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IEEE International Conference on bioinformatics and Biomedicine

作者： Jungsoo Gim Sungkyoung Choi Jongho Im Jae-Kwang Kim Taesung Park Interdisciplinary program for Bioinformatics and Department of Statistics Seoul National University Interdisciplinary program for Bioinformatics Seoul National University Department of Statistics Iowa State University

ISBN: (纸本)9781479956708

Genome-wide association studies have identified many candidate causal variants associated with common complex diseases and traits, but most of them have been drawn from nonrandomized case/control designs. In nonrandomized experiments, the results drawn from two different groups can be misleading because the units exposed to one group generally differ systematically from the units exposed to the other group. Propensity score is widely used to group case and control units for a more direct and significant comparison even with nonrandomized experiments. This propensity score matching can help with prioritizing additional uncovered variants on disease risk via sub-group analysis in genome-wide association studies. The aim of this work is to propose a post-hoc association test based on the subsets of samples. For that purpose, this paper presents a new paradigm for a post-hoc genome-wide association test when the sample size of controls are larger than that of cases: selecting control samples by equating the distribution of covariates in the case and control groups and re-performing association analysis upon these matched samples. We demonstrated the feasibility of this approach by applying it to 2752 type II diabetes patients in 8842 Korean population. Genome-wide association approach with matched samples is able to disclose 9 additional novel variants and 7 out of 9 have not identified from the association test of whole control samples. The process described here can successfully be combined with other types of case/control studies with large covariate information. This indicates that there a possibility of obtaining additional candidate causal variants responsible for common diseases through genome-wide association analysis with matched samples.

关键词： Propensity score Matching Genome-wide association study

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A normalization method for multiple reaction monitoring (MRM) data

A normalization method for multiple reaction monitoring (MRM...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Jungsoo Gim Taesung Park Interdisciplinary Program for Bioinformatics and Department of Statistics Seoul National University

ISBN: (纸本)9781479956708

High accuracy and reproducibility provided by selected or multiple reaction monitoring (SRM/MRM) experiments suggest that it is likely to become the platform of choice for identifying and verifying candidate biomarkers. Although several methods have been developed to quantify and discover the significant changes in SRM/MRM expression, we show normalization continues to be an essential step in the differential expression analysis. Here, we first investigate what makes normalization across the samples difficult in SRM assay. Then, we suggest a simple but effective normalization method to overcome the difficulty. Both real and simulated data sets are examined and improved results, compared to an existing method, are shown for inferring differential expression.

关键词： Proteomics Selected reaction monitoring (SRM) Normalization Differential expression test

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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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Biomarker development for pancreatic ductal adenocarcinoma using integrated analysis of mRNA and miRNA expression

Biomarker development for pancreatic ductal adenocarcinoma u...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Min-Seok Kwon Yongkang Kim Seungyeoun Lee Junghyun Namkung Taegyun Yun Sung Gon Yi Sangjo Han Meejoo Kang Sun Whe Kim Jin-Young Jang Taesung Park Interdisciplinary program in Bioinformatics Seoul National University Seoul Korea Department of Statistics Seoul National University Seoul Korea Department of Mathematics and Statistics Sejong University Seoul Korea Healthcare group SK telecom Bioinformatics Tech. Lab. Seoul Korea Department of Surgery Seoul National University Hospital Seoul Korea

ISBN: (纸本)9781479956708

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, which has dismal prognosis because of its silent early symptoms, high metastatic potential, and resistance to conventional therapies. Although a PDAC patient who is diagnosed at an early stage would have a substantial increase in chance of survival, the survival rate is poor because there is no efficient non-invasive diagnostic test in the early stage. In this study, we developed an efficient prediction models to detect PDAC in its early stages. Our prediction models use both mRNA and miRNA expression data from 104 PDAC tissues and 17 normal pancreatic tissues using microarray technology. After quality control, we built prediction models based on support vector machine (SVM) from mRNA and miRNA expressions for detecting early PDAC. To prevent over-fitting effect, we conducted leave-one-out cross validation (LOOCV) and 5-fold cross validation (CV). For independent validation of prediction models, we performed evaluation on independent datasets from Gene Expression Omnibus (GEO). After the validation, we identified 28 single markers and 231 combinations of markers with powerful prediction performance. In addition, the marker candidates are annotated with cancer pathways using gene ontology analysis. Our prediction models for PDAC may have potential for early diagnosis of PDAC.

