SIRA-HIV: A User-friendly System to Evaluate HIV-1 Drug

Resistance from Next-generation Sequencing Data

Letícia Martins Raposo

1,2 a

, Mônica Barcellos Arruda

, Rodrigo de Moraes Brindeiro

and Flavio Fonseca Nobre

Programa de Engenharia Biomédica, Universidade Federal do Rio de Janeiro,

Av. Horácio Macedo, 2030, Rio de Janeiro, Brazil

Departamento de Métodos Quantitativos, Universidade Federal do Estado do Rio de Janeiro,

Av. Pasteur, 458, Rio de Janeiro, Brazil

Departamento de Genética, Universidade Federal do Rio de Janeiro,

Rua Professor Rodolpho Paulo Rocco, Rio de Janeiro, Brazil

Keywords: HIV, Drug Resistance, Deep Sequencing, Sequence Analysis, User-Computer Interface, Software.

Abstract: Evaluating next-generation sequencing (NGS) data requires an extensive knowledge of bioinformatics and

programming commands, which could limit the studies in this area. We propose a user-friendly system to

analyse raw NGS data from HIV-1 patient samples to identify amino acid variants and the virus susceptibility

to antiretrovirals. SIRA-HIV was developed as an R Shiny web application. The software Segminator II was

applied to analyse viral data. Four genotypic interpretation systems were implemented in R language to

classify the HIV susceptibility: the French National Agency for AIDS Research (ANRS), the Stanford HIV

Drug Resistance Database (HIVdb), the Rega Institute (Rega) and the Brazilian Network for HIV-1

Genotyping (Brazilian Algorithm). SIRA-HIV was structured in two analysis components. The Drug

Resistance Positions module shows the resistance positions, their frequencies, and the coverage. In the

Genotypic Resistance Interpretation Algorithms module, the rule-based systems are available to interpret

HIV-1 drug resistance genotyping results. SIRA-HIV exhibited comparable results to Deep Gen HIV,

HyDRA, and PASeq. As advantage, the proposed application shows susceptibility levels from the most widely

used rule-based systems and works locally, allowing analysis not to rely on the internet. SIRA-HIV could be

a promising system to aid in HIV-1 patient data analysis.

1 INTRODUCTION

Human immunodeficiency virus type 1 (HIV-1) is a

viral agent responsible for one of the most impactful

pandemics in the world. Several antiretroviral drugs

are available to attempt to control HIV infection.

Despite the benefits of the therapy, the development

of drug resistance represents a significant obstacle to

the long-term effectiveness of antiretroviral therapy.

Resistance identification is a key issue for the

improved management of HIV-1 patients.

Most genotypic drug resistance testing is

established from expert-based rules using predefined

sets of known mutations. These interpretation

https://orcid.org/0000-0003-0613-5582

https://orcid.org/0000-0002-5311-5595

https://orcid.org/0000-0002-8675-4094

https://orcid.org/0000-0003-4261-8258

systems have been developed over the years to detect

resistance to antiretrovirals (ARVs). The most-used

rule-based algorithms are from the French National

Agency for AIDS Research (ANRS) (Meynard et al.,

2002), the Rega Institute (Rega) (Van Laethem et al.,

2002) and the Stanford HIV Reverse Transcriptase

and Protease Sequence Database (HIVdb) (Rhee et

al., 2003). In Brazil, the Brazilian Network for HIV-

1 Genotyping (http://50.116.24.135:8080/HIV/

resistencia.jsp) recommends the Brazilian algorithm

for the interpretation of mutations associated with

resistance to ARVs. These interpretation systems are

built on genotypic results from the Sanger sequencing

method, a traditional genotyping approach used in the

Raposo, L., Arruda, M., Brindeiro, R. and Nobre, F.

SIRA-HIV: A User-friendly System to Evaluate HIV-1 Drug Resistance from Next-generation Sequencing Data.

DOI: 10.5220/0008874700930100

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS, pages 93-100

ISBN: 978-989-758-398-8; ISSN: 2184-4305

detection of drug-resistance mutations (Gibson,

Schmotzer & Quiñones-Mateu, 2014). This assay

identifies HIV variants present over 15-20% of the

viral population, limiting its sensitivity to detect

minority variants (Erali, Page & Reimer, 2001;

Gibson et al., 2014; Palmer et al., 2005).

New techniques for sequencing DNA, such as

next-generation sequencing (NGS), have already

been explored in genotypic HIV resistance tests. They

produce a massive volume of sequences with a fast

processing time. Genotypic tests based on this

sequencing approach detect minority variants at

frequencies as low as 1% (Gibson et al., 2014; Wang

et al., 2007). These variants offer additional

information that may help to drive changes in ARV

regimens based on predicted future resistance profiles

that will benefit people living with HIV.

