Comparative genomic signature representations of the emerging COVID-19 coronavirus and other coronaviruses: High identity and possible recombination between Bat and Pangolin coronaviruses.

Coronaviruses are responsible on respiratory diseases in animal and human. The combination of numerical encoding techniques and digital signal processing methods are becoming increasingly important in handling large genomic data. In this paper, we propose to analyze the SARS-CoV-2 genomic signature using the combination of different nucleotide representations and signal processing tools in the aim to identify its genetic origin. The sequence of SARS-CoV-2 was compared with 21 relevant sequences including Bat, Yak and Pangolin coronavirus sequences. In addition, we developed a new algorithm to locate the nucleotide modifications. The results show that the Bat and Pangolin coronaviruses were the most related to SARS-CoV-2 with 96% and 86% of identity all along the genome. Within the S gene sequence, the Pangolin sequence presents local highest nucleotide identity. Those findings suggest genesis of SARS-Cov-2 through evolution from Bat and Pangolin strains. This study offers new ways to automatically characterize viruses.

The Helitron family classification using SVM based on Fourier transform features applied on an unbalanced dataset.

Helitrons are mobile sequences which belong to the class 2 of eukaryotic transposons. Their specificity resides in their mechanism of transposition: the rolling circle mechanism. They play an important role in remodeling proteomes due to their ability to modify existing genes and introducing new ones. A major difficulty in identifying and classifying Helitron families comes from the complex structure, the unspecified length, and the unbalanced appearance number of each Helitron type. The Helitron's recognition is still not solved in literature. The purpose of this paper is to characterize and classify Helitron types using spectral features and support vector machine (SVM) classification technique. Thus, the helitronic DNA is transformed into a numerical form using the FCGS2 coding technique. Then, a set of spectral features is extracted from the smoothed Fourier transform applied on the FCGS2 signals. Based on the spectral signature and the classification's confusion matrix, we demonstrated that some specific classes which do not show similarities, such as HelitronY2 and NDNAX3, are easily discriminated with important accuracy rates exceeding 90%. However, some Helitron types have great similarities such as the following: Helitron1, HelitronY1, HelitronY1A, and HelitronY4. Our system is also able to predict them with promising values reaching 70%. Graphical abstract The Helitron recognizer based on features extracted from smoothed Fourier transform.

A new way to visualize DNA's base succession: the Caenorhabditis elegans chromosome landscapes.

In the eukaryotic genomes, the genetic diseases are generally associated with the tandem repeats. These repeats seem to appear frequently. In this paper, we are describing a wavelet transform technique which provides a new way to represent the DNA succession bases as a DNA progression images. These images offer DNA landscapes, visualizing and following up periodicities through genomes. We investigated in a structural coding technique the Pnuc. Then, we illustrated, with time-frequency representation, the existence and the superposition of the periodicities in some biological features, their locations and the different ways in which they appear. The representations generated showed that one periodicity can sometimes be alone, but generally, it is incorporated to others. These periodicities associations create, in the Caenorhabditis elegans chromosome, a precise structural image of biological features, such as CeRep, Helitrons, repeats and satellites.

Building Specific Signals from Frequency Chaos Game and Revealing Periodicities Using a Smoothed Fourier Analysis.

Investigating the roles and functions of DNA within genomes is becoming a primary focus of genomic research. Thus, the research works are moving towards cooperation between different scientific disciplines which aims at facilitating the interpretation of genetic information. In order to characterize the DNA of living organisms, signal processing tools appear to be very suitable for such study. However, a DNA sequence must be converted into a numerical sequence before processing; which defines the concept of DNA coding. In line with this, we propose a new one dimensional model based on the chaos game representation theory called Frequency Chaos Game Signal: FCGS. Then, we perform a Smoothed Fourier Transform to enhance hidden periodicities in the C.elegans DNA sequences. Through this study, we demonstrate the performance of our coding approach in highlighting characteristic periodicities. Indeed, several periodicities are shown to be involved in the 1D spectra and the 2D spectrograms of FCGSs. To investigate further about the contribution of our method in the enhancement of characteristic spectral attributes, a comparison with a range of binary indicators is established.

Wavelet analysis of frequency chaos game signal: a time-frequency signature of the C. elegans DNA.

Challenging tasks are encountered in the field of bioinformatics. The choice of the genomic sequence's mapping technique is one the most fastidious tasks. It shows that a judicious choice would serve in examining periodic patterns distribution that concord with the underlying structure of genomes. Despite that, searching for a coding technique that can highlight all the information contained in the DNA has not yet attracted the attention it deserves. In this paper, we propose a new mapping technique based on the chaos game theory that we call the frequency chaos game signal (FCGS). The particularity of the FCGS coding resides in exploiting the statistical properties of the genomic sequence itself. This may reflect important structural and organizational features of DNA. To prove the usefulness of the FCGS approach in the detection of different local periodic patterns, we use the wavelet analysis because it provides access to information that can be obscured by other time-frequency methods such as the Fourier analysis. Thus, we apply the continuous wavelet transform (CWT) with the complex Morlet wavelet as a mother wavelet function. Scalograms that relate to the organism Caenorhabditis elegans (C. elegans) exhibit a multitude of periodic organization of specific DNA sequences.