Special Issue "Differential Gene Expression and Coexpression"

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 23678

Special Issue Editors

Dr. Ioannis Michalopoulos
E-Mail Website1 Website2
Guest Editor
Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 4 Soranou Efesiou, 11527 Athens, Greece
Interests: genomics; transcriptomics; epigenomics; proteomics; structural bioinformatics; phylogenomics; radiogenomics; cytogenetics and pharmacogenomics
Dr. Apostolos Malatras
E-Mail Website
Guest Editor
CY-Biobank, Centre of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia, Cyprus
Interests: bioinformatics; transcriptomics; genomics; NGS; biobanking

Special Issue Information

Dear Colleagues,

The most common approach in transcriptomics (RNA-seq and microarrays) is differential gene expression analysis. Genes identified as differentially expressed may be responsible for phenotype differences between various biological conditions. An alternative approach is gene co-expression analysis, which detects groups of genes with similar expression patterns across unrelated sets of transcriptomic data of the same organism. Co-expressed genes tend to be involved in similar biological processes. This Special Issue will include reviews and research articles on the topic differential gene expression and coexpression. The reviews will provide an overview of the methods available for transcriptomic analysis, while the research articles will provide an in-depth description of each state-of-the-art tool. Please send me an abstract prior to submission to make sure that your work falls within the scope of this Special Issue.

Dr. Ioannis Michalopoulos
Dr. Apostolos Malatras
Guest Editors

Manuscript Submission Information

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Keywords

  • transcriptomics
  • differential gene expression
  • gene co-expression
  • RNA-seq
  • microarrays
  • gene networks
  • bioinformatics tools

Published Papers (13 papers)

