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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P28. APPLICATION OF HIGH-THROUGHPUT SEQUENCING TO CIRCULATING MICRORNAS REVEALS NOVEL BIOMARKERS FOR DRUG- INDUCED LIVER INJURY Julian Krauskopf 1* , Florian Caiment 1 , Sandra Claessen 1 , Kent J. Johnson 2 , Roscoe L. Warner 2 , Shelli J. Schomaker 3 , Deborah A. Burt 3 , Jiri Aubrecht 3 , Jos C. Kleinjans 1 . Department of Toxicogenomics, Maastricht University, Maastricht 6200 MD, The Netherlands 1 ; Pathology Department, University of Michigan, Ann Arbor, MI 48109, USA 2 ; Drug Safety Research and Development, Pfizer, Inc., Groton, CT 06340, USA 2 . *j.krauskopf@maastrichtuniversity.nl Drug-induced liver-injury (DILI) is a leading cause of acute liver failure and the major reason for withdrawal of drugs from the market. Preclinical evaluation of drug candidates has failed to detect about 40% of potentially hepatotoxic compounds in humans. At the onset of liver injury in humans, currently used biomarkers have difficulty differentiating severe DILI from mild, and/or predict the outcome of injury for individual subjects. Therefore, new biomarker approaches for predicting and diagnosing DILI in humans are urgently needed. Recently, circulating microRNAs (miRNAs) such as miR-122 and miR-192 have emerged as promising biomarkers of liver injury in preclinical species and in DILI patients. In this study, we focused on examining global circulating miRNA profiles in serum samples from subjects with liver injury caused by accidental acetaminophen (APAP)-overdose. Upon applying next generation highthroughput sequencing of small RNA libraries, we identified 36 miRNAs, including three novel miRNA-like small nuclear RNAs, which were enriched in serum of APAP overdosed subjects. The set comprised miRNAs that are functionally associated with liver-specific biological processes and relevant to APAP toxic mechanisms. Although more patients need to be investigated, our study suggests that profiles of circulating miRNAs in human serum might provide additional biomarker candidates and possibly mechanistic information relevant to liver injury. 72
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P29. INFORMATION THEORETIC MODEL FOR GENE PRIORITIZATION Ajay Anand Kumar 1,2 * , Geert Vandeweyer 1,2 , Lut Van Laer 1,2 & Bart Loeys 1,2 . Department of Medical Genetics, University of Antwerp 1 ; Biomedical informatics, Antwerp University Hospital 2 . *ajay.kumar@uantwerpen.be The identification of top candidate genes involved in human diseases from a list of candidate genes remains computationally challenging. Many tools exist for this computational prioritization, of which the core typically utilizes fusion or integration of various genomic annotation data sources. However, due to the rapid generation of novel data high-throughput experiments, annotation sources often become outdated, lead to annotation errors. Hence, predictions based on these computational tools are not reliable. To tackle this, we propose an information theoretic model that effectively fuses annotation sources and regression model under Bayesian framework to prioritize candidate genes. Our method is fast and performs better as compared to four existing tools on their own benchmark dataset. INTRODUCTION Gene Prioritizaton has become a central research problem in the bioinformatics domain. With the advent of exome sequencing in clinical genetics, it became a necessity to automate the identification of the top most genes likely involved in the disease from a given pool of affected genes. Various annotation sources can be integrated or fused to learn multiple functionality of genes and then design a classifiers/regressor for prioritization. We propose here an early data integration method that implements an information retrieval model to fusing the data at functional feature level and then designing a discriminative regression model in Bayesian framework to prioritize candidate genes. METHODS Principle behind our approach is based on guilt-byassociation. Genes that are known to be disease associated might also share similar functions. The idea is that a classifier or regressor can be trained on the linear mapping between functional proximity profiles of genes and their phenotypic proximity profiles. We implemented Bayesian regressor to infer the degree of association of the test genes with the query disease. The work-flow of is shown in the Figure 1. The details are: 1. Functional annotation: Text, Ontologies (GO, MPO), Sequence similarity, Pathways, Interactions. Phenotype annotation: Human Phenotype Ontology (HPO), Disease Ontology (DO), HuGe/ MeSh terms and GAD 2. TF - IDF (Term Frequency – Inverse document frequency) methodology is used to assign statistical weights to the functional attributes of genes form these annotation sources. TF-IDF is data driven model traditionally used for information retrieval. We apply same methodology for weighing features. Together, it gives gene-by-gene functional & phenotypic proximity profiles. 3. Finally, the Bayesian linear regression model for a given set of query disease or training genes it learns the linear mapping between functional & phenotypic proximity profiles. Y = βX + η, where is Gaussian distributed. We have incorporated traditional noninformative Normal-Inverse Gama (NIG) priors for estimating the unknowns namely β and б. RESULTS & DISCUSSION We performed leave-one-out cross validation experiment on the benchmark data set that was used to compare four other tools whose design principles are similar to our method [1]. Our dataset consisted of 1040 disease genes categorized under manually curated 12 different disease classes [2]. In our preliminary results for 1154 prioritizations under the cut-off of top 5%, 10% and 30% genes ranked in random control dataset we achieved AUROC of 86.31 % against their best achieved score of 83.0%. This clearly indicates our method is comparatively better with other tools mentioned in the comparative analysis. FIGURE 1. Workflow of Bayesian regression model for gene prioritization. Currently, we are incurring large-scale cross-validation with manually curated 6762 disease gene association with more number of tools and benchmark data [3]. Additionally, we also plan to explore to develop probabilistic generative approach to model cooccurrences, dependencies of features for effective data fusion that can help in finding novel disease causing genes. REFERENCES 1. Chen B et.al BMC Med Genomics. 2015;8 Suppl 3:S2 2. Goh et.al Proc Natl Acad Sci USA 2007, 104(21):8685-8690 3. Börnigen, Daniela, et al. Bioinformatics 28.23 (2012): 3081-3088. 73
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