From DNA to protein: Why genetic code context of nucleotides for DNA signal processing? A review

Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis

Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.

Ab initio gene prediction for protein-coding regions

Motivation

Ab initio gene prediction in nonmodel organisms is a difficult task. While many ab initio methods have been developed, their average accuracy over long segments of a genome, and especially when assessed over a wide range of species, generally yields results with sensitivity and specificity levels in the low 60% range. A common weakness of most methods is the tendency to learn patterns that are species-specific to varying degrees. The need exists for methods to extract genetic features that can distinguish coding and noncoding regions that are not sensitive to specific organism characteristics.

Results

A new method based on a neural network (NN) that uses a collection of sensors to create input features is presented. It is shown that accurate predictions are achieved even when trained on organisms that are significantly different phylogenetically than test organisms. A consensus prediction algorithm for a CoDing Sequence (CDS) is subsequently applied to the first nucleotide level of NN predictions that boosts accuracy through a data-driven procedure that optimizes a CDS/non-CDS threshold. An aggregate accuracy benchmark at the nucleotide level shows that this new approach performs better than existing ab initio methods, while requiring significantly less training data.

Availability and implementation

https://github.com/BioMolecularPhysicsGroup-UNCC/MachineLearning.

Mechanical properties of coconut shell-based concrete: experimental and optimisation modelling

Excessive accumulation of waste materials has presented a serious environmental problem on a global scale. This has prompted many researchers to utilise agricultural, industrial, and by-product waste materials as the replacement of aggregate in the concrete matrix. In this present study, the prediction and optimisation of coconut shell (CA) content as the replacement of fine aggregate were evaluated based on the mechanical properties of the concrete (M30). Based on the suggested design array from the response surface methodology (RSM) model, experimental tests were carried out to achieve the goal of this study. The collected data was used to develop mathematical predictive equations using both GEP and RSM models. Analysis of variance (ANOVA) was also taken into account to appraise and verify the performance of the proposed models. Based on the results, the optimum content of replacing CA was 50%. In particular, the compressive, tensile, and flexural strength obtained after 28 days of curing were 46.2, 3.74, and 8.06 MPa, respectively, from the RSM model and 46.18, 3.85, and 7.99 MPa, respectively, from the GEP model. The obtained values were superior to those of the control concrete sample (43.12, 3.51 and 7.14 MPa, respectively). Beyond the optimum content, a loss in strength was observed. It was also found that both the GEP and RSM models exhibited high prediction accuracy with strong correlations (R2 = 0.97 and 0.95, respectively). In addition, minimum prediction error (RMSE < 0.945 (RSM), RMSE < 1.62 (GEP)) was achieved, indicating that both models were robust and reliable for further prediction. It was concluded that CA could serve as an excellent strategic material to address several serious environmental issues.

Detection of exon location in eukaryotic DNA using a fuzzy adaptive Gabor wavelet transform

The existing model-independent methods for the detection of exons in DNA could not prove to be ideal as commonly employed fixed window length strategy produces spectral leakage causing signal noise The Modified-Gabor-wavelet-transform exploits a multiscale strategy to deal with the issue to some extent. Yet, no rule regarding the occurrence of small and large exons has been specified. To overcome this randomness, scaling-factor of GWT has been adapted based on a fuzzy rule. Due to the nucleotides' genetic code and fuzzy behaviors in DNA configuration, this work could adopt the fuzzy approach. Two fuzzy membership functions (large and small) take care of the variation in the coding regions. The fuzzy-based learning parameter adaptively tunes the scale factor for fast and precise prediction of exons. The proposed approach has an immense plus point of being capable of isolating detailed sub-regions in each exon efficiently proving its efficacy comparing with existing techniques.

Hereditary disease prediction in eukaryotic DNA: an adaptive signal processing approach

Hereditary disease prediction in eukaryotic DNA using signal processing approaches is an incredible work in bioinformatics. Researchers of various fields are trying to put forth a noninvasive approach to forecast the disease-related genes. As diseased genes are more random than the healthy ones, in this work, a comparison of the diseased gene is made against the healthy ones. An adaptive signal processing method like functional link artificial neural network-based Levenberg-Marquardt filter has been proposed in this regard. For parameter upgradation, the algorithm is modified using particle swarm optimization. Here, disease genes are discriminated from healthy ones based on the magnitude of mean square error (MSE), which is calculated through the adaptive filter. The performance of the algorithm is inspected by computing some evaluation parameters. Since accuracy is the prime concern, authors in this work have taken an attempt to improve the accuracy level compared to the existing methods. Taking the reference gene as healthy, the overall process is accomplished by categorizing the diseased and healthy targets with MSE value at a threshold of 0.012. The proposed technique predicts the test gene sets successfully.

