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Moeckel C, Mareboina M, Konnaris MA, Chan CS, Mouratidis I, Montgomery A, Chantzi N, Pavlopoulos GA, Georgakopoulos-Soares I. A survey of k-mer methods and applications in bioinformatics. Comput Struct Biotechnol J 2024; 23:2289-2303. [PMID: 38840832 PMCID: PMC11152613 DOI: 10.1016/j.csbj.2024.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 06/07/2024] Open
Abstract
The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.
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Affiliation(s)
- Camille Moeckel
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Manvita Mareboina
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Maxwell A. Konnaris
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Candace S.Y. Chan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ioannis Mouratidis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Huck Institute of the Life Sciences, Penn State University, University Park, Pennsylvania, USA
| | - Austin Montgomery
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Nikol Chantzi
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | | | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Huck Institute of the Life Sciences, Penn State University, University Park, Pennsylvania, USA
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Guo LX, Wang L, You ZH, Yu CQ, Hu ML, Zhao BW, Li Y. Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations. Brief Bioinform 2024; 25:bbae020. [PMID: 38324624 PMCID: PMC10849193 DOI: 10.1093/bib/bbae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 01/01/2024] [Accepted: 01/11/2024] [Indexed: 02/09/2024] Open
Abstract
Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.
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Affiliation(s)
- Lu-Xiang Guo
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
| | - Lei Wang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China
- College of Information Science and Engineering, Zaozhuang University, Shandong 277100, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China
| | - Chang-Qing Yu
- College of Information Engineering, Xijing University, Xi’an 710123, China
| | - Meng-Lei Hu
- School of Medicine, Peking University, Beijing, 100091, China
| | - Bo-Wei Zhao
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
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Yi D, Nam JW, Jeong H. Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches. Brief Bioinform 2023; 24:bbad297. [PMID: 37587831 PMCID: PMC10516374 DOI: 10.1093/bib/bbad297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/05/2023] [Accepted: 07/23/2023] [Indexed: 08/18/2023] Open
Abstract
Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.
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Affiliation(s)
- Dohun Yi
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Research Institute for Convergence of Basic Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hyobin Jeong
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
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Schmidt M, Kutzner A. MSV: a modular structural variant caller that reveals nested and complex rearrangements by unifying breakends inferred directly from reads. Genome Biol 2023; 24:170. [PMID: 37461107 DOI: 10.1186/s13059-023-03009-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 07/06/2023] [Indexed: 07/20/2023] Open
Abstract
Structural variant (SV) calling belongs to the standard tools of modern bioinformatics for identifying and describing alterations in genomes. Initially, this work presents several complex genomic rearrangements that reveal conceptual ambiguities inherent to the representation via basic SV. We contextualize these ambiguities theoretically as well as practically and propose a graph-based approach for resolving them. For various yeast genomes, we practically compute adjacency matrices of our graph model and demonstrate that they provide highly accurate descriptions of one genome in terms of another. An open-source prototype implementation of our approach is available under the MIT license at https://github.com/ITBE-Lab/MA .
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Affiliation(s)
- Markus Schmidt
- Biomedical Center Munich, Department of Physiological Chemistry, Ludwig-Maximilians-Universität, Großhaderner Str. 9, 82152, Planegg-Martinsried, Germany
| | - Arne Kutzner
- Department of Information Systems, College of Engineering, Hanyang University, 222 Wangsimni-Ro, Seongdong-Gu, Seoul, 133-791, Republic of Korea.
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Choo ZN, Imieliński M. Faster detection of somatic structural variants. Nat Biomed Eng 2023; 7:847-848. [PMID: 37217613 DOI: 10.1038/s41551-023-01039-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Affiliation(s)
- Zi-Ning Choo
- Tri-Institutional MD-PhD Program, Weill Cornell Medicine, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Marcin Imieliński
- New York Genome Center, New York, NY, USA.
- Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY, USA.
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