CrossChip: a system supporting comparative analysis of different generations of Affymetrix arrays

Kong, Sek Won,Hwang, Kyu-Baek,Kim, Richard D.,Zhang, Byoung-Tak,Greenberg, Steven A.,Kohane, Isaac S.,Park, Peter J. Oxford University Press 2005 Bioinformatics Vol.21 No.9

<P><B>Summary:</B> To increase compatibility between different generations of Affymetrix GeneChip arrays, we propose a method of filtering probes based on their sequences. Our method is implemented as a web-based service for downloading necessary materials for converting the raw data files (*.CEL) for comparative analysis. The user can specify the appropriate level of filtering by setting the criteria for the minimum overlap length between probe sequences and the minimum number of usable probe pairs per probe set. Our website supports a within-species comparison for human and mouse GeneChip arrays.</P><P><B>Availability:</B> http://www.crosschip.org</P><P><B>Contact:</B> skong@cgr.harvard.edu</P>

Integrative analysis reveals the direct and indirect interactions between DNA copy number aberrations and gene expression changes.

Lee, Hyunju,Kong, Sek Won,Park, Peter J Oxford University Press 2008 Bioinformatics Vol.24 No.7

<P>MOTIVATION: DNA copy number aberrations (CNAs) and gene expression (GE) changes provide valuable information for studying chromosomal instability and its consequences in cancer. While it is clear that the structural aberrations and the transcript levels are intertwined, their relationship is more complex and subtle than initially suspected. Most studies so far have focused on how a CNA affects the expression levels of those genes contained within that CNA. RESULTS: To better understand the impact of CNAs on expression, we investigated the correlation of each CNA to all other genes in the genome. The correlations are computed over multiple patients that have both expression and copy number measurements in brain, bladder and breast cancer data sets. We find that a CNA has a direct impact on the gene amplified or deleted, but it also has a broad, indirect impact elsewhere. To identify a set of CNAs that is coordinately associated with the expression changes of a set of genes, we used a biclustering algorithm on the correlation matrix. For each of the three cancer types examined, the aberrations in several loci are associated with cancer-type specific biological pathways that have been described in the literature: CNAs of chromosome (chr) 7p13 were significantly correlated with epidermal growth factor receptor signaling pathway in glioblastoma multiforme, chr 13q with NF-kappaB cascades in bladder cancer, and chr 11p with Reck pathway in breast cancer. In all three data sets, gene sets related to cell cycle/division such as M phase, DNA replication and cell division were also associated with CNAs. Our results suggest that CNAs are both directly and indirectly correlated with changes in expression and that it is beneficial to examine the indirect effects of CNAs. AVAILABILITY: The code is available upon request.</P>

Poor Correlation Between the New Statistical and the Old Empirical Algorithms for DNA Microarray Analysis

Kim, Ju Han,Kuo, Winston P.,Kong, Sek-Won,Ohno-Machado, Lucila,Kohane, Isaac S. Korea Genome Organization 2003 Genomics & informatics Vol.1 No.2

DNA microarray is currently the most prominent tool for investigating large-scale gene expression data. Different algorithms for measuring gene expression levels from scanned images of microarray experiments may significantly impact the following steps of functional genomic analyses. $Affymetrix^{(R)}$ recently introduced high-density microarrays and new statistical algorithms in Microarray Suit (MAS) version 5.0$^{(R)}$. Very high correlations (0.92 - 0.97) between the new algorithms and the old algorithms (MAS 4.0) across several species and conditions were reported. We found that the column-wise array correlations had a tendency to be much higher than the row-wise gene correlations, which may be much more meaningful in the following higher-order data analyses including clustering and pattern analyses. In this paper, not only the detailed comparison of the two sets of algorithms is illustrated, but the impact of the introducing new algorithms on the further clustering analysis of microarray data and of possible pitfalls in mixing the old and the new algorithms were also described.

An Efficient Search Algorithm for Finding Genomic-Range Overlaps Based on the Maximum Range Length

Ho-Sik Seok,Taemin Song,Sek Won Kong,Kyu-Baek Hwang IEEE 2015 IEEE/ACM transactions on computational biology and Vol.12 No.4

<P>Efficient search algorithms for finding genomic-range overlaps are essential for various bioinformatics applications. A majority of fast algorithms for searching the overlaps between a query range (e.g., a genomic variant) and a set of N reference ranges (e.g., exons) has time complexity of O(k + logN), where kdenotes a term related to the length and location of the reference ranges. Here, we present a simple but efficient algorithm that reduces k, based on the maximum reference range length. Specifically, for a given query range and the maximum reference range length, the proposed method divides the reference range set into three subsets: always, potentially, and never overlapping. Therefore, search effort can be reduced by excluding never overlapping subset. We demonstrate that the running time of the proposed algorithm is proportional to potentially overlapping subset size, that is proportional to the maximum reference range length if all the other conditions are the same. Moreover, an implementation of our algorithm was 13.8 to 30.0 percent faster than one of the fastest range search methods available when tested on various genomic-range data sets. The proposed algorithm has been incorporated into a disease-linked variant prioritization pipeline for WGS (http://gnome.tchlab.org) and its implementation is available at http://ml.ssu.ac.kr/gSearch.</P>

Potential urinary biomarker panel to predict malignancy in women with pelvic masses

( Shin-wha Lee ),( Ha-young Lee ),( Sek Won Kong ),( Yong-man Kim ) 대한산부인과학회 2016 대한산부인과학회 학술대회 Vol.102 No.-

목적: The precise and expedient prediction of ovarian cancer (OC) in women with pelvic masses is crucial to improve prognosis through the timely identification and treatment at the early stage disease. Here we report an extended analysis of urinary proteins associated with OC and investigated the potential urinary biomarker panel to accurately predict malignancy in women with pelvic masses. 방법: We analyzed 23 biomarkers in urines samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAPTM bead-based multiplexed immunoassay. To identify the performance of each biomarker to predict cancer over benign, we used a repeated leave-group out cross validation strategy. Furthermore, prediction models using multi-makers were evaluated to develop a urinary ovarian cancer panel. 결과: The urinary concentration of 17 biomarkers exhibited significant differences between OC and benign tumor. HE4, VCAM, and TTR were the top three-biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples with OC, whereas TTR showed the highest efficacy in early-stage OC. Overall, HE4 was the most informative biomarker, followed by creatinine, CEA, NCAM, and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multi-marker panel consisted with HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity at 70.6% specificity to predict OC over the benign tumor. This panel performed well regardless of the disease status and demonstrated an improved performance by including the menopausal status. 결론: The urinary biomarker panel with HE4, creatinine, CEA, and TTR provides promising efficacy to predict OC over benign tumors in women with pelvic masses. It is also a non-invasive and easily available diagnostic tool. Further prospective studies would expand the clinical utility of the urinary multi-marker panel.