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Advances in Developing Genomics and Computational Approaches
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Advances in Developing Genomics and Computational Approaches

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 (25 November 2025) | Viewed by 5730

Special Issue Editor

Dr. Abedalrhman Alkhateeb

E-Mail Website
Guest Editor
Computer Science Department, Lakehead University, Thunder Bay, ON, Canada
Interests: genomics data analysis; machine learning in bioinformatics; computational algorithms for large-scale omics datasets; precision medicine; evolutionary genomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid advancements in genomics and computational technologies are revolutionizing our understanding of biological systems, disease mechanisms, and therapeutic development. This Special Issue, “Advances in Developing Genomics and Computational Approaches”, seeks to highlight innovative research at the intersection of genomics, bioinformatics, and computational biology. We invite submissions that address the challenges and opportunities in harnessing cutting-edge computational tools to decode complex biological data, enhance genomic biomedical prediction models and health outcomes, and translate findings into clinical and industrial applications.

Topics of interest include (but are not limited to) the following:

  • Novel algorithms for the analysis of next-generation sequencing (NGS) data;
  • Machine learning/AI applications in genomics and precision medicine;
  • Integrative analyses of multi-omics data and systems biology;
  • Cloud computing and scalable architectures for genomic workflows;
  • The ethical, legal, and social implications (ELSI) of genomic technologies;
  • Single-cell genomics and spatial transcriptomics methodologies;
  • Network biology and modeling of genetic interactions;
  • CRISPR-based genomic engineering and computational design tools.

We welcome original research articles, reviews, and methodological papers that combine computational innovation and biological discovery. Submissions emphasizing reproducibility, open-source tools, and translational impact are particularly encouraged.

Join us in advancing the frontiers of genomics and computational science to address pressing challenges in healthcare, agriculture, and biotechnology.

Dr. Abedalrhman Alkhateeb
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • genomics
  • computational biology
  • bioinformatics
  • machine learning
  • artificial intelligence (AI)
  • next-generation sequencing (NGS)
  • precision medicine
  • multi-omics integration
  • CRISPR-based engineering
  • single-cell genomics
  • ethical
  • legal
  • and social implications (ELSI)
  • translational genomics
  • biomedical prediction models
  • network biology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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23 pages, 5919 KB  
Article
Machine Learning Reveals Common Regulatory Mechanisms Mediated by Autophagy-Related Genes in the Development of Inflammatory Bowel Disease and Major Depressive Disorder
by Gengxian Wang, Luojin Wu, Jiyuan Shi, Mengmeng Sang and Liming Mao
Genes 2026, 17(1), 4; https://doi.org/10.3390/genes17010004 - 19 Dec 2025
Viewed by 758
Abstract
Background: Major Depressive Disorder (MDD) is more common in patients with Inflammatory Bowel Disease (IBD) than in the general population, suggesting a shared but unclear pathogenesis. Autophagy, a conserved intracellular cleaning process, maintains cellular health by removing debris and recycling nutrients. Given the [...] Read more.
Background: Major Depressive Disorder (MDD) is more common in patients with Inflammatory Bowel Disease (IBD) than in the general population, suggesting a shared but unclear pathogenesis. Autophagy, a conserved intracellular cleaning process, maintains cellular health by removing debris and recycling nutrients. Given the limited research on autophagy in this comorbidity, this study investigated the role of autophagy-related genes in both disorders. Aim: This study aimed to identify shared autophagy-related mechanisms between IBD and MDD and to explore potential therapeutic strategies. Methods: We identified differentially expressed autophagy-related genes (DE-ARGs) in diseased versus normal tissues. Shared DE-ARGs between IBD and MDD were designated Co-DEGs. We analyzed correlations among Co-DEGs and their association with immune cell infiltration. Four machine-learning algorithms were used to pinpoint key biomarkers. Potential therapeutic agents were predicted and validated via molecular docking. Results: We identified 47 shared Co-DEGs. Among these, CASP1 emerged as a cross-disease shared susceptibility-associated gene (SSAG), consistently selected by all machine-learning models. Drug-gene interaction analysis and molecular docking identified compounds that could regulate CASP1. Single-cell analysis suggested CASP1 helps reshape the immune microenvironment in Crohn’s disease. Furthermore, Mendelian randomization identified WDR6 as a shared genetic risk factor for both conditions. Conclusions: Our findings illuminate autophagy-mediated mechanisms linking gut and brain disorders. The identification of CASP1 as a SSAG, along with candidate therapeutics, provides a foundation for future research and targeted treatments for IBD and MDD comorbidity. Full article
(This article belongs to the Special Issue Advances in Developing Genomics and Computational Approaches)
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Review

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16 pages, 265 KB  
Review
TIGR-Tas and the Expanding Universe of RNA-Guided Genome Editing Systems: A New Era Beyond CRISPR-Cas
by Douglas M. Ruden
Genes 2025, 16(8), 896; https://doi.org/10.3390/genes16080896 - 28 Jul 2025
Cited by 3 | Viewed by 4483
Abstract
The recent discovery of TIGR-Tas (Tandem Interspaced Guide RNA-Targeting Systems) marks a major advance in the field of genome editing, introducing a new class of compact, programmable DNA-targeting systems that function independently of traditional CRISPR-Cas pathways. TIGR-Tas effectors use a novel dual-spacer guide [...] Read more.
The recent discovery of TIGR-Tas (Tandem Interspaced Guide RNA-Targeting Systems) marks a major advance in the field of genome editing, introducing a new class of compact, programmable DNA-targeting systems that function independently of traditional CRISPR-Cas pathways. TIGR-Tas effectors use a novel dual-spacer guide RNA (tigRNA) to recognize both strands of target DNA without requiring a protospacer adjacent motif (PAM). These Tas proteins introduce double-stranded DNA cuts with characteristic 8-nucleotide 3′ overhangs and are significantly smaller than Cas9, offering delivery advantages for in vivo editing. Structural analyses reveal homology to box C/D snoRNP proteins, suggesting a previously unrecognized evolutionary lineage of RNA-guided nucleases. This review positions TIGR-Tas at the forefront of a new wave of RNA-programmable genome-editing technologies. In parallel, I provide comparative insight into the diverse and increasingly modular CRISPR-Cas systems, including Cas9, Cas12, Cas13, and emerging effectors like Cas3, Cas10, CasΦ, and Cas14. While the CRISPR-Cas universe has revolutionized molecular biology, TIGR-Tas systems open a complementary and potentially more versatile path for programmable genome manipulation. I discuss mechanistic distinctions, evolutionary implications, and potential applications in human cells, synthetic biology, and therapeutic genome engineering. Full article
(This article belongs to the Special Issue Advances in Developing Genomics and Computational Approaches)
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