GSA-SNP: a general approach for gene set analysis of polymorphisms

Nam, Dougu,Kim, Jin,Kim, Seon-Young,Kim, Sangsoo Oxford University Press 2010 Nucleic acids research Vol.38 No.2

<P>Genome-wide association (GWA) study aims to identify the genetic factors associated with the traits of interest. However, the power of GWA analysis has been seriously limited by the enormous number of markers tested. Recently, the gene set analysis (GSA) methods were introduced to GWA studies to address the association of gene sets that share common biological functions. GSA considerably increased the power of association analysis and successfully identified coordinated association patterns of gene sets. There have been several approaches in this direction with some limitations. Here, we present a general approach for GSA in GWA analysis and a stand-alone software GSA-SNP that implements three widely used GSA methods. GSA-SNP provides a fast computation and an easy-to-use interface. The software and test datasets are freely available at http://gsa.muldas.org. We provide an exemplary analysis on adult heights in a Korean population.</P>

ADGO: analysis of differentially expressed gene sets using composite GO annotation

Nam, Dougu,Kim, Sang-Bae,Kim, Seon-Kyu,Yang, Sungjin,Kim, Seon-Young,Chu, In-Sun Oxford University Press 2006 Bioinformatics Vol.22 No.18

<P><B>Motivation:</B> Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets.</P><P><B>Results:</B> Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function <I>F</I> and the cellular component <I>C</I> that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many ‘filtered’ composite terms the number of which reached ∼34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data.</P><P><B>Availability:</B> We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast.</P><P><B>Contact:</B> chu@kribb.re.kr</P><P><B>Supplementary information:</B> http://array.kobic.re.kr/ADGO</P>

PREDICTION OF DISEASE SUBTYPES USING ENSEMBLE MACHINE LEARNING METHODS

Dougu Nam,Sang-Mun Chi 한국산업응용수학회 2012 한국산업응용수학회 학술대회 논문집 Vol.7 No.2

Ensemble learning of genetic networks from time-series expression data.

Nam, Dougu,Yoon, Sung Ho,Kim, Jihyun F Oxford University Press 2007 Bioinformatics Vol.23 No.23

<P>MOTIVATION: Inferring genetic networks from time-series expression data has been a great deal of interest. In most cases, however, the number of genes exceeds that of data points which, in principle, makes it impossible to recover the underlying networks. To address the dimensionality problem, we apply the subset selection method to a linear system of difference equations. Previous approaches assign the single most likely combination of regulators to each target gene, which often causes over-fitting of the small number of data. RESULTS: Here, we propose a new algorithm, named LEARNe, which merges the predictions from all the combinations of regulators that have a certain level of likelihood. LEARNe provides more accurate and robust predictions than previous methods for the structure of genetic networks under the linear system model. We tested LEARNe for reconstructing the SOS regulatory network of Escherichia coli and the cell cycle regulatory network of yeast from real experimental data, where LEARNe also exhibited better performances than previous methods. AVAILABILITY: The MATLAB codes are available upon request from the authors.</P>

Analysis of triangular motifs in protein interaction networks and their implications to protein ages and cancer genes

Jeon, Hyeonseong,Kim, Suh Ryung,Nam, Dougu,Yoo, Yun Joo Inderscience Enterprises Ltd. 2017 International journal of data mining and bioinform Vol.19 No.4

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

Kim, Eun-Youn,Kim, Seon-Young,Ashlock, Daniel,Nam, Dougu BioMed Central 2009 BMC bioinformatics Vol.10 No.-

<P><B>Background</B></P><P>Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance.</P><P><B>Results</B></P><P>We present a cluster-number-based ensemble clustering algorithm, called <I>MULTI-K</I>, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple <I>k</I>-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the <I>entropy-plot </I>to control the separation of singletons or small clusters. MULTI-K, unlike the simple <I>k</I>-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets.</P><P><B>Conclusion</B></P><P>The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.</P>

Computational Analysis of Neighboring Genes on Arabidopsis thaliana Chromosomes 4 and 5: Their Genomic Association as Functional Subunits

Goh, Sung-Ho,Kim, Tae-Hyung,Kim, Jee-Hyub,Nam, DouGu,Choi, Doil,Hur, Cheol-Goo Korea Genome Organization 2003 Genomics & informatics Vol.1 No.1

The genes related to specific events or pathways in bacteria are frequently localized proximate to the genome of their neighbors, as with the structures known as operon, but eukaryotic genes seem to be independent of their neighbors, and are dispersed randomly throughout genomes. Although cases are rare, the findings from structures similar to prokaryotic operons in the nematode genome, and the clustering of housekeeping genes on human genome, lead us to assess the genomic association of genes as functional subunits. We evaluated the genomic association of neighboring genes on chromosomes 4 and 5 of Arabidopsis thaliana with and without respectively consideration of the scaffold/matrix­attached regions (S/MAR) loci. The observed number of functionally identical bigrams and trig rams were significantly higher than expected, and these results were verified statistically by calculating p-values for weighted random distributions. The observed frequency of functionally identical big rams and trig rams were much higher in chromosome 4 than in chromosome 5, but the frequencies with, and without, consideration of the S/MAR in each chromosome were similar. In this study, a genomic association among functionally related neighboring genes in Arabidopsis thaliana was suggested.

Unleashing the full potential of Hsp90 inhibitors as cancer therapeutics through simultaneous inactivation of Hsp90, Grp94, and TRAP1

Hye-Kyung Park,윤남구,이지은,Sung Hu,윤소라,So Yeon Kim,홍준희,Dougu Nam,Young Chan Chae,박종배,Byoung Heon Kang 생화학분자생물학회 2020 Experimental and molecular medicine Vol.52 No.-

The Hsp90 family proteins Hsp90, Grp94, and TRAP1 are present in the cell cytoplasm, endoplasmic reticulum, and mitochondria, respectively; all play important roles in tumorigenesis by regulating protein homeostasis in response to stress. Thus, simultaneous inhibition of all Hsp90 paralogs is a reasonable strategy for cancer therapy. However, since the existing pan-Hsp90 inhibitor does not accumulate in mitochondria, the potential anticancer activity of pan-Hsp90 inhibition has not yet been fully examined in vivo. Analysis of The Cancer Genome Atlas database revealed that all Hsp90 paralogs were upregulated in prostate cancer. Inactivation of all Hsp90 paralogs induced mitochondrial dysfunction, increased cytosolic calcium, and activated calcineurin. Active calcineurin blocked prosurvival heat shock responses upon Hsp90 inhibition by preventing nuclear translocation of HSF1. The purine scaffold derivative DN401 inhibited all Hsp90 paralogs simultaneously and showed stronger anticancer activity than other Hsp90 inhibitors. PanHsp90 inhibition increased cytotoxicity and suppressed mechanisms that protect cancer cells, suggesting that it is a feasible strategy for the development of potent anticancer drugs. The mitochondria-permeable drug DN401 is a newly identified in vivo pan-Hsp90 inhibitor with potent anticancer activity.

Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2

Yoon, Sora,Nguyen, Hai ,C ,T,Yoo, Yun J,Kim, Jinhwan,Baik, Bukyung,Kim, Sounkou,Kim, Jin,Kim, Sangsoo,Nam, Dougu Oxford University Press 2018 Nucleic acids research Vol.46 No.10

<P><B>Abstract</B></P><P>Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS <I>P</I>-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.</P>