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Computational Methods for the Analysis of Genomic Data and Biological Processes

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A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (15 July 2020) | Viewed by 42769

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Prof. Dr. Francisco A. Gómez Vela

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Guest Editor

School of Engineerings, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: computer vision; machine learning; image and signal processing; 5G communications; IoT
Special Issues, Collections and Topics in MDPI journals

Dr. Federico Divina

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Computer Science Division, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: data mining; big data; artificial intelligence; soft computing; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Dr. Miguel García-Torres

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Guest Editor

Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Interests: machine learning; bioinformatics; astrostatistics; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms.

As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency.

The aim of this Special Issue is to collect the latest advances in the field of computational methods for the analysis of gene expression data, and in particular with the modelling of biological processes.

We encourage researchers to share their original works in the field of computational analysis of gene expression data. Topics of primary interest include, but are not limited to:

Computational methods or machine learning approaches for modelling biological processes;
Discovering genome–disease or genome–phenotype associations;
Gene–gene interactions and gene–environment interactions for disease association analysis;
New computational methods for gene expression data analysis;
Machine learning approaches for modelling gene regulatory networks;
Identification of expression patterns;
Reviews on computational methods for gene expression data analysis.

Update: A 2nd edition is being edited here https://www.mdpi.com/journal/genes/special_issues/comput_genetics_II

Prof. Dr. Francisco A. Gómez Vela
Prof. Dr. Federico Divina
Prof. Dr. Miguel García Torres
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Computational biology
Bioinformatics
Genomics
Gene expression
Gene regulation
Biomarker discovery
Gene network
Biomedical data analysis

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

4 pages, 182 KiB

Open AccessEditorial

Computational Methods for the Analysis of Genomic Data and Biological Processes

by Francisco Gómez-Vela, Federico Divina and Miguel García-Torres

Genes 2020, 11(10), 1230; https://doi.org/10.3390/genes11101230 - 20 Oct 2020

Cited by 2 | Viewed by 1984

Abstract

Today, new technologies, such as microarrays or high-performance sequencing, are producing more and more genomic data [...] Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

Research

Jump to: Editorial, Review

17 pages, 1500 KiB

Open AccessArticle

A Comparative Study of Supervised Machine Learning Algorithms for the Prediction of Long-Range Chromatin Interactions

by Thomas Vanhaeren, Federico Divina, Miguel García-Torres, Francisco Gómez-Vela, Wim Vanhoof and Pedro Manuel Martínez-García

Genes 2020, 11(9), 985; https://doi.org/10.3390/genes11090985 - 24 Aug 2020

Cited by 9 | Viewed by 4543

Abstract

The role of three-dimensional genome organization as a critical regulator of gene expression has become increasingly clear over the last decade. Most of our understanding of this association comes from the study of long range chromatin interaction maps provided by Chromatin Conformation Capture-based techniques, which have greatly improved in recent years. Since these procedures are experimentally laborious and expensive, in silico prediction has emerged as an alternative strategy to generate virtual maps in cell types and conditions for which experimental data of chromatin interactions is not available. Several methods have been based on predictive models trained on one-dimensional (1D) sequencing features, yielding promising results. However, different approaches vary both in the way they model chromatin interactions and in the machine learning-based strategy they rely on, making it challenging to carry out performance comparison of existing methods. In this study, we use publicly available 1D sequencing signals to model cohesin-mediated chromatin interactions in two human cell lines and evaluate the prediction performance of six popular machine learning algorithms: decision trees, random forests, gradient boosting, support vector machines, multi-layer perceptron and deep learning. Our approach accurately predicts long-range interactions and reveals that gradient boosting significantly outperforms the other five methods, yielding accuracies of about 95%. We show that chromatin features in close genomic proximity to the anchors cover most of the predictive information, as has been previously reported. Moreover, we demonstrate that gradient boosting models trained with different subsets of chromatin features, unlike the other methods tested, are able to produce accurate predictions. In this regard, and besides architectural proteins, transcription factors are shown to be highly informative. Our study provides a framework for the systematic prediction of long-range chromatin interactions, identifies gradient boosting as the best suited algorithm for this task and highlights cell-type specific binding of transcription factors at the anchors as important determinants of chromatin wiring mediated by cohesin. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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12 pages, 537 KiB

Open AccessArticle

DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool

by Mobeen Ur Rehman and Kil To Chong

Genes 2020, 11(8), 898; https://doi.org/10.3390/genes11080898 - 05 Aug 2020

Cited by 31 | Viewed by 3134

Abstract

DNA N

^{6}

DNA N

^{6}

-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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33 pages, 6934 KiB