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Research

Jump to: Review

Article
Selection and Evaluation of mRNA and miRNA Reference Genes for Expression Studies (qPCR) in Archived Formalin-fixed and Paraffin-embedded (FFPE) Colon Samples of DSS-induced Colitis Mouse Model
Biology 2023, 12(2), 190; https://doi.org/10.3390/biology12020190 - 26 Jan 2023
Viewed by 217
Abstract
The choice of appropriate reference genes is essential for correctly interpreting qPCR data and results. However, the majority of animal studies use a single reference gene without any prior evaluation. Therefore, many qPCR results from rodent studies can be misleading, affecting not only [...] Read more.
The choice of appropriate reference genes is essential for correctly interpreting qPCR data and results. However, the majority of animal studies use a single reference gene without any prior evaluation. Therefore, many qPCR results from rodent studies can be misleading, affecting not only reproducibility but also translatability. In this study, the expression stability of reference genes for mRNA and miRNA in archived FFPE samples of 117 C57BL/6JOlaHsd mice (males and females) from 9 colitis experiments (dextran sulfate sodium; DSS) were evaluated and their expression analysis was performed. In addition, we investigated whether normalization reduced/neutralized the influence of inter/intra-experimental factors which we systematically included in the study. Two statistical algorithms (NormFinder and Bestkeeper) were used to determine the stability of reference genes. Multivariate analysis was made to evaluate the influence of normalization with different reference genes on target gene expression in regard to inter/intra-experimental factors. Results show that archived FFPE samples are a reliable source of RNA and imply that the FFPE procedure does not change the ranking of stability of reference genes obtained in fresh tissues. Multivariate analysis showed that the histological picture is an important factor affecting the expression levels of target genes. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
Article
Selective Destabilization of Transcripts by mRNA Decapping Regulates Oocyte Maturation and Innate Immunity Gene Expression during Ageing in C. elegans
Biology 2023, 12(2), 171; https://doi.org/10.3390/biology12020171 - 21 Jan 2023
Viewed by 446
Abstract
Removal of the 5′ cap structure of RNAs (termed decapping) is a pivotal event in the life of cytoplasmic mRNAs mainly catalyzed by a conserved holoenzyme, composed of the catalytic subunit DCP2 and its essential cofactor DCP1. While decapping was initially considered merely [...] Read more.
Removal of the 5′ cap structure of RNAs (termed decapping) is a pivotal event in the life of cytoplasmic mRNAs mainly catalyzed by a conserved holoenzyme, composed of the catalytic subunit DCP2 and its essential cofactor DCP1. While decapping was initially considered merely a step in the general 5′-3′ mRNA decay, recent data suggest a great degree of selectivity that plays an active role in the post-transcriptional control of gene expression, and regulates multiple biological functions. Studies in Caenorhabditis elegans have shown that old age is accompanied by the accumulation of decapping factors in cytoplasmic RNA granules, and loss of decapping activity shortens the lifespan. However, the link between decapping and ageing remains elusive. Here, we present a comparative microarray study that was aimed to uncover the differences in the transcriptome of mid-aged dcap-1/DCP1 mutant and wild-type nematodes. Our data indicate that DCAP-1 mediates the silencing of spermatogenic genes during late oogenesis, and suppresses the aberrant uprise of immunity gene expression during ageing. The latter is achieved by destabilizing the mRNA that encodes the transcription factor PQM-1 and impairing its nuclear translocation. Failure to exert decapping-mediated control on PQM-1 has a negative impact on the lifespan, but mitigates the toxic effects of polyglutamine expression that are involved in human disease. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Article
BioTEA: Containerized Methods of Analysis for Microarray-Based Transcriptomics Data
Biology 2022, 11(9), 1346; https://doi.org/10.3390/biology11091346 - 13 Sep 2022
Viewed by 801
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Article
Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis
Biology 2022, 11(7), 1082; https://doi.org/10.3390/biology11071082 - 20 Jul 2022
Cited by 2 | Viewed by 1641
Abstract
The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty [...] Read more.
The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Article
MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies
Biology 2022, 11(6), 895; https://doi.org/10.3390/biology11060895 - 10 Jun 2022
Viewed by 1367
Abstract
MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis [...] Read more.
MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis of multiple outcomes. Importantly, the MAGE toolkit includes additional features for the conversion of probes to gene identifiers, and for conducting functional enrichment analysis, with annotated results, of statistically significant enriched terms in several formats. Along with the tool itself, a web-based infrastructure was also developed to support the features of this package. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Article
Procollagen C-Endopeptidase Enhancer 2 Secreted by Tonsil-Derived Mesenchymal Stem Cells Increases the Oxidative Burst of Promyelocytic HL-60 Cells
Biology 2022, 11(2), 255; https://doi.org/10.3390/biology11020255 - 07 Feb 2022
Cited by 2 | Viewed by 1026
Abstract
Reactive oxygen species (ROS) generated by neutrophils provide a frontline defence against invading pathogens. We investigated the supportive effect of tonsil-derived mesenchymal stem cells (TMSCs) on ROS generation from neutrophils using promyelocytic HL-60 cells. Methods: Differentiated HL-60 (dHL-60) cells were cocultured with TMSCs [...] Read more.
Reactive oxygen species (ROS) generated by neutrophils provide a frontline defence against invading pathogens. We investigated the supportive effect of tonsil-derived mesenchymal stem cells (TMSCs) on ROS generation from neutrophils using promyelocytic HL-60 cells. Methods: Differentiated HL-60 (dHL-60) cells were cocultured with TMSCs isolated from 25 independent donors, and ROS generation in dHL-60 cells was measured using luminescence. RNA sequencing and real-time PCR were performed to identify the candidate genes of TMSCs involved in augmenting the oxidative burst of dHL-60 cells. Transcriptome analysis of TMSCs derived from 25 independent donors revealed high levels of procollagen C-endopeptidase enhancer 2 (PCOLCE2) in TMSCs, which were highly effective in potentiating ROS generation in dHL-60 cells. In addition, PCOLCE2 knockdown in TMSCs abrogated TMSC-induced enhancement of ROS production in dHL-60 cells, indicating that TMSCs increased the oxidative burst in dHL-60 cells via PCOLCE2. Furthermore, the direct addition of recombinant PCOLCE2 protein increased ROS production in dHL-60 cells. These results suggest that PCOLCE2 secreted by TMSCs may be used as a therapeutic candidate to enhance host defences by increasing neutrophil oxidative bursts. PCOLCE2 levels in TMSCs could be used as a marker to select TMSCs exhibiting high efficacy for enhancing neutrophil oxidative bursts. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Article