The analysis of NGS data to identify these HIV

variants often requires extensive knowledge of

computing and bioinformatics, such as programming

skills and the use of the UNIX-based operating

system. These requirements make the broad use of

NGS data interpretation difficult and restrict the range

of studies in this area. To overcome these limitations,

we present SIRA-HIV, a user-friendly system

developed in R (R Core Team, Vienna, Austria) to

process raw NGS reads generated from HIV-infected

patient samples. This tool provides a list of amino

acid mutations annotated with their frequencies and

the levels of susceptibility for each ARV from

different genotypic interpretation systems.

2 MATERIALS AND METHODS

The system works in three steps: (i) next-generation

sequence analysis, (ii) HIV-1 amino acid variant

identification and (iii) HIV-1 susceptibility

classification.

2.1 Next-generation Sequence Analysis

The raw NGS reads, from outputs in the FASTQ

format, are analysed using Segminator II, software

developed by Archer and colleagues (Archer et al.,

2012). This program is a variant calling algorithm that

analyses viral deep sequencing data from different

platforms, providing a precise mapping and

alignment of the reads against the reference sequence.

Segminator II was implemented in Java and has a

user-friendly interface, simplifying the analysis of

NGS data. Some studies have already employed this

software to analyse viral populations (Aoudjane et al.,

2014; Gibson et al., 2014; Macalalad et al., 2012;

Vrancken et al., 2016). In particular, Gibson et al.,

2014 used Segminator II in their study to assess HIV-

1 susceptibilities to ARVs and to predict HIV-1

coreceptor tropism. More information about variant

calling steps, see Archer et al., 2012.

2.2 HIV-1 Amino Acid Variant

Identification

The output file of Segminator II, called VEME Table,

is used to identify the amino acids present in the

structure of three enzymes: protease (PR), reverse

transcriptase (RT) and integrase (IN). This file has

information about coverage (the number of times a

genome has been sequenced) and the nucleotide

frequencies at each position. From these data, for

each one of the three enzymes, all possible codons are

assembled and translated providing all amino acid

variants and their frequencies for each position. As

minority variants may be present at similar

frequencies as sequencing artefacts, a threshold of 1%

was chosen to select the variants. This value has

already been used in previous studies (Mohamed et

al., 2014; Paredes et al., 2010; Vandenbroucke et al.,

2011).

The analysis to assemble and identify the HIV-1

amino acid variants from the VEME Table results

was developed in R language.

2.3 HIV-1 Susceptibility Classification

The classification rules from the genotypic resistance

interpretation systems including ANRS version 29

(http://www.hivfrenchresistance.org/archives.html),

HIVdb version 8.7 (https://hivdb.stanford.edu/

page/release-notes/#algorithm.updates), Rega

version 10.0.0 (https://rega.kuleuven.be/cev/avd/

software/rega-algorithm), and the Brazilian

Algorithm version 13 (http://50.116.24.135:8080/

HIV/resistencia.jsp) were implemented in the R

language and are used to classify HIV-1 susceptibility

to ARVs.

The interpretation systems that were incorporated

into SIRA-HIV provide predictions for 24 drugs: PIs

(atazanavir/r (ATV/r), darunavir/r (DRV/r),

fosamprenavir/r (FPV/r), indinavir/r (IDV/r),

lopinavir/r (LPV/r), nelfinavir (NFV), saquinavir/r

(SQV/r), and tipranavir/r (TPV/r)); NRTIs (abacavir

(ABC), zidovudine (AZT), stavudine (D4T),

didanosine (DDI), emtricitabine (FTC), lamivudine

(3TC) and tenofovir (TDF)), NNRTIs (doravirine

(DOR), efavirenz (EFV), etravirine (ETR),

nevirapine (NVP), and rilpivirine (RPV)); and INIs

(bictegravir (BIC), dolutegravir (DTG), elvitegravir

BIOINFORMATICS 2020 - 11th International Conference on Bioinformatics Models, Methods and Algorithms

(EVG), and raltegravir (RAL)).

2.4 Implementation

The program was implemented using R software

version 3.2.5 (R Development Core Team, 2013).

SIRA-HIV is based on the use of libraries seqinr

(Charif & Lobry, 2007), gtools (Warnes, Bolker &

Lumley, 2015), plotly (Sievert et al., 2017), DT (Xie,

2016), shinyBS (Bailey, 2015), and shiny (Chang et

al., 2017) to create a system requiring no

programming experience from the user. The output of

SIRA-HIV comprises a list of amino acid mutations,

with their respective frequencies for each sample and

the levels of drug resistance predicted by the rule-

based algorithms for each ARV.