Performance Evaluation and Validation of QCM (Query Control Mechanism) for QoS-Enabled Layered-Based Clustering for Reactive Flooding in the Internet of Things

Internet of Things (IoT) facilitates a wide range of applications through sensor-based connected devices that require bandwidth and other network resources. Enhancement of efficient utilization of a heterogeneous IoT network is an open optimization problem that is mostly suffered by network flooding. Redundant, unwanted, and flooded queries are major causes of inefficient utilization of resources. Several query control mechanisms in the literature claimed to cater to the issues related to bandwidth, cost, and Quality of Service (QoS). This research article presented a statistical performance evaluation of different query control mechanisms that addressed minimization of energy consumption, energy cost and network flooding. Specifically, it evaluated the performance measure of Query Control Mechanism (QCM) for QoS-enabled layered-based clustering for reactive flooding in the Internet of Things. By statistical means, this study inferred the significant achievement of the QCM algorithm that outperformed the prevailing algorithms, i.e., Divide-and-Conquer (DnC), Service Level Agreements (SLA), and Hybrid Energy-aware Clustering Protocol for IoT (Hy-IoT) for identification and elimination of redundant flooding queries. The inferential analysis for performance evaluation of algorithms was measured in terms of three scenarios, i.e., energy consumption, delays and throughput with different intervals of traffic, malicious mote and malicious mote with realistic condition. It is evident from the results that the QCM algorithm outperforms the existing algorithms and the statistical probability value "P" < 0.05 indicates the performance of QCM is significant at the 95% confidence interval. Hence, it could be inferred from findings that the performance of the QCM algorithm was substantial as compared to that of other algorithms.

A degeneration-reducing criterion for optimal digital mapping of genetic codes

Bioinformatics may seem to be a scientific field processing primarily large string datasets, as nucleotides and amino acids are represented with dedicated characters. On the other hand, many computational tasks that bioinformatics challenges are mathematical problems understandable as operations with digits. In fact, many computational tasks are solved this way in the background. One of the most widely used digital representations is mapping of nucleotides and amino acids with integers 0–3 and 0–20, respectively. The limitation of this mapping occurs when the digital signal of nucleotides has to be translated into a digital signal of amino acids as the genetic code is degenerated. This causes non-monotonies in a mapping function. Although map for reducing this undesirable effect has already been proposed, it is defined theoretically and for standard genetic codes only. In this study, we derived a novel optimal criterion for reducing the influence of degeneration by utilizing a large dataset of real sequences with various genetic codes. As a result, we proposed a new robust global optimal map suitable for any genetic code as well as specialized optimal maps for particular genetic codes.

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Optimization of 1D numerical representation for DNA to protein translation.

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Reducing genetic code degeneracy in numerical representation of DNA sequences.

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More robust numerical conversion used for genomic-proteomic analysis.

An integrated approach for identification of exon locations using recursive Gauss Newton tuned adaptive Kaiser window

Identification of exon location in a DNA sequence has been considered as the most demanding and challenging research topic in the field of Bioinformatics. This work proposes a robust approach combining the Trigonometric mapping with Adaptive tuned Kaiser Windowing approach for locating the protein coding regions (EXONS) in a genetic sequence. For better convergence as well as improved accurateness, the side lobe height control parameter (β) of Kaiser Window in the proposed algorithm is made adaptive to track the changing dynamics of the genetic sequence. This yields better tracking potential of the anticipated Adaptive Kaiser algorithm as it uses the recursive Gauss Newton tuning which in turn utilizes the covariance of the error signal to tune the β factor which has been shown through numerous simulation results under a variety of practical test conditions. A detailed comparative analysis with the existing mapping schemes, windowing techniques, and other signal processing methods like SVD, AN, DFT, STDFT, WT, and ST has also been included in the paper to focus on the strength and efficiency of the proposed approach. Moreover, some critical performance parameters have been computed using the proposed approach to investigate the effectiveness and robustness of the algorithm. In addition to this, the proposed approach has also been successfully applied on a number of benchmark gene sets like Musmusculus, Homosapiens, and C. elegans, etc., where the proposed approach revealed efficient prediction of exon location in contrast to the other existing mapping methods.

Detecting Periodicities in Eukaryotic Genomes by Ramanujan Fourier Transform

Ramanujan Fourier transform (RFT) nowadays is becoming a popular signal processing method. RFT is used to detect periodicities in exons and introns of eukaryotic genomes in this article. Genomic sequences of nine species were analyzed. The highest peak in the spectrum amplitude corresponding to each exon or intron is regarded as the significant signal. Accordingly, the periodicity corresponding to the significant signal can be also regarded as a valuable periodicity. Exons and introns have different periodic phenomena. The computational results reveal that the 2-, 3-, 4-, and 6-base periodicities of exons and introns are four kinds of important periodicities based on RFT. It is the first time that the 2-base periodicity of introns is discovered through signal processing method. The frequencies of the 2-base periodicity and the 3-base periodicity occurrence are polar opposite between the exons and the introns. With regard to the cyclicality of the Ramanujan sums, which is the base function of the transformation, RFT is suggested for studying the periodic features of dinucleotides, trinucleotides, and q nucleotides.

A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing

BACKGROUND AND OBJECTIVE

Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals.

METHODS

This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise.

RESULTS

Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.

CONCLUSION

This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.