Differential Gene Expression
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2021 ◽  
Guy P Hunt ◽  
Rafael Henkin ◽  
Fabrizio Smeraldi ◽  
Michael R Barnes

Background: Over the past three decades there have been numerous molecular biology developments that have led to an explosion in the number of gene expression studies being performed. Many of these gene expression studies publish their data to the public database GEO, making them freely available. By analysing gene expression datasets, researchers can identify genes that are differentially expressed between two groups. This can provide insights that lead to the development of new tests and treatments for diseases. Despite the wide availability of gene expression datasets, analysing them is difficult for several reasons. These reasons include the fact that most methods for performing gene expression analysis require programming proficiency. Results: We developed the GEOexplorer software package to overcome several of the difficulties in performing gene expression analysis. GEOexplorer was therefore developed as a web application, that can perform interactive and reproducible microarray gene expression analysis, while producing a wealth of interactive visualisations to facilitate result exploration. GEOexplorer is implemented in R using the Shiny framework and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of exploratory data analysis and differential gene expression analysis intuitively and generate a broad spectrum of publication ready outputs. Conclusion: GEOexplorer is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/GEOexplorer/). GEOexplorer provides a solution for performing interactive and reproducible analyses of microarray gene expression data, empowering life scientists to perform exploratory data analysis and differential gene expression analysis on GEO microarray datasets.

3 Biotech ◽  
2021 ◽  
Vol 11 (11) ◽  
Manjunatha Channappa ◽  
Sapna Sharma ◽  
Deepika Kulshreshtha ◽  
Kartar Singh ◽  
Subhash C. Bhardwaj ◽  

2021 ◽  
pp. 1-11
Xiaojuan Wang ◽  
Deyin Zhang ◽  
Weiming Wang ◽  
Feng Lv ◽  
Xin Pang ◽  

Joanna Houghton ◽  
Angela Rodgers ◽  
Graham Rose ◽  
Alexandre D’Halluin ◽  
Terry Kipkorir ◽  

Control of gene expression via small regulatory RNAs (sRNAs) is poorly understood in one of the most successful pathogens, Mycobacterium tuberculosis . Here, we present an in-depth characterization of the sRNA F6, including its expression in different infection models and the differential gene expression observed upon deletion of the sRNA.

Yuhui Dong ◽  
Hongcheng Fang ◽  
Yujiao Hou ◽  
Yaping Zhao ◽  
Xiudong Sun ◽  

Aquaculture ◽  
2022 ◽  
Vol 547 ◽  
pp. 737434
Monica Janeth Cabrera-Stevens ◽  
Arturo Sánchez-Paz ◽  
Fernando Mendoza-Cano ◽  
Cristina Escobedo-Fregoso ◽  
Trinidad Encinas-García ◽  

Animals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2667
Irina Chadaeva ◽  
Petr Ponomarenko ◽  
Rimma Kozhemyakina ◽  
Valentin Suslov ◽  
Anton Bogomolov ◽  

Belyaev’s concept of destabilizing selection during domestication was a major achievement in the XX century. Its practical value has been realized in commercial colors of the domesticated fox that never occur in the wild and has been confirmed in a wide variety of pet breeds. Many human disease models involving animals allow to test drugs before human testing. Perhaps this is why investigators doing transcriptomic profiling of domestic versus wild animals have searched for breed-specific patterns. Here we sequenced hypothalamic transcriptomes of tame and aggressive rats, identified their differentially expressed genes (DEGs), and, for the first time, applied principal component analysis to compare them with all the known DEGs of domestic versus wild animals that we could find. Two principal components, PC1 and PC2, respectively explained 67% and 33% of differential-gene-expression variance (hereinafter: log2 value) between domestic and wild animals. PC1 corresponded to multiple orthologous DEGs supported by homologs; these DEGs kept the log2 value sign from species to species and from tissue to tissue (i.e., a common domestication pattern). PC2 represented stand-alone homologous DEG pairs reversing the log2 value sign from one species to another and from tissue to tissue (i.e., representing intraspecific and interspecific variation).

