Volkan Yazar

Dr. Yazar graduated with high honors from METU in 2008 with a major in Biological Life Sciences. Dr. Yazar then pursued his Master’s studies at Radboud University Nijmegen and Harvard Medical School. After receiving his M.Sc. degree in 2010, he continued his graduate studies as a Ph.D. student in Biochemistry and Molecular Pharmacology at University of Massachusetts Medical School. Dr. Yazar switched his focus of research from experimental biomedical research to bioinformatics in 2013. He pursued his Ph.D. studies in the field of mammalian transcriptomics and was granted his Ph.D. degree in 2019 at Bilkent University. Afterwards, he continued his postdoctoral training at the Institute for Cell Engineering at Johns Hopkins Medical School (2020-2022) and then at DZNE (Deutsches Zentrum für Neurodegenerative Erkrankungen; 2022-2023), with a focus on multiomics approaches in neuroinformatics.

He is a faculty member at the Department of Molecular Biology and Genetics, Bilkent University since September 2023. Dr. Yazar's research interests include systems biology approaches, as well as multi-omics data wrangling and analysis, in neurodegenerative disorders, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). He is also interested in the immune component of neurodegeneration and how it shapes the response of the neurons to the ongoing neurotoxicity in disease.

We aim to shed light on underlying molecular mechanisms of complex progressive neurodegenerative disorders of the central nervous system (CNS), including Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS), using various bioinformatics approaches. More specifically, we leverage data sets deposited in public repositories as well as disease-specific databases to discover novel candidate molecules and master regulators associated with disease initiation and progression using a range of multi-modal, multi-omics data integration and analysis methods. Considering intrinsic characteristics of raw data, we develop customized analysis workflows for individual data sets after rounds of data wrangling and use multivariate statistical techniques and systems biology approaches to extract valuable biological information from high dimensional sequencing and array data at the epigenome, transcriptome, and proteome levels.