A Hopfield Neural Classifier and Its FPGA Implementation for Identification of Symmetrically Structured DNA Motifs

Authors:
Maria Stepanova;Feng Lin;Valerie C.-L. Lin
Affiliations:
School of Computer Engineering, Nanyang Technological University, Nanyang, Singapore 639798;School of Computer Engineering, Nanyang Technological University, Nanyang, Singapore 639798;School of Biological Science, Nanyang Technological University, Nanyang, Singapore 639798
Venue:
Journal of VLSI Signal Processing Systems
Year:
2007

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Abstract

Some specialized transcription factors recognize specific DNA sequences arranged in inverted and direct repeats with a short nucleotide spacer in between. Identification of these motifs has been challenging due to their high divergence. In this paper, we describe a novel computational approach that can greatly improve the efficiency and accuracy in prediction of these DNA binding sites. A Hopfield neural classifier was designed with the flexibility of internal structure being adapted recurrently for the target motif structure. An FPGA implementation of this recurrent neural network is presented. It contains 60 neurons, and is described by the Verilog HDL modules. The circuitry was mapped onto an Alpha Data Virtex-4LX160 FPGA board. A set of 600 experimentally verified steroid hormone binding sites was used as the training set, and the developed Hopfield neural classifier has been used to identify and classify actual Hormone Response Elements. The program has been proven to be an effective tool in studying hormone-regulated gene networks.