Jie Zheng1, Huan Li2, Qingzhi Liu3, Yongqun He4,5,6,7. 1. Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA. 2. Health Science Center, Shenzhen University, Shenzhen 518000, China. 3. Department of Mathematics, University of Maryland, College Park, MD 20742, USA. 4. Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA. 5. Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA. 6. Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA. 7. Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Abstract
BACKGROUND: The community-based Ontology of Biological and Clinical Statistics (OBCS) represents and standardizes biological and clinical data and statistical methods. METHODS: Both OBCS and the Vaccine Ontology (VO) were used to ontologically model various components and relations in a typical host response to vaccination study. Such a model was then applied to represent and compare three microarray studies of host responses to the yellow fever vaccine YF-17D. A literature meta-analysis was then conducted to survey yellow fever vaccine response papers and summarize statistical methods, using OBCS. RESULTS: A general ontological model was developed to identify major components in a typical host response to vaccination. Our ontology modeling of three similar studies identified common and different components which may contribute to varying conclusions. Although these three studies all used the same vaccine, human blood samples, similar sample collection time post vaccination, and microarray assays, statistically differentially expressed genes and associated gene functions differed, likely due to the differences in specific variables (e.g., microarray type and human variations). Our manual annotation of 95 papers in human responses to yellow fever vaccines identified 38 data analysis methods. These statistical methods were consistently represented and classified with OBCS. Eight statistical methods not available in existing ontologies were added to OBCS. CONCLUSIONS: The study represents the first single use case of applying OBCS ontology to standardize, integrate, and use biomedical data and statistical methods. Our ontology-based meta-analysis showed that different experimental results might be due to different experimental assays and conditions, sample variations, and data analysis methods.
BACKGROUND: The community-based Ontology of Biological and Clinical Statistics (OBCS) represents and standardizes biological and clinical data and statistical methods. METHODS: Both OBCS and the Vaccine Ontology (VO) were used to ontologically model various components and relations in a typical host response to vaccination study. Such a model was then applied to represent and compare three microarray studies of host responses to the yellow fever vaccine YF-17D. A literature meta-analysis was then conducted to survey yellow fever vaccine response papers and summarize statistical methods, using OBCS. RESULTS: A general ontological model was developed to identify major components in a typical host response to vaccination. Our ontology modeling of three similar studies identified common and different components which may contribute to varying conclusions. Although these three studies all used the same vaccine, human blood samples, similar sample collection time post vaccination, and microarray assays, statistically differentially expressed genes and associated gene functions differed, likely due to the differences in specific variables (e.g., microarray type and human variations). Our manual annotation of 95 papers in human responses to yellow fever vaccines identified 38 data analysis methods. These statistical methods were consistently represented and classified with OBCS. Eight statistical methods not available in existing ontologies were added to OBCS. CONCLUSIONS: The study represents the first single use case of applying OBCS ontology to standardize, integrate, and use biomedical data and statistical methods. Our ontology-based meta-analysis showed that different experimental results might be due to different experimental assays and conditions, sample variations, and data analysis methods.
Entities:
Keywords:
OBCS; host response to vaccination; ontology; statistical data analysis; vaccine
Authors: Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov Journal: Proc Natl Acad Sci U S A Date: 2005-09-30 Impact factor: 11.205
Authors: Anita Bandrowski; Ryan Brinkman; Mathias Brochhausen; Matthew H Brush; Bill Bug; Marcus C Chibucos; Kevin Clancy; Mélanie Courtot; Dirk Derom; Michel Dumontier; Liju Fan; Jennifer Fostel; Gilberto Fragoso; Frank Gibson; Alejandra Gonzalez-Beltran; Melissa A Haendel; Yongqun He; Mervi Heiskanen; Tina Hernandez-Boussard; Mark Jensen; Yu Lin; Allyson L Lister; Phillip Lord; James Malone; Elisabetta Manduchi; Monnie McGee; Norman Morrison; James A Overton; Helen Parkinson; Bjoern Peters; Philippe Rocca-Serra; Alan Ruttenberg; Susanna-Assunta Sansone; Richard H Scheuermann; Daniel Schober; Barry Smith; Larisa N Soldatova; Christian J Stoeckert; Chris F Taylor; Carlo Torniai; Jessica A Turner; Randi Vita; Patricia L Whetzel; Jie Zheng Journal: PLoS One Date: 2016-04-29 Impact factor: 3.240
Authors: Kimberly Berke; Peter Sun; Edison Ong; Nasim Sanati; Anthony Huffman; Timothy Brunson; Fred Loney; Joseph Ostrow; Rebecca Racz; Bin Zhao; Zuoshuang Xiang; Anna Maria Masci; Jie Zheng; Guanming Wu; Yongqun He Journal: Front Immunol Date: 2021-03-12 Impact factor: 7.561