SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis

<p>Abstract</p> <p>Background</p> <p>Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly.</p> <p>Results</p> <p>We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', r²and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient (r²). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis.</p> <p>Conclusion</p> <p>SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use.</p

Impact factor of Korean Journal of Pediatrics on Korean Medical Citation Index and Science Citation Index of Web of Science

PurposeThe total number of times a paper is cited, also known as the impact factor (IF) of a medical journal, is widely implied in evaluating the quality of a research paper. We evaluated the citation index data as an IF of Korean J Pediatr in Korean Medical Citation Index (KoMCI) and JCI of Web of Science.MethodsWe calculated the IF of Korean J Pediatr at KoMCI supervised by Korean Association of Medical Journal Editors. And we estimated the IF of Korean J Pediatr by the JCI of Web of Science although it was never officially reported.ResultsThe IF of Korean J Pediatr on KoMCI has increased from 0.100 in the year 2000, to 0.205 in 2008, and 0.326 in 2009. Although the IF of Korean J Pediatr was 0.006 in 2005, 0.018 in 2006, 0.028 in 2008, 0.066 in 2009, and 0.018 in 2010 according to the JCI of Web of Science, the number of citations are steadily increasing.ConclusionUnderstanding and realizing the current status will be a stepping stone for further improvement. The next objective of the Korean J Pediatr is to become registered in the SCI or SCIE. Increasing the IF according to the JCI of Web of Science is crucial in order to achieve this goal

Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions

<p>Abstract</p> <p>Background</p> <p>The information from different data sets experimented under different conditions may be inconsistent even though they are performed with the same research objectives. More than that, even when the data sets were generated from the same platform, the data agreement may be affected by the technical variation among the laboratories. In this case, it is necessary to use the combined data set after adjusting the differences between such data sets, for detecting the more reliable information.</p> <p>Results</p> <p>The proposed method combines data sets posterior to the discretization of data sets based on the ranks of the gene expression ratios, and the statistical method is applied to the combined data set for predictive gene selection. The efficiency of the proposed method was evaluated using five colon cancer related data sets, which were experimented using cDNA microarrays with different RNA sources, and one experiment utilized oligonucleotide arrays. NCI-60 cell lines data sets were used, which were performed with two different platforms of cDNA microarrays and Affymetrix HU6800 oligonucleotide arrays. The combined data set by the proposed method predicted the test data sets more accurately than the separated data sets did. The biological significant genes were detected from the combined data set, which were missed on the separated data sets.</p> <p>Conclusion</p> <p>By transforming gene expressions using ranks, the proposed method is not influenced by systematic bias among chips and normalization method. The method may be especially more useful to find predictive genes from data sets which have different scale in gene expressions.</p

Green Tea Consumption and Stomach Cancer Risk: A Meta-Analysis

OBJECTIVES: Green tea has been suggested to have a chemopreventive effect against various cancers including stomach cancer. The aim of this study is to elucidate the relationship between green tea consumption and stomach cancer risk by meta-analysis. METHODS: Eighteen observational studies were identified using MEDLINE, THE COCHRANE LIBRARY, RISS, and a manual search. Summary relative risks/odds ratios (RR/ORs) for the highest versus non/lowest green tea consumption levels were calculated on the basis of fixed and random effect models. Subgroup analyses were used to examine heterogeneity across the studies. RESULTS: The combined results indicate a reduced risk of stomach cancer with intake of green tea (RR/OR=0.86, 95% CI=0.74-1.00). Subgroup analysis with six studies that reported differences between the highest and lowest consumption levels equal to or greater than five cups/day revealed a statistically significant protective effect (RR/OR=0.68, 95% CI=0.53-0.87). CONCLUSION: Green tea appears to play a protective role against the development of stomach cancer. The results also suggest that a higher level of green tea consumption might be needed for a clear preventive effect to appear. This conclusion, however, should be interpreted with caution because various biases can affect the results of a meta-analysis.ope

Novel and simple transformation algorithm for combining microarray data sets

<p>Abstract</p> <p>Background</p> <p>With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis.</p> <p>Results</p> <p>Two microarray data sets based on a 17k cDNA microarray system were used, consisting of 82 normal colon mucosa and 72 colorectal cancer tissues. Each data set was prepared from either total RNA or amplified mRNA, and the difference of RNA source between these two data sets was detected by ANOVA (Analysis of variance) model. A simple integration method was introduced which was based on the distributions of gene expression ratios among different microarray data sets. The method transformed gene expression ratios into the form of a reference data set on a gene by gene basis. Hierarchical clustering analysis, density and box plots, and mixture scores with correlation coefficients revealed that the two data sets were well intermingled, indicating that the proposed method minimized the experimental bias. In addition, any RNA source effect was not detected by the proposed transformation method. In the mixed data set, two previously identified subgroups of normal and tumor were well separated, and the efficiency of integration was more prominent in tumor groups than normal groups. The transformation method was slightly more effective when a data set with strong homogeneity in the same experimental group was used as a reference data set.</p> <p>Conclusion</p> <p>Proposed method is simple but useful to combine several data sets from different experimental conditions. With this method, biologically useful information can be detectable by applying various analytic methods to the combined data set with increased sample size.</p