2.5 Validation against Software

Pipelines

To confirm the results provided by SIRA-HIV, nine

HIV-1 genotype samples sequenced using the Ion

Torrent® PGM platform at the Molecular Virology

Laboratory of the Health Sciences Centre of the

Federal University of Rio de Janeiro (CCS - UFRJ /

Brazil) were used.

The mutations identified by SIRA-HIV were

compared to those defined by three already existing

software pipelines: DeepGen HIV (Gibson et al.,

2014), HyDRA (https://hydra.canada.ca/), and

PASeq (Noguera-Julian et al., 2017). The software

analysed the same samples, and only the mutations

with a frequency greater than or equal to 1% were

considered in the comparison.

3 RESULTS

This section describes the final graphical interface of

the system and the results of the comparison of SIRA-

HIV to the others software.

SIRA-HIV is structured in two modules that are

dependent on each other. The first one, called Drug

Resistance Positions, manages next-generation

sequence analysis and HIV-1 amino acid variant

identification. The second module, Genotypic

Resistance Interpretation Algorithms, is responsible

for the HIV-1 susceptibility classification from the

four rules-based interpretation systems: ANRS,

HIVdb, Rega and the Brazilian algorithm.

The median runtime required for analysing HIV-

1 sequence since the insertion of FASTA and FASTQ

files until SIRA-HIV shows the results is about 2

minutes.

3.1 Drug Resistance Positions

This module maps the reads generated by the NGS to

the HIV-1 reference genome, analyses the mapping

results, and identifies the amino acids present in the

drug resistance-associated positions.

To start the analysis, the user provides a name to

be assigned to the report files. In step 1, by pressing

the “Run” button, the user tells SIRA-HIV to open the

Segminator II. Before NGS data input, Segminator II

requires a project to be set up, which involves

entering a project name (using the “Add Project”

menu option) and providing a reference template in

FASTA format. To this version of SIRA-HIV, the

HIV-1 B HXB2 reference strain (Accession number:

K03455) is used as a template. After setting up a

project, NGS datasets in the FASTQ format are added

using the “Add Dataset” menu. After the dataset is

added, Segminator II automatically generates an

assembly by first mapping and then pairwise

alignment each read using the default parameters. The

user can also adjust alignment and mapping

parameters before the alignment. The results are

exported using the “Tools > VEME Table” menu. If

the user already has the VEME Table, step 1 of SIRA-

HIV can be skipped.

In step 2, the VEME Table file is loaded, and in

step 3, the region of the HIV-1 pol gene (PR, RT or

IN) is chosen. Each region is evaluated separately,

according to the option selected. SIRA-HIV displays

a main table with the drug resistance-associated

positions, accompanied by the wild-type HIV-1

amino acid (before the position) and the amino acid

identified in the sequences (after the position), the

frequency in percent for each amino acid and the

coverage. A coverage plot displaying the number of

times a genome has been sequenced can be displayed

on the screen using the “Coverage plot” button. Fig 1

shows the Drug Resistance Positions module.

After this first analysis, the user can download a

printable report. The program can export to three

different file formats: CSV (.csv), Excel (.xls), and

PDF (.pdf). The coverage plot can be saved in the

.png format.

The HIV-1 drug resistance-associated positions

displayed in the system are based on those from the

HIVdb list, found at https://hivdb.stanford.edu/

hivdb/by-mutations/, together with other positions

cited in the literature (Kantor et al., 2001; Rhee et al.,

2006).

SIRA-HIV: A User-friendly System to Evaluate HIV-1 Drug Resistance from Next-generation Sequencing Data

Figure 1: First module of SIRA-HIV. Users provide the

NGS sequences to Segminator II and select the region of

the pol gene to be analysed. The system provides the

information for the amino acids and their frequencies in

each drug resistance position. In this example, the protease

was chosen, and the “Coverage plot” button was selected.

Figure 2: Second module of SIRA-HIV. The genotypic

resistance interpretation algorithms depict the resistance

classifications. The 4 algorithms and the threshold ≥ 1%

were chosen in this example.

3.2 Genotypic Resistance

Interpretation Algorithms

This module classifies the HIV-1 susceptibility level

to ARVs by the rule-based systems ANRS, HIVdb,

Rega and the Brazilian Algorithm.

