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Special Issue Information

Dear Colleagues,

This collection is the second edition of the previous Special Issue, "Differential Gene Expression and Coexpression".

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 from 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 of 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

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Related Special Issue

Published Papers (1 paper)

Research

13 pages, 2183 KiB

Open AccessArticle

Comparison of Alternative Splicing Landscapes Revealed by Long-Read Sequencing in Hepatocyte-Derived HepG2 and Huh7 Cultured Cells and Human Liver Tissue

by Anna Kozlova, Elizaveta Sarygina, Kseniia Deinichenko, Sergey Radko, Konstantin Ptitsyn, Svetlana Khmeleva, Leonid Kurbatov, Pavel Spirin, Vladimir Prassolov, Ekaterina Ilgisonis, Andrey Lisitsa and Elena Ponomarenko

Biology 2023, 12(12), 1494; https://doi.org/10.3390/biology12121494 - 06 Dec 2023

Viewed by 1197

Abstract

The long-read RNA sequencing developed by Oxford Nanopore Technologies provides a direct quantification of transcript isoforms, thereby making it possible to present alternative splicing (AS) profiles as arrays of single splice variants with different abundances. Additionally, AS profiles can be presented as arrays of genes characterized by the degree of alternative splicing (the DAS—the number of detected splice variants per gene). Here, we successfully utilized the DAS to reveal biological pathways influenced by the alterations in AS in human liver tissue and the hepatocyte-derived malignant cell lines HepG2 and Huh7, thus employing the mathematical algorithm of gene set enrichment analysis. Furthermore, analysis of the AS profiles as abundances of single splice variants by using the graded tissue specificity index τ provided the selection of the groups of genes expressing particular splice variants specifically in liver tissue, HepG2 cells, and Huh7 cells. The majority of these splice variants were translated into proteins products and appeal to be in focus regarding further insights into the mechanisms underlying cell malignization. The used metrics are intrinsically suitable for transcriptome-wide AS profiling using long-read sequencing. Full article

(This article belongs to the Special Issue Differential Gene Expression and Coexpression 2.0)

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