title: SHIVA - a web application for drug resistance and tropism testing in HIV.
abstract: BACKGROUND
  Drug resistance testing is mandatory in antiretroviral therapy in human immunodeficiency virus (HIV) infected patients for successful treatment. The emergence of resistances against antiretroviral agents remains the major obstacle in inhibition of viral replication and thus to control infection. Due to the high mutation rate the virus is able to adapt rapidly under drug pressure leading to the evolution of resistant variants and finally to therapy failure.
  
  
  RESULTS
  We developed a web service for drug resistance prediction of commonly used drugs in antiretroviral therapy, i.e., protease inhibitors (PIs), reverse transcriptase inhibitors (NRTIs and NNRTIs), and integrase inhibitors (INIs), but also for the novel drug class of maturation inhibitors. Furthermore, co-receptor tropism (CCR5 or CXCR4) can be predicted as well, which is essential for treatment with entry inhibitors, such as Maraviroc. Currently, SHIVA provides 24 prediction models for several drug classes. SHIVA can be used with single RNA/DNA or amino acid sequences, but also with large amounts of next-generation sequencing data and allows prediction of a user specified selection of drugs simultaneously. Prediction results are provided as clinical reports which are sent via email to the user.
  
  
  CONCLUSIONS
  SHIVA represents a novel high performing alternative for hitherto developed drug resistance testing approaches able to process data derived from next-generation sequencing technologies. SHIVA is publicly available via a user-friendly web interface.
authors.affiliation: Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315, Germany.|University of Applied Science Weihenstephan-Triesdorf, Am Hofgarten 4, Freising, 85354, Germany.
authors.forename: Mona
authors.initials: M
authors.lastname: Riemenschneider
authors.affiliation: Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315, Germany.|University of Applied Science Weihenstephan-Triesdorf, Am Hofgarten 4, Freising, 85354, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Hummel
authors.affiliation: Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315, Germany. d.heider@wz-straubing.de.|University of Applied Science Weihenstephan-Triesdorf, Am Hofgarten 4, Freising, 85354, Germany. d.heider@wz-straubing.de.|Wissenschaftszentrum Weihenstephan, Technische Universität München, Alte Akademie 8, Freising, 85354, Germany. d.heider@wz-straubing.de.
authors.forename: Dominik
authors.initials: D
authors.lastname: Heider
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology*; D024882:Drug Resistance, Viral; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics / Q000502:physiology; D006801:Humans; D020407:Internet; D019718:Receptors, CXCR4 / Q000235:genetics / Q000378:metabolism; D018894:Reverse Transcriptase Inhibitors / Q000494:pharmacology; D012984:Software; D056189:Viral Tropism; D014779:Virus Replication / Q000187:drug effects
chemical_list: D019380:Anti-HIV Agents; C527276:CXCR4 protein, human; D019718:Receptors, CXCR4; D018894:Reverse Transcriptase Inhibitors
keywords: HIV therapy; Infectious diseases; Machine learning; Retrovirus