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Background: PSEUDO RESPONSE REGULATOR (PRR) genes are essential components of circadian clock, playing vital roles in multiple processes including plant growth, flowering and stress response. Nonetheless, little is known about the evolution and function of PRR family in Rosaceae species. Results: In this study, a total of 43 PRR genes in seven Rosaceae species were identified through comprehensive analysis. The evolutionary relationships were analyzed with phylogenetic tree, duplication events and synteny. PRR genes were classified into three groups (PRR1, PRR5/9, PRR3/7). The expansion of PRR family was mainly derived from dispersed and whole-genome duplication events. Purifying selection was the major force for PRR family evolution. Synteny analysis indicated the existence of multiple orthologous PRR gene pairs between pear and other Rosaceae species. Moreover, the conserved motifs of eight PbPRR proteins supported the phylogenetic relationship. PRR genes showed diverse expression pattern in various tissues of pear (Pyrus bretschneideri). Transcript analysis under 12-h light/ dark cycle and constant light conditions revealed that PRR genes exhibited distinct rhythmic oscillations in pear. PbPRR59a and PbPRR59b highly homologous to AtPRR5 and AtPRR9 were cloned for further functional verification. PbPRR59a and PbPRR59b proteins were localized in the nucleus. The ectopic overexpression of PbPRR59a and PbPRR59b significantly delayed flowering in Arabidopsis transgenic plants by repress the expression of AtGI, AtCO and AtFT under long-day conditions. Conclusions: These results provide information for exploring the evolution of PRR genes in plants, and contribute to the subsequent functional studies of PRR genes in pear and other Rosaceae species. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are 19-25 nucleotides non-coding RNAs known to have important post-transcriptional regulatory functions. The computational target prediction algorithm is vital to effective experimental testing. However, since different existing algorithms rely on different features and classifiers, there is a poor agreement among the results of different algorithms. To benefit from the advantages of different algorithms, we proposed an algorithm called BCmicrO that combines the prediction of different algorithms with Bayesian Network. BCmicrO was evaluated using the training data and the proteomic data. The results show that BCmicrO improves both the sensitivity and the specificity of each individual algorithm. All the related materials including genome-wide prediction of human targets and a web-based tool are available at http://compgenomics.utsa.edu/gene/gene_1.php.

Background: MicroRNAs (miRNAs) are single-stranded non-coding RNAs shown to plays important regulatory roles in a wide range of biological processes and diseases. The functions and regulatory mechanisms of most of miRNAs are still poorly understood in part because of the difficulty in identifying the miRNA regulatory targets. To this end, computational methods have evolved as important tools for genome-wide target screening. Although considerable work in the past few years has produced many target prediction algorithms, most of them are solely based on sequence, and the accuracy is still poor. In contrast, gene expression profiling from miRNA transfection experiments can provide additional information about miRNA targets. However, most of existing research assumes down-regulated mRNAs as targets. Given the fact that the primary function of miRNA is protein inhibition, this assumption is neither sufficient nor necessary., Results: A novel Bayesian approach is proposed in this paper that integrates sequence level prediction with expression profiling of miRNA transfection. This approach does not restrict the target to be down-expressed and thus improve the performance of existing target prediction algorithm. The proposed algorithm was tested on simulated data, proteomics data, and IP pull-down data and shown to achieve better performance than existing approaches for target prediction. All the related materials including source code are available at http://compgenomics.utsa.edu/expmicro.html., Conclusions: The proposed Bayesian algorithm integrates properly the sequence paring data and mRNA expression profiles for miRNA target prediction. This algorithm is shown to have better prediction performance than existing algorithms.