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The importance of microRNA (miRNA) at the post-transcriptional regulation level has recently been recognized in both animals and plants. In recent years, many studies focused on miRNA target identification and functional analysis. However, little is known about the transcription and regulation of miRNAs themselves. In this study, the transcription start sites (TSSs) for 11 miRNA primary transcripts of soybean from 11 miRNA loci (of 50 loci tested) were cloned by a 5′ rapid amplification of cDNA ends (5′ RACE) procedure using total RNA from 30-d-old seedlings. The features consistent with a RNA polymerase II mechanism of transcription were found among these miRNA loci. A position weight matrix algorithm was used to identify conserved motifs in miRNA core promoter regions. A canonical TATA box motif was identified upstream of the major start site at 8 (76%) of the mapped miRNA loci. Several cis-acting elements were predicted in the 2 kb 5′ to the TSSs. Potential spatial and temporal expression patterns of the miRNAs were found. The target genes for these miRNAs were also predicted and further elucidated for the potential function of the miRNAs. This research provides a molecular basis to explore regulatory mechanisms of miRNA expression, and a way to understand miRNA-mediated regulatory pathways and networks in soybean.

Abstract  TagSNP selection, which aims to select a small subset of informative single nucleotide polymorphisms (SNPs) to represent the whole large SNP set, has played an important role in current genomic research. Not only can this cut down the cost of genotyping by filtering a large number of redundant SNPs, but also it can accelerate the study of genome-wide disease association. In this paper, we propose a new hybrid method called CMDStagger that combines the ideas of the clustering and the graph algorithm, to find the minimum set of tagSNPs. The proposed algorithm uses the information of the linkage disequilibrium association and the haplotype diversity to reduce the information loss in tagSNP selection, and has no limit of block partition. The approach is tested on eight benchmark datasets from Hapmap and chromosome 5q31. Experimental results show that the algorithm in this paper can reduce the selection time and obtain less tagSNPs with high prediction accuracy. It indicates that this method has better performance than previous ones. [ABSTRACT FROM AUTHOR]

Background: Recent studies have shown genetic variation is the basis of the genome-wide disease association research. However, due to the high cost on genotyping large number of single nucleotide polymorphisms (SNPs), it is essential to choose a small subset of informative SNPs (tagSNPs), which are able to capture most variation in a population, to represent the rest SNPs. Several methods have been proposed to find the minimum set of tagSNPs, but most of them still have some disadvantages such as information loss and block-partition limit. Results: This paper proposes a new hybrid method named CGTS which combines the ideas of the clustering and the graph algorithms to select tagSNPs on genotype data. This method aims to maximize the number of the discarding nontagSNPs in the given set. CGTS integrates the information of the LD association and the genotype diversity using the site graphs, discards redundant SNPs using the algorithm based on these graph structures. The clustering algorithm is used to reduce the running time of CGTS. The efficiency of the algorithm and quality of solutions are evaluated on biological data and the comparisons with three popular selecting methods are shown in the paper. Conclusion: Our theoretical analysis and experimental results show that our algorithm CGTS is not only more efficient than other methods but also can be get higher accuracy in tagSNP selection. [ABSTRACT FROM AUTHOR]

Background: Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results: To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion: Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time. [ABSTRACT FROM AUTHOR]