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Overview of steps in a typical gene expression microarray experiment. Topics in blue boxes with solid borders are addressed in the Experimental Design section, those in green boxes with dashed borders are covered in the section on data preparation, and those in purple boxes with dash-dotted borders are discussed in the Data Analysis section of this review.
Source publication
Gene expression microarrays provide a snapshot of all the transcriptional activity in a biological sample. Unlike most traditional molecular biology tools, which generally allow the study of a single gene or a small set of genes, microarrays facilitate the discovery of totally novel and unexpected functional roles of genes. The power of these tools...
Citations
... During exposure to environmental stress factors, plants modulate various biochemical and metabolic alterations, which results in the alteration of thousands of genes. Massive parallel analysis of biological data using microarray provides an excellent tool for the analysis of thousands of genes at the same time in the same reaction (Slonim and Yanai 2009). Microarray technology is rapidly becoming a central platform for functional genomics. ...
Plants are sessile beings and therefore, they inevitably encounter several biotic and abiotic stress factors during their entire life cycle. Environmental and climate changes can alter the physiological state of a plant and trigger several modulations for acclimation and adaptation to unfavorable conditions. To meet the challenges of climate change, understanding the mechanisms that crop plants employ to resist and combat environmental stress factors is of considerable interest to ensure sustainable agricultural production and food security. Growing global demand for food for expanding populations in the era of climate change demands the development of stress-tolerant crop cultivars. For several decades, researchers are working toward improving crop plants for developing climate-resilient elite genotypes. Within the last two decades, the combination of two major “omics” tools has advanced our knowledge of stress regulatory networks, thereby benefitting crop improvement programs. This chapter is intended to be a synopsis of updated and comprehensive knowledge concerning all possible combinations of advanced genomic and proteomic approaches in this regard. It includes information on the most significant achievements of the genomic and proteomic approaches, which will assist sustainable crop production under various suboptimal conditions, including biotic and abiotic, and the future directions for harnessing these sophisticated, high-throughput methodologies to enhance yield stability in terms of agricultural production. In this chapter, attention is paid to highlighting the currently available tools in the genomic and proteomic research areas and the synergistic knowledge of these two major omics strategies for designing our future crops with new tactics to withstand hostile environment.
... In silico studies of diverse types of functional and structural members of the SARS-CoV-2 proteome have significantly enriched protein information in structural resources like the Protein Data Bank (PDB) [152]. Many unique functional gene interactions contributing to the generation of novel disease subtypes, together with the characterization of disease pathomechanisms or modes of drug response, have been elucidated by computational studies of drug repurposing using data on gene regulation, based on the presumption that drugs aim to bind the same proteins with equivalent gene expression profiles [153]. Lastly, gene-expression data from transcriptome resources like the Genotype-Tissue Expression (GTEx) program and the LINCS L1000 database have also been referred to in several COVID-19 drug-repurposing approaches [154,155]. ...
SARS-CoV-2 is a highly contagious and dangerous coronavirus that has been spreading around the world since late December 2019. Severe COVID-19 has been observed to induce severe damage to the alveoli, and the slow loss of lung function led to the deaths of many patients. Scientists from all over the world are now saying that SARS-CoV-2 can spread through the air, which is a very frightening prospect for humans. Many scientists thought that this virus would evolve during the first wave of the pandemic and that the second wave of reinfection with the coronavirus would also be very dangerous. In late 2020 and early 2021, researchers found different genetic versions of the SARS-CoV-2 virus in many places around the world. Patients with different types of viruses had different symptoms. It is now evident from numerous case studies that many COVID-19 patients who are released from nursing homes or hospitals are more prone to developing multi-organ dysfunction than the general population. Understanding the pathophysiology of COVID-19 and its impact on various organ systems is crucial for developing effective treatment strategies and managing long-term health consequences. The case studies highlighted in this review provide valuable insights into the ongoing health concerns of individuals affected by COVID-19.
