Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

The five pillars of computational reproducibility: bioinformatics and beyond.

Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.

COVID-19 drug repurposing: A review of computational screening methods, clinical trials, and protein interaction assays.

The situation of coronavirus disease 2019 (COVID-19) pandemic is rapidly evolving, and medical researchers around the globe are dedicated to finding cures for the disease. Drug repurposing, as an efficient way for drug development, has received a lot of attention. However, the huge amount of studies makes it challenging to keep up to date with the literature on COVID-19 therapeutic development. This review addresses this challenge by grouping the COVID-19 drug repurposing research into three large groups, including clinical trials, computational research, and in vitro protein-binding experiments. Particularly, to facilitate future drug discovery and the creation of effective drug combinations, drugs are organized by their mechanisms of action and reviewed by their efficacy measured by clinical trials. Providing this subtyping information, we hope this review would serve the scientists, clinicians, and the pharmaceutical industry who are looking at the new therapeutics for COVID-19 treatment.

Advances in Metal-Organic Frameworks for the Removal of Chemical Warfare Agents: Insights into Hydrolysis and Oxidation Reaction Mechanisms.

The destruction of chemical warfare agents (CWAs) is a crucial area of research due to the ongoing evolution of toxic chemicals. Metal-organic frameworks (MOFs), a class of porous crystalline solids, have emerged as promising materials for this purpose. Their remarkable porosity and large surface areas enable superior adsorption, reactivity, and catalytic abilities, making them ideal for capturing and decomposing target species. Moreover, the tunable networks of MOFs allow customization of their chemical functionalities, making them practicable in personal protective equipment and adjustable to dynamic environments. This review paper focuses on experimental and computational studies investigating the removal of CWAs by MOFs, specifically emphasizing the removal of nerve agents (GB, GD, and VX) via hydrolysis and sulfur mustard (HD) via selective photooxidation. Among the different MOFs, zirconium-based MOFs exhibit extraordinary structural stability and reusability, rendering them the most promising materials for the hydrolytic and photooxidative degradation of CWAs. Accordingly, this work primarily concentrates on exploring the intrinsic catalytic reaction mechanisms in Zr-MOFs through first-principles approximations, as well as the design of efficient degradation strategies in the aqueous and solid phases through the establishment of Zr-MOF structure-property relationships. Recent progress in the tuning and functionalization of MOFs is also examined, aiming to enhance practical CWA removal under realistic battlefield conditions. By providing a comprehensive overview of experimental findings and computational insights, this review paper contributes to the advancement of MOF-based strategies for the destruction of CWAs and highlights the potential of these materials to address the challenges associated with chemical warfare.

"Big Data" Approaches for Prevention of the Metabolic Syndrome.

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these "big data", the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of "big data" applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.

Synthesis and Characterization of the Mixed Metal Oxide of ZnO-TiO2 Decorated by Polyaniline as a Protective Film for Acidic Steel Corrosion: Experimental, and Computational Inspections.

In this work, the preparation, characterization, and evaluation of a novel nanocomposite using polyaniline (PANi) functionalized bi-metal oxide ZnO-TiO2 (ZnTiO@PANi) as shielding film for carbon steel (CS)-alloy in acidic chloride solution at 298 K was studied. Different spectroscopic characterization techniques, such as UV-visible spectroscopy, dynamic light scattering (DLS), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectroscopy (FTIR) approaches, as well as other physicochemical methods, such as X-ray diffraction (XRD), high-resolution transmission electron microscopy (HR-TEM), and field emission scanning electron microscope (FESEM), were used to describe the produced nanocomposites. The significance of these films lies in the ZnO-TiO2 nanoparticle's functionalization by polyaniline, a material with high conductivity and electrochemical stability in acidic solutions. The mechanistic findings of the corrosion inhibition method were obtained by the use of electrochemical methods including open-circuit potentials (OCP) vs. time, potentiodynamic polarization (PDP), and electrochemical impedance spectroscopy (EIS). The results indicate that the synthesized ZnTiO@PANi is a powerful acidic corrosion inhibitor, and its inhibition effectiveness is 98.86% in the presence of 100 ppm. Additionally, the charge transfer resistance (Rp) value augmented from 51.8 to 432.7, and 963.7 Ω cm2 when the dose of PANi, and ZnTiO@PANi reached 100 ppm, respectively. The improvement in Rp and inhibition capacity values with an increase in nanocomposite dose is produced by the nanocomposite additives covering a larger portion of the surface, resulting in a decrease in alloy corrosion. By identifying the probable regions for molecule adsorption on the steel substrate, theoretical and computational studies provided significant details regarding the corrosion mitigation mechanism. The possibility of substituting old poisonous small substances with inexpensive and non-hazardous polymeric materials as shielding layers for utilization in the oilfield sectors is an important suggestion made by this research.

Green Algorithms: Quantifying the Carbon Footprint of Computation.

Climate change is profoundly affecting nearly all aspects of life on earth, including human societies, economies, and health. Various human activities are responsible for significant greenhouse gas (GHG) emissions, including data centers and other sources of large-scale computation. Although many important scientific milestones are achieved thanks to the development of high-performance computing, the resultant environmental impact is underappreciated. In this work, a methodological framework to estimate the carbon footprint of any computational task in a standardized and reliable way is presented and metrics to contextualize GHG emissions are defined. A freely available online tool, Green Algorithms (www.green-algorithms.org) is developed, which enables a user to estimate and report the carbon footprint of their computation. The tool easily integrates with computational processes as it requires minimal information and does not interfere with existing code, while also accounting for a broad range of hardware configurations. Finally, the GHG emissions of algorithms used for particle physics simulations, weather forecasts, and natural language processing are quantified. Taken together, this study develops a simple generalizable framework and freely available tool to quantify the carbon footprint of nearly any computation. Combined with recommendations to minimize unnecessary CO2 emissions, the authors hope to raise awareness and facilitate greener computation.

