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Research Advances in the Bioinformatics of Genome Editing and Gene Function Analysis

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 6694

Special Issue Editor

Prof. Dr. Hidemasa Bono

E-Mail Website
Guest Editor
1. Graduate School of Integrated Sciences for Life, Hiroshima University, 3-10-23 Kagamiyama, Higashi-Hiroshima City 739-0046, Japan
2. Genome Editing Innovation Center, Hiroshima University, 3-10-23 Kagamiyama, Higashi-Hiroshima City 739-0046, Japan
3. Database Center for Life Science (DBCLS), Joint Support-Center for Data Science Research, Research Organization of Information and Systems, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
Interests: bioinformatics; biological databases; genome editing; functional genomics; genome sequencing; meta-analysis; transcriptome analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions for our upcoming Special Issue (SI) focusing on the bioinformatics of genome editing and gene function analysis. Genome editing technologies have become widely used and indispensable tools for gene function analysis. Additionally, there is a growing need to process vast amounts of base sequence information obtained through Next Generation Sequencing (NGS) and large-scale image data for functional analysis. However, the details of such bioinformatics research often remain under-represented and relegated to supplementary materials. In this SI, we aim to bring these critical discussions to the forefront. We welcome a broad range of papers that shine a light on this pivotal area of research. Join us in advancing the field of genome editing and gene function analysis through your valuable contributions.

Prof. Dr. Hidemasa Bono
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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 editing
  • functional genomics
  • genome analysis
  • transcriptome analysis
  • metagenome analysis
  • meta-analysis
  • bioinformatics
  • large language model (LLM)
  • public databases

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Published Papers (4 papers)

