Traditional sequence analysis depends on sequence alignment. In this study, we analyzed various functional regions of the human genome based on sequence features, including word frequency, dinucleotide relative abundance, and base-base correlation. We analyzed the human chromosome 22 and classified the upstream, exon, intron, downstream, and intergenic regions by principal component analysis and discriminant analysis of these features. The results show that we could classify the functional regions of genome based on sequence feature and discriminant analysis.

Domestic animals show considerable genetic diversity. Previous studies suggested that animal phenotypes were affected by miRNA-mRNA interplay, but these studies focused mainly on the analysis of one or several miRNA-mRNA interactions. However, in this study, we investigated miRNA-mRNA and miRNA-lncRNA interactions on a genomic scale using miranda and targetscan algorithms. There has been strong directional artificial selection practiced during the domestication of animals. Thus, we investigated SNPs that were located in miRNAs and miRNA binding sites and found that several SNPs located in 3'-UTRs of mRNAs had the potential to affect miRNA-mRNA interactions. In addition, a database, named miRBond, was developed to provide visualization, analysis and downloading of the resulting datasets. Our results open the way to further experimental verification of miRNA-mRNA and miRNA-lncRNA interactions as well as the influence of SNPs upon such interplay.

MicroRNAs (miRNAs) play an indispensable role in cancer initiation and progression. Different cancers have some common hallmarks in general. Analyzing miRNAs that consistently contribute to different cancers can help us to discover the relationship between miRNAs and traits shared by cancers. Most previous works focus on analyzing single miRNA. However, dysregulation of a single miRNA is generally not sufficient to contribute to complex cancer processes. In this study, we put emphasis on analyzing cooperation of miRNAs across cancers. We assume that miRNAs can cooperatively regulate oncogenic pathways and contribute to cancer hallmarks. Such a cooperation is modeled by a miRNA module referred to as a pan-cancer conserved miRNA module. The module consists of miRNAs which simultaneously regulate cancers and are significantly intra-correlated. A novel computational workflow for the module discovery is presented. Multiple modules are discovered from miRNA expression profiles using the method. The function of top two ranked modules are analyzed using the mRNAs which correlate to all the miRNAs in a module across cancers, inferring that the two modules function in regulating the cell cycle which relates to cancer hallmarks as self sufficiency in growth signals and insensitivity to antigrowth signals. Additionally, two novel miRNAs mir-590 and mir-629 are found to cooperate with well-known onco-miRNAs in the modules to contribute to cancers. We also found that PTEN, which is a well known tumor suppressor that regulates the cell cycle, is a common target of miRNAs in the top-one module and cooperative control of PTEN can be a reason for the miRNAs' cooperation. We believe that analyzing the cooperative mechanism of the miRNAs in modules rather than focusing on only single miRNAs may help us know more about the complicated relationship between miRNAs and cancers and develop more effective treatment strategies for cancers.

ABSTRACT. Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs. Distinguishing piRNAs from other non-coding RNAs is important because of their important role in the physiological regulation of spermatogenesis, genome protection from transposons, and regulation of mRNAs and long non-coding RNAs. Few computational studies have addressed piRNAs detection, and both effectiveness and efficiency of piRNA detection tools require improvement. In this study, a piRNA detection method based on sequence features and a support vector machine was developed. Four types of features are proposed: weighted k-mer, weighted k-mer with wildcards, position-specific base, and piRNA length. The piRNA sequences from human, mouse, rat, and drosophila were respectively used in this experiment. Compared to existing algorithms, the proposed method provides a better balance between precision and sensitivity (both are approximately 90%), and although these values were slightly slower than those obtained using the piRNA annotation approach, the proposed method was four-fold faster than piRPred and 229-fold faster than piRNA predictor.