关键词： Cancer Barium Blood Lungs Predictive models Ontologies Reliability

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Abstracts from the 20th International Symposium on Signal Transduction at the Blood-Brain Barriers Abstracts

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FLUIDS AND BARRIERS OF THE CNS 2017年第SUPPL 2期14卷 27-27页

作者： [Anonymous] The Jerzy Kukuczka Academy of Physical Education Katowice Poland The Institute of Animal Physiology and Food Research Polish Academy of Sciences Olsztyn Poland University of Miami Miami FL USA Institute of Pharmaceutical Science King’s College London London United Kingdom Department of Ophthalmology and Molecular and Integrative Physiology The University of Michigan Ann Arbor MI USA Department of Internal Medicine and Cardiology The University of Michigan Ann Arbor MI USA School of Medicine Department of Cellular and Molecular Medicine Koc University Istanbul Turkey Department of Cellular and Molecular Medicine Medical Faculty Koc University Istanbul Turkey Department of Neuroscience School of Medicine Koc University Istanbul Turkey Department of Neuroscience Medical Faculty Koc University Istanbul Turkey Department of Laboratory Animals Science Aziz Sancar Experimental Medicine Research Institute Istanbul University Istanbul Turkey Department of Neuroscience Aziz Sancar Experimental Medicine Research Institute Istanbul University Istanbul Turkey School of Medicine Koc University Istanbul Turkey Department of Forensic Science Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Histology and Embryology School of Medicine Koc University Istanbul Turkey Department of Histology and Embryology Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Physiology School of Medicine Koc University Istanbul Turkey Theodor Kocher Institute University of Bern Bern Switzerland Institute of Veterinary Physiology Vetsuisse Faculty University of Zurich Zurich Switzerland Zurich Center for Integrative Human Physiology University of Zurich Zurich Switzerland Zurich Center of Integrative Human Physiology University of Zurich Zurich Switzerland Institute for Veterinary Physiology and Zurich Center for Integrative Human Physiology University of Zurich Zurich Switzerland Institute of Veterinary Physiology Univer

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11th German Conference on Chemoinformatics (GCC 2015) Abstracts

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JOURNAL OF CHEMINFORMATICS 2016年第SUPPL 1期8卷 1-27页

作者： [Anonymous] GDCh-CIC Division Associated Board Member Beilstein-Institut zur Förderung der Chemischen Wissenschaften Trakehner Str. 7-9 60487 Frankfurt Germany. ufechner@beilstein-institut.de. Division Medicinal Chemistry Amsterdam Institute for Molecules Medicines and Systems (AIMMS) VU University Amsterdam The Netherlands. Centre for Bioinformatics Uni Hamburg Bundesstr. 43 20146 Hamburg Germany. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. stefan.guessregen@***. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. GlaxoSmithKline Stevenage SG1 2NY UK. nicola.j.richmond@***. Discngine Paris 75011 France. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. marwin.segler@wwu.de. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. Bundeskriminalamt Wiesbaden Central Analytics II 65173 Wiesbaden Germany. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. shea@chem.ucsb.edu. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. Chemistry Department Ludwig-Maximilians-Universität München Butenandtstr. 7 81377 Munich Germany. Inorganic Chemistry and Center for Nanointegration University of Duisburg-Essen Essen Germany. CAM-D Technologies Essen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. achim.zielesny@w-hs.de. Leiden University Leiden Netherlands. j.fraaije@chem.leidenuniv.nl. Culgi BV Berlin Germany. Physikalische Chemie III TU Dortmund 44227 Dortmund Germany. stefan.kast@tu-dortmund.de. Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW United Kingdom. kcb27@cam.ac.uk. Centre for Molecular Informatics Department of Chemistry

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Erratum to: DNA methylation and transcriptional noise

