Feature Papers in Technologies and Resources for Genetics 2023

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

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 6320

Special Issue Editors


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Guest Editor
Department of Molecular Medicine, University of Padova, Padua, Italy
Interests: cancer genomics and transcriptomics; bioinformatics; systems biology; microRNAs; circular RNAs
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20052, USA
Interests: genomics; transcriptomics; cancer genomics; computational biology; bioinformatics; RNA seq; bioinformatic tools
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, “Feature Papers in Technologies and Resources for Genetics”, aims to collect high-quality review articles or research articles on all aspects of novel advances in technological methods, protocols, and software for the generation and interpretation of genome-derived data. It is dedicated to recent advances in the research area of genomics and genetics and comprises a selection of exclusive papers from the Editorial Board Members (EBMs) of the Technologies and Resources for Genetics Section, as well as invited papers from relevant experts. We also welcome senior experts in the field to make contributions to this Special Issue. We aim to represent our Section as an attractive open access publishing platform for genomics and genetic research.

The topics of this issue will describe novel advances in technological methods, protocols, and software for the generation and interpretation of genome-derived data. The topics covered will include but are not limited to:

  1. Genome sequencing, genomic technologies, and novel sequencing strategies;
  2. Functional genomics and genome annotation;
  3. Computational biology, bioinformatics, and biostatistics;
  4. Bioinformatics analysis of proteomics and genomics data, including new online data resources and tools;
  5. New approaches for phylogenomic analyses;
  6. Genome editing;
  7. Genetic reprogramming;
  8. Single-molecule, real-time (SMRT) sequencing;
  9. Comparative genomics;
  10. Conservation genetics and genomics;
  11. Metagenomics;
  12. Noncoding genomics;
  13. Circular RNA;
  14. Machine learning applications to genetics and genomics.

Prof. Dr. Stefania Bortoluzzi
Prof. Dr. Anelia D. Horvath
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

  • genome sequencing, genomic technologies, and novel sequencing strategies
  • functional genomics and genome annotation
  • computational biology, bioinformatics, and biostatistics
  • bioinformatics analysis of proteomics and genomics data including new online data resources and tools
  • new approaches for phylogenomic analyses

Published Papers (4 papers)

