Among 147 patients on NNRTI and NRTIbased 1st line ART regimen, 116 had mutations belonging to these 2 categories of drugs. M184V was the most common NRTI mutation detected in 132 (64.08%) patients. 76 (36.89%) of the patients harbored at least one Thymidine Analog Mutations (TAMs). The distribution of TAMs in the clinical panel samples were M41L-40 (19.42%), D67N-37 (17.96%), K70R-31 (15.05%), L210W-12 (5.83%), T215F/Y-60 (29.12%) and K219E- 8 (3.88%) respectively. Three or more TAMs were detected in 32 (15.53%)samples while Q151M complex (Q151M, V75I, F77L and F116Y) was observed only in 2 (0.97%) patients. The details of other NRTI related mutations detected in this study are described in Figure 3A. K103N was the most common NNRTI mutation 154447-36-6 present in 72 (34.95%) patients. The prevalence of other common NNRTI mutations in the study population was K101E/H-24 (11.65%), V106M-20 (9.71%), Y181C/I-49 (23.79%) and G190A/S-39 (18.93%) respectively. The details of other NNRTI-related mutations are described in Figure 3B. At least one PI major or minor resistance mutations were detected in 23 and 38 patients respectively among the 59 successfully genotyped, who were exposed to ritonavir-boosted PI based 1st line of antiretroviral therapy. Out of this, 20 samples harbored both PI major and minor mutations while 18 and 3 had only PI minor and only PI major mutations respectively. Eighteen samples did not have any PI-related mutations. The most common major PI mutations detected were M46I/L-14 (6.80%), I54A/T/ V-16 (7.77%), V82A/C/F-16 (7.77%) and L90M-8 (3.88%) respectively. L10F/I/V-29 (14.08%) and A71T/V-14 (6.80%) were the two common PI minor mutations observed in the clinical panel. The details of other PI-related mutations are described in Figure 3C.Table 6 describes the hands- on time and cost of different stages of the analysis starting from 315706-13-9 distributor collection of clinical sample to interpretation of drug resistance mutations. All costs are presented in US dollars. Cost of establishing the reference laboratory capable of performing the assay is however not included in the analysis. Further, some major costs common to both the assays such as logistics and manpower were also excluded. The hands-on times for the ViroSeq genotyping system and the in-house method were 18 hour 45 min and 17 hour 15 min respectively while the running cost of the in-house assay was computed at 85 compared to 303 for the ViroSeq genotyping system.The HIV-1 drug resistance genotyping assay is not feasible for routine monitoring of patients taking 1st line antiretroviral drugs in resource limited settings like India mainly due to high cost of commercial HIV-1 genotyping assays presently available in the market . But increased access to antiretroviral drugs without proper monitoring results in transmission of drug resistant HIV-1 strains in newly infected individuals . Laboratory methods to monitor the treatment outcome and proper guidelines regarding course of action in case of therapeutic failure is critical in management of HIV-1/AIDS. HIV-1 drug resistance genotyping assay for patients with virologic failure acts as a guiding tool during switching to next line of treatment . We performed a cost analysis of the drug resistance genotyping assay described in this study and compared it with the running cost of ViroSeq genotyping system which indicated that our assay is around 71.9% cost effective compared to the later. This attribute make Figure 2. Phylogenetic tree of clinical panel samples. Phylogenetic analysis of sequences obtained from clinical panel samples. The construction of phylogenetic tree is described in the text. All HIV-1 subtype reference sequences used to construct the tree were obtained from Los Alamos National Laboratory HIV sequence database (http://www.hiv.lanl.gov/content/index). The reference sequence IDs shown in the tree are in the following sequence: subtype.country of origin.isolate number.accession number. doi:10.1371/journal.pone.0105790.g002 this assay more suitable for routine monitoring of transmitted HIV-1 drug resistance strains as well as for detection of drug resistance mutations in patients with virologic failure. The drug resistance mutations detected by our in-house assay exhibited excellent concordance when compared with corresponding results from the ViroSeq genotyping system. The assay was able to detect all clinically relevant mutations according to the IAS 2013 mutation list . These findings demonstrate both utility and feasibility of this home brew assay in HIV-1 drug resistance surveillance and monitoring in resource limited settings like India. None of the HIV-1 drug resistance genotyping assays including the US-FDA approved commercial assays as well as various home brew assays can successfully amplify 100% clinical samples mainly due to high genetic variability of HIV-1  and occurrence of spontaneous mutation within primer binding regions of the viral genome . In this backdrop, the home brew assay described in this study could successfully genotype 91% of samples from the clinical panel which was found to be satisfactory. This high rate of success is possibly due to the geographical region-specific primers designed for this assay coupled with incorporation of a nested PCR protocol.