... Gene expressions describe the information of a gene generated by its transcription and translation process, which can be compiled into profiles to e.g. understand the genetic mechanisms of diseases or the response to complementary treatments proven to be helpful in cancer treatments (Brazma and Vilo, 2000;Slonim and Yanai, 2009). ...
Motivation: Colorectal Cancer has the second-highest mortality rate worldwide, which requires advanced diagnostics and individualized therapies to be developed. Information about the interactions between molecular entities provides valuable information to detect the responsible genes driving cancer progression. Graph Convolutional Neural Networks are able to utilize the prior knowledge provided by interaction networks and the Spektral library adds a performance increase in contrast to standard implementations. Furthermore, machine learning technology shows great potential to assist medical professionals through guided clinical decision support. However, the deep learning models are limited in their application in precision medicine due to their lack to explain the factors contributing to a prediction. Adaption of the Graph Layer-Wise Relevance Propagation methodology to graph-based deep learning models allows to attribute the learned outcome to single genes and determine their relevance. The resulting patient-specific subnetworks then can be used to identify potentially targetable genes.
Results: We present an implementation of Graph Convolutional Neural Networks using the Spektral library in combination with adapted functions for Graph Layer-Wise Relevance Propagation. Deep learning models were trained on a newly composed large gene expression dataset of Colorectal Cancer patients with different molecular interaction networks as prior knowledge: Protein-protein interactions from the Human Protein Reference Database and STRING, and pathways from the Reactome database. Our implementation performs comparably with the original implementation while reducing the computation time, especially for large networks. Further, the generated subnetworks are similar to those of the initial implementation and reveal possible, and even more distant, biomarkers and drug targets.
... Although it can be monitored dynamically, the technique is limited by the fact that it only visualises what has been labelled, which assumes an a priori knowledge of the process to be monitored, and/or multiple probes for a more systemic understanding. More systemic approaches, which can be considered top-down, employ different omics techniques, including genomics [132,133], transcriptomics [134][135][136], proteomics [137,138] and metabolomics [139], by which the entire genome, transcriptome, proteome, and metabolome of the system can be analysed as a snapshot of the quenched system using highthroughput techniques [140][141][142]. These techniques are usually used in a targeted manner, focusing on specific aspects of a systemic response, such that they can also be considered bottom-up approaches [143][144][145]. ...
Background
The dynamics of the cellular glycolysis pathway underpin cellular function and dysfunction, and therefore ultimately health, disease, diagnostic and therapeutic strategies. Evolving our understanding of this fundamental process and its dynamics remains critical.
Scope of review
This paper reviews the medical relevance of glycolytic pathway in depth and explores the current state of the art for monitoring and modelling the dynamics of the process. The future perspectives of label free, vibrational microspectroscopic techniques to overcome the limitations of the current approaches are considered.
Major conclusions
Vibrational microspectroscopic techniques can potentially operate in the niche area of limitations of other omics technologies for non-destructive, real-time, in vivo label-free monitoring of glycolysis dynamics at a cellular and subcellular level.
... Commercial and custom-designed microarrays evolved through two main generations of chips: the first two-color (or twochannel) spotted microarrays [3] and the next single-channel (or one-color) high-density microarrays [4], mainly popularized by Affymetrix (Santa Clara, California) and capable of a much more reliable genome-wide quantification of the expression changes across multiple samples and different experimental conditions. The need for acquiring, processing and analyzing the ensuing high-throughput transcriptomics data streams has been a powerful driver for the development of innovative computational methods and statistics approaches within the context of bioinformatics across the postgenomic era [5][6][7][8][9]. The birth and the rapid growth of the Bioconductor project [10], together with the two most important public repositories for gene expression data-namely the Gene Expression Omnibus (GEO) [11,12] and ArrayExpress (AE) [13,14]-is probably what best embodies the deep impact of that first technological revolution in -omics disciplines. ...
