Protein-Protein Interaction Network Exploration Using Cytoscape.

As the protein-protein interaction (PPI) data increase exponentially, the development and usage of computational methods to analyze these datasets have become a new research horizon in systems biology. The PPI network analysis and visualization can help identify functional modules of the network, pathway genes involved in common cellular functions, and functional annotations of novel genes. Currently, a variety of tools are available for network graph visualization and analysis. Cytoscape, an open-source software tool, is one of them. It provides an interactive visualization interface along with other core features to import, navigate, filter, cluster, search, and export networks. It comes with hundreds of in-built Apps in App Manager to resolve research questions related to network visualization and integration. This chapter aims to illustrate the Cytoscape application to visualize and analyze the PPI network using Arabidopsis interactome-1 main (AI-1MAIN) PPI network dataset from Plant Interactome Database.

Protein-Protein Interaction Network Analysis Using NetworkX.

In recent years, extracting information from biological data has become a particularly valuable way of gaining knowledge. Molecular interaction networks provide a framework for visualizing cellular processes, but their complexity frequently makes their interpretation difficult. Proteins are one of the primary determinants of biological function. Indeed, most biological activities in the living cells are functionally regulated by protein-protein interactions (PPIs). Thus, studying protein interactions is critical for understanding their roles within the cell. Exploring the PPI networks can open new avenues for future experimental studies and offer interspecies predictions for effective interaction mapping. In this chapter we will demonstrate how to construct, visualize, and analyze a protein-protein interaction network using NetworkX.

Population and mutational assessment of novel repeats in 13RM Y-STRs in unrelated males born in Gilgit, Pakistan.

Because they are totally transferred to the future generations until mutations occur, Y chromosome genetic markers are commonly utilised in forensics for the classification of male lineages for criminal justice purposes. The mutation rate of Rapidly Mutating Y-STRs (RM Y-STRs) markers is high. That is not seen in other Y-STRs markers, and they appear to be effective in distinguishing paternally related men. This study aimed to estimate the population and mutational parameters of 13 RM Y-STRs in 13 unrelated males born in Gilgit, Pakistan. Repeat there was no population substructure and strong discriminating capacity in the counts. In this population, there were higher mutation rates with the unusual structure of repeats. More research is needed to better characterize these loci in diverse Pakistani groups.

Comparative analysis of the serum microbiome of HIV infected individuals.

HIV infects the CD4 cells which marks the suppression of our immune system. DNA from serum of healthy, treated and untreated HIV infected individuals was extracted. The DNA was subjected to 16S metagenomic sequencing and analyzed using QIIME2 pipeline. 16S sequencing analysis showed serum microbiome was dominated by Firmicutes, Proteobacteria, Bacteroidota and Actinobacteria. Treated HIV infection showed highest abundance of Firmicutes (66.40%) significantly higher than untreated HIV infection (35.88%) and control (41.89%). Bacilli was most abundant class in treated (63.59%) and second most abundant in untreated (34.53%) while control group showed highest abundance of class Gamma-proteobacteria (45.86%). Untreated HIV infection group showed Enterococcus (10.72%) and Streptococcus (6.599%) as the most abundant species. Untreated HIV infection showed significantly higher (p = 0.0039) species richness than treated and control groups. An altered serum microbiome of treated HIV infection and higher microbial abundance in serum of untreated HIV infection was observed.