Yesim Aydin Son

Modeling the combined effect of RNA-binding proteins and microRNAs in post-transcriptional regulation

Recent studies show that RNA-binding proteins (RBPs) and microRNAs (miRNAs) function in coordination with each other to control post-transcriptional regulation (PTR). Despite this, the majority of research to date has focused on the regulatory effect of individual RBPs or miRNAs. Here, we mapped both RBP and miRNA binding sites on human 3′UTRs and utilized this collection to better understand PTR. We show that the transcripts that lack competition for HuR binding are destabilized more after HuR depletion. We also confirm this finding for PUM1(2) by measuring genome-wide expression changes following the knockdown of PUM1(2) in HEK293 cells. Next, to find potential cooperative interactions, we identified the pairs of factors whose sites co-localize more often than expected by random chance. Upon examining these results for PUM1(2), we found that transcripts where the sites of PUM1(2) and its interacting miRNA form a stem-loop are more stabilized upon PUM1(2) depletion. Finally, using dinucleotide frequency and counts of regulatory sites as features in a regression model, we achieved an AU-ROC of 0.86 in predicting mRNA half-life in BEAS-2B cells. Altogether, our results suggest that future studies of PTR must consider the combined effects of RBPs and miRNAs, as well as their interactions.

Reconstruction of the temporal signaling network in Salmonella-infected human cells.

Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Given that the bacterial infection modifies the response network of the host, a more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic dataset. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.

A Prostate Cancer Model Build by a Novel SVM-ID3 Hybrid Feature Selection Method Using Both Genotyping and Phenotype Data from dbGaP

Through Genome Wide Association Studies (GWAS) many Single Nucleotide Polymorphism (SNP)-complex disease relations can be investigated. The output of GWAS can be high in amount and high dimensional, also relations between SNPs, phenotypes and diseases are most likely to be nonlinear. In order to handle high volume-high dimensional data and to be able to find the nonlinear relations we have utilized data mining approaches and a hybrid feature selection model of support vector machine and decision tree has been designed. The designed model is tested on prostate cancer data and for the first time combined genotype and phenotype information is used to increase the diagnostic performance. We were able to select phenotypic features such as ethnicity and body mass index, and SNPs those map to specific genes such as CRR9, TERT. The performance results of the proposed hybrid model, on prostate cancer dataset, with 90.92% of sensitivity and 0.91 of area under ROC curve, shows the potential of the approach for prediction and early detection of the prostate cancer.

METU-SNP: An Integrated Software System for SNP- Complex Disease Association Analysis

Recently, there has been increasing research to discover genomic biomarkers, haplotypes, and potentially other variables that together contribute to the development of diseases. Single Nucleotide Polymorphisms (SNPs) are the most common form of genomic variations and they can represent an individual’s genetic variability in greatest detail. Genome-wide association studies (GWAS) of SNPs, high-dimensional case-control studies, are among the most promising approaches for identifying disease causing variants. METU-SNP software is a Java based integrated desktop application specifically designed for the prioritization of SNP biomarkers and the discovery of genes and pathways related to diseases via analysis of the GWAS case-control data. Outputs of METU-SNP can easily be utilized for the downstream biomarkers research to allow the prediction and the diagnosis of diseases and other personalized medical approaches. Here, we introduce and describe the system functionality and architecture of the METU-SNP. We believe that the METU-SNP will help researchers with the reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting the development of personalized medicine approaches and targeted drug discoveries.

Designing and Implementing Pharmacogenomics Study

Pharmacogenomics of today has its origins in the 1950s with pioneering studies of monogenic variations in drug metabolism and pharmacokinetics. With the completion of the Human Genome Project in 2003 and the advances in genomics such as the high-throughput genomics technologies, we are now in the postgenomics era. This transition is increasingly marked with study of polygenic and multifactorial traits such as common complex human diseases as well as pharmacodynamic differences among populations. Changes that emerge from postgenomics medicine are not, however, limited to seismic shifts in scale and scope of pharmacogenetics research. Importantly, many low- and middle-income countries (LMICs) of the South, Asia-Pacific, Eastern Mediterranean, and the Middle East are becoming notable contributors with rapid globalization of science and increasing access to genomics technologies. This brings about, in parallel, an acute demand for regional capacity building in LMICs so that the future evaluation and implementation of postgenomics technologies in personalized medicine take place in an integrated, sustainable, and equitable manner. This chapter aims to highlight the potential applications and opportunities as well as technical and strategic issues that this field offers to influence medical care.