Genome-wide genetic aberrations of thymoma using cDNA microarray based comparative genomic hybridization

BACKGROUND: Thymoma is a heterogeneous group of tumors in biology and clinical behavior. Even though thymoma is divided into five subgroups following the World Health Organization classification, the nature of the disease is mixed within the subgroups. RESULTS: We investigated the molecular characteristics of genetic changes variation of thymoma using cDNA microarray based-comparative genomic hybridization (CGH) with a 17 K cDNA microarray in an indirect, sex-matched design. Genomic DNA from the paraffin embedded 39 thymoma tissues (A 6, AB 11, B1 7, B2 7, B3 8) labeled with Cy-3 was co-hybridized with the reference placenta gDNA labeled with Cy-5. Using the CAMVS software, we investigated the deletions on chromosomes 1, 2, 3, 4, 5, 6, 8, 12, 13 and 18 throughout the thymoma. Then, we evaluated the genetic variations of thymoma based on the subgroups and the clinical behavior. First, the 36 significant genes differentiating five subgroups were selected by Significance Analysis of Microarray. Based on these genes, type AB was suggested to be heterogeneous at the molecular level as well as histologically. Next, we observed that the thymoma was divided into A, B (1, 2) and B3 subgroups with 33 significant genes. In addition, we selected 70 genes differentiating types A and B3, which differ largely in clinical behaviors. Finally, the 11 heterogeneous AB subtypes were able to correctly assign into A and B (1, 2) types based on their genetic characteristics. CONCLUSION: In our study, we observed the genome-wide chromosomal aberrations of thymoma and identified significant gene sets with genetic variations related to thymoma subgroups, which might provide useful information for thymoma pathobiology.ope

Institutional Board Review for Clinical Investigations on Inflammatory Bowel Diseases: A Single-Center Study

Background/AimsThe growing volume and the diversity of clinical research has led to related laws and regulations as well as the Institutional Review Board (IRB) approval process becoming more stringent. To conduct clinical research efficiently and while following regulations, information about the IRB approval process and feedback is important for investigators. This has yet to be studied.MethodsWe included 381 gastrointestinal disease research proposals (79 with inflammatory bowel disease [IBD], and 302 with non-IBD) reviewed by the IRB of Severance Hospital between January 2009 and December 2013. We retrospectively analyzed research characteristics including research risk levels, results of initial reviews, frequencies of continuing review, numbers of IRB comments, frequencies of IRB comments, and durations from submission to approval.ResultsInvestigators' decisions on risk level were higher in the IBD group than in the non-IBD group (P<0.05). Results of initial reviews, frequencies of continuing reviews, the numbers of IRB review comments, and durations from submission to approval were not different between the two groups, but IRB decisions on risk level were higher in the IBD group (P<0.05). In subgroup analysis, the number of IRB comments from initial review on informed consent forms and procedures as well were quest of more information were significantly higher in the IBD group than in the non-IBD group (P<0.001 and 0.01, respectively).ConclusionsIn Korea, rare diseases such as IBD require more information for the IRB process due to their distinct characteristics. IBD researchers should develop research protocols more carefully and make their research as subject-friendly as possible

SFRP4 drives invasion in gastric cancer and is an early predictor of recurrence.

OBJECTIVE: Gastric cancer patients generally have a poor outcome, particularly those with advanced-stage disease which is defined by the increased invasion of cancer locally and is associated with higher metastatic potential. This study aimed to identify genes that were functional in the most fundamental hallmark of cancer, namely invasion. We then wanted to assess their value as biomarkers of gastric cancer progression and recurrence. DESIGN: Data from a cohort of patients profiled on cDNA expression arrays was interrogated using K-means analysis. This genomic approach classified the data based on patterns of gene expression allowing the identification of the genes most correlated with the invasion of GC. We evaluated the functional role of a key protein from this analysis in invasion and as a biomarker of recurrence after curative resection. RESULTS: Expression of secreted frizzled-related protein 4 (SFRP4) was identified as directly proportional to gastric cancer invasion. This finding was validated in multiple, independent datasets and its functional role in invasion was also confirmed using invasion assays. A change in serum levels of SFRP4 after curative resection, when coupled with AJCC stage, can accurately predict the risk of disease recurrence after curative therapy in an assay we termed PredictR. CONCLUSIONS: This simple ELISA-based assay can help predict recurrence of disease after curative gastric cancer surgery irrespective of adjuvant therapy. The results require further evaluation in a prospective trial but would help in the rational prescription of cancer therapies and surveillance to prevent under or over treatment of patients after curative resection