Dr. Yasin KAYMAZ

  • Ege University

Title: Yet another new era for transcriptomics: single-cell


Transcriptomics dynamism has always been at the center of regulatory mechanisms in a cell. This motivated many research fields to seek a snapshot of molecular states. Tissue level averaged-out transcript signal measurements have already become obsolete, leaving the stage for cells to be treated equally and independently, a single-cell era. Thanks to the improvements in microfluidic systems and especially DNA barcoding methods, we are now opening pandora’s box yet again. Although certain companies are eager to push the limits and develop new approaches to profile many more cells at once, we are still struggling to resolve common issues, such as data clustering, classification, pseudo-time inference, or multi-batch integration problem as hard as iron. As our research team focuses on contributing to ease such analytical limitations, we are developing new methods that will provide a paradigm shift in cell type clustering and classification. We are interested in isoform-level transcriptome dynamism to investigate cell type profiles that can be linked with tumor-associated macrophages in non-small cell lung cancer. With this talk, we will have a journey with our research perspective centering the single-cell sequencing applications and future projections.


Dr. Yasin KAYMAZ completed his master’s and doctorate education in the United States of America in 2008 with the state scholarship he received after receiving his Biochemistry BSc from Ege University. He returned to the Bioengineering Department at the same university in Turkey. During his education in the USA, he received his master’s degree in Bioinformatics from Boston University Engineering Faculty (2009-2011), and then he received his doctorate education at the University of Massachusetts Medical School Computational Biology and Bioinformatics department (2011-2017). He conducted various studies on the relationship between Endemic Burkitt Lymphoma and Epstein Barr Virus based on molecular biology and bioinformatics for his doctoral thesis. He has been involved in joint studies focusing on data generation, data analytics, and method development, especially for NGS (next-generation sequencing). While continuing his post-doctoral studies in the Department of Informatics at Harvard University, he has worked on developing analytical methods, especially for single-cell sequencing data (single cell RNAseq, etc.).