Cluster Analysis of Coronavirus Sequences using Computational Sequence Descriptors : With Applications to SARS, MERS and SARS-CoV-2 (CoVID-19)
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
INTRODUCTION: Coronaviruses comprise a group of enveloped, positive-sense single-stranded RNA viruses that infect humans as well as a wide range of animals. The study was performed on a set of 573 sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) viruses. The sequences were represented with alignment-free sequence descriptors and analyzed with different chemometric methods: Euclidean/Mahalanobis distances, principal component analysis and self-organizing maps (Kohonen networks). We report the cluster structures of the data. The sequences are well-clustered regarding the type of virus; however, some of them show the tendency to belong to more than one virus type.
BACKGROUND: This is a study of 573 genome sequences belonging to SARS, MERS and SARS-- CoV-2 (CoVID-19) coronaviruses.
OBJECTIVES: The aim was to compare the virus sequences, which originate from different places around the world.
METHODS: The study used alignment free sequence descriptors for the representation of sequences and chemometric methods for analyzing clusters.
RESULTS: Majority of genome sequences are clustered with respect to the virus type, but some of them are outliers.
CONCLUSION: We indicate 71 sequences, which tend to belong to more than one cluster.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
Current computer-aided drug design - 17(2021), 7 vom: 12., Seite 936-945 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Vračko, Marjan [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 17.01.2022 Date Revised 17.01.2022 published: Print Citation Status MEDLINE |
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| doi: |
10.2174/1573409917666210202092646 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM320918203 |
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| 500 | |a Date Revised 17.01.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
| 520 | |a INTRODUCTION: Coronaviruses comprise a group of enveloped, positive-sense single-stranded RNA viruses that infect humans as well as a wide range of animals. The study was performed on a set of 573 sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) viruses. The sequences were represented with alignment-free sequence descriptors and analyzed with different chemometric methods: Euclidean/Mahalanobis distances, principal component analysis and self-organizing maps (Kohonen networks). We report the cluster structures of the data. The sequences are well-clustered regarding the type of virus; however, some of them show the tendency to belong to more than one virus type | ||
| 520 | |a BACKGROUND: This is a study of 573 genome sequences belonging to SARS, MERS and SARS-- CoV-2 (CoVID-19) coronaviruses | ||
| 520 | |a OBJECTIVES: The aim was to compare the virus sequences, which originate from different places around the world | ||
| 520 | |a METHODS: The study used alignment free sequence descriptors for the representation of sequences and chemometric methods for analyzing clusters | ||
| 520 | |a RESULTS: Majority of genome sequences are clustered with respect to the virus type, but some of them are outliers | ||
| 520 | |a CONCLUSION: We indicate 71 sequences, which tend to belong to more than one cluster | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Euclidean distance | |
| 650 | 4 | |a MERS | |
| 650 | 4 | |a Mahalanobis distance | |
| 650 | 4 | |a SARS | |
| 650 | 4 | |a SARS-CoV-2 (CoVID-19) | |
| 650 | 4 | |a alignment-free sequenc descriptors. | |
| 650 | 4 | |a clustering | |
| 650 | 4 | |a mathematical representation of sequences | |
| 650 | 4 | |a principal component analysis | |
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| 700 | 1 | |a Dey, Tathagata |e verfasserin |4 aut | |
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