FET: Small: AlignMEM: Fast and Efficient DNA Sequence Alignment in Non-Volatile Magnetic RAM

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The state-of-the-art DNA sequencing technologies could generate Terabytes of DNA sequence data in a single run, and their throughput is expected to increase 3-5 times each year in the coming years. In order to apply these big DNA-data into follow-up complex disease diagnostics/prognostics, such as cancer risk assessment, tailor patient treatment, and prenatal testing, they must be first aligned to a 3.2-billion-length human reference genome. However, the existing software tools for this purpose may need hours or days to align such large amount of DNA sequence data even with very powerful computing systems of today due to the 'memory wall' challenge in state-of-the-art computing architecture that describes the speed mismatch between memory units and computing units. To this end this, project leverages innovations from non-volatile nano-magnet based Magnetic Random Access Memory (MRAM) technology and in-memory computing architecture. If successful, it can achieve up to two orders magnitude higher computing performance, speed and energy efficiency for next-generation DNA sequence analysis system, which enables large-scale fast genomic data analytics to support research on various disease studies and biomedical applications. This project will develop new undergraduate/graduate level course modules on in-memory computing architecture and bioinformatics. This project will follow two main research tracks. The first one explores how to leverage the intrinsic non-volatile MRAM device property to efficiently develop ultra-parallel, reconfigurable in-memory logic required by DNA alignment computation and its big DNA-data Processing-in-Memory (PIM) accelerator architecture. The second research track will investigate how to develop fast DNA alignment-in-memory algorithm based on Burrows-Wheeler Transformation to match with the proposed MRAM based PIM platform and its large-scale genomic analysis application in disease phenotype prediction. Alignments generated will be used to estimate gene expression, and identify single nucleotide mutation events for patient samples, leading to molecular signatures for disease risk assessment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.