Feature Papers in Technologies and Resources for Genetics

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 (15 January 2023) | Viewed by 24292

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 Medical Sciences, University of Torino, 10126 Torino, Italy
Interests: machine learning; computational biomedicine; bioinformatics; physical modelling of biological systems

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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 genetic 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 topic will describe novel advances in technological methods, protocols, and software for the generation and interpretation of genome-derived data. The topic 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. Piero Fariselli
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.

Published Papers (9 papers)

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Research

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19 pages, 793 KiB  
Article
Adaptively Integrative Association between Multivariate Phenotypes and Transcriptomic Data for Complex Diseases
by Yujia Li, Yusi Fang, Hung-Ching Chang, Michael Gorczyca, Peng Liu and George C. Tseng
Genes 2023, 14(4), 798; https://doi.org/10.3390/genes14040798 - 26 Mar 2023
Viewed by 1399
Abstract
Phenotype–gene association studies can uncover disease mechanisms for translational research. Association with multiple phenotypes or clinical variables in complex diseases has the advantage of increasing statistical power and offering a holistic view. Existing multi-variate association methods mostly focus on SNP-based genetic associations. In [...] Read more.
Phenotype–gene association studies can uncover disease mechanisms for translational research. Association with multiple phenotypes or clinical variables in complex diseases has the advantage of increasing statistical power and offering a holistic view. Existing multi-variate association methods mostly focus on SNP-based genetic associations. In this paper, we extend and evaluate two adaptive Fisher’s methods, namely AFp and AFz, from the p-value combination perspective for phenotype–mRNA association analysis. The proposed method effectively aggregates heterogeneous phenotype–gene effects, allows association with different data types of phenotypes, and performs the selection of the associated phenotypes. Variability indices of the phenotype–gene effect selection are calculated by bootstrap analysis, and the resulting co-membership matrix identifies gene modules clustered by phenotype–gene effect. Extensive simulations demonstrate the superior performance of AFp compared to existing methods in terms of type I error control, statistical power and biological interpretation. Finally, the method is separately applied to three sets of transcriptomic and clinical datasets from lung disease, breast cancer, and brain aging and generates intriguing biological findings. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics)
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16 pages, 5030 KiB  
Article
Evaluation of Candidate Reference Genes for Gene Expression Analysis in Wild Lamiophlomis rotata
by Luhao Wang, Feng Qiao, Guigong Geng and Yueheng Lu
Genes 2023, 14(3), 573; https://doi.org/10.3390/genes14030573 - 24 Feb 2023
Cited by 2 | Viewed by 1145
Abstract
Lamiophlomis rotata (Benth.) Kudo is a perennial and unique medicinal plant of the Qinghai–Tibet Plateau. It has the effects of diminishing inflammation, activating blood circulation, removing blood stasis, reducing swelling, and relieving pain. However, thus far, reliable reference gene identifications have not been [...] Read more.
Lamiophlomis rotata (Benth.) Kudo is a perennial and unique medicinal plant of the Qinghai–Tibet Plateau. It has the effects of diminishing inflammation, activating blood circulation, removing blood stasis, reducing swelling, and relieving pain. However, thus far, reliable reference gene identifications have not been reported in wild L. rotata. In this study, we identified suitable reference genes for the analysis of gene expression related to the medicinal compound synthesis in wild L. rotata subjected to five different-altitude habitats. Based on the RNA-Seq data of wild L. rotata from five different regions, the stability of 15 candidate internal reference genes was analyzed using geNorm, NormFinder, BestKeeper, and RefFinder. TFIIS, EF-1α, and CYP22 were the most suitable internal reference genes in the leaves of L. rotata from different regions, while OBP, TFIIS, and CYP22 were the optimal reference genes in the roots of L. rotata. The reference genes identified here would be very useful for gene expression studies with different tissues in L. rotata from different habitats. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics)
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17 pages, 1404 KiB  
Article
Identification of New Toxicity Mechanisms in Drug-Induced Liver Injury through Systems Pharmacology
by Aurelio A. Moya-García, Andrés González-Jiménez, Fernando Moreno, Camilla Stephens, María Isabel Lucena and Juan A. G. Ranea
