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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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Chantzi N, Mareboina M, Konnaris MA, Montgomery A, Patsakis M, Mouratidis I, Georgakopoulos-Soares I. The determinants of the rarity of nucleic and peptide short sequences in nature. NAR Genom Bioinform 2024; 6:lqae029. [PMID: 38584871 PMCID: PMC10993293 DOI: 10.1093/nargab/lqae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/21/2024] [Accepted: 03/18/2024] [Indexed: 04/09/2024] Open
Abstract
The prevalence of nucleic and peptide short sequences across organismal genomes and proteomes has not been thoroughly investigated. We examined 45 785 reference genomes and 21 871 reference proteomes, spanning archaea, bacteria, eukaryotes and viruses to calculate the rarity of short sequences in them. To capture this, we developed a metric of the rarity of each sequence in nature, the rarity index. We find that the frequency of certain dipeptides in rare oligopeptide sequences is hundreds of times lower than expected, which is not the case for any dinucleotides. We also generate predictive regression models that infer the rarity of nucleic and proteomic sequences across nature or within each domain of life and viruses separately. When examining each of the three domains of life and viruses separately, the R² performance of the model predicting rarity for 5-mer peptides from mono- and dipeptides ranged between 0.814 and 0.932. A separate model predicting rarity for 10-mer oligonucleotides from mono- and dinucleotides achieved R² performance between 0.408 and 0.606. Our results indicate that the mono- and dinucleotide composition of nucleic sequences and the mono- and dipeptide composition of peptide sequences can explain a significant proportion of the variance in their frequencies in nature.
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Affiliation(s)
- Nikol Chantzi
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Manvita Mareboina
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Maxwell A Konnaris
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
- Department of Statistics, Penn State University, University Park, PA, 16802, USA
- Huck Institutes of the Life Sciences, Penn State University, University Park, PA, 16802, USA
| | - Austin Montgomery
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Michail Patsakis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Ioannis Mouratidis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
- Huck Institutes of the Life Sciences, Penn State University, University Park, PA, 16802, USA
| | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
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Mouratidis I, Chantzi N, Khan U, Konnaris MA, Chan CSY, Mareboina M, Moeckel C, Georgakopoulos-Soares I. Frequentmers - a novel way to look at metagenomic next generation sequencing data and an application in detecting liver cirrhosis. BMC Genomics 2023; 24:768. [PMID: 38087204 PMCID: PMC10714505 DOI: 10.1186/s12864-023-09861-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/29/2023] [Indexed: 12/17/2023] Open
Abstract
Early detection of human disease is associated with improved clinical outcomes. However, many diseases are often detected at an advanced, symptomatic stage where patients are past efficacious treatment periods and can result in less favorable outcomes. Therefore, methods that can accurately detect human disease at a presymptomatic stage are urgently needed. Here, we introduce "frequentmers"; short sequences that are specific and recurrently observed in either patient or healthy control samples, but not in both. We showcase the utility of frequentmers for the detection of liver cirrhosis using metagenomic Next Generation Sequencing data from stool samples of patients and controls. We develop classification models for the detection of liver cirrhosis and achieve an AUC score of 0.91 using ten-fold cross-validation. A small subset of 200 frequentmers can achieve comparable results in detecting liver cirrhosis. Finally, we identify the microbial organisms in liver cirrhosis samples, which are associated with the most predictive frequentmer biomarkers.
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Affiliation(s)
- Ioannis Mouratidis
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA.
| | - Nikol Chantzi
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA
| | - Umair Khan
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA
| | - Maxwell A Konnaris
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA
- Department of Statistics, Penn State, University Park, PA, USA
- Huck Institutes of the Life Sciences, Penn State, University Park, PA, USA
| | - Candace S Y Chan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Manvita Mareboina
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA
| | - Camille Moeckel
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA
| | - Ilias Georgakopoulos-Soares
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA.
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Li X, Li H, Yang Z, Wu Y, Zhang M. Exploring objective feature sets in constructing the evolution relationship of animal genome sequences. BMC Genomics 2023; 24:634. [PMID: 37872534 PMCID: PMC10594854 DOI: 10.1186/s12864-023-09747-x] [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: 08/07/2023] [Accepted: 10/17/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Exploring evolution regularities of genome sequences and constructing more objective species evolution relationships at the genomic level are high-profile topics. Based on the evolution mechanism of genome sequences proposed in our previous research, we found that only the 8-mers containing CG or TA dinucleotides correlate directly with the evolution of genome sequences, and the relative frequency rather than the actual frequency of these 8-mers is more suitable to characterize the evolution of genome sequences. RESULT Therefore, two types of feature sets were obtained, they are the relative frequency sets of CG1 + CG2 8-mers and TA1 + TA2 8-mers. The evolution relationships of mammals and reptiles were constructed by the relative frequency set of CG1 + CG2 8-mers, and two types of evolution relationships of insects were constructed by the relative frequency sets of CG1 + CG2 8-mers and TA1 + TA2 8-mers respectively. Through comparison and analysis, we found that evolution relationships are consistent with the known conclusions. According to the evolution mechanism, we considered that the evolution relationship constructed by CG1 + CG2 8-mers reflects the evolution state of genome sequences in current time, and the evolution relationship constructed by TA1 + TA2 8-mers reflects the evolution state in the early stage. CONCLUSION Our study provides objective feature sets in constructing evolution relationships at the genomic level.
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Affiliation(s)
- Xiaolong Li
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
| | - Hong Li
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China.
| | - Zhenhua Yang
- School of Economics and Management, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Yuan Wu
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
| | - Mengchuan Zhang
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
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Raju RS, Al Nahid A, Chondrow Dev P, Islam R. VirusTaxo: Taxonomic classification of viruses from the genome sequence using k-mer enrichment. Genomics 2022; 114:110414. [PMID: 35718090 DOI: 10.1016/j.ygeno.2022.110414] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 04/27/2022] [Accepted: 06/13/2022] [Indexed: 11/04/2022]
Abstract
Classification of viruses into their taxonomic ranks (e.g., order, family, and genus) provides a framework to organize an abundant population of viruses. Next-generation metagenomic sequencing technologies lead to a rapid increase in generating sequencing data of viruses which require bioinformatics tools to analyze the taxonomy. Many metagenomic taxonomy classifiers have been developed to study microbiomes, but it is particularly challenging to assign the taxonomy of diverse virus sequences and there is a growing need for dedicated methods to be developed that are optimized to classify virus sequences into their taxa. For taxonomic classification of viruses from metagenomic sequences, we developed VirusTaxo using diverse (e.g., 402 DNA and 280 RNA) genera of viruses. VirusTaxo has an average accuracy of 93% at genus level prediction in DNA and RNA viruses. VirusTaxo outperformed existing taxonomic classifiers of viruses where it assigned taxonomy of a larger fraction of metagenomic contigs compared to other methods. Benchmarking of VirusTaxo on a collection of SARS-CoV-2 sequencing libraries and metavirome datasets suggests that VirusTaxo can characterize virus taxonomy from highly diverse contigs and provide a reliable decision on the taxonomy of viruses.
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Affiliation(s)
- Rajan Saha Raju
- Department of Computer Science and Engineering, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Abdullah Al Nahid
- Department of Biochemistry and Molecular Biology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Preonath Chondrow Dev
- Department of Biochemistry and Molecular Biology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Rashedul Islam
- Omics Lab, Dhaka, Bangladesh; Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V5Z 4S6, Canada.
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