Grant: Discovery Grants Program - Individual

I propose the realization of specialized computer chips for biomolecule measurement and analysis, specifically DNA sequencing. Because these chips will be very small and because they will consume very little power, they shall allow DNA measurement machines ("sequencers") to become less costly and small and portable too. In effect, this research will help make DNA sequencing much more accessible and affordable to a broader set of users thus empowering applications such as personalized healthcare, biomolecular environmental sensing, and even next-generation information technologies such as archival data storage using DNA itself. Three chips are proposed, all for a form of sequencing whereby individual DNA molecules slip through a nano-scale aperture (a so-called "nanopore" sensor), an action that generates a minute signature-current of the DNA. An analysis of this current reveals the make-up of the DNA thus achieving sequencing. The first custom chip (analog mixed-signal) is responsible for amplifying and digitizing the extremely weak signature-currents emerging from the DNA-sensing nanopores. The second (digital) custom chip processes these digitized measurement signals to compute the sequence of sub-molecules (the "bases" A, C, G, T) that the measured DNA consists of, a process referred to as "basecalling". The third (digital) semi-custom chip combines the signals from the first and second chip to make even more accurate predictions of the sequence of bases that make up the measured DNA, thus making the results useful for detailed clinical analysis. Each of these three chips aims to significantly advance the state-of-the-art in DNA signal processing. My research into the first chip is focused on extending its speed (so that it can handle more DNA measurements relative to competing approaches) by at least 10X without compromising signal quality. My research also intends to make the first chip programmable and will also enable it to interact with the nanopore sensing apparatus in order to improve the sequencing system's adaptability to manufacturing and experimental variations. My research into the second chip is focused on integrating machine-learning DNA sequence analysis inference algorithms into a sub-0.5 W DNA basecaller. To date, no such integrated basecaller exists for nanopore-based DNA sequencers. My research into the third chip is focused on a field-programmable-gate-array (FPGA) hardware accelerator to work in conjunction with a general-purpose computer to increase the rate at which DNA sequences can be corrected by 100X with a substantial drop in required power consumption. To date, no such hardware-based accelerator for nanopore-based sequencing correction has been reported.