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Special Issue "Multi-Omics Co-expression Network Analysis"

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

Deadline for manuscript submissions: 31 July 2023 | Viewed by 2885

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Dr. Thomas Ulas

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Guest Editor

Platform foR SinglE Cell GenomIcS and Epigenomics (PRECISE), University of Bonn and Deutsches Zentrum für Neurodegenerative Erkrankungen e.V., 53127 Bonn, Germany
Interests: computational biology; bioinformatics; innate immunity; neonate immunity; R; bulk and single-cell transcriptomics co-expression network analysis, data integration

Special Issue Information

Dear Colleagues,

Independent high-throughput omic studies (e.g., genomics, epigenomics, transcriptomics, proteomics, and metagenomics) that aimed to understand a specific problem, such as human disease, have been successfully applied to a large extent in the last decade. In transcriptomics, statistical analysis methods are used to explore biological functions which contribute to the discovery of multiple pathways and the understanding of gene–disease relationships. These studies create a large amount of data, which may be used to answer even broader questions than originally planned by carefully integrating them using suitable mathematical frameworks. To fully comprehend the complex interactions that occur in cellular processes, approaches that can also capture the relationships between the genes involved are very beneficial. Co-expression networks have been utilized as a method to describe and study gene relationships in order to overcome this problem. Furthermore, co-expression networks can be utilized to integrate different omics datasets.

We invite original research and short communications on tools that enable the combination of co-expression network analysis with at least one other omic layer, including an application demonstrating the benefits of your approach, as well as review articles on topics related to "Multi-Omics Co-Expression Network Analysis" to be submitted to this Special Issue. We look forward to receiving your contributions.

Dr. Thomas Ulas
Guest Editor

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Keywords

multi-omics
co-expression network analysis
transcriptomics
data integration
bioinformatics
network visualization
network comparison

Published Papers (3 papers)

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Research

Open AccessArticle

Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification

and

Biology 2023, 12(4), 518; https://doi.org/10.3390/biology12040518 - 29 Mar 2023

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Abstract

Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score. Full article

(This article belongs to the Special Issue Multi-Omics Co-expression Network Analysis)

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Open AccessArticle

Exploiting Multi-Omics Profiling and Systems Biology to Investigate Functions of TOMM34

Ekaterina V. Poverennaya

Mikhail A. Pyatnitskiy

Georgii V. Dolgalev

Viktoria A. Arzumanian

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Biology 2023, 12(2), 198; https://doi.org/10.3390/biology12020198 - 28 Jan 2023

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Abstract

Although modern biology is now in the post-genomic era with vastly increased access to high-quality data, the set of human genes with a known function remains far from complete. This is especially true for hundreds of mitochondria-associated genes, which are under-characterized and lack clear functional annotation. However, with the advent of multi-omics profiling methods coupled with systems biology algorithms, the cellular role of many such genes can be elucidated. Here, we report genes and pathways associated with TOMM34, Translocase of Outer Mitochondrial Membrane, which plays role in the mitochondrial protein import as a part of cytosolic complex together with Hsp70/Hsp90 and is upregulated in various cancers. We identified genes, proteins, and metabolites altered in TOMM34^-/- HepG2 cells. To our knowledge, this is the first attempt to study the functional capacity of TOMM34 using a multi-omics strategy. We demonstrate that TOMM34 affects various processes including oxidative phosphorylation, citric acid cycle, metabolism of purine, and several amino acids. Besides the analysis of already known pathways, we utilized de novo network enrichment algorithm to extract novel perturbed subnetworks, thus obtaining evidence that TOMM34 potentially plays role in several other cellular processes, including NOTCH-, MAPK-, and STAT3-signaling. Collectively, our findings provide new insights into TOMM34’s cellular functions. Full article

(This article belongs to the Special Issue Multi-Omics Co-expression Network Analysis)

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Open AccessArticle

Integrative Analysis of Transcriptome and Metabolome Reveals Molecular Responses in Eriocheir sinensis with Hepatopancreatic Necrosis Disease

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Biology 2022, 11(9), 1267; https://doi.org/10.3390/biology11091267 - 26 Aug 2022

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Abstract

Hepatopancreatic necrosis disease (HPND) is a highly lethal disease that first emerged in 2015 in Jiangsu Province, China. So far, most researchers believe that this disease is caused by abiotic factors. However, its true pathogenic mechanism remains unknown. In this study, the effects of HPND on the metabolism and other biological indicators of the Chinese mitten crab (Eriocheir sinensis) were evaluated by integrating transcriptomics and metabolomics. Our findings demonstrate that the innate immunity, antioxidant activity, detoxification ability, and nervous system of the diseased crabs were affected. Additionally, metabolic pathways such as lipid metabolism, nucleotide metabolism, and protein metabolism were dysregulated, and energy production was slightly increased. Moreover, the IL-17 signaling pathway was activated and high levels of autophagy and apoptosis occurred in diseased crabs, which may be related to hepatopancreas damage. The abnormal mitochondrial function and possible anaerobic metabolism observed in our study suggested that functional hypoxia may be involved in HPND progression. Furthermore, the activities of carboxylesterase and acetylcholinesterase were significantly inhibited, indicating that the diseased crabs were likely stressed by pesticides such as pyrethroids. Collectively, our findings provide new insights into the molecular mechanisms altered in diseased crabs, as well as the etiology and pathogenic mechanisms of HPND. Full article

(This article belongs to the Special Issue Multi-Omics Co-expression Network Analysis)

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