Computational investigation of molecular structures, spectroscopic properties and antitumor-antibacterial activities of some Schiff bases.

Molecular structures, spectroscopic properties (IR, 1H NMR and 13C NMR, UV-VIS), molecular electrostatic potential maps and some molecular properties (ionization energy, electron affinity, energy gap, hardness, electronegativity, electrophilicity index, static dipole moment and average linear polarizability) of three Schiff bases which are 2-((ethylamino)methyl)-6-methoxyphenol (HL1), 2-((ethylamino) methyl)-6-methylphenol (HL2) and 2-((ethylamino)methyl)-6-chlorophenol (HL3) were computed at B3LYP/6-31G(d) level in aqueous phase. The effects of methoxy, methyl and chloro substituents on Schiff bases were examined and it was found that the electron donating property of methyl and chlorine substituents was higher than the methoxy substituent. In order to investigate the antitumor activities of Schiff bases were docked against the breast cancer (MCF7) cell line. Molecular docking results were compared with antitumor standard 5-fluorouracil. Antitumor activity of HL2 and HL3 molecule was found to be higher than HL1 against MCF-7 cell line. In addition, in order to predict the antibacterial activities of Schiff bases were docked against the Mycobacterium tuberculosis (H37Rv) cell line. Docking results were compared with the antibacterial reference N-(salicylidene)-2-hydroxyaniline. Antibacterial activity of HL2 and HL3 molecules was found to be higher than HL1. It is estimated that the binding of the electron donating group to the ortho position of the hydroxyl group in studied Schiff bases increases both antitumor and antibacterial activity.

Improving the Catalytic CO2 Reduction on Cs2AgBiBr6 by Halide Defect Engineering: A DFT Study.

Pb-free double halide perovskites have drawn immense attention in the potential photocatalytic application, due to the regulatable bandgap energy and nontoxicity. Herein, we first present a study for CO2 conversion on Pb-free halide perovskite Cs2AgBiBr6 under state-of-the-art first-principles calculation with dispersion correction. Compared with the previous CsPbBr3, the cell parameter of Cs2AgBiBr6 underwent only a small decrease of 3.69%. By investigating the adsorption of CO, CO2, NO, NO2, and catalytic reduction of CO2, we found Cs2AgBiBr6 exhibits modest adsorption ability and unsatisfied potential determining step energy of 2.68 eV in catalysis. We adopted defect engineering (Cl doping, I doping and Br-vacancy) to regulate the adsorption and CO2 reduction behavior. It is found that CO2 molecule can be chemically and preferably adsorbed on Br-vacancy doped Cs2AgBiBr6 with a negative adsorption energy of -1.16 eV. Studying the CO2 reduction paths on pure and defect modified Cs2AgBiBr6, Br-vacancy is proved to play a critical role in decreasing the potential determining step energy to 1.25 eV. Finally, we probe into the electronic properties and demonstrate Br-vacancy will not obviously promote the process of catalysis deactivation, as there is no formation of deep-level electronic states acting as carrier recombination center. Our findings reveal the process of gas adsorption and CO2 reduction on novel Pb-free Cs2AgBiBr6, and propose a potential strategy to improve the efficiency of catalytic CO2 conversion towards practical implementation.

The role of metadata in reproducible computational research.

Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

Journal of Open Source Software (JOSS): design and first-year review.

This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision; while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a digital object identifier (DOI), deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative (OSI).

Genomics pipelines and data integration: challenges and opportunities in the research setting.

INTRODUCTION: The emergence and mass utilization of high-throughput (HT) technologies, including sequencing technologies (genomics) and mass spectrometry (proteomics, metabolomics, lipids), has allowed geneticists, biologists, and biostatisticians to bridge the gap between genotype and phenotype on a massive scale. These new technologies have brought rapid advances in our understanding of cell biology, evolutionary history, microbial environments, and are increasingly providing new insights and applications towards clinical care and personalized medicine. Areas covered: The very success of this industry also translates into daunting big data challenges for researchers and institutions that extend beyond the traditional academic focus of algorithms and tools. The main obstacles revolve around analysis provenance, data management of massive datasets, ease of use of software, interpretability and reproducibility of results. Expert commentary: The authors review the challenges associated with implementing bioinformatics best practices in a large-scale setting, and highlight the opportunity for establishing bioinformatics pipelines that incorporate data tracking and auditing, enabling greater consistency and reproducibility for basic research, translational or clinical settings.

India's Computational Biology Growth and Challenges.

India's computational science is growing swiftly due to the outburst of internet and information technology services. The bioinformatics sector of India has been transforming rapidly by creating a competitive position in global bioinformatics market. Bioinformatics is widely used across India to address a wide range of biological issues. Recently, computational researchers and biologists are collaborating in projects such as database development, sequence analysis, genomic prospects and algorithm generations. In this paper, we have presented the Indian computational biology scenario highlighting bioinformatics-related educational activities, manpower development, internet boom, service industry, research activities, conferences and trainings undertaken by the corporate and government sectors. Nonetheless, this new field of science faces lots of challenges.