Open AccessFeature PaperArticle

Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks

by Fernando M. Delgado-Chaves, Francisco Gómez-Vela, Federico Divina, Miguel García-Torres and Domingo S. Rodriguez-Baena

Genes 2020, 11(7), 831; https://doi.org/10.3390/genes11070831 - 21 Jul 2020

Cited by 7 | Viewed by 3999

Abstract

Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E

^{Δ H S C}

compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E

^{Δ H S C}

mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E

^{Δ H S C}

mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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28 pages, 1158 KiB

Open AccessArticle

Classification of Microarray Gene Expression Data Using an Infiltration Tactics Optimization (ITO) Algorithm

by Javed Zahoor and Kashif Zafar

Genes 2020, 11(7), 819; https://doi.org/10.3390/genes11070819 - 18 Jul 2020

Cited by 17 | Viewed by 3807

Abstract

A number of different feature selection and classification techniques have been proposed in literature including parameter-free and parameter-based algorithms. The former are quick but may result in local maxima while the latter use dataset-specific parameter-tuning for higher accuracy. However, higher accuracy may not necessarily mean higher reliability of the model. Thus, generalized optimization is still a challenge open for further research. This paper presents a warzone inspired “infiltration tactics” based optimization algorithm (ITO)—not to be confused with the ITO algorithm based on the Itõ Process in the field of Stochastic calculus. The proposed ITO algorithm combines parameter-free and parameter-based classifiers to produce a high-accuracy-high-reliability (HAHR) binary classifier. The algorithm produces results in two phases: (i) Lightweight Infantry Group (LIG) converges quickly to find non-local maxima and produces comparable results (i.e., 70 to 88% accuracy) (ii) Followup Team (FT) uses advanced tuning to enhance the baseline performance (i.e., 75 to 99%). Every soldier of the ITO army is a base model with its own independently chosen Subset selection method, pre-processing, and validation methods and classifier. The successful soldiers are combined through heterogeneous ensembles for optimal results. The proposed approach addresses a data scarcity problem, is flexible to the choice of heterogeneous base classifiers, and is able to produce HAHR models comparable to the established MAQC-II results. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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14 pages, 6966 KiB

Open AccessArticle

Comparative Pathway Integrator: A Framework of Meta-Analytic Integration of Multiple Transcriptomic Studies for Consensual and Differential Pathway Analysis

by Xiangrui Zeng, Wei Zong, Chien-Wei Lin, Zhou Fang, Tianzhou Ma, David A. Lewis, John F. Enwright and George C. Tseng

Genes 2020, 11(6), 696; https://doi.org/10.3390/genes11060696 - 24 Jun 2020

Cited by 6 | Viewed by 2477

Abstract

Pathway enrichment analysis provides a knowledge-driven approach to interpret differentially expressed genes associated with disease status. Many tools have been developed to analyze a single study. However, when multiple studies of different conditions are jointly analyzed, novel integrative tools are needed. In addition, pathway redundancy introduced by combining multiple public pathway databases hinders interpretation and knowledge discovery. We present a meta-analytic integration tool, Comparative Pathway Integrator (CPI), to address these issues using adaptively weighted Fisher’s method to discover consensual and differential enrichment patterns, a tight clustering algorithm to reduce pathway redundancy, and a text mining algorithm to assist interpretation of the pathway clusters. We applied CPI to jointly analyze six psychiatric disorder transcriptomic studies to demonstrate its effectiveness, and found functions confirmed by previous biological studies as well as novel enrichment patterns. CPI’s R package is accessible online on Github metaOmics/MetaPath. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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14 pages, 1207 KiB

Open AccessArticle

metaRE R Package for Meta-Analysis of Transcriptome Data to Identify the cis-Regulatory Code behind the Transcriptional Reprogramming

by Daria D. Novikova, Pavel A. Cherenkov, Yana G. Sizentsova and Victoria V. Mironova

Genes 2020, 11(6), 634; https://doi.org/10.3390/genes11060634 - 09 Jun 2020

Cited by 7 | Viewed by 4038

Abstract

At the molecular level, response to an external factor or an internal condition causes reprogramming of temporal and spatial transcription. When an organism undergoes physiological and/or morphological changes, several signaling pathways are activated simultaneously. Examples of such complex reactions are the response to temperature changes, dehydration, various biologically active substances, and others. A significant part of the regulatory ensemble in such complex reactions remains unidentified. We developed metaRE, an R package for the systematic search for cis-regulatory elements enriched in the promoters of the genes significantly changed their transcription in a complex reaction. metaRE mines multiple expression profiling datasets generated to test the same organism’s response and identifies simple and composite cis-regulatory elements systematically associated with differential expression of genes. Here, we showed metaRE performance for the identification of low-temperature-responsive cis-regulatory code in Arabidopsis thaliana and Danio rerio. MetaRE identified potential binding sites for known as well as unknown cold response regulators. A notable part of cis-elements was found in both searches discovering great conservation in low-temperature responses between plants and animals. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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22 pages, 6560 KiB