The user can select one or more systems to

classify the data in step 1 and can select two

thresholds (≥ 1% and ≥ 20%) in step 2. The first one

selects the amino acids from the drug resistance

positions with frequencies greater than or equal to

20%, and the second one chooses the amino acids

with frequencies greater than or equal to 1%. When

the user selects the first option (≥ 20%), minority

variants are not included in the set of mutations

allocated to the rule-based systems. When selecting

the second threshold (≥1%), minority variants

detected by NGS are included in the analysis (Fig 2).

We chose to look at percentage cut-offs 20% and 1%

because the upper end (20%) reflects what can be

detected using Sanger-based platforms, while the

lower end (1%) reflects what is possible using NGS

platforms.

SIRA-HIV shows the classifications of the

selected rule-based systems for the ARVs that act on

the proteins chosen in the module Drug Resistance

Positions. The user can also download a printable

report in this module. The program can export to three

different file formats: CSV (.csv), Excel (.xls), and

PDF (.pdf).

3.3 Validation

In order to evaluate the mutations identified by SIRA-

HIV, three other available software were used:

DeepGen HIV, HyDRA, and PASeq. Since the lists

of mutations that could be identified varied among the

software, only those common to the four pipelines

were used in the comparison.

Fig 3 shows the number of mutations found by

SIRA-HIV and the other three pipelines, according to

the pol gene regions analysed. Mutations with a

frequency between 1% and 20% were classified as

minority, and those with a frequency above 20% were

classified as a majority mutation. Regarding majority

mutations, similar values were observed for all

software, with the exception of PASeq in the PR

region, which presented a smaller number of

mutations. In relation to minority mutations, SIRA-

HIV and DeepGen HIV had a higher number of

observations, with closer results, while HyDRA and

PASeq identified a smaller number.

Figure 3: Number of minority and majority mutations found

in protease (PR), reverse transcriptase (RT) and integrase

(IN). Nine HIV-1 sequences were analysed by SIRA-HIV,

DeepGen HIV, HyDRA and PASeq.

PR RT IN

SIRA-HIVDeepGen HyDRA PASeq SIRA-HIVDeepGen HyDRA PASeq SIRA-HIVDeepGen HyDRA PASeq

Software

Number of Mutations

Mutation

Major Minor

BIOINFORMATICS 2020 - 11th International Conference on Bioinformatics Models, Methods and Algorithms

When comparing the concordant mutations

between SIRA-HIV and each of the other three

software, it can be observed in Fig 4 that the mutation

frequency measurements determined by SIRA-HIV

showed a high agreement with the frequency reported

by the other pipelines.

Additionally, the quality of agreement was

evaluated according to the minority or majority

mutation classification. Fig 4 shows that SIRA-HIV

disagreed with the other three software in five points.

They represent only two mutations found in the RT

region. While SIRA-HIV reported a frequency below

20% for the Y181C mutation, the other three software

found values above 20%. For the G190A mutation,

SIRA-HIV, as well as DeepGen HIV, had a frequency

above 20%, while HyDRA and PASeq presented

frequencies below 20%.

Figure 4: Agreement between SIRA-HIV and the three

others software in the analysis of nine HIV-1 patient

samples. The linearity in mutation frequency measurements

shows a great concordance between the evaluated software

and the others systems. SIRA-HIV disagreed with only for

two mutations, Y181C and G190A, found in reverse

transcriptase sequence. These frequency discrepancies are

marked in grey on the graph.

4 DISCUSSION

This work developed a user-friendly system called

SIRA-HIV, implemented in R language, in which

users unfamiliar with command lines and other

programming skills can analyse NGS data. The

system identifies mutations present in the HIV-1

genome and categorizes the virus susceptibility level

to each ARV by using two thresholds (≥ 1% and ≥

20%). The first range includes the minor and major

population of resistance mutations, whereas the

second range comprises only the major resistance

mutation population.

To validate SIRA-HIV, three others next-

generation sequencing analysis pipelines were

selected: DeepGen HIV, HyDRA, and PASeq.

Although DeepGen HIV is not publicly available, it

was used in the validation step since it also uses

Segminator II as a mapping algorithm. Segminator II

was chosen to perform sequence mapping and

alignment due to its wide usage in other HIV studies

(Aoudjane et al., 2014; Gibson, Meyer, et al., 2014;

Vrancken et al., 2016), its specificity in

characterizing viral data and its easy-to-use graphical

interface.

