Uncovering Algorithms and Rapid Development Tools for Next-generation Sequencing Data
New genome sequencing technologies are generating unprecedented amounts of data. But clearer and more efficient computational analysis is needed to make sense of this data. The sheer scale of genomic data makes many traditional analysis methods impractical. The inherent limitations also prevent us from easily understanding the repetitive regions in the human genome that harbour many clinically important genes.
Dr. Ibrahim Numanagic, Canada Research Chair in Computational Biology and Data Science, is exploring new algorithmic and data science methods that can help alleviate the scale and ambiguity issues that are inherent in sequencing data in a fast and cost-efficient manner. He and his research team will also develop a comprehensive, domain-specific framework for developing high-performance computational tools that can produce fast, scalable genomics analysis software for different computer architectures.
Ultimately, Numanagic’s goal is to make it easier to access genes that harbour important clinical information, allowing for easier, less expensive clinical diagnostics. He and his team envision that the domain-specific framework for building bioinformatics software will boost both small- and large-scale genomics and health projects. It will also enable researchers to explore sequencing data and develop scalable tools for data analysis in rapidly and easily, saving time and costs.