Genes 2022, 13(7), 1292; https://doi.org/10.3390/genes13071292 - 21 Jul 2022
Viewed by 1795
Abstract
Among adverse drug reactions, drug-induced liver injury presents particular challenges because of its complexity, and the underlying mechanisms are still not completely characterized. Our knowledge of the topic is limited and based on the assumption that a drug acts on one molecular target. [...] Read more.
Among adverse drug reactions, drug-induced liver injury presents particular challenges because of its complexity, and the underlying mechanisms are still not completely characterized. Our knowledge of the topic is limited and based on the assumption that a drug acts on one molecular target. We have leveraged drug polypharmacology, i.e., the ability of a drug to bind multiple targets and thus perturb several biological processes, to develop a systems pharmacology platform that integrates all drug–target interactions. Our analysis sheds light on the molecular mechanisms of drugs involved in drug-induced liver injury and provides new hypotheses to study this phenomenon. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics)
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17 pages, 1525 KiB  
Article
Development of Transformation for Genome Editing of an Emerging Model Organism
by Yutaka Yamamoto and Susan A. Gerbi
Genes 2022, 13(7), 1108; https://doi.org/10.3390/genes13071108 - 21 Jun 2022
Cited by 5 | Viewed by 1664 | Correction
Abstract
With the advances in genomic sequencing, many organisms with novel biological properties are ripe for use as emerging model organisms. However, to make full use of them, transformation methods need to be developed to permit genome editing. Here, we present the development of [...] Read more.
With the advances in genomic sequencing, many organisms with novel biological properties are ripe for use as emerging model organisms. However, to make full use of them, transformation methods need to be developed to permit genome editing. Here, we present the development of transformation for the fungus fly Bradysia (Sciara) coprophila; this may serve as a paradigm for the development of transformation for other emerging systems, especially insects. Bradysia (Sciara) has a variety of unique biological features, including locus-specific developmentally regulated DNA amplification, chromosome imprinting, a monopolar spindle in male meiosis I, non-disjunction of the X chromosome in male meiosis II, X chromosome elimination in early embryogenesis, germ-line-limited (L) chromosomes and high resistance to radiation. Mining the unique biology of Bradysia (Sciara) requires a transformation system to test mutations of DNA sequences that may play roles for these features. We describe a Bradysia (Sciara) transformation system using a modified piggyBac transformation vector and detailed protocols we have developed to accommodate Bradysia (Sciara) specific requirements. This advance will provide a platform for us and others in the growing Bradysia (Sciara) community to take advantage of this unique biological system. In addition, the versatile piggyBac vectors described here and transformation methods will be useful for other emerging model systems. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics)
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15 pages, 2419 KiB  
Article
Inferring Potential Cancer Driving Synonymous Variants
by Zishuo Zeng and Yana Bromberg
Genes 2022, 13(5), 778; https://doi.org/10.3390/genes13050778 - 27 Apr 2022
Cited by 2 | Viewed by 2715
Abstract
Synonymous single nucleotide variants (sSNVs) are often considered functionally silent, but a few cases of cancer-causing sSNVs have been reported. From available databases, we collected four categories of sSNVs: germline, somatic in normal tissues, somatic in cancerous tissues, and putative cancer drivers. We [...] Read more.
Synonymous single nucleotide variants (sSNVs) are often considered functionally silent, but a few cases of cancer-causing sSNVs have been reported. From available databases, we collected four categories of sSNVs: germline, somatic in normal tissues, somatic in cancerous tissues, and putative cancer drivers. We found that screening sSNVs for recurrence among patients, conservation of the affected genomic position, and synVep prediction (synVep is a machine learning-based sSNV effect predictor) recovers cancer driver variants (termed proposed drivers) and previously unknown putative cancer genes. Of the 2.9 million somatic sSNVs found in the COSMIC database, we identified 2111 proposed cancer driver sSNVs. Of these, 326 sSNVs could be further tagged for possible RNA splicing effects, RNA structural changes, and affected RBP motifs. This list of proposed cancer driver sSNVs provides computational guidance in prioritizing the experimental evaluation of synonymous mutations found in cancers. Furthermore, our list of novel potential cancer genes, galvanized by synonymous mutations, may highlight yet unexplored cancer mechanisms. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics)
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Review

