In recent years, a deep learning model called convolutional neural network with an ability of extracting features of high-level abstraction from minimum preprocessing data has been widely used. In this research, we proposed a new approach in classifying DNA sequences using the convolutional neural network while considering these sequences as text data. We used one-hot vectors to represent sequences as input to the model; therefore, it conserves the essential position information of each nucleotide in sequences. Using 12 DNA sequence datasets, we evaluated our proposed model and achieved significant improvements in all of these datasets. This result has shown a potential of using convolutional neural network for DNA sequence to solve other sequence problems in bioinformatics.

Inflammation has an important role in cancer development through various mechanisms. It has been shown that dysregulation of microRNAs (miRNAs) that function as oncogenes or tumor suppressors contributes to tumorigenesis. However, the relationship between inflammation and cancer-related miRNA expression in tumorigenesis has not yet been fully understood. Using K19-C2mE and Gan mouse models that develop gastritis and gastritis-associated tumors, respectively, we found that 21 miRNAs were upregulated, and that 29 miRNAs were downregulated in gastric tumors in an inflammation-dependent manner. Among these miRNAs, the expression of miR-7, a possible tumor suppressor, significantly decreased in both gastritis and gastric tumors. Moreover, the expression of miR-7 in human gastric cancer was inversely correlated with the levels of interleukin-1b and tumor necrosis factor-a, suggesting that miR-7 downregulation is related to the severity of inflammatory responses. In the normal mouse stomach, miR-7 expression was at a basal level in undifferentiated gastric epithelial cells, and was induced during differentiation. Moreover, transfection of a miR-7 precursor into gastric cancer cells suppressed cell proliferation and soft agar colony formation. These results suggest that suppression of miR-7 expression is important for maintaining the undifferentiated status of gastric epithelial cells, and thus contributes to gastric tumorigenesis. Although epigenetic changes were not found in the CpG islands around miR-7-1 of gastritis and gastric tumor cells, we found that activated macrophage-derived small molecule(s) (o3 kDa) are responsible for miR-7 repression in gastric cancer cells. Furthermore, the miR-7 expression level significantly decreased in the inflamed gastric mucosa of Helicobacter-infected mice, whereas it increased in the stomach of germfree K19-C2mE and Gan mice wherein inflammatory responses were suppressed.Taken together, these results indicate that downregulation of tumor suppressor miR-7 is a novel mechanism by which the inflammatory response promotes gastric tumorigenesis.

Background: Upstream open reading frames (uORFs) in the 5′-untranslated regions (5′-UTRs) of certain eukaryotic mRNAs encode evolutionarily conserved functional peptides, such as cis-acting regulatory peptides that control translation of downstream main ORFs (mORFs). For genome-wide searches for uORFs with conserved peptide sequences (CPuORFs), comparative genomic studies have been conducted, in which uORF sequences were compared between selected species. To increase chances of identifying CPuORFs, we previously developed an approach in which uORF sequences were compared using BLAST between Arabidopsis and any other plant species with available transcript sequence databases. If this approach is applied to multiple plant species belonging to phylogenetically distant clades, it is expected to further comprehensively identify CPuORFs conserved in various plant lineages, including those conserved among relatively small taxonomic groups.

Background: MicroRNAs (miRNAs) are a class of small non-coding RNA molecules (20-24 nt), which are believed to participate in repression of gene expression. They play important roles in several biological processes (e.g. cell death and cell growth). Both experimental and computational approaches have been used to determine the function of miRNAs in cellular processes. Most efforts have concentrated on identification of miRNAs and their target genes. However, understanding the regulatory mechanism of miRNAs in the gene regulatory network is also essential to the discovery of functions of miRNAs in complex cellular systems. To understand the regulatory mechanism of miRNAs in complex cellular systems, we need to identify the functional modules involved in complex interactions between miRNAs and their target genes.