Kay Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Northwest A&F University Yangling, China.
authors.forename: Jingjing
authors.initials: J
authors.lastname: Zhai
authors.affiliation: State Kay Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Northwest A&F University Yangling, China.
authors.forename: Yunjia
authors.initials: Y
authors.lastname: Tang
authors.affiliation: State Kay Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Northwest A&F University Yangling, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Yuan
authors.affiliation: State Kay Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Northwest A&F University Yangling, China.
authors.forename: Longteng
authors.initials: L
authors.lastname: Wang
authors.affiliation: State Kay Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Northwest A&F University Yangling, China.
authors.forename: Haoli
authors.initials: H
authors.lastname: Shang
authors.affiliation: State Kay Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Northwest A&F University Yangling, China.
authors.forename: Chuang
authors.initials: C
authors.lastname: Ma
keywords: biological network; data fusion; flowering time; gene prioritization; machine learning; meta-analysis; rank aggregation; systems biology