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35 pages, 1554 KiB  
Review
Advancement in Human Face Prediction Using DNA
by Aamer Alshehhi, Aliya Almarzooqi, Khadija Alhammadi, Naoufel Werghi, Guan K. Tay and Habiba Alsafar
Genes 2023, 14(1), 136; https://doi.org/10.3390/genes14010136 - 3 Jan 2023
Cited by 5 | Viewed by 5582
Abstract
The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are [...] Read more.
The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are not available in the deoxyribose nucleic acid (DNA) database. This review summarizes the currently used methods for predicting human phenotypes such as age, ancestry, pigmentation, and facial features based on genetic variations. To identify the facial features affected by DNA, various two-dimensional (2D)- and three-dimensional (3D)-scanning techniques and analysis tools are reviewed. A comparison between the scanning technologies is also presented in this review. Face-landmarking techniques and face-phenotyping algorithms are discussed in chronological order. Then, the latest approaches in genetic to 3D face shape analysis are emphasized. A systematic review of the current markers that passed the threshold of a genome-wide association (GWAS) of single nucleotide polymorphism (SNP)-face traits from the GWAS Catalog is also provided using the preferred reporting items for systematic reviews and meta-analyses (PRISMA), approach. Finally, the current challenges in forensic DNA phenotyping are analyzed and discussed. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics)
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15 pages, 970 KiB  
Review
Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View
by Juan A. G. Ranea, James Perkins, Mónica Chagoyen, Elena Díaz-Santiago and Florencio Pazos
Genes 2022, 13(6), 1081; https://doi.org/10.3390/genes13061081 - 17 Jun 2022
Cited by 4 | Viewed by 2761
Abstract
Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is [...] Read more.
Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is traditionally partitioned into discrete “diseases”; however, that partition is sometimes problematic, as diseases are highly heterogeneous and can differ greatly from one patient to another. Moreover, for many pathological states, the set of symptoms (phenotypes) manifested by the patient is not enough to diagnose a particular disease. On the contrary, phenotypes, by definition, are directly observable and can be closer to the molecular basis of the pathology. These clinical phenotypes are also important for personalised medicine, as they can help stratify patients and design personalised interventions. For these reasons, network and systemic approaches to pathologies are gradually incorporating phenotypic information. This review covers the current landscape of phenotype-centred network approaches to study different aspects of human diseases. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics)
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16 pages, 767 KiB  
Review
Inferring Signatures of Positive Selection in Whole-Genome Sequencing Data: An Overview of Haplotype-Based Methods
by Paolo Abondio, Elisabetta Cilli and Donata Luiselli
Genes 2022, 13(5), 926; https://doi.org/10.3390/genes13050926 - 22 May 2022
Cited by 9 | Viewed by 4167
Abstract
Signatures of positive selection in the genome are a characteristic mark of adaptation that can reveal an ongoing, recent, or ancient response to environmental change throughout the evolution of a population. New sources of food, climate conditions, and exposure to pathogens are only [...] Read more.
Signatures of positive selection in the genome are a characteristic mark of adaptation that can reveal an ongoing, recent, or ancient response to environmental change throughout the evolution of a population. New sources of food, climate conditions, and exposure to pathogens are only some of the possible sources of selective pressure, and the rise of advantageous genetic variants is a crucial determinant of survival and reproduction. In this context, the ability to detect these signatures of selection may pinpoint genetic variants that are responsible for a significant change in gene regulation, gene expression, or protein synthesis, structure, and function. This review focuses on statistical methods that take advantage of linkage disequilibrium and haplotype determination to reveal signatures of positive selection in whole-genome sequencing data, showing that they emerge from different descriptions of the same underlying event. Moreover, considerations are provided around the application of these statistics to different species, their suitability for ancient DNA, and the usefulness of discovering variants under selection for biomedicine and public health in an evolutionary medicine framework. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics)
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Other

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4 pages, 749 KiB  
Brief Report
PolyX2: Fast Detection of Homorepeats in Large Protein Datasets
by Pablo Mier and Miguel A. Andrade-Navarro
Genes 2022, 13(5), 758; https://doi.org/10.3390/genes13050758 - 25 Apr 2022
Cited by 6 | Viewed by 1477
Abstract
Homorepeat sequences, consecutive runs of identical amino acids, are prevalent in eukaryotic proteins. It has become necessary to annotate and evaluate this feature in entire proteomes. The definition of what constitutes a homorepeat is not fixed, and different research approaches may require different [...] Read more.
Homorepeat sequences, consecutive runs of identical amino acids, are prevalent in eukaryotic proteins. It has become necessary to annotate and evaluate this feature in entire proteomes. The definition of what constitutes a homorepeat is not fixed, and different research approaches may require different definitions; therefore, flexible approaches to analyze homorepeats in complete proteomes are needed. Here, we present polyX2, a fast, simple but tunable script to scan protein datasets for all possible homorepeats. The user can modify the length of the window to scan, the minimum number of identical residues that must be found in the window, and the types of homorepeats to be found. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics)
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