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Evolution of Gene Regulatory Networks
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Evolution of Gene Regulatory Networks

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

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 23961

Special Issue Editors

Dr. Marek Mutwil

E-Mail Website
Guest Editor
School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
Interests: gene function prediction; secondary metabolism; co-expression; gene expression; plant evolution
Dr. Federico Manuel Giorgi

E-Mail Website
Co-Guest Editor
Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy
Interests: bioinformatics; transcriptomics; gene networks; plant biology; cancer biology; genomics; biostatistics

Special Issue Information

Dear Colleagues,

Prediction of gene regulatory networks and gene function is currently among the most active topics in computational biology. The overwhelming accumulation of high-throughput sequencing data, capturing genomes and gene expression data for thousands of organisms species, requires us to invent novel bioinformatical approaches to efficiently process this data. Furthermore, it is becoming clear that the evolution of novel traits, such as organs, tissues, and metabolites, cannot be fully explained by genomic approaches since genomics might not reveal which genes work together to express a given trait. Consequently, current approaches use transcriptomics, proteomics, metabolomics and novel data processing and machine learning algorithms to try to infer the gene function and regulation.

This Special Issue invites research articles, reviews, and short communications including but not limited to: methods to construct functional and gene regulatory networks, novel approaches to process high-throughput data to produce these networks, and comparative approaches that study the evolution of these networks. 

Dr. Marek Mutwil
Dr. Federico Manuel Giorgi
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

  • regulatory
  • network
  • sequencing
  • function
  • co-expression
  • transcriptomics
  • computational
  • bioinformatics
  • cloud

Published Papers (3 papers)

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Research

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17 pages, 2874 KiB  
Article
Predicting Functions of Uncharacterized Human Proteins: From Canonical to Proteoforms
by Ekaterina Poverennaya, Olga Kiseleva, Anastasia Romanova and Mikhail Pyatnitskiy
Genes 2020, 11(6), 677; https://doi.org/10.3390/genes11060677 - 21 Jun 2020
Cited by 7 | Viewed by 2882
Abstract
Despite tremendous efforts in genomics, transcriptomics, and proteomics communities, there is still no comprehensive data about the exact number of protein-coding genes, translated proteoforms, and their function. In addition, by now, we lack functional annotation for 1193 genes, where expression was confirmed at [...] Read more.
Despite tremendous efforts in genomics, transcriptomics, and proteomics communities, there is still no comprehensive data about the exact number of protein-coding genes, translated proteoforms, and their function. In addition, by now, we lack functional annotation for 1193 genes, where expression was confirmed at the proteomic level (uPE1 proteins). We re-analyzed results of AP-MS experiments from the BioPlex 2.0 database to predict functions of uPE1 proteins and their splice forms. By building a protein–protein interaction network for 12 ths. identified proteins encoded by 11 ths. genes, we were able to predict Gene Ontology categories for a total of 387 uPE1 genes. We predicted different functions for canonical and alternatively spliced forms for four uPE1 genes. In total, functional differences were revealed for 62 proteoforms encoded by 31 genes. Based on these results, it can be carefully concluded that the dynamics and versatility of the interactome is ensured by changing the dominant splice form. Overall, we propose that analysis of large-scale AP-MS experiments performed for various cell lines and under various conditions is a key to understanding the full potential of genes role in cellular processes. Full article
(This article belongs to the Special Issue Evolution of Gene Regulatory Networks)
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10 pages, 1370 KiB  
Article
LSTrAP-Cloud: A User-Friendly Cloud Computing Pipeline to Infer Coexpression Networks
by Qiao Wen Tan, William Goh and Marek Mutwil
Genes 2020, 11(4), 428; https://doi.org/10.3390/genes11040428 - 16 Apr 2020
Cited by 8 | Viewed by 3826
Abstract
As genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our [...] Read more.
As genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, the Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes involved in a biological process that they might be interested in, by using a single gene of interest as bait. The LSTrAP-Cloud is based on Google Colaboratory, and provides user-friendly tools that process quality-control RNA sequencing data streamed from the European Nucleotide Archive. The LSTRAP-Cloud outputs a gene coexpression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway of Nicotiana tabacum as a case study to demonstrate how enzymes, transporters, and transcription factors involved in the synthesis, transport, and regulation of nicotine can be identified using our pipeline. Full article
(This article belongs to the Special Issue Evolution of Gene Regulatory Networks)
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Review

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49 pages, 13573 KiB  
Review
Histone Deacetylases (HDACs): Evolution, Specificity, Role in Transcriptional Complexes, and Pharmacological Actionability
by Giorgio Milazzo, Daniele Mercatelli, Giulia Di Muzio, Luca Triboli, Piergiuseppe De Rosa, Giovanni Perini and Federico M. Giorgi
Genes 2020, 11(5), 556; https://doi.org/10.3390/genes11050556 - 15 May 2020
Cited by 171 | Viewed by 16708
Abstract
Histone deacetylases (HDACs) are evolutionary conserved enzymes which operate by removing acetyl groups from histones and other protein regulatory factors, with functional consequences on chromatin remodeling and gene expression profiles. We provide here a review on the recent knowledge accrued on the zinc-dependent [...] Read more.
Histone deacetylases (HDACs) are evolutionary conserved enzymes which operate by removing acetyl groups from histones and other protein regulatory factors, with functional consequences on chromatin remodeling and gene expression profiles. We provide here a review on the recent knowledge accrued on the zinc-dependent HDAC protein family across different species, tissues, and human pathologies, specifically focusing on the role of HDAC inhibitors as anti-cancer agents. We will investigate the chemical specificity of different HDACs and discuss their role in the human interactome as members of chromatin-binding and regulatory complexes. Full article
(This article belongs to the Special Issue Evolution of Gene Regulatory Networks)
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