Oxford Nanopore MinION Direct RNA-Seq for Systems Biology
Biology 2021, 10(11), 1131; https://doi.org/10.3390/biology10111131 - 04 Nov 2021
Cited by 8 | Viewed by 2506
Abstract
Long-read direct RNA sequencing developed by Oxford Nanopore Technologies (ONT) is quickly gaining popularity for transcriptome studies, while fast turnaround time and low cost make it an attractive instrument for clinical applications. There is a growing interest to utilize transcriptome data to unravel [...] Read more.
Long-read direct RNA sequencing developed by Oxford Nanopore Technologies (ONT) is quickly gaining popularity for transcriptome studies, while fast turnaround time and low cost make it an attractive instrument for clinical applications. There is a growing interest to utilize transcriptome data to unravel activated biological processes responsible for disease progression and response to therapies. This trend is of particular interest for precision medicine which aims at single-patient analysis. Here we evaluated whether gene abundances measured by MinION direct RNA sequencing are suited to produce robust estimates of pathway activation for single sample scoring methods. We performed multiple RNA-seq analyses for a single sample that originated from the HepG2 cell line, namely five ONT replicates, and three replicates using Illumina NovaSeq. Two pathway scoring methods were employed—ssGSEA and singscore. We estimated the ONT performance in terms of detected protein-coding genes and average pairwise correlation between pathway activation scores using an exhaustive computational scheme for all combinations of replicates. In brief, we found that at least two ONT replicates are required to obtain reproducible pathway scores for both algorithms. We hope that our findings may be of interest to researchers planning their ONT direct RNA-seq experiments. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Article
ARGEOS: A New Bioinformatic Tool for Detailed Systematics Search in GEO and ArrayExpress
Biology 2021, 10(10), 1026; https://doi.org/10.3390/biology10101026 - 11 Oct 2021
Cited by 4 | Viewed by 1745
Abstract
Conduct a reanalysis of transcriptome data for studying intracellular signaling or solving other experimental problems is becoming increasingly popular. Gene expression data are archived as microarray or RNA-seq datasets mainly in two public databases: Gene Expression Omnibus (GEO) and ArrayExpress (AE). These databases [...] Read more.
Conduct a reanalysis of transcriptome data for studying intracellular signaling or solving other experimental problems is becoming increasingly popular. Gene expression data are archived as microarray or RNA-seq datasets mainly in two public databases: Gene Expression Omnibus (GEO) and ArrayExpress (AE). These databases were not initially intended to systematically search datasets, making it challenging to conduct a secondary study. Therefore, we have created the ARGEOS service, which has the following advantages that facilitate the search: (1) Users can simultaneously send several requests that are supposed to be used for systematic searches, and it is possible to correct the requests; (2) advanced analysis of information about the dataset is available. The service collects detailed protocols, information on the number of datasets, analyzes the availability of raw data, and provides other reference information. All this contributes to both rapid data analysis with the search for the most relevant datasets and to the systematic search with detailed analysis of the information of the datasets. The efficiency of the service is shown in the example of analyzing transcriptome data of activated (polarized) cells. We have performed a systematic search of studies of cell polarization (when cells are exposed to different immune stimuli). The web interface for ARGEOS is user-friendly and straightforward. It can be used by a person who is not familiar with database searching. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Article
Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis
Biology 2021, 10(10), 957; https://doi.org/10.3390/biology10100957 - 24 Sep 2021
Cited by 9 | Viewed by 2487
Abstract
Liver cancer is one of the most common cancers and the top leading cause of cancer death globally. However, the molecular mechanisms of liver tumorigenesis and progression remain unclear. In the current study, we investigated the hub genes and the potential molecular pathways [...] Read more.
Liver cancer is one of the most common cancers and the top leading cause of cancer death globally. However, the molecular mechanisms of liver tumorigenesis and progression remain unclear. In the current study, we investigated the hub genes and the potential molecular pathways through which these genes contribute to liver cancer onset and development. The weighted gene co-expression network analysis (WCGNA) was performed on the main data attained from the GEO (Gene Expression Omnibus) database. The Cancer Genome Atlas (TCGA) dataset was used to evaluate the association between prognosis and these hub genes. The expression of genes from the black module was found to be significantly related to liver cancer. Based on the results of protein–protein interaction, gene co-expression network, and survival analyses, DNA topoisomerase II alpha (TOP2A), ribonucleotide reductase regulatory subunit M2 (RRM2), never in mitosis-related kinase 2 (NEK2), cyclin-dependent kinase 1 (CDK1), and cyclin B1 (CCNB1) were identified as the hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the differentially expressed genes (DEGs) were enriched in the immune-associated pathways. These hub genes were further screened and validated using statistical and functional analyses. Additionally, the TOP2A, RRM2, NEK2, CDK1, and CCNB1 proteins were overexpressed in tumor liver tissues as compared to normal liver tissues according to the Human Protein Atlas database and previous studies. Our results suggest the potential use of TOP2A, RRM2, NEK2, CDK1, and CCNB1 as prognostic biomarkers in liver cancer. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Article
