Characterization of Genetic Mutations Using DNA Walks and Multifractal Detrended Fluctuation Analysis

Monitoring genetic mutations in DNA sequences and their subsequent characterisation provide the possibility for rapid development of diagnostics and therapeutic tools. Here, it is shown that the "DNA walk" (DNAW) representation together with multifractal detrended fluctuation analysis (MFDFA), i.e. DNAW/MFDFA, form a reliable characterization method for studying local and global properties of similar DNA sequences. The DNAW/MFDFA approach allows to study the stochastic properties of genetic sequences by constructing a one-to-one map of the sequence onto a walk, and is able to uncover the self-similarity properties of DNA walks. These features are illustrated on a set of similar DNA sequences of SARS-CoV-2 virus, in which the differences in nucleotide bases arise due to genetic mutations. The results show that DNAW/MFDFA can be used to extract long-range correlation information and type and degree of fractal complexity.

Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods.

Corona disease has become one of the problems and challenges of humankind over the past two years. One of the problems that existed from the first days of this epidemic was clinical symptoms similar to other infectious viruses such as colds and influenza. Therefore, diagnosis of this disease and its coping and treatment approaches is also been difficult. In this study, Attempts has been made to investigate the origin of this disease and the genetic structure of the virus leading to it. For this purpose, signal processing and linear predictive coding approaches were used which are widely used in data compression. A pattern recognition model was presented for the detection and separation of covid samples from the influenza virus case studies. This model, which was based on support vector machine classifier, was tested successfully on several datasets collected from different countries. The obtained results performed on all datasets by more than 98% accuracy. The proposed model, in addition to its good performance accuracy, can be a step forward in quantifying and digitizing medical big data information.

kmer2vec: A Novel Method for Comparing DNA Sequences by word2vec Embedding.

The comparison of DNA sequences is of great significance in genomics analysis. Although the traditional multiple sequence alignment (MSA) method is popularly used for evolutionary analysis, optimally aligning k sequences becomes computationally intractable when k increases due to the intrinsic computational complexity of MSA. Despite numerous k-mer alignment-free methods being proposed, the existing k-mer alignment-free methods may not truly capture the contextual structures of the sequences. In this study, we present a novel k-mer contextual alignment-free method (called kmer2vec), in which the sequence k-mers are semantically embedded to word2vec vectors, an essential technique in natural language processing. Consequently, the method converts each DNA/RNA sequence into a point in the word2vec high-dimensional space and compares DNA sequences in the space. Because the word2vec vectors are trained from the contextual relationship of k-mers in the genomes, the method may extract valuable structural information from the sequences and reflect the relationship among them properly. The proposed method is optimized on the parameters from word2vec training and verified in the phylogenetic analysis of large whole genomes, including coronavirus and bacterial genomes. The results demonstrate the effectiveness of the method on phylogenetic tree construction and species clustering. The method running speed is much faster than that of the MSA method, especially the phylogenetic relationships constructed by the kmer2vec method are more accurate than the conventional k-mer alignment-free method. Therefore, this approach can provide new perspectives for phylogeny and evolution and make it possible to analyze large genomes. In addition, we discuss special parameterization in the k-mer word2vec embedding construction. An effective tool for rapid SARS-CoV-2 typing can also be derived when combining kmer2vec with clustering methods.

Exploring the Role of 2D-Graphdiyne as a Charge Carrier Layer in Field-Effect Transistors for Non-Covalent Biological Immobilization against Human Diseases.

Graphdiyne's (GDY's) outstanding features have made it a novel 2D nanomaterial and a great candidate for electronic gadgets and optoelectronic devices, and it has opened new opportunities for the development of highly sensitive electronic and optical detection methods as well. Here, we testified a non-covalent grafting strategy in which GDY serves as a charge carrier layer and a bioaffinity substrate to immobilize biological receptors on GDY-based field-effect transistor (FET) devices. Firm non-covalent anchoring of biological molecules via pyrene groups and electrostatic interactions in addition to preserved electrical properties of GDY endows it with features of an ultrasensitive and stable detection mechanism. With emerging new forms and extending the subtypes of the already existing fatal diseases, genetic and biological knowledge demands more details. In this regard, we constructed simple yet efficient platforms using GDY-based FET devices in order to detect different kinds of biological molecules that threaten human health. The resulted data showed that the proposed non-covalent bioaffinity assays in GDY-based FET devices could be considered reliable strategies for novel label-free biosensing platforms, which still reach a high on/off ratio of over 10⁴. The limits of detection of the FET devices to detect DNA strands, the CA19-9 antigen, microRNA-155, the CA15-3 antigen, and the COVID-19 antigen were 0.2 aM, 0.04 pU mL^-1, 0.11 aM, 0.043 pU mL^-1, and 0.003 fg mL^-1, respectively, in the linear ranges of 1 aM to 1 pM, 1 pU mL^-1 to 0.1 µU mL^-1, 1 aM to 1 pM, 1 pU mL^-1 to 10 µU mL^-1, and 1 fg mL^-1 to 10 ng mL^-1, respectively. Finally, the extraordinary performance of these label-free FET biosensors with low detection limits, high sensitivity and selectivity, capable of being miniaturized, and implantability for in vivo analysis makes them a great candidate in disease diagnostics and point-of-care testing.

A comprehensive tool for rapid and accurate prediction of disease using DNA sequence classifier.

In the current pandemic situation where the coronavirus is spreading very fast that can jump from one human to another. Along with this, there are millions of viruses for example Ebola, SARS, etc. that can spread as fast as the coronavirus due to the mobilization and globalization of the population and are equally deadly. Earlier identification of these viruses can prevent the outbreaks that we are facing currently as well as can help in the earlier designing of drugs. Identification of disease at a prior stage can be achieved through DNA sequence classification as DNA carries most of the genetic information about organisms. This is the reason why the classification of DNA sequences plays an important role in computational biology. This paper has presented a solution in which samples collected from NCBI are used for the classification of DNA sequences. DNA sequence classification will in turn gives the pattern of various diseases; these patterns are then compared with the samples of a newly infected person and can help in the earlier identification of disease. However, feature extraction always remains a big issue. In this paper, a machine learning-based classifier and a new technique for extracting features from DNA sequences based on a hot vector matrix have been proposed. In the hot vector representation of the DNA sequence, each pair of the word is represented using a binary matrix which represents the position of each nucleotide in the DNA sequence. The resultant matrix is then given as an input to the traditional CNN for feature extraction. The results of the proposed method have been compared with 5 well-known classifiers namely Convolution neural network (CNN), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) algorithm, Decision Trees, Recurrent Neural Networks (RNN) on several parameters including precision rate and accuracy and the result shows that the proposed method gives an accuracy of 93.9%, which is highest compared to other classifiers.