2021 ◽  
Rance Nault ◽  
Satabdi Saha ◽  
Sudin Bhattacharya ◽  
Jack Dodson ◽  
Samiran Sinha ◽  

AbstractThe application of single-cell RNA sequencing (scRNAseq) for the evaluation of chemicals, drugs, and food contaminants presents the opportunity to consider cellular heterogeneity in pharmacological and toxicological responses. Current differential gene expression analysis (DGEA) methods focus primarily on two group comparisons, not multi-group dose-response study designs used in safety assessments. To benchmark DGEA methods for dose-response scRNAseq experiments, we proposed a multiplicity corrected Bayesian testing approach and compare it against 8 other methods including two frequentist fit-for-purpose tests using simulated and experimental data. Our Bayesian test method outperformed all other tests for a broad range of accuracy metrics including control of false positive error rates. Most notable, the fit-for-purpose and standard multiple group DGEA methods were superior to the two group scRNAseq methods for dose-response study designs. Collectively, our benchmarking of DGEA methods demonstrates the importance in considering study design when determining the most appropriate test methods.

2021 ◽  
Vol 22 (18) ◽  
pp. 9684
Jiao Sun ◽  
Naima Ahmed Fahmi ◽  
Heba Nassereddeen ◽  
Sze Cheng ◽  
Irene Martinez ◽  

Microbes and viruses are known to alter host transcriptomes by means of infection. In light of recent challenges posed by the COVID-19 pandemic, a deeper understanding of the disease at the transcriptome level is needed. However, research about transcriptome reprogramming by post-transcriptional regulation is very limited. In this study, computational methods developed by our lab were applied to RNA-seq data to detect transcript variants (i.e., alternative splicing (AS) and alternative polyadenylation (APA) events). The RNA-seq data were obtained from a publicly available source, and they consist of mock-treated and SARS-CoV-2 infected (COVID-19) lung alveolar (A549) cells. Data analysis results show that more AS events are found in SARS-CoV-2 infected cells than in mock-treated cells, whereas fewer APA events are detected in SARS-CoV-2 infected cells. A combination of conventional differential gene expression analysis and transcript variants analysis revealed that most of the genes with transcript variants are not differentially expressed. This indicates that no strong correlation exists between differential gene expression and the AS/APA events in the mock-treated or SARS-CoV-2 infected samples. These genes with transcript variants can be applied as another layer of molecular signatures for COVID-19 studies. In addition, the transcript variants are enriched in important biological pathways that were not detected in the studies that only focused on differential gene expression analysis. Therefore, the pathways may lead to new molecular mechanisms of SARS-CoV-2 pathogenesis.

Inmaculada Gómez ◽  
M. Carmen Thomas ◽  
Génesis Palacios ◽  
Adriana Egui ◽  
Bartolomé Carrilero ◽  

Infection by the Trypanosoma cruzi parasite causes Chagas disease and triggers multiple immune mechanisms in the host to combat the pathogen. Chagas disease has a variable clinical presentation and progression, producing in the chronic phase a fragile balance between the host immune response and parasite replication that keeps patients in a clinically silent asymptomatic stage for years. Since the parasite is intracellular and replicates within cells, the cell-mediated response of the host adaptive immunity plays a critical role. This function is mainly orchestrated by T lymphocytes, which recognize parasite antigens and promote specific functions to control the infection. However, little is known about the immunological markers associated with this asymptomatic stage of the disease. In this large-scale analysis, the differential expression of 106 immune system-related genes has been analyzed using high-throughput qPCR in T. cruzi antigen-stimulated PBMC from chronic Chagas disease patients with indeterminate form (IND) and healthy donors (HD) from endemic and non-endemic areas of Chagas disease. This analysis revealed that there were no differences in the expression level of most genes under study between healthy donors from endemic and non-endemic areas determined by PCA and differential gene expression analysis. Instead, PCA revealed the existence of different expression profiles between IND patients and HD (p < 0.0001), dependent on the 32 genes included in PC1. Differential gene expression analysis also revealed 23 upregulated genes (expression fold change > 2) and 11 downregulated genes (expression fold change < 0.5) in IND patients versus HD. Enrichment analysis showed that several upregulated genes in IND patients participate in relevant immunological pathways such as antigen-dependent B cell activation, stress induction of HSP regulation, NO2-dependent IL12 pathway in NK cells, and cytokine-inflammatory response. The antigen-specific differential gene expression profile detected in these patients and the relevant immunological pathways that seem to be activated could represent potential biomarkers of the asymptomatic form of Chagas disease, helpful to diagnosis and infection control.

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