Survival-Based Biomarker Module Identification Associated with Oral Squamous Cell Carcinoma (OSCC)
Biology 2021, 10(8), 760; https://doi.org/10.3390/biology10080760 - 08 Aug 2021
Cited by 7 | Viewed by 2618
Abstract
Head and neck squamous cell carcinoma (HNSC) is one of the most common malignant tumors worldwide with a high rate of morbidity and mortality, with 90% of predilections occurring for oral squamous cell carcinoma (OSCC). Cancers of the mouth account for 40% of [...] Read more.
Head and neck squamous cell carcinoma (HNSC) is one of the most common malignant tumors worldwide with a high rate of morbidity and mortality, with 90% of predilections occurring for oral squamous cell carcinoma (OSCC). Cancers of the mouth account for 40% of head and neck cancers, including squamous cell carcinomas of the tongue, floor of the mouth, buccal mucosa, lips, hard and soft palate, and gingival. OSCC is the most devastating and commonly occurring oral malignancy, with a mortality rate of 500,000 deaths per year. This has imposed a strong necessity to discover driver genes responsible for its progression and malignancy. In the present study we filtered oral squamous cell carcinoma tissue samples from TCGA-HNSC cohort, which we followed by constructing a weighted PPI network based on the survival of patients and the expression profiles of samples collected from them. We found a total of 46 modules, with 18 modules having more than five edges. The KM and ME analyses revealed a single module (with 12 genes) as significant in the training and test datasets. The genes from this significant module were subjected to pathway enrichment analysis for identification of significant pathways and involved genes. Finally, the overlapping genes between gene sets ranked on the basis of weighted PPI module centralities (i.e., degree and eigenvector), significant pathway genes, and DEGs from a microarray OSCC dataset were considered as OSCC-specific hub genes. These hub genes were clinically validated using the IHC images available from the Human Protein Atlas (HPA) database. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Article
FLAME: A Web Tool for Functional and Literature Enrichment Analysis of Multiple Gene Lists
Biology 2021, 10(7), 665; https://doi.org/10.3390/biology10070665 - 14 Jul 2021
Cited by 11 | Viewed by 3319
Abstract
Functional enrichment is a widely used method for interpreting experimental results by identifying classes of proteins/genes associated with certain biological functions, pathways, diseases, or phenotypes. Despite the variety of existing tools, most of them can process a single list per time, thus making [...] Read more.
Functional enrichment is a widely used method for interpreting experimental results by identifying classes of proteins/genes associated with certain biological functions, pathways, diseases, or phenotypes. Despite the variety of existing tools, most of them can process a single list per time, thus making a more combinatorial analysis more complicated and prone to errors. In this article, we present FLAME, a web tool for combining multiple lists prior to enrichment analysis. Users can upload several lists and use interactive UpSet plots, as an alternative to Venn diagrams, to handle unions or intersections among the given input files. Functional and literature enrichment, along with gene conversions, are offered by g:Profiler and aGOtool applications for 197 organisms. FLAME can analyze genes/proteins for related articles, Gene Ontologies, pathways, annotations, regulatory motifs, domains, diseases, and phenotypes, and can also generate protein–protein interactions derived from STRING. We have validated FLAME by interrogating gene expression data associated with the sensitivity of the distal part of the large intestine to experimental colitis-propelled colon cancer. FLAME comes with an interactive user-friendly interface for easy list manipulation and exploration, while results can be visualized as interactive and parameterizable heatmaps, barcharts, Manhattan plots, networks, and tables. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Article
Genome-Wide Atlas of Promoter Expression Reveals Contribution of Transcribed Regulatory Elements to Genetic Control of Disuse-Mediated Atrophy of Skeletal Muscle
Biology 2021, 10(6), 557; https://doi.org/10.3390/biology10060557 - 20 Jun 2021
Viewed by 2240
Abstract
The prevention of muscle atrophy carries with it clinical significance for the control of increased morbidity and mortality following physical inactivity. While major transcriptional events associated with muscle atrophy-recovery processes are the subject of active research on the gene level, the contribution of [...] Read more.
The prevention of muscle atrophy carries with it clinical significance for the control of increased morbidity and mortality following physical inactivity. While major transcriptional events associated with muscle atrophy-recovery processes are the subject of active research on the gene level, the contribution of non-coding regulatory elements and alternative promoter usage is a major source for both the production of alternative protein products and new insights into the activity of transcription factors. We used the cap-analysis of gene expression (CAGE) to create a genome-wide atlas of promoter-level transcription in fast (m. EDL) and slow (m. soleus) muscles in rats that were subjected to hindlimb unloading and subsequent recovery. We found that the genetic regulation of the atrophy-recovery cycle in two types of muscle is mediated by different pathways, including a unique set of non-coding transcribed regulatory elements. We showed that the activation of “shadow” enhancers is tightly linked to specific stages of atrophy and recovery dynamics, with the largest number of specific regulatory elements being transcriptionally active in the muscles on the first day of recovery after a week of disuse. The developed comprehensive database of transcription of regulatory elements will further stimulate research on the gene regulation of muscle homeostasis in mammals. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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Review

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Review
Approaches in Gene Coexpression Analysis in Eukaryotes
Biology 2022, 11(7), 1019; https://doi.org/10.3390/biology11071019 - 06 Jul 2022
Cited by 1 | Viewed by 1334
Abstract
Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes [...] Read more.
Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs. Full article
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
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