Evaluation of novel candidate variations and their interactions related to bipolar disorders: Analysis of GWAS data

Background Multifactor dimensionality reduction (MDR) is a nonparametric approach that can be used to detect relevant interactions between single-nucleotide polymorphisms (SNPs). The aim of this study was to build the best genomic model based on SNP associations and to identify candidate polymorphisms that are the underlying molecular basis of the bipolar disorders. Methods This study was performed on Whole-Genome Association Study of Bipolar Disorder (dbGaP [database of Genotypes and Phenotypes] study accession number: phs000017.v3.p1) data. After preprocessing of the genotyping data, three classification-based data mining methods (ie, random forest, naïve Bayes, and k-nearest neighbor) were performed. Additionally, as a nonparametric, model-free approach, the MDR method was used to evaluate the SNP profiles. The validity of these methods was evaluated using true classification rate, recall (sensitivity), precision (positive predictive value), and F-measure. Results Random forests, naïve Bayes, and k-nearest neighbors identified 16, 13, and ten candidate SNPs, respectively. Surprisingly, the top six SNPs were reported by all three methods. Random forests and k-nearest neighbors were more successful than naïve Bayes, with recall values >0.95. On the other hand, MDR generated a model with comparable predictive performance based on five SNPs. Although different SNP profiles were identified in MDR compared to the classification-based models, all models mapped SNPs to the DOCK10 gene. Conclusion Three classification-based data mining approaches, random forests, naïve Bayes, and k-nearest neighbors, have prioritized similar SNP profiles as predictors of bipolar disorders, in contrast to MDR, which has found different SNPs through analysis of two-way and three-way interactions. The reduced number of associated SNPs discovered by MDR, without loss in the classification performance, would facilitate validation studies and decision support models, and would reduce the cost to develop predictive and diagnostic tests. Nevertheless, we need to emphasize that translation of genomic models to the clinical setting requires models with higher classification performance.

Use of open linked data in bioinformatics space: A case study

In the life sciences, semantic web can support many aspects of bio- and health informatics, with exciting applications appearing in areas ranging from plant genetics to drug discovery. Using semantic technologies with open linked data, provides two kinds of advantages: ability to search multiple datasets through a single framework and ability to search relationships and paths of relationships that go across different datasets. The Bio2RDF project creates a network of coherently linked data across the biological databases. As part of the Bio2RDF project, an integrated bioinformatics warehouse on the semantic web is built. In this paper, a use case with a query for multiple distant data sources which are semantically available through Bio2RDF is defined. The validation of the results by traditional search techniques and discussion for future directions is presented.

Emerging Technologies in Health Information Systems: Genomics Driven Wellness Tracking and Management System (GO-WELL)

Today, with the technology-driven developments, healthcare systems and services are being radically transformed to become more effective and efficient. Omics technologies along with mobile sensors and monitoring systems are emerging disruptive technologies, which will provide us the opportunities of a paradigm shifting in medical theory, research and practice. Traditional methods are beginning to convert to a new personalized, predictive, preventive and participatory paradigm based on big data approaches. We anticipate that; next-generation health information systems will be constructed based on tracking all aspects of health status on 24/7, and returning evidence based recommendations to empower individuals. As an example of future personal health record (PHR) concept, GO-WELL is based on clinical envirogenomic knowledge base (CENG-KB) to engage patients for predictive care. In this chapter, we present the design principles of this system, after describing several concepts, including personalized medicine, omics revolution, incorporation of genomic data into medical decision processes, and the utilization of enviro-behavioural parameters for disease risk assessment.

iSNP: An integrated, automatically updated SNP database

Single Nucleotide Polymorphisms (SNP) is a DNA sequence variation that occurs when a single nucleotide differs between members of a biological species or paired chromosomes in an individual. Studies on SNP data are important for scientists to shed light on identifying genetic variations underlying complex diseases. While recent advances in high-throughput genotyping technologies are resulting in data accumulation at large scales, the need for collection and service of this data under a standard format and globally normalized and structured metadata that houses the structured SNP data is becoming essential. Here, we present iSNP that is a regularly updated, machine curated database that holds SNP and its associated metadata from publicly available databases under a structured standard format that can be efficiently utilized within different applications.

Building Dicer1 regulation network of mouse liver hepatocytes

RNA induced gene silencing complex (RISC) has a role in many cellular processes which includes regulation of gene expression, immune response, cell differentiation and embryonic development. Dicer protein is a key regulator of these processes. Dicer1 specifically has central role in maturation of RISC substrate RNA and RISC assembly in mouse. Here the results of a microarray study that investigates the molecular level changes in Dicer1 knockout mouse liver hepatocytes is re-analysed and the differentially regulated genes are hierarchically clustered based on molecular function and co-expression to construct the suggested regulation network of Dicer1 in mouse hepatocytes.