Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. We compared the evolutionary conservation between human drug target genes and non- target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. Lv, Wenhua Xu, Yongdeng Guo, Yiying Yu, Ziqi Feng, Guanglong Liu, Panpan Luan, Meiwei Zhu, Hongjie Liu, Guiyou Zhang, Mingming Lv, Hongchao Duan, Lian Shang, Zhenwei Li, Jin Jiang, Yongshuai Zhang, RuijieĪlthough evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. The drug target genes show higher evolutionary conservation than non- target genes.
The hub genes discovered by ARNetMiT based GCNs are consistent with the literature. According to the evaluation of the topological features of ARNetMiT based GCNs, the degrees of nodes have power-law distribution. We see that using high confidence values in scGCNs increase the ratio of the overlapped gene-gene interactions between the compared methods. Overlap analysis results show that ARNetMiT outperforms the compared GNI algorithms. We also infer GCNs with popular GNI algorithms for comparison with the GCNs of ARNetMiT. We use overlap analysis and the topological features for the performance analysis of GCNs. Support and confidence values are used to prune association rules on miRNA- target genes data to construct support based GCNs (sGCNs) along with support and confidence based GCNs (scGCNs). Our approach assumes miRNAs as transactions and target genes as their items. We also present R package of ARNetMiT, which infers and visualizes GCNs of diseases that are selected by users. In this study, we introduce ARNetMiT that utilize a hash based association rule algorithm in a novel way to infer the GCNs on miRNA- target genes data. With the usage of these co-expressed genes, we can theoretically construct co-expression networks (GCNs) related to 152 diseases.
We hypothesize that target genes of miRNAs are co-expressed, when they are regulated by multiple miRNAs. The relations between miRNA- target genes enable users to derive co-expressed genes that may be involved in similar biological processes and functions in cells. MiRNAs are key regulators that bind to target genes to suppress their gene expression level. The cis-element based targeted gene finding approach is expected to be widely applicable since a large number of cis-elements in many species are available.ĪRNetMiT R Package: association rules based gene co-expression networks of miRNA targets.
Based on RT-PCR verification and the results from literature, this method has an accuracy rate of 67.5% for the top 40 predictions. We then use the ABRE-CE module to identify putative ABA-responsive genes in A.thaliana. We first construct and analyze two ABA specific cis-elements, ABA-responsive element (ABRE) and its coupling element (CE), in A.thaliana, based on their conservation in rice and other cereal plants. As a case study, we apply the above approach to predict the genes in model plant Arabidopsis thaliana which are inducible by a phytohormone, abscisic acid (ABA), and abiotic stress, such as drought, cold and salinity. Given such cis-elements, putative target genes whose promoters contain the elements can be identified. A viable approach to targeted gene finding is to exploit the cis-regulatory elements that are known to be responsible for the transcription of target genes.
Since gene regulation is mainly determined by the binding of transcription factors and cis-regulatory DNA sequences, most existing gene annotation methods, which exploit the conservation of open reading frames, are not effective in finding target genes. This problem can be referred to as targeted gene finding. Zhang, Weixiong Ruan, Jianhua Ho, Tuan-Hua David You, Youngsook Yu, Taotao Quatrano, Ralph SĪ fundamental problem of computational genomics is identifying the genes that respond to certain endogenous cues and environmental stimuli. Cis-regulatory element based targeted gene finding: genome-wide identification of abscisic acid- and abiotic stress-responsive genes in Arabidopsis thaliana.