Lab Members

Assistant Professor

Volkan Yazar

Assistant Professor in the Department of Molecular Biology and Genetics at Bilkent University.

Research Keywords:

• Data Science, Statistical Learning, Systems Biology, Transcriptomics, Epigenetics, Neurodegeneration (HD, PD, and ALS), Data Wrangling, NGS-based and Array-based data analysis

Ongoing Research Projects:

• Transcriptome Dynamics of Suppressive and Adjuvant Oligodeoxynucleotide (ODN)-Based Immune Modulation: A Single-Cell-Guided Approach Integrating Differential Expression, Co-Expression Network, and Longitudinal Analyses. This project involves the temporal characterization of cellular and molecular targets of immunomodulatory CpG (adjuvant) and A151 (suppressive) ODNs in a murine system using Statistical Learning, based on a large set of expression arrays from our multi-group, time-series study.

• Investigation of the molecular mechanisms underlying Huntington’s disease (HD), Parkinson’s disease (PD), and Amyotrophic Lateral Sclerosis (ALS) with a focus on immune dysfunction and oxidative stress, using Data Mining and Systems Biology approaches. In this project, we generate novel proteomics data and also leverage publicly available datasets and disease-specific databases to identify candidate molecules and master regulators involved in disease initiation and progression, using multi-modal, multi-omics data integration and network-level analysis methods.

• Investigation of regeneration-associated gene (RAG) network activation by the master regulator ATF3 in SOD1 mutant familial ALS (fALS): Building on our recent discovery that the ATF3 arm of the RAG network is activated during the early stages of SOD1-mutant familial ALS (fALS), this project aims to provide novel insights into the prodromal phase of fALS in patients carrying various SOD1 mutations. Our approach leverages multi-omics data from SOD1 mutation carriers to perform longitudinal analyses of induced pluripotent stem cells (iPSCs) differentiating into motor neurons.

Taking into account all intrinsic characteristics of raw data, we develop customized analysis workflows for individual datasets following iterative data wrangling. We then apply statistical learning and systems biology approaches to extract meaningful biological insights from high-dimensional sequencing and array data across the epigenome, transcriptome, and proteome levels.