Dynamic Deep Genomics Sequence Encoder for Managed File Transfer

Abstract

The standard communication protocols transfer big data over the internet. However, the challenge lies while sharing the massive amount of genomics data quickly over limited bandwidth networks. Managed File Transfer (MFT) that uses File Transfer Protocol Secure (FTPS) or Secure Shell File Transfer Protocol (SFTP) is a scalable and secure alternative for the purpose. This paper proposes a framework consisting of a Variable-length Genomic Binary Encoding (VGBE) scheme and a hybrid Gated Recurrent Unit-Convolutional Neural Network model (GRU-CNN). The Method exploits Managed File Transfer to reduce the bit size before transmission and increases the bit rate during the transmission of genomics data. Consequently, the method ensures a minimum number of bits per word and less latency. The methodology is tested on various communication protocols. It is validated in terms of performance metrics viz. transfer rate and the size of transferred data using diverse benchmark datasets such as protein sequences and human reference genome taken from the University of California Santa Cruz (UCSC) and National Center for Biotechnology Information (NCBI), respectively. Furthermore, simulation and implementation results show that the model is 98% faster, has 96% less data size, and is more secure than standard communication protocols. Thus, the proposed method is an efficient and secure genomics data transfer method using deep learning techniques.