Tens of thousands of gene expression data sets describing a variety of model organisms in many different pathophysiological conditions are currently stored in publicly available databases such as the Gene Expression Omnibus (GEO) and ArrayExpress (AE). As microarray technology is giving way to RNA-seq, it becomes strategic to develop high-level tools of analysis to preserve access to this huge amount of information through the most sophisticated methods of data preparation and processing developed over the years, while ensuring, at the same time, the reproducibility of the results. To meet this need, here we present bioTEA (biological Transcript Expression Analyzer), a novel software tool that combines ease of use with the versatility and power of an R/Bioconductor-based differential expression analysis, starting from raw data retrieval and preparation to gene annotation. BioTEA is an R-coded pipeline, wrapped in a Python-based command line interface and containerized with Docker technology. The user can choose among multiple options—including gene filtering, batch effect handling, sample pairing, statistical test type—to adapt the algorithm flow to the structure of the particular data set. All these options are saved in a single text file, which can be easily shared between different laboratories to deterministically reproduce the results. In addition, a detailed log file provides accurate information about each step of the analysis. Overall, these features make bioTEA an invaluable tool for both bioinformaticians and wet-lab biologists interested in transcriptomics. BioTEA is free and open-source.
... A significant amount of unigenes was identified in cotton by Zhang et al. [40] through a suppression subtractive hybridization approach that was applied to roots growing in a saline medium [40]. The strategy first required sufficient knowledge of involved gene sets [41]. In the recent decade, bioinformatics tools and next-generation sequencing (NGS) technologies have made significant advancements Agronomy 2022, 12, 1849 3 of 21 that aided in identifying and characterization of related differentially expressed genes (DEGs). ...
Abiotic stresses adversely influence crop productivity and salt stress is one limiting factor. Plants need to evolve their defense mechanisms to survive in such fluctuating scenarios at either the biochemical, physiological, or molecular level. The analytical/critical investigations of cotton (Gossypium hirsutum) plants that involve looking into transcriptomic and metabolomic profiles could give a comprehensive picture of the response of the cotton plant to salt stress. This study was conducted on pre-treated cotton seeds by soaking them in a 3% sodium chloride (NaCl) solution at room temperature for 0.5, 1, and 1.5 h. In total, 3738 and 285 differentially expressed genes (DEGs) and metabolites, respectively, were discovered. The prominent DEGs included AtCCC1, EP1, NHE, AtpOMT, GAST1, CLC-c, ARP, AtKIN14, AtC3H2, COP9, AtHK-2, and EID1 to code for the regulation of seed growth, abscisic acid receptor PYR/PYL, a cellular response regarding stress tolerance (especially to salt) and germination, jasmonic acid, salicylic acid, and auxin-activated signaling pathways. A more significant amount of transcription factors, including the ethylene-responsive TFs ERF (205), bHLH (252), ZF-domains (167), bHLH (101), MYB (92), NAC (83), GATA (43), auxin-responsive proteins (30), MADs-box (23), bZIP (27), and HHO (13) were discovered in samples of NaCl-pretreated cotton seedlings under different treatments. The functional annotations of DEGs exposed their important roles in regulating different phytohormones and signal-transduction-mediated pathways in salt-treated seeds. The metabolites analysis revealed differential accumulation of flavonols, phenolic acid, amino acids, and derivatives in seedling samples treated for 0.5 h with NaCl. The conjoint analysis that showed most of the DEGs were associated with the production and regulation of glucose-1-phosphate, uridine 5′-diphospho-D-glucose, and 2-deoxyribose 1-phosphate under salt stress conditions. These results indicated positive effects of NaCl 0.5 h treatments on seedlings’ germination and growth, seemingly by activating specific growth-promoting enzymes and metabolites to alleviate adverse effects of salt stress. Hence, seed pre-treatment with NaCl can be beneficial in future cotton management and breeding programs to enhance growth and development under salt stress.