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Epigenetics & Chromatin 2014年第1期7卷 1-1页

作者： Iksoo Huh Taesung Park Jia Zeng Soojin V Yi Department of Statistics Bioinformatics and Biostatistics Laboratory Interdisciplinary Program in Bioinformatics Seoul National University Seoul 151-742 South Korea School of Biology Institute of Bioengineering and Biosciences Georgia Institute of Technology 310 Ferst Drive Atlanta GA 30332 USA

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Cancer Informatics: Profiling Age-Related Epigenetic Markers of Stomach Adenocarcinoma in Young and Old Subjects

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Cancer Informatics 2015年 14卷

作者： Byoung-Chul Kim Hyoung Oh Jeong Daeui Park Chul-Hong Kim Eun Kyeong Lee Dae Hyun Kim Eunok Im Nam Deuk Kim Sunghoon Lee Byung Pal Yu Jong Bhak Hae Young Chung Molecular Inflammation Research Center for Aging Intervention Pusan National University Busan Republic of Korea.Interdisciplinary Research Program of Bioinformatics and Longevity Science Pusan National University Busan Republic of Korea. Genomictree Inc. Yuseong-gu Daejeon Republic of Korea. Molecular Inflammation Research Center for Aging Intervention Pusan National University Busan Republic of Korea. Personal Genomics Institute Genome Research Foundation Suwon Republic of Korea.BioMedical Engineering UNIST Ulsan Republic of Korea. Department of Physiology University of Texas Health Science Center at San Antonio San Antonio TX USA.

The purpose of our study is to identify epigenetic markers that are differently expressed in the stomach adenocarcinoma (STAD) condition. Based on data from The Cancer Genome Atlas (TCGA), we were able to detect an age-related difference in methylation patterns and changes in gene and miRNA expression levels in young (n = 14) and old (n = 70) STAD subjects. Our analysis identified 323 upregulated and 653 downregulated genes in old STAD subjects. We also found 76 miRNAs with age-related expression patterns and 113 differentially methylated genes (DMGs), respectively. Our further analysis revealed that significant upregulated genes (n = 35) were assigned to the cell cycle, while the muscle system process (n = 27) and cell adhesion-related genes (n = 57) were downregulated. In addition, by comparing gene and miRNA expression with methylation change, we identified that three upregulated genes (ELF3, IL1β, and MMP13) known to be involved in inflammatory responses and cell growth were significantly hypomethylated in the promoter region. We further detected target candidates for age-related, downregulated miRNAs (hsa-mir-124-3, hsa-mir-204, and hsa-mir-125b-2) in old STAD subjects. This is the first report of the results from a study exploring age-related epigenetic biomarkers of STAD using high-throughput data and provides evidence for a complex clinicopathological condition expressed by the age-related STAD progression.

关键词： stomach adenocarcinoma age TCGA RNAseq miRNA DNA methylation

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Proceedings of the 15th Annual UT-KBRIN bioinformatics Summit 2016 : Cadiz, KY, USA. 8-10 April 2016

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BMC bioinformatics 2016年第SUPPL 10期17卷 297页

