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New Advances and Challenges in Bioinformatics. IWBBIO-2023 Selection
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New Advances and Challenges in Bioinformatics. IWBBIO-2023 Selection

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 (31 January 2024) | Viewed by 2979

Special Issue Editors

Prof. Dr. Olga Valenzuela

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Guest Editor
Department of Applied Mathematics, University of Granada, 18071 Granada, Spain
Interests: deep learning; statistical analysis in big data; machine learning algorithms; data mining; bioinformatics; computational biology
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco Ortuño

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Guest Editor
Department of Computer Engineering, Automatics and Robotics (ICAR), Information and Communications Technology Centre (CITIC-UGR), University of Granada, 18010 Granada, Spain
Interests: machine learning algorithms; data mining; bioinformatics; computational biology
Special Issues, Collections and Topics in MDPI journals
Dr. Jan Urban

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Guest Editor
Institute of Complex Systems, South Bohemian Research Centre of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, University of South Bohemia in České Budějovice, Zámek 136, 37 333 Nové Hrady, Czech Republic
Interests: signal processing; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Jean-Fred Fontaine

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Faculty of Biology, Johannes Gutenberg-Universität Mainz, Biozentrum I, Hans-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
Interests: bioinformatics; computational biology; machine learning
Prof. Dr. Ignacio Rojas

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Guest Editor
Information Technology and Telecommunications Engineering, University of Granada, 18010 Granada, Spain
Interests: time series analysis; forecasting; artificial intelligence; biomedical engineering; complex problem
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 10th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2023) will be held in Gran Canaria, Spain, the 12–14th July 2023. It will serve as a discussion forum for scientists regarding the latest ideas and realizations on the foundations, theory, models, and applications of interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, biology, bioinformatics, and biomedicine: https://iwbbio.ugr.es.

Following the success of last year’s edition (https://www.mdpi.com/journal/genes/special_issues/IWBBIO2022), we are editing a new Special Issue for the current edition, IWBBIO 2023.

The current Special Issue solicits high-quality original research papers on Bioinformatics. New computational techniques and methods in machine learning; data mining; data integration; genomics and evolution; next-generation sequencing data; protein and RNA structure; medical informatics and translational bioinformatics; computational systems biology; modeling and simulations; and their application in the life science domain, biomedicine, and biomedical engineering are especially encouraged.

Prof. Dr. Olga Valenzuela
Dr. Francisco Ortuño
Dr. Jan Urban
Dr. Jean-Fred Fontaine
Prof. Dr. Ignacio Rojas
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 (3 papers)