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Research

18 pages, 2944 KiB  
Article
Risk Prediction of RNA Off-Targets of CRISPR Base Editors in Tissue-Specific Transcriptomes Using Language Models
by Kazuki Nakamae, Takayuki Suzuki, Sora Yonezawa, Kentaro Yamamoto, Taro Kakuzaki, Hiromasa Ono, Yuki Naito and Hidemasa Bono
Int. J. Mol. Sci. 2025, 26(4), 1723; https://doi.org/10.3390/ijms26041723 - 18 Feb 2025
Cited by 1 | Viewed by 1026
Abstract
Base-editing technologies, particularly cytosine base editors (CBEs), allow precise gene modification without introducing double-strand breaks; however, unintended RNA off-target effects remain a critical concern and are under studied. To address this gap, we developed the Pipeline for CRISPR-induced Transcriptome-wide Unintended RNA Editing (PiCTURE), [...] Read more.
Base-editing technologies, particularly cytosine base editors (CBEs), allow precise gene modification without introducing double-strand breaks; however, unintended RNA off-target effects remain a critical concern and are under studied. To address this gap, we developed the Pipeline for CRISPR-induced Transcriptome-wide Unintended RNA Editing (PiCTURE), a standardized computational pipeline for detecting and quantifying transcriptome-wide CBE-induced RNA off-target events. PiCTURE identifies both canonical ACW (W = A or T/U) motif-dependent and non-canonical RNA off-targets, revealing a broader WCW motif that underlies many unanticipated edits. Additionally, we developed two machine learning models based on the DNABERT-2 language model, termed STL and SNL, which outperformed motif-only approaches in terms of accuracy, precision, recall, and F1 score. To demonstrate the practical application of our predictive model for CBE-induced RNA off-target risk, we integrated PiCTURE outputs with the Predicting RNA Off-target compared with Tissue-specific Expression for Caring for Tissue and Organ (PROTECTiO) pipeline and estimated RNA off-target risk for each transcript showing tissue-specific expression. The analysis revealed differences among tissues: while the brain and ovaries exhibited relatively low off-target burden, the colon and lungs displayed relatively high risks. Our study provides a comprehensive framework for RNA off-target profiling, emphasizing the importance of advanced machine learning-based classifiers in CBE safety evaluations and offering valuable insights to inform the development of safer genome-editing therapies. Full article
(This article belongs to the Special Issue Research Advances in the Bioinformatics of Genome Editing and Gene Function Analysis)
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28 pages, 30710 KiB  
Article
Time-Course Transcriptomics Analysis Reveals Molecular Mechanisms of Salt-Tolerant and Salt-Sensitive Cotton Cultivars in Response to Salt Stress
by Hang Li, Li Liu, Xianhui Kong, Xuwen Wang, Aijun Si, Fuxiang Zhao, Qian Huang, Yu Yu and Zhiwen Chen
Int. J. Mol. Sci. 2025, 26(1), 329; https://doi.org/10.3390/ijms26010329 - 2 Jan 2025
Cited by 2 | Viewed by 1153
Abstract
Salt stress is an environmental factor that limits plant seed germination, growth, and survival. We performed a comparative RNA sequencing transcriptome analysis during germination of the seeds from two cultivars with contrasting salt tolerance responses. A transcriptomic comparison between salt-tolerant cotton cv Jin-mian [...] Read more.
Salt stress is an environmental factor that limits plant seed germination, growth, and survival. We performed a comparative RNA sequencing transcriptome analysis during germination of the seeds from two cultivars with contrasting salt tolerance responses. A transcriptomic comparison between salt-tolerant cotton cv Jin-mian 25 and salt-sensitive cotton cv Su-mian 3 revealed both similar and differential expression patterns between the two genotypes during salt stress. The expression of genes related to aquaporins, kinases, reactive oxygen species (ROS) scavenging, trehalose biosynthesis, and phytohormone biosynthesis and signaling that include ethylene (ET), gibberellin (GA), abscisic acid (ABA), jasmonic acid (JA), and brassinosteroid (BR) were systematically investigated between the cultivars. Despite the involvement of these genes in cotton’s response to salt stress in positive or negative ways, their expression levels were mostly similar in both genotypes. Interestingly, a PXC2 gene (Ghir_D08G025150) was identified, which encodes a leucine-rich repeat receptor-like protein kinase (LRR-RLK). This gene showed an induced expression pattern after salt stress treatment in salt-tolerant cv Jin-mian 25 but not salt-sensitive cv Su-mian 3. Our multifaceted transcriptome approach illustrated a differential response to salt stress between salt-tolerant and salt-sensitive cotton. Full article
(This article belongs to the Special Issue Research Advances in the Bioinformatics of Genome Editing and Gene Function Analysis)
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11 pages, 2273 KiB  
Article
KOnezumi-AID: Automation Software for Efficient Multiplex Gene Knockout Using Target-AID
by Taito Taki, Kento Morimoto, Seiya Mizuno and Akihiro Kuno
Int. J. Mol. Sci. 2024, 25(24), 13500; https://doi.org/10.3390/ijms252413500 - 17 Dec 2024
Cited by 1 | Viewed by 1217
Abstract
With the groundbreaking advancements in genome editing technologies, particularly CRISPR-Cas9, creating knockout mutants has become highly efficient. However, the CRISPR-Cas9 system introduces DNA double-strand breaks, increasing the risk of chromosomal rearrangements and posing a major obstacle to simultaneous multiple gene knockout. Base-editing systems, [...] Read more.
With the groundbreaking advancements in genome editing technologies, particularly CRISPR-Cas9, creating knockout mutants has become highly efficient. However, the CRISPR-Cas9 system introduces DNA double-strand breaks, increasing the risk of chromosomal rearrangements and posing a major obstacle to simultaneous multiple gene knockout. Base-editing systems, such as Target-AID, are safe alternatives for precise base modifications without requiring DNA double-strand breaks, serving as promising solutions for existing challenges. Nevertheless, the absence of adequate tools to support Target-AID-based gene knockout highlights the need for a comprehensive system to design guide RNAs (gRNAs) for the simultaneous knockout of multiple genes. Here, we aimed to develop KOnezumi-AID, a command-line tool for gRNA design for Target-AID-mediated genome editing. KOnezumi-AID facilitates gene knockout by inducing the premature termination codons or promoting exon skipping, thereby generating experiment-ready gRNA designs for mouse and human genomes. Additionally, KOnezumi-AID exhibits batch processing capacity, enabling rapid and precise gRNA design for large-scale genome editing, including CRISPR screening. In summary, KOnezumi-AID is an efficient and user-friendly tool for gRNA design, streamlining genome editing workflows and advancing gene knockout research. Full article
(This article belongs to the Special Issue Research Advances in the Bioinformatics of Genome Editing and Gene Function Analysis)
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16 pages, 5040 KiB  
Article
Crispr-SGRU: Prediction of CRISPR/Cas9 Off-Target Activities with Mismatches and Indels Using Stacked BiGRU
by Guishan Zhang, Ye Luo, Huanzeng Xie and Zhiming Dai
Int. J. Mol. Sci. 2024, 25(20), 10945; https://doi.org/10.3390/ijms252010945 - 11 Oct 2024
Cited by 1 | Viewed by 2106
Abstract
CRISPR/Cas9 is a popular genome editing technology, yet its clinical application is hindered by off-target effects. Many deep learning-based methods are available for off-target prediction. However, few can predict off-target activities with insertions or deletions (indels) between single guide RNA and DNA sequence [...] Read more.
CRISPR/Cas9 is a popular genome editing technology, yet its clinical application is hindered by off-target effects. Many deep learning-based methods are available for off-target prediction. However, few can predict off-target activities with insertions or deletions (indels) between single guide RNA and DNA sequence pairs. Additionally, the analysis of off-target data is challenged due to a data imbalance issue. Moreover, the prediction accuracy and interpretability remain to be improved. Here, we introduce a deep learning-based framework, named Crispr-SGRU, to predict off-target activities with mismatches and indels. This model is based on Inception and stacked BiGRU. It adopts a dice loss function to solve the inherent imbalance issue. Experimental results show our model outperforms existing methods for off-target prediction in terms of accuracy and robustness. Finally, we study the interpretability of this model through Deep SHAP and teacher–student-based knowledge distillation, and find it can provide meaningful explanations for sequence patterns regarding off-target activity. Full article
(This article belongs to the Special Issue Research Advances in the Bioinformatics of Genome Editing and Gene Function Analysis)
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