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    1997 …2023

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    Detecting local similarities among biosequences is fundamental to the task of assigning identity, historical context, and putative function to parts of genomes. While efficient and sensitive similarity search algorithms such as BLAST have served the biological community well in the past, we must adapt these tools to meet emerging analytical needs in genomics. Exponential growth of both our sequence databases and our capacity to generate new sequence can obsolete methods that were "fast enough" in the past; moreover, new applications, such as discovering new protein families from metagenomic data or alignment of short reads with errors to a sequenced genome, pose bioinformatic challenges that require us to extend existing algorithms and to propose new ones.

    My lab explores new algorithmic techniques for designing biosequence similarity search tools. We seek to anticipate the needs of the biological community for new tools, to establish firm theoretical foundations for the design of existing tools, and to discover techniques that make new classes of search problem feasible. For example, our work on the PROJECTION motif-finding algorithm and on Wang and Stormo`s PhyloNet motif search tool extended these tools to problem sizes and complexities that were previously beyond their scope. Our ongoing work on Mercury BLAST, an FPGA-accelerated engine for BLAST, seeks to exploit the low-level parallelism in today`s high-performance computing architectures to accelerate similarity search. We are also exploring challenges in clustering, assembling, and interpreting reads from metagenomic datasets, as well as fundamental questions of how to enable simpler, faster construction of new bioinformatics tools for high-performance computing architectures.

    Available to Mentor:

    • PhD/MSTP Students

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