Inference of genetic relatedness between viral quasispecies from sequencing data
Abstract Background RNA viruses such as HCV and HIV mutate at extremely high rates, and as a result, they exist in infected hosts as populations of genetically related variants. Recent advances in sequencing technologies make possible to identify such populations at great depth. In particular, these...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-12-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12864-017-4274-5 |
| _version_ | 1818508735234965504 |
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| author | Olga Glebova Sergey Knyazev Andrew Melnyk Alexander Artyomenko Yury Khudyakov Alex Zelikovsky Pavel Skums |
| author_facet | Olga Glebova Sergey Knyazev Andrew Melnyk Alexander Artyomenko Yury Khudyakov Alex Zelikovsky Pavel Skums |
| author_sort | Olga Glebova |
| collection | DOAJ |
| description | Abstract Background RNA viruses such as HCV and HIV mutate at extremely high rates, and as a result, they exist in infected hosts as populations of genetically related variants. Recent advances in sequencing technologies make possible to identify such populations at great depth. In particular, these technologies provide new opportunities for inference of relatedness between viral samples, identification of transmission clusters and sources of infection, which are crucial tasks for viral outbreaks investigations. Results We present (i) an evolutionary simulation algorithm Viral Outbreak InferenCE (VOICE) inferring genetic relatedness, (ii) an algorithm MinDistB detecting possible transmission using minimal distances between intra-host viral populations and sizes of their relative borders, and (iii) a non-parametric recursive clustering algorithm Relatedness Depth (ReD) analyzing clusters’ structure to infer possible transmissions and their directions. All proposed algorithms were validated using real sequencing data from HCV outbreaks. Conclusions All algorithms are applicable to the analysis of outbreaks of highly heterogeneous RNA viruses. Our experimental validation shows that they can successfully identify genetic relatedness between viral populations, as well as infer transmission clusters and outbreak sources. |
| first_indexed | 2024-12-10T22:36:03Z |
| format | Article |
| id | doaj.art-5074477777af4cdb8a4abca9314772fe |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-10T22:36:03Z |
| publishDate | 2017-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-5074477777af4cdb8a4abca9314772fe2022-12-22T01:30:53ZengBMCBMC Genomics1471-21642017-12-0118S10818810.1186/s12864-017-4274-5Inference of genetic relatedness between viral quasispecies from sequencing dataOlga Glebova0Sergey Knyazev1Andrew Melnyk2Alexander Artyomenko3Yury Khudyakov4Alex Zelikovsky5Pavel Skums6Computer Science Department, Georgia State UniversityComputer Science Department, Georgia State UniversityComputer Science Department, Georgia State UniversityComputer Science Department, Georgia State UniversityCenters for Disease Control and PreventionComputer Science Department, Georgia State UniversityComputer Science Department, Georgia State UniversityAbstract Background RNA viruses such as HCV and HIV mutate at extremely high rates, and as a result, they exist in infected hosts as populations of genetically related variants. Recent advances in sequencing technologies make possible to identify such populations at great depth. In particular, these technologies provide new opportunities for inference of relatedness between viral samples, identification of transmission clusters and sources of infection, which are crucial tasks for viral outbreaks investigations. Results We present (i) an evolutionary simulation algorithm Viral Outbreak InferenCE (VOICE) inferring genetic relatedness, (ii) an algorithm MinDistB detecting possible transmission using minimal distances between intra-host viral populations and sizes of their relative borders, and (iii) a non-parametric recursive clustering algorithm Relatedness Depth (ReD) analyzing clusters’ structure to infer possible transmissions and their directions. All proposed algorithms were validated using real sequencing data from HCV outbreaks. Conclusions All algorithms are applicable to the analysis of outbreaks of highly heterogeneous RNA viruses. Our experimental validation shows that they can successfully identify genetic relatedness between viral populations, as well as infer transmission clusters and outbreak sources.http://link.springer.com/article/10.1186/s12864-017-4274-5Genetic relatednessTransmission networksOutbreaks investigationsSimulationClustering |
| spellingShingle | Olga Glebova Sergey Knyazev Andrew Melnyk Alexander Artyomenko Yury Khudyakov Alex Zelikovsky Pavel Skums Inference of genetic relatedness between viral quasispecies from sequencing data BMC Genomics Genetic relatedness Transmission networks Outbreaks investigations Simulation Clustering |
| title | Inference of genetic relatedness between viral quasispecies from sequencing data |
| title_full | Inference of genetic relatedness between viral quasispecies from sequencing data |
| title_fullStr | Inference of genetic relatedness between viral quasispecies from sequencing data |
| title_full_unstemmed | Inference of genetic relatedness between viral quasispecies from sequencing data |
| title_short | Inference of genetic relatedness between viral quasispecies from sequencing data |
| title_sort | inference of genetic relatedness between viral quasispecies from sequencing data |
| topic | Genetic relatedness Transmission networks Outbreaks investigations Simulation Clustering |
| url | http://link.springer.com/article/10.1186/s12864-017-4274-5 |
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