RDF Curator: A Novel Workflow that Generates Semantic Graph from Literature for Curation Using Text Mining

There exist few databases that enable cross-reference among various research fields related to bioenergy. Cross-reference is highly desired among bioinformatics databases related to environment, energy, and agriculture for better mutual cooperation. By uniting Semantic Graph, we can economically construct a distributed database, regardless of the size of research laboratories and research endeavors.

Our purpose is to design and develop a workflow based on RDF (Resource Description Framework) that generates Semantic Graph for a set of technical terms extracted from documents of various formats, such as PDF, HTML, and plain text. Our attempt is to generate Semantics Graph as a result of text mining including morphological analysis and syntax analysis.

We have developed a prototype of workflow program named "RDF Curator". By using this system, various types of documents can be automatically converted into RDF. "RDF Curator" is composed of general tools and libraries so that no special environment is needed. Hence, “RDF Curator” can be used on many platforms, such as MacOSX, Linux, and Windows (Cygwin). We expect that our system can assist human curators in constructing Semantic Graph. Although fast and high throughput, the accuracy of the present version of "RDF Curator" is lower than that of human curators. As a future task, we have to improve the accuracy of the workflow. In addition, we also plan to apply our system to analysis of network similarity

Microarray-Based Cancer Prediction Using Soft Computing Approach

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably

Inference of Cancer-specific Gene Regulatory Networks Using Soft Computing Rules

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer

A Robust Gene Selection Method for Microarray-based Cancer Classification

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers

Cgaln: fast and space-efficient whole-genome alignment

<p>Abstract</p> <p>Background</p> <p>Whole-genome sequence alignment is an essential process for extracting valuable information about the functions, evolution, and peculiarities of genomes under investigation. As available genomic sequence data accumulate rapidly, there is great demand for tools that can compare whole-genome sequences within practical amounts of time and space. However, most existing genomic alignment tools can treat sequences that are only a few Mb long at once, and no state-of-the-art alignment program can align large sequences such as mammalian genomes directly on a conventional standalone computer.</p> <p>Results</p> <p>We previously proposed the CGAT (Coarse-Grained AlignmenT) algorithm, which performs an alignment job in two steps: first at the block level and then at the nucleotide level. The former is "coarse-grained" alignment that can explore genomic rearrangements and reduce the sizes of the regions to be analyzed in the next step. The latter is detailed alignment within limited regions. In this paper, we present an update of the algorithm and the open-source program, Cgaln, that implements the algorithm. We compared the performance of Cgaln with those of other programs on whole genomic sequences of several bacteria and of some mammalian chromosome pairs. The results showed that Cgaln is several times faster and more memory-efficient than the best existing programs, while its sensitivity and accuracy are comparable to those of the best programs. Cgaln takes less than 13 hours to finish an alignment between the whole genomes of human and mouse in a single run on a conventional desktop computer with a single CPU and 2 GB memory.</p> <p>Conclusions</p> <p>Cgaln is not only fast and memory efficient but also effective in coping with genomic rearrangements. Our results show that Cgaln is very effective for comparison of large genomes, especially of intact chromosomal sequences. We believe that Cgaln provides novel viewpoint for reducing computational complexity and will contribute to various fields of genome science.</p

The Microenvironment of Freeze-Injured Mouse Urinary Bladders Enables Successful Tissue Engineering

Mouse bone marrow-derived cells implanted into freeze-injured bladder walls form smooth muscle layers, but not in intact walls. We determined if the microenvironment within injured urinary bladders was supportive of smooth muscle layer development. The urinary bladders of female nude mice were freeze-injured for 30 s. Three days later, the rate of blood flow in the wounded areas and in comparable areas of intact control urinary bladders was observed by charge-coupled device (CCD) video microscopy. Injured and control bladder walls were also analyzed histologically and cytologically. Growth factor mRNA expression was determined by real-time reverse transcription polymerase chain reaction arrays. The injured regions maintained a partial microcirculation in which blood flow velocity was significantly less than in controls. The injured bladder walls had few typical smooth muscle layers, and blood vessels in the walls had reduced smooth muscle content. The loss of smooth muscle cells in the bladder walls may have resulted in the formation of large porous spaces seen by scanning electron microscopy of the injured areas. The expression of nineteen growth-related mRNAs, including secreted phosphoprotein 1, inhibin beta-A, glial cell line-derived neurotrophic factor, and transforming growth factor beta 1, were significantly upregulated in the injured urinary bladders. In conclusion, the microenvironment in freeze-injured urinary bladders enables successful tissue engineering.ArticleTISSUE ENGINEERING PART A. 15(11):3367-3375 (2009)journal articl

