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Genes
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Gene Regulation and Bioinformatics
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Gene Regulation and Bioinformatics

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

Deadline for manuscript submissions: 10 June 2024 | Viewed by 2854

Special Issue Editors

Dr. Jinghua Zhao

E-Mail Website
Guest Editor
Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
Interests: genetic epidemiology; biostatistics; public health; machine learning; proteogenomics
Dr. Jiadong Ji

E-Mail Website
Guest Editor
Institute for Financial Studies, Shandong University, Jinan 250100, China
Interests: bioinformatics; gene network; multi-omic integration; machine learning; biostatistics

Special Issue Information

Dear Colleagues,

Gene regulation is a fundamental process that governs gene expression, which in turn plays a critical role in various biological processes such as development, differentiation, and disease. The advent of high-throughput technologies has led to a significant amount of genomic and transcriptomic data, creating an urgent need for computational approaches to analyze and interpret these data. Bioinformatics has emerged as a powerful tool for understanding the complex mechanisms of gene regulation and identifying novel targets for therapeutic intervention. To date, the field of gene regulation has been transformed by the advent of bioinformatics, which provides powerful tools for analyzing large amounts of genomic data.

This Special Issue aims to explore the intersection of computational and biological sciences in the context of gene regulation. We invite submissions that use bioinformatics approaches to study gene regulation at all levels, from transcriptional to post-transcriptional regulation, and in all organisms, from bacteria to humans. We are particularly interested in studies that use integrative approaches to combine genomic, epigenomic, and transcriptomic data to gain insights into gene regulation. The Special Issue will cover a broad range of topics including, but not limited to, regulatory networks, epigenomics, non-coding RNAs, machine learning algorithms, and network-based analysis.

Dr. Jinghua Zhao
Dr. Jiadong Ji
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

  • gene regulatory networks
  • epigenomics
  • machine learning algorithms
  • network-based analysis
  • non-coding RNAs
  • bioinformatics
 

Published Papers (2 papers)

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Research

Jump to: Review

18 pages, 774 KiB  
Article
Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops
by Angelos Kolaitis, Evangelos Makris, Alexandros Anastasios Karagiannis, Panayiotis Tsanakas and Christos Pavlatos
Genes 2024, 15(6), 670; https://doi.org/10.3390/genes15060670 - 23 May 2024
Viewed by 311
Abstract
Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact [...] Read more.
Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact of the widespread COVID-19 pandemic. This paper presents a framework, Knotify_V2.0, which makes use of syntactic pattern recognition techniques in order to predict RNA structures, with a specific emphasis on tackling the demanding task of predicting H-type pseudoknots that encompass bulges and hairpins. By leveraging the expressive capabilities of a Context-Free Grammar (CFG), the suggested framework integrates the inherent benefits of CFG and makes use of minimum free energy and maximum base pairing criteria. This integration enables the effective management of this inherently ambiguous task. The main contribution of Knotify_V2.0 compared to earlier versions lies in its capacity to identify additional motifs like bulges and hairpins within the internal loops of the pseudoknot. Notably, the proposed methodology, Knotify_V2.0, demonstrates superior accuracy in predicting core stems compared to state-of-the-art frameworks. Knotify_V2.0 exhibited exceptional performance by accurately identifying both core base pairing that form the ground truth pseudoknot in 70% of the examined sequences. Furthermore, Knotify_V2.0 narrowed the performance gap with Knotty, which had demonstrated better performance than Knotify and even surpassed it in Recall and F1-score metrics. Knotify_V2.0 achieved a higher count of true positives (tp) and a significantly lower count of false negatives (fn) compared to Knotify, highlighting improvements in Prediction and Recall metrics, respectively. Consequently, Knotify_V2.0 achieved a higher F1-score than any other platform. The source code and comprehensive implementation details of Knotify_V2.0 are publicly available on GitHub. Full article
(This article belongs to the Special Issue Gene Regulation and Bioinformatics)

Review

Jump to: Research

15 pages, 580 KiB  
Review
Workability of mRNA Sequencing for Predicting Protein Abundance
by Elena A. Ponomarenko, George S. Krasnov, Olga I. Kiseleva, Polina A. Kryukova, Viktoriia A. Arzumanian, Georgii V. Dolgalev, Ekaterina V. Ilgisonis, Andrey V. Lisitsa and Ekaterina V. Poverennaya
Genes 2023, 14(11), 2065; https://doi.org/10.3390/genes14112065 - 11 Nov 2023
Cited by 3 | Viewed by 1904
Abstract
Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its [...] Read more.
Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell’s state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of “transcript-protein” pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome–proteome levels for different tissues does not exceed 0.3–0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction. Full article
(This article belongs to the Special Issue Gene Regulation and Bioinformatics)
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