Geno2pheno[ngs-freq]: a genotypic interpretation system for identifying viral drug resistance using next-generation sequencing data. Issue Volume 46:Issue W1(2018) (1st May 2018)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Nucleic acids research. Volume 46:Issue W1(2018)
• Page Start: W271
• Page End: W277
• Publication Date: 2018-05-01
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gky349
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: 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 .