gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.

The tRNA adaptation index (tAI) is a translation efficiency metric that considers weighted values (S ij values) for codon-tRNA wobble interaction efficiencies. The initial implementation of the tAI had significant flaws. For instance, generated S ij weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. Consequently, a species-specific approach (stAI) was developed to overcome those limitations. However, the stAI method employed a hill climbing algorithm to optimize the S ij weights, which is not ideal for obtaining the best set of S ij weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. In addition, it did not perform well in computing the tAI of fungal genomes in comparison with the original implementation. We developed a novel approach named genetic tAI (gtAI) implemented as a Python package (https://github.com/AliYoussef96/gtAI), which employs a genetic algorithm to obtain the best set of S ij weights and follows a new codon usage-based workflow that better computes the tAI of genomes from the three domains of life. The gtAI has significantly improved the correlation with the codon adaptation index (CAI) and the prediction of protein abundance (empirical data) compared to the stAI.

Integrated multiomics analysis to infer COVID-19 biological insights.

Three years after the pandemic, we still have an imprecise comprehension of the pathogen landscape and we are left with an urgent need for early detection methods and effective therapy for severe COVID-19 patients. The implications of infection go beyond pulmonary damage since the virus hijacks the host's cellular machinery and consumes its resources. Here, we profiled the plasma proteome and metabolome of a cohort of 57 control and severe COVID-19 cases using high-resolution mass spectrometry. We analyzed their proteome and metabolome profiles with multiple depths and methodologies as conventional single omics analysis and other multi-omics integrative methods to obtain the most comprehensive method that portrays an in-depth molecular landscape of the disease. Our findings revealed that integrating the knowledge-based and statistical-based techniques (knowledge-statistical network) outperformed other methods not only on the pathway detection level but even on the number of features detected within pathways. The versatile usage of this approach could provide us with a better understanding of the molecular mechanisms behind any biological system and provide multi-dimensional therapeutic solutions by simultaneously targeting more than one pathogenic factor.

Xconnector: Retrieving and visualizing metabolites and pathways information from various database resources.

Metabolomics databases contain crucial information collected from various biological systems and experiments. Developers and scientists performed massive efforts to make the database public and accessible. The diversity of the metabolomics databases arises from the different data types included within the database originating from various sources and experiments can be confusing for biologists and researchers who need further manual investigation for the retrieved data. Xconnector is a software package designed to easily retrieve and visualize metabolomics data from different databases. Xconnector can parse information from Human Metabolome Database (HMDB), Livestock Metabolome Database (LMDB), Yeast Metabolome Database (YMDB), Toxin and Toxin Target Database (T3DB), ReSpect Phytochemicals Database (ReSpectDB), The Blood Exposome Database, Phenol-Explorer Database, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Small Molecule Pathway Database (SMPDB). Using Python language, Xconnector connects the targeted databases, recover requested metabolites from single or different database sources, reformat and repack the data to generate a single Excel CSV file containing all information from the databases, in an application programming interface (API)/ Python dependent manner seamlessly. In addition, Xconnector automatically generates graphical outputs in a time-saving approach ready for publication. SIGNIFICANCE: The powerful ability of Xconnector to summarize metabolomics information from different sources would enable researchers to get a closer glimpse on the nature of potential molecules of interest toward medical diagnostics, better biomarker discovery, and personalized medicine. The software is available as an executable application and as a python package compatible for different operating systems.

UniprotR: Retrieving and visualizing protein sequence and functional information from Universal Protein Resource (UniProt knowledgebase).

UniprotR is a software package designed to easily retrieve, cluster and visualize protein data from UniProt knowledgebase (UniProtKB) using R language. The package is implemented mainly to process, parse and illustrate proteomics data in a handy and time-saving approach allowing researchers to summarize all required protein information available at UniProtKB in a readable data frame, Excel CSV file, and/or graphical output. UniprotR generates a set of graphics including gene ontology, chromosomal location, protein scoring and status, protein networking, sequence phylogenetic tree, and physicochemical properties. In addition, the package supports clustering of proteins based on primary gene name or chromosomal location, facilitating additional downstream analysis. SIGNIFICANCE: In this work, we implemented a robust package for retrieving and visualizing information from multiple sources such UniProtKB, SWISS-MODEL, and STRING. UniprotR Contains functions that enable retrieving and cluster data in a handy way and visualize data in publishable graphs to facilitate researcher's work and fulfill their needs. UniprotR will aid in saving time for downstream data analysis instead of manual time consuming data analysis. AVAILABILITY AND IMPLEMENTATION: UniprotR released as free open source code under the license of GPLv3, and available in CRAN (The Comprehensive R Archive Network) and GitHub. (https://cran.r-project.org/web/packages/UniprotR/index.html). (https://github.com/Proteomicslab57357/UniprotR).