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Research

16 pages, 1887 KiB  
Article
Optimization and Performance Analysis of CAT Method for DNA Sequence Similarity Searching and Alignment
by Veska Gancheva and Hristo Stoev
Genes 2024, 15(3), 341; https://doi.org/10.3390/genes15030341 - 07 Mar 2024
Viewed by 735
Abstract
Bioinformatics is a rapidly developing field enabling scientific experiments via computer models and simulations. In recent years, there has been an extraordinary growth in biological databases. Therefore, it is extremely important to propose effective methods and algorithms for the fast and accurate processing [...] Read more.
Bioinformatics is a rapidly developing field enabling scientific experiments via computer models and simulations. In recent years, there has been an extraordinary growth in biological databases. Therefore, it is extremely important to propose effective methods and algorithms for the fast and accurate processing of biological data. Sequence comparisons are the best way to investigate and understand the biological functions and evolutionary relationships between genes on the basis of the alignment of two or more DNA sequences in order to maximize the identity level and degree of similarity. This paper presents a new version of the pairwise DNA sequences alignment algorithm, based on a new method called CAT, where a dependency with a previous match and the closest neighbor are taken into consideration to increase the uniqueness of the CAT profile and to reduce possible collisions, i.e., two or more sequence with the same CAT profiles. This makes the proposed algorithm suitable for finding the exact match of a concrete DNA sequence in a large set of DNA data faster. In order to enable the usage of the profiles as sequence metadata, CAT profiles are generated once prior to data uploading to the database. The proposed algorithm consists of two main stages: CAT profile calculation depending on the chosen benchmark sequences and sequence comparison by using the calculated CAT profiles. Improvements in the generation of the CAT profiles are detailed and described in this paper. Block schemes, pseudo code tables, and figures were updated according to the proposed new version and experimental results. Experiments were carried out using the new version of the CAT method for DNA sequence alignment and different datasets. New experimental results regarding collisions, speed, and efficiency of the suggested new implementation are presented. Experiments related to the performance comparison with Needleman–Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. A performance analysis of the proposed algorithm based on the CAT method against the Knuth–Morris–Pratt algorithm, which has a complexity of O(n) and is widely used for biological data searching, was performed. The impact of prior matching dependencies on uniqueness for generated CAT profiles is investigated. The experimental results from sequence alignment demonstrate that the proposed CAT method-based algorithm exhibits minimal deviation, which can be deemed negligible if such deviation is considered permissible in favor of enhanced performance. It should be noted that the performance of the CAT algorithm in terms of execution time remains stable, unaffected by the length of the analyzed sequences. Hence, the primary benefit of the suggested approach lies in its rapid processing capabilities in large-scale sequence alignment, a task that traditional exact algorithms would require significantly more time to perform. Full article
(This article belongs to the Special Issue New Advances and Challenges in Bioinformatics. IWBBIO-2023 Selection)
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19 pages, 968 KiB  
Article
Gene Expression Analysis for Uterine Cervix and Corpus Cancer Characterization
by Lucía Almorox, Laura Antequera, Ignacio Rojas, Luis Javier Herrera and Francisco M. Ortuño
Genes 2024, 15(3), 312; https://doi.org/10.3390/genes15030312 - 28 Feb 2024
Viewed by 966
Abstract
The analysis of gene expression quantification data is a powerful and widely used approach in cancer research. This work provides new insights into the transcriptomic changes that occur in healthy uterine tissue compared to those in cancerous tissues and explores the differences associated [...] Read more.
The analysis of gene expression quantification data is a powerful and widely used approach in cancer research. This work provides new insights into the transcriptomic changes that occur in healthy uterine tissue compared to those in cancerous tissues and explores the differences associated with uterine cancer localizations and histological subtypes. To achieve this, RNA-Seq data from the TCGA database were preprocessed and analyzed using the KnowSeq package. Firstly, a kNN model was applied to classify uterine cervix cancer, uterine corpus cancer, and healthy uterine samples. Through variable selection, a three-gene signature was identified (VWCE, CLDN15, ADCYAP1R1), achieving consistent 100% test accuracy across 20 repetitions of a 5-fold cross-validation. A supplementary similar analysis using miRNA-Seq data from the same samples identified an optimal two-gene miRNA-coding signature potentially regulating the three-gene signature previously mentioned, which attained optimal classification performance with an 82% F1-macro score. Subsequently, a kNN model was implemented for the classification of cervical cancer samples into their two main histological subtypes (adenocarcinoma and squamous cell carcinoma). A uni-gene signature (ICA1L) was identified, achieving 100% test accuracy through 20 repetitions of a 5-fold cross-validation and externally validated through the CGCI program. Finally, an examination of six cervical adenosquamous carcinoma (mixed) samples revealed a pattern where the gene expression value in the mixed class aligned closer to the histological subtype with lower expression, prompting a reconsideration of the diagnosis for these mixed samples. In summary, this study provides valuable insights into the molecular mechanisms of uterine cervix and corpus cancers. The newly identified gene signatures demonstrate robust predictive capabilities, guiding future research in cancer diagnosis and treatment methodologies. Full article
(This article belongs to the Special Issue New Advances and Challenges in Bioinformatics. IWBBIO-2023 Selection)
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16 pages, 5961 KiB  
Article
Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation
by Lorenzo Martini, Roberta Bardini, Alessandro Savino and Stefano Di Carlo
Genes 2024, 15(3), 268; https://doi.org/10.3390/genes15030268 - 21 Feb 2024
Viewed by 628
Abstract
It is well known how sequencing technologies propelled cellular biology research in recent years, providing incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with single-cell ATAC sequencing supporting it and becoming more popular. [...] Read more.
It is well known how sequencing technologies propelled cellular biology research in recent years, providing incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to simultaneously perform the mentioned sequencing modalities on the same cells. Yet, there still needs to be a clear and dedicated way to analyze these multi-modal data. One of the current methods is to calculate the Gene Activity Matrix (GAM), which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well defined, and it is unclear how various accessible regions impact the expression of the genes. Moreover, the transcription process is highly regulated by the transcription factors that bind to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of Genomic-Annotated Gene Activity Matrix (GAGAM) contributions, aiming to investigate the correlation between the TF expression and motif information in the different functional genomic regions to understand the different Transcription Factors (TFs) dynamics involved in different cell types. Full article
(This article belongs to the Special Issue New Advances and Challenges in Bioinformatics. IWBBIO-2023 Selection)
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