Low-dimensional representation of genomic sequences

Incipient functional SARS-CoV-2 diversification identified through neural network haplotype maps

Since its introduction in the human population, SARS-CoV-2 has evolved into multiple clades, but the events in its intrahost diversification are not well understood. Here, we compare three-dimensional (3D) self-organized neural haplotype maps (SOMs) of SARS-CoV-2 from thirty individual nasopharyngeal diagnostic samples obtained within a 19-day interval in Madrid (Spain), at the time of transition between clades 19 and 20. SOMs have been trained with the haplotype repertoire present in the mutant spectra of the nsp12- and spike (S)-coding regions. Each SOM consisted of a dominant neuron (displaying the maximum frequency), surrounded by a low-frequency neuron cloud. The sequence of the master (dominant) neuron was either identical to that of the reference Wuhan-Hu-1 genome or differed from it at one nucleotide position. Six different deviant haplotype sequences were identified among the master neurons. Some of the substitutions in the neural clouds affected critical sites of the nsp12-nsp8-nsp7 polymerase complex and resulted in altered kinetics of RNA synthesis in an in vitro primer extension assay. Thus, the analysis has identified mutations that are relevant to modification of viral RNA synthesis, present in the mutant clouds of SARS-CoV-2 quasispecies. These mutations most likely occurred during intrahost diversification in several COVID-19 patients, during an initial stage of the pandemic, and within a brief time period.

Computing dominant metric dimensions of certain connected networks

In the studies of the connected networks, metric dimension being a distance-based parameter got much more attention of the researches due to its wide range of applications in different areas of chemistry and computer science. At present its various types such as local metric dimension, mixed metric dimension, solid metric dimension, and dominant metric dimension have been used to solve the problems related to drug discoveries, embedding of biological sequence data, classification of chemical compounds, linear optimization, robot navigation, differentiating the interconnected networks, detecting network motifs, image processing, source localization and sensor networking. Dominant resolving sets are better than resolving sets because they carry the property of domination. In this paper, we obtain the dominant metric dimension of wheel, gear and anti-web wheel network in the form of integral numbers. We observe that the aforesaid networks have bounded dominant metric dimension as the order of the network increases. In particular, we also discuss the importance of the obtained results for the robot navigation networking.

Applications of Network Analysis in Biomedicine

The abundance of high-throughput data and technical refinements in graph theories have allowed network analysis to become an effective approach for various medical fields. This chapter introduces co-expression, Bayesian, and regression-based network construction methods, which are the basis of network analysis. Various methods in network topology analysis are explained, along with their unique features and applications in biomedicine. Furthermore, we explain the role of network embedding in reducing the dimensionality of networks and outline several popular algorithms used by researchers today. Current literature has implemented different combinations of topology analysis and network embedding techniques, and we outline several studies in the fields of genetic-based disease prediction, drug-target identification, and multi-level omics integration.