Inference of genetic relatedness between viral quasispecies from sequencing data
| العنوان: | Inference of genetic relatedness between viral quasispecies from sequencing data |
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| المؤلفون: | Andrew Melnyk, Olga Glebova, Alexander Zelikovsky, Pavel Skums, Alexander Artyomenko, Sergey Knyazev, Yury Khudyakov |
| المصدر: | BMC Genomics, Vol 18, Iss S10, Pp 81-88 (2017) BMC Genomics |
| بيانات النشر: | BMC, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 0301 basic medicine, Transmission networks, lcsh:QH426-470, lcsh:Biotechnology, 030106 microbiology, Inference, Genome, Viral, Hepacivirus, Computational biology, Viral quasispecies, Biology, Clustering, law.invention, 03 medical and health sciences, Genetic relatedness, Phylogenetics, law, lcsh:TP248.13-248.65, Outbreaks investigations, Genetics, Cluster Analysis, Cluster analysis, Phylogeny, Sequence Analysis, RNA, Research, Computational Biology, Outbreak, Quasispecies, lcsh:Genetics, 030104 developmental biology, Transmission (mechanics), RNA, Viral, Identification (biology), DNA microarray, Algorithms, Simulation, Biotechnology |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 1471-2164 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::055fbb62b885d24ea57b21abaec3ec78 http://link.springer.com/article/10.1186/s12864-017-4274-5 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....055fbb62b885d24ea57b21abaec3ec78 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 14712164 |
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