PhyloPi: An affordable, purpose built phylogenetic pipeline for the HIV drug resistance testing facility

Phillip Armand Bester; Andrie De Vries; Stephanus Riekert; Kim Steegen; Gert van Zyl; Dominique Goedhals

doi:10.1371/journal.pone.0213241

PhyloPi: An affordable, purpose built phylogenetic pipeline for the HIV drug resistance testing facility

PLoS One. 2019 Mar 5;14(3):e0213241. doi: 10.1371/journal.pone.0213241. eCollection 2019.

Authors

Phillip Armand Bester¹, Andrie De Vries², Stephanus Riekert³, Kim Steegen⁴, Gert van Zyl⁵, Dominique Goedhals¹

Affiliations

¹ Division of Virology, University of the Free State and National Health Laboratory Service (NHLS), Bloemfontein, South Africa.
² RStudio Team (2016). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA, United States of America.
³ Information and Communication Technology Services, University of the Free State, Bloemfontein, South Africa.
⁴ Department of Molecular Medicine and Haematology, University of the Witwatersrand, Johannesburg, South Africa.
⁵ Division of Medical Virology, Stellenbosch University and National Health Laboratory Service (NHLS) Tygerberg, Cape Town, South Africa.

Abstract

Introduction: Phylogenetic analysis plays a crucial role in quality control in the HIV drug resistance testing laboratory. If previous patient sequence data is available sample swaps can be detected and investigated. As Antiretroviral treatment coverage is increasing in many developing countries, so is the need for HIV drug resistance testing. In countries with multiple languages, transcription errors are easily made with patient identifiers. Here a self-contained blastn integrated phylogenetic pipeline can be especially useful. Even though our pipeline can run on any unix based system, a Raspberry Pi 3 is used here as a very affordable and integrated solution.

Performance benchmarks: The computational capability of this single board computer is demonstrated as well as the utility thereof in the HIV drug resistance laboratory. Benchmarking analysis against a large public database shows excellent time performance with minimal user intervention. This pipeline also contains utilities to find previous sequences as well as phylogenetic analysis and a graphical sequence mapping utility against the pol area of the HIV HXB2 reference genome. Sequence data from the Los Alamos HIV database was analyzed for inter- and intra-patient diversity and logistic regression was conducted on the calculated genetic distances. These findings show that allowable clustering and genetic distance between viral sequences from different patients is very dependent on subtype as well as the area of the viral genome being analyzed.

Availability: The Raspberry Pi image for PhyloPi, source code of the pipeline, sequence data, bash-, python- and R-scripts for the logistic regression, benchmarking as well as helper scripts are available at http://scholar.ufs.ac.za:8080/xmlui/handle/11660/7638 and https://github.com/ArmandBester/phylopi. The PhyloPi image and the source code are published under the GPLv3 license. A demo version of the PhyloPi pipeline is available at http://phylopi.hpc.ufs.ac.za/.

MeSH terms

Anti-HIV Agents / pharmacology*
Computational Biology
Databases, Factual
Drug Resistance, Viral*
HIV / drug effects*
HIV / genetics
HIV Infections / drug therapy*
HIV Infections / genetics
HIV Infections / virology
Humans
Phylogeny*
Software*

Substances

Anti-HIV Agents

Grants and funding

The authors received no specific funding for this work. Andrie De Vries is employed by RStudio, Inc. RStudio, Inc., provided support in the form of salary for author A De Vries, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of this author is articulated in the ‘author contributions’ section.