Improvement in accuracy of multiple sequence alignment using novel group-to-group sequence alignment algorithm with piecewise linear gap cost

BACKGROUND: Multiple sequence alignment (MSA) is a useful tool in bioinformatics. Although many MSA algorithms have been developed, there is still room for improvement in accuracy and speed. In the alignment of a family of protein sequences, global MSA algorithms perform better than local ones in many cases, while local ones perform better than global ones when some sequences have long insertions or deletions (indels) relative to others. Many recent leading MSA algorithms have incorporated pairwise alignment information obtained from a mixture of sources into their scoring system to improve accuracy of alignment containing long indels. RESULTS: We propose a novel group-to-group sequence alignment algorithm that uses a piecewise linear gap cost. We developed a program called PRIME, which employs our proposed algorithm to optimize the well-defined sum-of-pairs score. PRIME stands for Profile-based Randomized Iteration MEthod. We evaluated PRIME and some recent MSA programs using BAliBASE version 3.0 and PREFAB version 4.0 benchmarks. The results of benchmark tests showed that PRIME can construct accurate alignments comparable to the most accurate programs currently available, including L-INS-i of MAFFT, ProbCons, and T-Coffee. CONCLUSION: PRIME enables users to construct accurate alignments without having to employ pairwise alignment information. PRIME is available at

Antibacterial Effects of Disulfiram in Helicobacter pylori

Background: Helicobacter pylori infection poses a risk of the occurrence of gastrointestinal diseases, such as gastric cancer. Its incidence rate is significantly reduced by eradication, and thereby, eradication therapy is generally performed. Disulfiram is an oral prescription drug mainly used for the treatment of alcohol dependence. In recent years, reports have been made on its anticancer and antibacterial effects, and thus, it has recently become an interesting subject. This study aimed to examine the antibacterial activity of disulfiram, investigate the presence or absence of its antibacterial activity on H. pylori, and determine whether it could be a new bactericidal drug against drug-resistant H. pylori. Materials and Methods: Drug-sensitive strains of H. pylori and amoxicillin-resistant, clarithromycin-resistant, and metronidazole-resistant strains were used, and a growth inhibition test of H. pylori using disulfiram was performed. Furthermore, the expression of urease, vacuolating cytotoxin A (VacA), and CagA, the virulence proteins of H. pylori, was quantitatively analyzed using the Western blotting method. In addition, for H. pylori used in this study, the 16SrDNA sequence, a ribosomal gene involved in protein production, was analyzed to examine the presence or absence of gene mutation. Results: Disulfiram suppressed the growth of 7 out of 12 H. pylori strains at 1 mu g/mL, and no correlation was observed between their susceptibility/resistance to current eradication antimicrobial drugs and disulfiram resistance. Disulfiram reduced the expression levels of urease, VacA, and CagA proteins. H. pylori, which showed resistance to disulfiram, tended to have fewer gene deletions/insertions in the 16S rDNA sequence; however, no specific mutation was detected. Conclusion: Disulfiram has a bactericidal effect on H. pylori at low concentrations, suggesting that it can be used as a supplement for current H. pylori eradication drugs

Serodiagnosis and Bacterial Genome of Helicobacter pylori Infection

The infection caused by Helicobacter pylori is associated with several diseases, including gastric cancer. Several methods for the diagnosis of H. pylori infection exist, including endoscopy, the urea breath test, and the fecal antigen test, which is the serum antibody titer test that is often used since it is a simple and highly sensitive test. In this context, this study aims to find the association between different antibody reactivities and the organization of bacterial genomes. Next-generation sequences were performed to determine the genome sequences of four strains of antigens with different reactivity. The search was performed on the common genes, with the homology analysis conducted using a genome ring and dot plot analysis. The two antigens of the highly reactive strains showed a high gene homology, and Western blots for CagA and VacA also showed high expression levels of proteins. In the poorly responsive antigen strains, it was found that the inversion occurred around the vacA gene in the genome. The structure of bacterial genomes might contribute to the poor reactivity exhibited by the antibodies of patients. In the future, an accurate serodiagnosis could be performed by using a strain with few gene mutations of the antigen used for the antibody titer test of H. pylori