... The targeted genes and specific probe design, which vary between manufacturers and in different products, typically has as many as 20 different probes for different regions of 1 target gene. The frame size per probe on the microarray has been reduced to 5 μm or fewer squares so that as many as 30,000 genes can be targeted (355,356). There are technical considerations when using gene chips, and algorithms are used to correct for nonspecific binding and to determine average expression across the multiple probes used for each targeted gene. ...
... Statistical analysis using open-source statistics software (e.g., edgeR) can identify differentially expressed transcripts, with P values adjusted for multiple testing to adjust for FDR (often identified as a q-value) because 1 in 20 (P = 0.05) is not appropriate when analyzing datasets with thousands of data points (369). Usually, expression of individual transcripts isrelative to expression in a control treatment, which is known as the differential expression value (355,356). The result is a list of individual transcripts that are differentially expressed relative to the control. ...
The ASN Board of Directors appointed the Nutrition Research Task Force to develop a report on scientific methods used in nutrition science to advance discovery, interpretation, and application of knowledge in the field. The genesis of this report was growing concern about the tone of discourse among nutrition professionals and the implications of acrimony on the productive study and translation of nutrition science. Too often, honest differences of opinion are cast as conflicts instead of areas of needed collaboration. Recognition of the value (and limitations) of contributions from well-executed nutrition science derived from the various approaches used in the discipline, as well as appreciation of how their layering will yield the strongest evidence base, will provide a basis for greater productivity and impact. Greater collaborative efforts within the field of nutrition science will require an understanding that each method or approach has a place and function that should be valued and used together to create the nutrition evidence base. Precision nutrition was identified as an important emerging nutrition topic by the preponderance of task force members, and this theme was adopted for the report because it lent itself to integration of many approaches in nutrition science. Although the primary audience for this report is nutrition researchers and other nutrition professionals, a secondary aim is to develop a document useful for the various audiences that translate nutrition research, including journalists, clinicians, and policymakers. The intent is to promote accurate, transparent, verifiable evidence-based communication about nutrition science. This will facilitate reasoned interpretation and application of emerging findings and, thereby, improve understanding and trust in nutrition science and appropriate characterization, development, and adoption of recommendations.
... As a variation of 1-channel array, Illumina BeadArray synthesizes barcoded probes on the surface of microbeads (59) (https://www.ncbi.nlm.nih.gov/probe/docs/techbeadarray). The DNA microarray technology is relatively mature, with various well-established experimental platforms and analytical tools available (60). Yet, the main drawback of DNA microarray technologies lies in its inability to detect de novo transcripts, since such technologies rely on probes designed according to known nucleotide sequences. ...
The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.
... Today, two main approaches are used: the first is to identify individual DEGs; the second involves the identification of functionally related gene sets with an altered expression. There are many commercial tools and open-source software packages for finding DEGs in microarray experiments [56]. The main tool for the second approach is Gene Set Enrichment Analysis (GSEA) software. ...
Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.
... DNA microarray technology studies are available in Monograph titled DNA Microarrays: A Practical Approach has been edited by Mark Schena (Schena, 1999), Basic Methods in Molecular Biology: DNA arrays methods and protocols by Rampal JB (Davis, 2012). The broad areas of microarray applications include microarrays for gene expression studies (Bulyk, Huang, Choo, & Church, 2001;Slonim & Yanai, 2009;Ziauddin & Sabatini, 2001), analysis of gene expression for drug development, metabolism, and toxicity (Gerhold et al., 2001;Madden, Wang, & Landes, 2000), neuroscience application (Luo & Geschwind, 2001;Parrish et al., 2004;Sanfilippo & Di Rosa, 2021), for genotyping (Hacia et al., 2000;Hacia & Collins, 1999;Mahalingam & Fedoroff, 2001), pharmacogenomics (Chicurel & Dalma-Weiszhausz, 2002). The present review enlightens the growth of microarray technology and its applicability to cancer research. ...
Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.
