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geno2pheno[ngs-freq]: a genotypic interpretation system for identifying viral drug resistance using next-generation sequencing data

Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). In contrast to Sanger sequencing, next-generation sequencing (NGS) can detect resistance mutations...

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Detalles Bibliográficos
Autores principales: Döring, Matthias, Büch, Joachim, Friedrich, Georg, Pironti, Alejandro, Kalaghatgi, Prabhav, Knops, Elena, Heger, Eva, Obermeier, Martin, Däumer, Martin, Thielen, Alexander, Kaiser, Rolf, Lengauer, Thomas, Pfeifer, Nico
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6031006/
https://www.ncbi.nlm.nih.gov/pubmed/29718426
http://dx.doi.org/10.1093/nar/gky349
Descripción
Sumario:Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). In contrast to Sanger sequencing, next-generation sequencing (NGS) can detect resistance mutations in minority populations. Thus, genotypic resistance testing based on NGS data can offer novel, treatment-relevant insights. Since existing web services for analyzing resistance in NGS samples are subject to long processing times and follow strictly rules-based approaches, we developed geno2pheno[ngs-freq], a web service for rapidly identifying drug resistance in HIV-1 and HCV samples. By relying on frequency files that provide the read counts of nucleotides or codons along a viral genome, the time-intensive step of processing raw NGS data is eliminated. Once a frequency file has been uploaded, consensus sequences are generated for a set of user-defined prevalence cutoffs, such that the constructed sequences contain only those nucleotides whose codon prevalence exceeds a given cutoff. After locally aligning the sequences to a set of references, resistance is predicted using the well-established approaches of geno2pheno[resistance] and geno2pheno[hcv]. geno2pheno[ngs-freq] can assist clinical decision making by enabling users to explore resistance in viral populations with different abundances and is freely available at http://ngs.geno2pheno.org.