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Research

18 pages, 5679 KiB  
Article
Genomic and Functional Evaluation of Two Lacticaseibacillus paracasei and Two Lactiplantibacillus plantarum Strains, Isolated from a Rearing Tank of Rotifers (Brachionus plicatilis), as Probiotics for Aquaculture
by Diogo Contente, Lara Díaz-Formoso, Javier Feito, Pablo E. Hernández, Estefanía Muñoz-Atienza, Juan Borrero, Patrícia Poeta and Luis M. Cintas
Genes 2024, 15(1), 64; https://doi.org/10.3390/genes15010064 - 1 Jan 2024
Viewed by 1636
Abstract
Aquaculture plays a crucial role in meeting the increasing global demand for food and protein sources. However, its expansion is followed by increasing challenges, such as infectious disease outbreaks and antibiotic misuse. The present study focuses on the genetic and functional analyses of [...] Read more.
Aquaculture plays a crucial role in meeting the increasing global demand for food and protein sources. However, its expansion is followed by increasing challenges, such as infectious disease outbreaks and antibiotic misuse. The present study focuses on the genetic and functional analyses of two Lacticaseibacillus paracasei (BF3 and RT4) and two Lactiplantibacillus plantarum (BF12 and WT12) strains isolated from a rotifer cultivation tank used for turbot larviculture. Whole-genome sequencing (WGS) and bioinformatics analyses confirmed their probiotic potential, the absence of transferable antibiotic resistance genes, and the absence of virulence and pathogenicity factors. Bacteriocin mining identified a gene cluster encoding six plantaricins, suggesting their role in the antimicrobial activity exerted by these strains. In vitro cell-free protein synthesis (IV-CFPS) analyses was used to evaluate the expression of the plantaricin genes. The in vitro-synthesized class IIb (two-peptide bacteriocins) plantaricin E/F (PlnE/F) exerted antimicrobial activity against three indicator microorganisms, including the well-known ichthyopathogen Lactococcus garvieae. Furthermore, MALDI-TOF MS on colonies detected the presence of a major peptide that matches the dimeric form of plantaricins E (PlnE) and F (PlnF). This study emphasizes the importance of genome sequencing and bioinformatic analysis for evaluating aquaculture probiotic candidates. Moreover, it provides valuable insights into their genetic features and antimicrobial mechanisms, paving the way for their application as probiotics in larviculture, which is a major bottleneck in aquaculture. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics 2023)
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10 pages, 1216 KiB  
Article
Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations
by Cesare Rollo, Corrado Pancotti, Giovanni Birolo, Ivan Rossi, Tiziana Sanavia and Piero Fariselli
Genes 2023, 14(12), 2228; https://doi.org/10.3390/genes14122228 - 17 Dec 2023
Cited by 1 | Viewed by 1251
Abstract
Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are [...] Read more.
Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics 2023)
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18 pages, 5301 KiB  
Article
One Step Closer to the Understanding of the Relationship IDR-LCR-Structure
by Mariane Gonçalves-Kulik, Friederike Schmid and Miguel A. Andrade-Navarro
Genes 2023, 14(9), 1711; https://doi.org/10.3390/genes14091711 - 28 Aug 2023
Cited by 3 | Viewed by 1065
Abstract
Intrinsically disordered regions (IDRs) in protein sequences are emerging as functionally important elements for interaction and regulation. While being generally flexible, we previously showed, by observation of experimentally obtained structures, that they contain regions of reduced sequence complexity that have an increased propensity [...] Read more.
Intrinsically disordered regions (IDRs) in protein sequences are emerging as functionally important elements for interaction and regulation. While being generally flexible, we previously showed, by observation of experimentally obtained structures, that they contain regions of reduced sequence complexity that have an increased propensity to form structure. Here we expand the universe of cases taking advantage of structural predictions by AlphaFold. Our studies focus on low complexity regions (LCRs) found within IDRs, where these LCRs have only one or two residue types (polyX and polyXY, respectively). In addition to confirming previous observations that polyE and polyEK have a tendency towards helical structure, we find a similar tendency for other LCRs such as polyQ and polyER, most of them including charged residues. We analyzed the position of polyXY containing IDRs within proteins, which allowed us to show that polyAG and polyAK accumulate at the N-terminal, with the latter showing increased helical propensity at that location. Functional enrichment analysis of polyXY with helical propensity indicated functions requiring interaction with RNA and DNA. Our work adds evidence of the function of LCRs in interaction-dependent structuring of disordered regions, encouraging the development of tools for the prediction of their dynamic structural properties. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics 2023)
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14 pages, 5790 KiB  
Article
Deep-Learning-Based Hepatic Ploidy Quantification Using H&E Histopathology Images
by Zhuoyu Wen, Yu-Hsuan Lin, Shidan Wang, Naoto Fujiwara, Ruichen Rong, Kevin W. Jin, Donghan M. Yang, Bo Yao, Shengjie Yang, Tao Wang, Yang Xie, Yujin Hoshida, Hao Zhu and Guanghua Xiao
Genes 2023, 14(4), 921; https://doi.org/10.3390/genes14040921 - 16 Apr 2023
Cited by 1 | Viewed by 1989
Abstract
Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) imaging, which are not widely available in [...] Read more.
Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) imaging, which are not widely available in clinical settings due to high financial and time costs. To improve accessibility for clinical samples, we developed a computational algorithm to quantify hepatic ploidy using hematoxylin-eosin (H&E) histopathology images, which are commonly obtained during routine clinical practice. Our algorithm uses a deep learning model to first segment and classify different types of cell nuclei in H&E images. It then determines cellular ploidy based on the relative distance between identified hepatocyte nuclei and determines nuclear ploidy using a fitted Gaussian mixture model. The algorithm can establish the total number of hepatocytes and their detailed ploidy information in a region of interest (ROI) on H&E images. This is the first successful attempt to automate ploidy analysis on H&E images. Our algorithm is expected to serve as an important tool for studying the role of polyploidy in human liver disease. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics 2023)
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