作者： Eric C. Rouchka Julia H. Chariker Benjamin J. Harrison Juw Won Park Xueyuan Cao Stanley Pounds Susana Raimondi James Downing Raul Ribeiro Jeffery Rubnitz Jatinder Lamba Bernie J. Daigle Deborah Burgess Stephanie Gehrlich John C. Carmen Nicholas Johnson Chandrakanth Emani Kalpani De Silva Michael P. Heaton Theodore S. Kalbfleisch Teeradache Viangteeravat Rahul Mudunuri Oluwaseun Ajayi Fatih Şen Eunice Y. Huang Mohammad Mohebbi Luaire Florian Douglas J. Jackson John F. Naber AKM Sabbir Sally R. Ellingson Yuping Lu Charles A Phillips Michael A. Langston Rahul K. Sevakula Raghuveer Thirukovalluru Nishchal K. Verma Yan Cui Mohammed Sayed Jing Wang Qi Liu Yu Shyr Xiaofei Zhang Naresh Prodduturi Gavin R. Oliver Diane Grill Jie Na Jeanette Eckel-Passow Eric W. Klee Michael M. Goodin Mark Farman Harrison Inocencio Chanyong Jang Jerzy W. Jaromczyk Neil Moore Kelly Sovacool Leon Dent Mike Izban Sammed Mandape Shruti Sakhare Siddharth Pratap Dana Marshall M Scotty DePriest James N. MacLeod Hanady Adam Ethan Blandford Joel Campbell Joshua Castlen Brittany Dixon Ginger Gilbert Aaron Hall Philip Kreisle Jessica Lasher Bethany Oakes Allison Speer Maximilian Valentine Naga Satya V. Rao Nagisetty Rony Jose Robert Rooney David Hains Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core Louisville USA Department of Psychological and Brain Sciences University of Louisville Louisville USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville USA Department of Biostatistics St. Jude Children’s Research Hospital Memphis USA Department of Pathology St. Jude Children’s Research Hospital Memphis USA Department of Oncology St. Jude Children’s Research Hospital Memphis USA Department of Pharmacotherapy and Translational Research College of Pharmacy University of Florida Gainesville USA Department of Biological Sciences University of Memphis Memphis USA Department of Computer Science University of Memphis Memphis USA Department of Computer Science Northern Kentucky University Highland Heights USA Department of Biological Sciences Northern Kentucky University Highland Heights USA Department of Biology Western Kentucky University Bowling Green USA Interdisciplinary Studies Program: Specialization in Bioinformatics University of Louisville Louisville USA United States Department of Agriculture Agricultural Research Service United States Meat Animal Research Center Clay Center USA Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Louisville USA Biomedical Informatics Core Children’s Foundation Research Institute Memphis USA Department of Pediatrics University of Tennessee Health Science Center Memphis USA Department of Electrical and Computer Engineering University of Louisville Louisville USA Department of Computer Science University of Kentucky Lexington USA Division of Biomedical Informatics College of Medicine University of Kentucky Lexington USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville USA Department of Electrical E

I1 Proceedings of the Fifteenth Annual UT- KBRIN bioinformatics Summit 2016 Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park P1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpoints Xueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder Lamba P2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental power Bernie J Daigle, Jr. P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomes Deborah Burgess, Stephanie Gehrlich, John C Carmen P4 Tracking the molecular evolution of Pax gene Nicholas Johnson; Chandrakanth Emani P5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparison Stephanie Gehrlich, Deborah Burgess, John C Carmen P6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencing Kalpani De Silva, Michael P Heaton, Theodore S Kalbfleisch P7 Mining physiological data to identify patients with similar medical events and phenotypes Teeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y Huang P8 Smart brief for home health monitoring Mohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F Naber P9 Side-effect term matching for computational adverse drug reaction predictions AKM Sabbir, Sally R Ellingson P10 Enrichment vs robustness: A comparison of transcriptomic data clustering metrics Yuping Lu, Charles A Phillips, Michael A Langston P11 Deep neural networks for transcriptome-based cancer classification Rahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan Cui P12 Motif discovery using K-means clustering Mohammed Sayed, Juw Won Park P13 Large scale discovery of active enhancers from nasce

关键词： True Negative Differential Expression UCSC Genome Browser Laboratory Information Management System Biomedical Informatics

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Pathway based identification of rare mutation effect in cancer

Pathway based identification of rare mutation effect in canc...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： TaeJin Ahn Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea Department of statistics Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781479913114

Identifying driver mutation is important in understanding disease mechanism and future application of custom tailored therapeutic decision. Functional analysis of mutational impact usually focuses on the gene expression level of the mutated gene. However, complex regulatory network may cause differential gene expression among functional neighbors of the mutated gene. We suggest a new approach for discovering rare mutations that have real impact in the context of pathway; philosophy of our method is iteratively combining rare mutations until no more mutations can be added, under the condition that the combined mutational event can statistically discriminate pathway level mRNA expression between groups with and without mutation events. Our approach is shown to sensitively capture mutations that change pathway level gene expression at breast cancer data.

关键词： Gene expression Breast cancer Drugs Tumors Proteins

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