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Bioinformatics and Computational Genomics
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Bioinformatics and Computational Genomics

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

Deadline for manuscript submissions: closed (5 May 2025) | Viewed by 3515

Special Issue Editor

Dr. Quan Li

E-Mail Website
Guest Editor
Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON, Canada
Interests: bioinformatics; genome; cancer; big data; human complex disease; machine learning/deep learning; NGS; variant annotation and interpretation

Special Issue Information

Dear Colleagues,

Bioinformatics, as an interdisciplinary field, has extensively covered the application of computer science, information, mathematics and computing to biological and clinical scenarios in recent decades. Now, the volume of genome data is exponentially increasing due to the application of sequencing methods such as next-generation technologies. These enormous volumes of genetic data need to be properly analysed and interpreted. Computational genomics and bioinformatics are efficient methods by which to extract biological information from complex genomic data.

This Special Issue focuses on recent developments or advances in computational genomics and bioinformatics methods in biology for the clinical field. It aims to provide both breadth in its diversity and depth in its consideration of cutting-edge techniques for computational genomics and bioinformatics.

We welcome the submission of research articles addressing significant research findings and novel methods/tools, as well as reviews in bioinformatics and computational genomics.

Dr. Quan Li
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 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

  • bioinformatics
  • computational genomics
  • computational biology
  • genomics, genome annotation
  • sequence alignment, variant
  • metagenomic
  • high-throughput sequencing
  • single-cell sequencing
  • gene expression analysis
  • multi-dimensional omics
  • systems biology
  • evolutionary and phylogenetic analysis
  • AI and machine learning methods in bioinformatics

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

Published Papers (3 papers)

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Research

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33 pages, 9229 KiB  
Article
UNet with Attention Networks: A Novel Deep Learning Approach for DNA Methylation Prediction in HeLa Cells
by Apoorva, Vikas Handa, Shalini Batra and Vinay Arora
Genes 2025, 16(6), 655; https://doi.org/10.3390/genes16060655 - 28 May 2025
Viewed by 284
Abstract
Background: The purpose of the proposed study is to investigate the efficacy of UNet in predicting Deoxyribonucleic Acid methylation patterns in a cervical cancer cell line. The application of deep learning to analyse the factors affecting methylation in the context of cervical [...] Read more.
Background: The purpose of the proposed study is to investigate the efficacy of UNet in predicting Deoxyribonucleic Acid methylation patterns in a cervical cancer cell line. The application of deep learning to analyse the factors affecting methylation in the context of cervical cancer has not yet been fully explored. Methods: A comprehensive performance evaluation has been conducted based on multiple window sizes of DNA sequences. For this purpose, three different parameter-analysis techniques, namely, autoencoders, Generative Adversarial Networks, and Multi-Head Attention Networks, were used. This work presents a novel framework for methylation prediction in promoter regions of various genes. Results and Conclusions: Experimental results have proved that attention networks in association with UNet achieved a significant accuracy level of 91.01% along with a sensitivity of 89.65%, specificity of around 92.35%, and an area under curve of 0.910 on ENCODE database. The proposed model outperformed three state-of-the-art models: Convolutional Neural Network, Transfer Learning, and Feed Forward Neural Network with K-Nearest Neighbour. Moreover, validation of the model in five gene promoters achieved an accuracy of 81.60% with an area under curve score of 0.814, a p-value of 3.62×10−19, and Cohen’s Kappa value of 0.631. This novel approach has led to a better understanding of epigenetic variables and their implications in cervical cancer, offering potential insights into therapeutic strategies. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Genomics)
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15 pages, 15327 KiB  
Article
Colorectal Cancer Biomarker Identification via Joint DNA-Methylation and Transcriptomics Analysis Workflow
by Olajumoke B. Oladapo and Marmar R. Moussa
Genes 2025, 16(6), 620; https://doi.org/10.3390/genes16060620 - 23 May 2025
Viewed by 368
Abstract
Background: Colorectal cancer (CRC) is a term that refers to the combination of colon and rectal cancer as they are being treated as a single tumor. In CRC, 72% of tumors are colon cancer, while the other 28% represent rectal cancer. CRC [...] Read more.
Background: Colorectal cancer (CRC) is a term that refers to the combination of colon and rectal cancer as they are being treated as a single tumor. In CRC, 72% of tumors are colon cancer, while the other 28% represent rectal cancer. CRC is a multifactorial disease caused by both genetic and epigenetic changes in the colon mucosal cells, affecting the oncogenes, DNA repair genes, and tumor suppressor genes. Currently, two DNA methylation-based biomarkers for CRC have received FDA approval: SEPT9, used in blood-based screening tests, and a combination of NDRG4 and BMP3 for stool-based tests. Although DNA methylation biomarkers have been explored in colorectal cancer (CRC), the identification of robust and clinically valuable biomarkers remains a challenge, particularly for early-stage detection and precancerous lesions. Patients often receive diagnoses at the locally advanced stage, which limits the potential utility of current biomarkers in clinical settings. Methods: The datasets used in this study were retrieved from the GEO database, specifically GSE75548 and GSE75546 for rectal cancer and GSE50760 and GSE101764 for colon cancer, summing up to a total of 130 paired samples. These datasets represent expression profiling by array, methylation profiling by genome tiling array, and expression profiling by high-throughput sequencing and include rectal and colon cancer samples paired with adjacent normal tissue samples. Differential analysis was used to identify differentially methylated CPG sites (DMCs) and identify differentially expressed genes (DEGs). Results: From the integration of DMCs with DEGs in colorectal cancer, we identified 150 candidates for methylation-regulated genes (MRGs) with two genes common across all cohorts (GNG7 and PDX1) highlighted as candidate biomarkers in CRC. The functional enrichment analysis and protein–protein interactions (PPIs) identified relevant pathways involved in CRC, including the Wnt signaling pathway, extracellular matrix (ECM) organization, among other enriched pathways. Conclusions: Our findings show the strength of our in silco computational approach in jointly identifying methylation-regulated biomarkers for colon cancer and highlight several genes and pathways as biomarker candidates for further investigations. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Genomics)
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Review

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15 pages, 687 KiB  
Review
Federated Learning: Breaking Down Barriers in Global Genomic Research
by Giulia Calvino, Cristina Peconi, Claudia Strafella, Giulia Trastulli, Domenica Megalizzi, Sarah Andreucci, Raffaella Cascella, Carlo Caltagirone, Stefania Zampatti and Emiliano Giardina
Genes 2024, 15(12), 1650; https://doi.org/10.3390/genes15121650 - 22 Dec 2024
Cited by 3 | Viewed by 1807
Abstract
Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as [...] Read more.
Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as a transformative solution, enabling decentralized data analysis while preserving privacy and complying with regulations such as the General Data Protection Regulation (GDPR). This review explores the potential use of FL in genomics, detailing its methodology, including local model training, secure aggregation, and iterative improvement. Key challenges, such as heterogeneous data integration and cybersecurity risks, are examined alongside regulations like GDPR. In conclusion, successful implementations of FL in global and national initiatives demonstrate its scalability and role in supporting collaborative research. Finally, we discuss future directions, including AI integration and the necessity of education and training, to fully harness the potential of FL in advancing precision medicine and global health initiatives. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Genomics)
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