Wrote the paper: MR, DH. inhibitors. Furthermore, co-receptor tropism (CCR5 or CXCR4) can be L-690330 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. for the selected specificity of 95.0 and geno2pheno are only able to predict up to 8 and 50 sequences, respectively, while HIVdb and WebPSSM are restricted to 500 sequences. For co-receptor prediction based on NGS data generated with 454 pyrosequencing, geno2pheno454 can be used as well, however the preprocessing of the data needs to be done offline. There are also differences in run times for the prediction of 8 protease and 50 V3 sequences, respectively. It turned out that HIVdb is the fastest tool, followed by SHIVA with 2.89 and 6.02 seconds for the prediction of 8 protease sequences, respectively. In contrast, geno2pheno needs 24.37 seconds. For the prediction of co-receptor tropism, SHIVA is slower than geno2pheno and WebPSSM, which is mainly L-690330 due to the Rabbit Polyclonal to OPN5 internal 3D-modeling process in TCUP 2.0 [16]. Except WebPSSM, all other servers provide a clinical report that can be used by the clinicans, however, the HIVdb report is not very intuitively and thus only of limited use. One major drawback of geno2pheno compared to the other servers is the lack of detailed data access, which is in particular important for large amounts of data. Conclusion SHIVA represents a novel high performing alternative for hitherto developed drug resistance testing approaches. SHIVA allows the processing of large amounts of data derived from high-throughput technologies [18]. Moreover, SHIVA is platform independent, easy to use and publicly available. In future, additional prediction models that are based on multi-label classification techniques and structural descriptors will be incorporated. Recent studies have demonstrated that such approaches have great potential to further improve drug resistance predictions [19, 20]. Moreover, we will incorporate GPU-based implementations [21] of our models in the near future to speed up the prediction of large data sets. Availability and requirements Project name: SHIVA Project home page: http://shiva.heiderlab.de Operating system(s): Platform independent Programming language: Java, R Other requirements: Javascript License: GNU LGPL Any restrictions to use by non-academics: no licence needed Funding This work was supported by the German Research Foundation (DFG) and the Technische Universit?t Mnchen within the funding programme Open Access Publishing. Authors contributions Conceived and designed the experiments: MR, TH, DH. Performed the experiments: MR, TH. Interpreted results: MR, TH, DH. Wrote the paper: MR, DH. All authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. Consent for publication Not applicable. Ethics approval and consent to participate Not applicable. Abbreviations ARTAntiretroviral therapyBVMBevirimatDNADesoxyribonucleic acidGPUGraphics processor unitHIVHuman immunodeficiency virusIDidentifierINIIntegrase inhibitorNGSNext-generation sequencingNRTINucleotide reverse transcriptase inhibitorNNRTINon-nucleoside reverse transcriptase inhibitorPIProtease inhibitorRNARibonucleic acidRTVRitonavir Contributor Information Mona Riemenschneider, Email: ed.gnibuarts-zw@redienhcsnemeir.m. Thomas Hummel, Email: ed.twsh.tneduts@lemmuh.samoht. Dominik Heider, Email: ed.gnibuarts-zw@redieh.d..There are also differences in run times for the prediction of 8 protease and 50 V3 sequences, respectively. 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. for the selected specificity of 95.0 and geno2pheno are only able to predict up to 8 and 50 sequences, respectively, while HIVdb and WebPSSM are restricted to 500 sequences. For co-receptor prediction based on NGS data generated with 454 pyrosequencing, geno2pheno454 can be used as well, however the preprocessing of the data needs to be done offline. There are also differences in run times for the prediction of 8 protease and 50 V3 sequences, respectively. It turned out that HIVdb is the fastest tool, followed by SHIVA with 2.89 and 6.02 seconds for the prediction of 8 protease sequences, respectively. In contrast, geno2pheno needs 24.37 seconds. For the prediction of co-receptor tropism, SHIVA is slower than geno2pheno and WebPSSM, which is mainly due to the internal 3D-modeling process in TCUP 2.0 [16]. Except WebPSSM, all other servers provide a clinical report that can be used by the clinicans, however, the HIVdb report is not very intuitively and thus only of limited use. One major drawback of geno2pheno compared to the other servers is the lack of detailed data access, which is in particular important for large amounts of data. Conclusion SHIVA represents a novel high performing alternative for hitherto developed drug resistance testing approaches. SHIVA allows the processing of large amounts of data derived from high-throughput technologies [18]. Moreover, SHIVA is platform independent, easy to use and publicly available. L-690330 In future, additional prediction models that are based on multi-label classification techniques and structural descriptors will be incorporated. Recent studies have demonstrated that such approaches have great potential L-690330 to further improve drug resistance predictions [19, 20]. Moreover, we will incorporate GPU-based implementations [21] of our models in the near future to speed up the prediction of large data sets. Availability and requirements Project name: SHIVA Project home page: http://shiva.heiderlab.de Operating system(s): Platform independent Programming language: Java, R Other requirements: Javascript License: GNU LGPL Any restrictions to use L-690330 by non-academics: no licence needed Funding This work was supported by the German Research Foundation (DFG) and the Technische Universit?t Mnchen within the funding programme Open Access Publishing. Authors contributions Conceived and designed the experiments: MR, TH, DH. Performed the experiments: MR, TH. Interpreted results: MR, TH, DH. Wrote the paper: MR, DH. All authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. Consent for publication Not applicable. Ethics approval and consent to participate Not applicable. Abbreviations ARTAntiretroviral therapyBVMBevirimatDNADesoxyribonucleic acidGPUGraphics processor unitHIVHuman immunodeficiency virusIDidentifierINIIntegrase inhibitorNGSNext-generation sequencingNRTINucleotide reverse transcriptase inhibitorNNRTINon-nucleoside reverse transcriptase inhibitorPIProtease inhibitorRNARibonucleic acidRTVRitonavir Contributor Information Mona Riemenschneider, Email: ed.gnibuarts-zw@redienhcsnemeir.m. Thomas Hummel, Email: ed.twsh.tneduts@lemmuh.samoht. Dominik Heider, Email: ed.gnibuarts-zw@redieh.d..