In general, it was observed that SIRA-HIV and

DeepGen HIV showed the highest agreement in the

identification of mutations in the nine HIV + patients

samples. This can be explained due to the use of

Segminator II as the sequence mapping software and

the use of a reference sequence with the same length

(position 1807 to 5096 relative to HXB2 isolate

genome). One of the possible explanations for the

differences found between these systems may be

related to the mapping parameters of the Segminator

II. In the present study, the default values of the

program were used, except for the “Replace Template

with the Con option”. DeepGen HIV also uses this

option; however, we were unable to obtain

information about the other parameters used by this

pipeline. Variations in the values can cause changes

in the mapping and, consequently, can generate

different results between analyses.

Another possible source of mismatch among

identified mutations may be related to the reference

sequence used in the mapping. In DeepGen HIV, the

reference sequence is chosen from the Los Alamos

HIV Sequence Database. The most similar sequence

to 100 readings randomly selected from the NGS

dataset is used as a reference. In this study, the HXB2

reference sequence, corresponding to the wild-type

genome of the HIV-1 subtype B virus, was used.

In relation to HyDRA and PASeq, both software

identified a smaller number of mutations, mainly the

minority variants in the PR and RT region. These

regions have a greater number of positions, which

may explain this increase of disagreement in the

number of mutations identified. In addition, some

analysed sequences presented lower coverage for the

RT and IN region, which may have influenced the

identification of minority mutations by these two

software. PASeq also identified a smaller number of

majority mutations for the PR region.

In relation to the graphical interface, SIRA-HIV

was structured in two main analysis components. In

the Drug Resistance Positions module, the results are

shown in table form, containing the resistance

SIRA-HIV: A User-friendly System to Evaluate HIV-1 Drug Resistance from Next-generation Sequencing Data

positions, their original and sampled amino acids,

their frequencies, and the coverage. A coverage graph

per position was also included to ease the

visualization of the values.

It is important to know this variable since a

minimum coverage of approximately 450 nucleotides

in nonhomopolymeric regions (without nucleotide

repeats) is suggested to ensure the detection of

minority variants present in over 1% of the population

(Wang et al., 2007). In the Genotypic Resistance

Interpretation Algorithms module, the international

algorithms ANRS, HIVdb, and Rega and the national

Brazilian algorithm were included in the SIRA-HIV

to provide different classification options to users.

Most pipelines, even being user-friendly, do not show

the level of HIV-1 drug resistance or only show the

predictions according to HIVdb, as it can be observed

in DeepGen HIV and PASeq. SIRA-HIV is more

complete in this respect. Accessing the most widely

used rule-based systems (ANRS, HIVdb, and Rega),

the user can check if the systems are discordant in

their classifications or if there is a consensus between

them.

As well as Hydra and PASeq, SIRA-HIV has the

advantage of not requiring computer-programming

skills, which are often necessary for bioinformatics.

Users only need the NGS sequences and the reference

template to start the analyses. Several health analytics

tools have been developed as user-friendly systems to

facilitate data analysis that often requires

programming skills, including the shiny R package

(Moraga, 2017; Tarvainen et al., 2014).

Another positive aspect is the capability of the

system to consider two threshold variant levels. When

all mutations greater than or equal to 1% are

considered, the user can infer the possible impact of

drug-resistant minority variants over future ARV

regimen success. Nevertheless, there is still much

debate about their clinical relevance. Drug-resistant

minority variants are not yet fully considered in

decision making on the best therapeutic regimen (Li

& Kuritzkes, 2013). However, it is expected that

some of these minority mutations may be selected,

increasing their frequencies in the population and

leading to future therapeutic failure. Therefore, this

information can assist the physician in the decision-

making about the best treatment regimen to be

adopted for each HIV-1 patient.

In future versions, we intend to add classifiers

designed to predict HIV-1 coreceptor tropism as well

as to add ensemble models based on genotypic

interpretation systems to provide a single HIV-1

resistance profile, since these algorithms use different

rules to predict drug susceptibility, resulting in

possible differences between these methods (Eberle

& Gürtler, 2012; Kijak et al., 2003; Snoeck et al.,

2006; Vergne et al., 2006).

In conclusion, the user-friendly interface

presented in this work could be a promising system to

aid in the data analysis of HIV+ patient data.

Physicians and laboratories can access HIV genome

information that can help better understand the drug

resistance problem and can provide the appropriate

and personalized treatment for each patient. In

addition, working with NGS data, SIRA-HIV

includes additional information not found in Sanger

sequencing, promoting the detection of minority

populations of resistant viruses and improving drug

resistance interpretations. SIRA-HIV is available on

https://github.com/leticiaraposo/sira-hiv and works

locally allowing analysis not to rely on the internet,

another advantage compared to the systems

mentioned here.

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