Open AccessArticle

Computational Analysis of Transcriptomic and Proteomic Data for Deciphering Molecular Heterogeneity and Drug Responsiveness in Model Human Hepatocellular Carcinoma Cell Lines

by Panagiotis C. Agioutantis, Heleni Loutrari and Fragiskos N. Kolisis

Genes 2020, 11(6), 623; https://doi.org/10.3390/genes11060623 - 05 Jun 2020

Cited by 3 | Viewed by 3456

Abstract

Hepatocellular carcinoma (HCC) is associated with high mortality due to its inherent heterogeneity, aggressiveness, and limited therapeutic regimes. Herein, we analyzed 21 human HCC cell lines (HCC lines) to explore intertumor molecular diversity and pertinent drug sensitivity. We used an integrative computational approach based on exploratory and single-sample gene-set enrichment analysis of transcriptome and proteome data from the Cancer Cell Line Encyclopedia, followed by correlation analysis of drug-screening data from the Cancer Therapeutics Response Portal with curated gene-set enrichment scores. Acquired results classified HCC lines into two groups, a poorly and a well-differentiated group, displaying lower/higher enrichment scores in a “Specifically Upregulated in Liver” gene-set, respectively. Hierarchical clustering based on a published epithelial–mesenchymal transition gene expression signature further supported this stratification. Between-group comparisons of gene and protein expression unveiled distinctive patterns, whereas downstream functional analysis significantly associated differentially expressed genes with crucial cancer-related biological processes/pathways and revealed concrete driver-gene signatures. Finally, correlation analysis highlighted a diverse effectiveness of specific drugs against poorly compared to well-differentiated HCC lines, possibly applicable in clinical research with patients with analogous characteristics. Overall, this study expanded the knowledge on the molecular profiles, differentiation status, and drug responsiveness of HCC lines, and proposes a cost-effective computational approach to precision anti-HCC therapies. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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12 pages, 1185 KiB

Open AccessArticle

iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm

by Omid Mahmoudi, Abdul Wahab and Kil To Chong

Genes 2020, 11(5), 529; https://doi.org/10.3390/genes11050529 - 09 May 2020

Cited by 21 | Viewed by 2983

Abstract

One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequences through the high-throughput laboratory techniques but still, these lab processes are time consuming and costly. Diverse computational methods have been proposed to identify m6A sites accurately. In this paper, we proposed a computational model named iMethyl-deep to identify m6A Saccharomyces Cerevisiae on two benchmark datasets M6A2614 and M6A6540 by using single nucleotide resolution to convert RNA sequence into a high quality feature representation. The iMethyl-deep obtained 89.19% and 87.44% of accuracy on M6A2614 and M6A6540 respectively which show that our proposed method outperforms the state-of-the-art predictors, at least 8.44%, 8.96%, 8.69% and 0.173 on M6A2614 and 15.47%, 28.52%, 25.54 and 0.5 on M6A6540 higher in terms of four metrics Sp, Sn, ACC and MCC respectively. Meanwhile, M6A6540 dataset never used to train a model. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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18 pages, 2381 KiB

Open AccessArticle

Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-Map Regulatory Intervals

by Ruoyu Tian, Yidan Pan, Thomas H. A. Etheridge, Harshavardhan Deshmukh, Dalia Gulick, Greg Gibson, Gang Bao and Ciaran M Lee

Genes 2020, 11(5), 504; https://doi.org/10.3390/genes11050504 - 04 May 2020

Cited by 5 | Viewed by 3347

Abstract

The majority of genetic variants affecting complex traits map to regulatory regions of genes, and typically lie in credible intervals of 100 or more SNPs. Fine mapping of the causal variant(s) at a locus depends on assays that are able to discriminate the effects of polymorphisms or mutations on gene expression. Here, we evaluated a moderate-throughput CRISPR-Cas9 mutagenesis approach, based on replicated measurement of transcript abundance in single-cell clones, by deleting candidate regulatory SNPs, affecting four genes known to be affected by large-effect expression Quantitative Trait Loci (eQTL) in leukocytes, and using Fluidigm qRT-PCR to monitor gene expression in HL60 pro-myeloid human cells. We concluded that there were multiple constraints that rendered the approach generally infeasible for fine mapping. These included the non-targetability of many regulatory SNPs, clonal variability of single-cell derivatives, and expense. Power calculations based on the measured variance attributable to major sources of experimental error indicated that typical eQTL explaining 10% of the variation in expression of a gene would usually require at least eight biological replicates of each clone. Scanning across credible intervals with this approach is not recommended. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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20 pages, 3482 KiB

