معرفی
Dr Ian M Carr is a Lecturer in Medical Bioinformatics at the School of Medicine, University of Leeds, where he is affiliated with the Leeds Institute of Medical Research at St James's. He is based at the WTBB, St James’s University Hospital, and serves as the academic lead of the Next Generation Sequencing Facility and module lead for the Human Medicine MSc programme.
His research focuses on the development and application of bioinformatics tools to solve practical challenges in molecular biology. Key interests include rare disease gene identification, variant detection using both long and short-read NGS data, de novo genome assembly of viruses, bacteria, and eukaryotes, and the study of ecological population structures and species distribution. He specializes in creating automated software solutions for sequence and genotyping data analysis, as well as clinical data collection.
The trend in his research outputs reflects a strong emphasis on interdisciplinary bioinformatics, integrating molecular biology with computational methods. His work spans genomics, epigenetics, copy number analysis, and transcriptional regulation, often involving the development of bespoke software applications in C++, C#, R, Bash, and Python for both Windows and Linux environments.
Dr Carr is a Microsoft Certified Solutions Developer, demonstrating his technical proficiency in software development. While no specific awards are listed, his contributions to bioinformatics through published software and research outputs are significant.
He actively supervises postgraduate research and welcomes PhD enquiries from qualified candidates worldwide. His leadership in the NGS Facility and MSc teaching underscores his commitment to both research innovation and education. He has developed standalone and web-based applications to streamline data analysis workflows in clinical and research settings.
Dr Carr is part of the Leeds Institute of Medical Research at St James's, contributing to a collaborative environment focused on advancing medical genomics and bioinformatics. His work bridges the gap between molecular biology and computational science, fostering efficient, scalable solutions for complex biological data.
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