Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

MetumpX-a metabolomics support package for untargeted mass spectrometry.

MOTIVATION: Metabolomics is a data analysis and interpretation field aiming to study functions of small molecules within the organism. Consequently Metabolomics requires researchers in life sciences to be comfortable in downloading, installing and scripting of software that are mostly not user friendly and lack basic GUIs. As the researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines truly speeding up the learning curve to build software workstations. Therefore, this paper aims to provide MetumpX, a software package that eases in the installation of 103 software by automatically resolving their individual dependencies and also allowing the users to choose which software works best for them. RESULTS: MetumpX is a Ubuntu-based software package that facilitate easy download and installation of 103 tools spread across the standard metabolomics pipeline. As far as the authors know MetumpX is the only solution of its kind where the focus lies on automating development of software workstations. AVAILABILITY AND IMPLEMENTATION: https://github.com/hasaniqbal777/MetumpX-bin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics and systems biology analysis revealed PMID26394986-Compound-10 as potential repurposable drug against covid-19.

The global health pandemic known as COVID-19, which stems from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a significant concern worldwide. Several treatment methods exist for COVID-19; however, there is an urgent demand for previously established drugs and vaccines to effectively combat the disease. Since, discovering new drugs poses a significant challenge, making the repurposing of existing drugs can potentially reduce time and costs compared to developing entirely new drugs from scratch. The objective of this study is to identify hub genes and associated repurposed drugs targeting them. We analyzed differentially expressed genes (DEGs) by analyzing RNA-seq transcriptomic datasets and integrated with genes associated with COVID-19 present in different databases. We detected 173 Covid-19 associated genes for the construction of a protein-protein interaction (PPI) network which resulted in the identification of the top 10 hub genes/proteins (STAT1, IRF7, MX1, IRF9, ISG15, OAS3, OAS2, OAS1, IRF3, and IRF1). Hub genes were subjected to GO functional and KEGG pathway enrichment analyses, which indicated some key roles and signaling pathways that were strongly related to SARS-CoV-2 infections. We conducted drug repurposing analysis using CMap, TTD, and DrugBank databases with these 10 hub genes, leading to the identification of Piceatannol, CKD-712, and PMID26394986-Compound-10 as top-ranked candidate drugs. Finally, drug-gene interactions analysis through molecular docking and validated via molecular dynamic simulation for 80 ns suggests PMID26394986-Compound-10 as the only potential drug. Our research demonstrates how in silico analysis might produce repurposing candidates to help respond faster to new disease outbreaks.Communicated by Ramaswamy H. Sarma.

Unearthing Insights into Metabolic Syndrome by Linking Drugs, Targets, and Gene Expressions Using Similarity Measures and Graph Theory.

AIMS AND OBJECTIVES: Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive insilico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS. METHODS: For this purpose, we (a) investigated associated drugs and pathways for MetS, (b) employed eight different similarity measures to construct eight gene regulatory networks, (c) chose an optimal network, where a maximum number of drug targets were central, (d) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (e) employed these candidate genes to propose suitable drugs. RESULTS: Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS. CONCLUSION: Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.

Whole-genome sequencing as a first-tier diagnostic framework for rare genetic diseases.

Rare diseases affect nearly 300 million people globally with most patients aged five or less. Traditional diagnostic approaches have provided much of the diagnosis; however, there are limitations. For instance, simply inadequate and untimely diagnosis adversely affects both the patient and their families. This review advocates the use of whole genome sequencing in clinical settings for diagnosis of rare genetic diseases by showcasing five case studies. These examples specifically describe the utilization of whole genome sequencing, which helped in providing relief to patients via correct diagnosis followed by use of precision medicine.

An Overview of Therapeutic Options in Relapsing-remitting Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic disorder of the central nervous system (CNS). MS affects 2.1 million individuals every year and is also considered a major cause of economic health burden around the world. Genetics and environmental factors both play a role in the pathogenesis of MS by activating the immune response and causing inflammation. Patients with MS can have various clinical courses, but the most common pattern seen is relapsing-remitting multiple sclerosis (RRMS). Multiple therapeutic options have been studied to prevent RRMS patients from frequent relapses. The oldest and most frequently used medication for MS is interferon beta, either used alone or as add-on therapy with other drugs. Newer treatment options that have been recently approved to control MS symptoms and suppress the inflammation are glatiramer acetate and siponimod. Infusion therapies consisting of monoclonal antibodies and immunosuppressive drugs have also been studied in the recent past. Some trials have been conducted on the use of stem cells for RRMS patients. We have briefly discussed all treatment options and the response of RRMS patients in multiple trials.