Open AccessArticle

Biosystem Analysis of the Hypoxia Inducible Domain Family Member 2A: Implications in Cancer Biology

by Celia Salazar, Osvaldo Yañez, Alvaro A. Elorza, Natalie Cortes, Olimpo García-Beltrán, William Tiznado and Lina María Ruiz

Genes 2020, 11(2), 206; https://doi.org/10.3390/genes11020206 - 18 Feb 2020

Cited by 7 | Viewed by 3371

Abstract

The expression of HIGD2A is dependent on oxygen levels, glucose concentration, and cell cycle progression. This gene encodes for protein HIG2A, found in mitochondria and the nucleus, promoting cell survival in hypoxic conditions. The genomic location of HIGD2A is in chromosome 5q35.2, where several chromosomal abnormalities are related to numerous cancers. The analysis of high definition expression profiles of HIGD2A suggests a role for HIG2A in cancer biology. Accordingly, the research objective was to perform a molecular biosystem analysis of HIGD2A aiming to discover HIG2A implications in cancer biology. For this purpose, public databases such as SWISS-MODEL protein structure homology-modelling server, Catalogue of Somatic Mutations in Cancer (COSMIC), Gene Expression Omnibus (GEO), MethHC: a database of DNA methylation and gene expression in human cancer, and microRNA-target interactions database (miRTarBase) were accessed. We also evaluated, by using Real-Time Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR), the expression of Higd2a gene in healthy bone marrow-liver-spleen tissues of mice after quercetin (50 mg/kg) treatment. Thus, among the structural features of HIG2A protein that may participate in HIG2A translocation to the nucleus are an importin α-dependent nuclear localization signal (NLS), a motif of DNA binding residues and a probable SUMOylating residue. HIGD2A gene is not implicated in cancer via mutation. In addition, DNA methylation and mRNA expression of HIGD2A gene present significant alterations in several cancers; HIGD2A gene showed significant higher expression in Diffuse Large B-cell Lymphoma (DLBCL). Hypoxic tissues characterize the “bone marrow-liver-spleen” DLBCL type. The relative quantification, by using qRT-PCR, showed that Higd2a expression is higher in bone marrow than in the liver or spleen. In addition, it was observed that quercetin modulated the expression of Higd2a gene in mice. As an assembly factor of mitochondrial respirasomes, HIG2A might be unexpectedly involved in the change of cellular energetics happening in cancer. As a result, it is worth continuing to explore the role of HIGD2A in cancer biology. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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Review

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16 pages, 1327 KiB

Open AccessReview

Exercise and High-Fat Diet in Obesity: Functional Genomics Perspectives of Two Energy Homeostasis Pillars

by Abdelaziz Ghanemi, Aicha Melouane, Mayumi Yoshioka and Jonny St-Amand

Genes 2020, 11(8), 875; https://doi.org/10.3390/genes11080875 - 31 Jul 2020

Cited by 23 | Viewed by 4474

Abstract

The heavy impact of obesity on both the population general health and the economy makes clarifying the underlying mechanisms, identifying pharmacological targets, and developing efficient therapies for obesity of high importance. The main struggle facing obesity research is that the underlying mechanistic pathways are yet to be fully revealed. This limits both our understanding of pathogenesis and therapeutic progress toward treating the obesity epidemic. The current anti-obesity approaches are mainly a controlled diet and exercise which could have limitations. For instance, the “classical” anti-obesity approach of exercise might not be practical for patients suffering from disabilities that prevent them from routine exercise. Therefore, therapeutic alternatives are urgently required. Within this context, pharmacological agents could be relatively efficient in association to an adequate diet that remains the most efficient approach in such situation. Herein, we put a spotlight on potential therapeutic targets for obesity identified following differential genes expression-based studies aiming to find genes that are differentially expressed under diverse conditions depending on physical activity and diet (mainly high-fat), two key factors influencing obesity development and prognosis. Such functional genomics approaches contribute to elucidate the molecular mechanisms that both control obesity development and switch the genetic, biochemical, and metabolic pathways toward a specific energy balance phenotype. It is important to clarify that by “gene-related pathways”, we refer to genes, the corresponding proteins and their potential receptors, the enzymes and molecules within both the cells in the intercellular space, that are related to the activation, the regulation, or the inactivation of the gene or its corresponding protein or pathways. We believe that this emerging area of functional genomics-related exploration will not only lead to novel mechanisms but also new applications and implications along with a new generation of treatments for obesity and the related metabolic disorders especially with the modern advances in pharmacological drug targeting and functional genomics techniques. Full article

(This article belongs to the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes)

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