New Exon Prediction Techniques Using Adaptive Signal Processing Algorithms for Genomic Analysis

Abstract

Substantial research and monetary aids to healthcare establishments are provided by cloud computing. A benign position to store and handle vast genome data is offered by cloud services. Labs for gene sequencing send out raw and contingent data over the Internet to multiple sequence collections under conservative flow of gene information. The use of cloud services also reduces the storage costs of deoxyribonucleic acid (DNA) sequencing. Here, an efficient and new bio-informatics genomic system is proposed by the use of cloud services from Amazon to access the stored gene data and process it. A key task in bio-informatics is to locate protein-coding sections in a gene sequence based on three base periodicity (TBP) is for disease diagnosis and design drugs. Here, a novel cloud-based adaptive exon predictor (AEP) using Amazon cloud services is proposed to improve the accuracy in exon finding ability as well as aimed at superior convergence. Noise in the input gene sequence given to the proposed AEPs is pre-processed using normalized LMS filtering. Computational complexity can be reduced using proposed data normalized form of least logarithmic absolute difference (NLLAD) algorithm and its error normalized variants. It was shown that sign regressor NLLAD (SRNLLAD) dependent AEP is efficient in exon forecast applications using different metrics for a performance like sensitivity 0.8037, precision 0.8052 along with specificity 0.8146 by different gene sequences considered from the National Center for Biotechnology Information (NCBI) databank. The proposed AEPs have shown upright performance than typical LMS and other AEPs in terms of exon prediction accuracy, convergence, and computational complexity. Their less computational complexity will be found attractive, and they are suitable to use in bio-informatics nano devices.INDEX TERMS Amazon cloud services, bio-informatics, convergence, deoxyribonucleic acid, National Center for Biotechnology Information, base three periodicity.

An Adaptive Mapping Method Using Spectral Envelope Approach for DNA Spectral Analysis

“…The first exon is the shortest (112 bases) and is usually the most difficult to detect. In this scenario, the coding regions are identified as follows [ 8 , 10 , 15 ]. The energy density spectrum at frequency rad/sample is evaluated over a window of W samples, then the window is slid by one or more samples, and the energy density is recalculated in a process that analyzes the entire DNA sequence.…”

“…Furthermore, we use the portion of gene F56F11 from chromosome III of Caenorhabditis elegans that transcribes the protein F56F11.4 , isoform a . The F56F11.4a is used as a benchmark problem for different exon detection techniques [ 8 , 13 , 14 ]. It has 7990 bp starting at nucleotide position 7021 of gene F56F11 .…”

“…This periodic phenomenon has attracted the attention of many biologists who are trying to understand and explain it [ 3 , 4 , 5 , 6 ]. Thus, it is possible to discriminate between coding and non-coding regions of a DNA sequence by observing its energy spectrum [ 7 , 8 , 9 , 10 ].…”

Pervasive underwater passive target tracking for the computation of standard deviation solution in a 3D environment

“…These measurements are related nonlinearly to the system dynamic function. To reduce the nonlinearity in the system, use highly nonlinear filtering algorithms like EKF (Extended Kalman Filter) (Harsha and Ratnam, 2018; Divya and Rao, 2019), MGEKF (Modified Gain Kalman Filter), UKF (Unscented Kalman Filter) and PF (Particle Filter) (Putluri et al , 2019; Jahan and Rao, 2019). The Kalman filter is an optimal linear filter; hence, it is not suitable for nonlinear systems.…”

Prevalence of shifted Rayleigh filter for passive surveillance in underwater

“…Signal processing and its various advancements like stochastic signal processing (Allen and Blackman, 1991; Lindgren and Gong, 1978), adaptive signal processing and multirate signal processing have wide range of applications GPS-TEC variations (Suraj et al , 2018; Ratnam et al , 2019), medical field (Bhagat et al , 2018; Putluri et al , 2019; Nagaraju et al , 2019), wireless communications (Mahender et al ., 2017) and computer vision models (Prasad et al , 2017; Kishore and Prasad, 2017; Maddala et al , 2019). In stochastic signal processing, underwater target tracking has a lot of applications like underwater surveillance, finding ship decks, school of fish, target tracking, etc.…”