Ram Krishna Shrestha

Background: Many high throughput sequencing (HTS) approaches, such as the Roche/454 platform, produce sequences in which the quality of the sequence (as measured by a Phred-like quality scores) decreases linearly across a sequence read. Undertaking quality trimming of this data is essential to enable confidence in the results of subsequent downstream analysis. Here, we have developed a novel, highly sensitive and accurate approach (QTrim) for the quality trimming of sequence reads generated using the Roche/454 sequencing platform (or any platform with long reads that outputs Phred-like quality scores). Results: The performance of QTrim was evaluated against all other available quality trimming approaches on both poor and high quality 454 sequence data. In all cases, QTrim appears to perform equally as well as the best other approach (PRINSEQ) with these two methods significantly outperforming all other methods. Further analysis of the trimmed data revealed that the novel trimming approach implemented in QTrim ensures that the prevalence of low quality bases in the resulting trimmed data is substantially lower than PRINSEQ or any of the other approaches tested. Conclusions: QTrim is a novel, highly sensitive and accurate algorithm for the quality trimming of Roche/454 sequence reads. It is implemented both as an executable program that can be integrated with standalone sequence analysis pipelines and as a web-based application to enable individuals with little or no bioinformatics experience to quality trim their sequence data.

Background: The role of HIV-1 RNA in the emergence of resistance to antiretroviral therapies (ARTs) is well documented while less is known about the role of historical viruses stored in the proviral DNA. The primary focus of this work was to characterize the genetic diversity and evolution of HIV drug resistant variants in an individual’s provirus during antiretroviral therapy using next generation sequencing. Methods: Blood samples were collected prior to antiretroviral therapy exposure and during the course of treatment from five patients in whom drug resistance mutations had previously been identified using consensus sequencing. The spectrum of viral variants present in the provirus at each sampling time-point were characterized using 454 pyrosequencing from multiple combined PCR products. The prevalence of viral variants containing drug resistant mutations (DRMs) was characterized at each time-point. Results: Low abundance drug resistant viruses were identified in 14 of 15 sampling time-points from the five patients. In all individuals DRMs against current therapy were identified at one or more of the sampling time-points. In two of the five individuals studied these DRMs were present prior to treatment exposure and were present at high prevalence within the amplified and sequenced viral population. DRMs to drugs other than those being currently used were identified in four of the five individuals. Conclusion: The presence of DRMs in the provirus, regardless of their observed prevalence did not appear to have an effect on clinical outcomes in the short term suggesting that the drug resistant viral variants present in the proviral DNA do not appear to play a role in the short term in facilitating the emergence of drug resistance.

Background: In human immunodeficiency virus type 1 (HIV-1) infection, transmitted viruses generally use the CCR5 chemokine receptor as a coreceptor for host cell entry. In more than 50% of subtype B infections, a switch in coreceptor tropism from CCR5- to CXCR4-use occurs during disease progression. Phenotypic or genotypic approaches can be used to test for the presence of CXCR4-using viral variants in an individual’s viral population that would result in resistance to treatment with CCR5-antagonists. While genotyping approaches for coreceptor-tropism prediction in subtype B are well established and verified, they are less so for subtype C.Methods: Here, using a dataset comprising V3 loop sequences from 349 CCR5-using and 56 CXCR4-using HIV-1 subtype C viruses we perform a comparative analysis of the predictive ability of 11 genotypic algorithms in their prediction of coreceptor tropism in subtype C. We calculate the sensitivity and specificity of each of the approaches as well as determining their overall accuracy. By separating the CXCR4-using viruses into CXCR4-exclusive (25 sequences) and dual-tropic (31 sequences) we evaluate the effect of the possible conflicting signal from dual-tropic viruses on the ability of a of the approaches to correctly predict coreceptor phenotype.Results: We determined that geno2pheno with a false positive rate of 5% is the best approach for predicting CXCR4-usage in subtype C sequences with an accuracy of 94% (89% sensitivity and 99% specificity). Contrary to what has been reported for subtype B, the optimal approaches for prediction of CXCR4-usage in sequence from viruses that use CXCR4 exclusively, also perform best at predicting CXCR4-use in dual-tropic viral variants.Conclusions: The accuracy of genotyping approaches at correctly predicting the coreceptor usage of V3 sequences from subtype C viruses is very high. We suggest that genotyping approaches can be used to test for coreceptor tropism in HIV-1 group M subtype C with a high degree of confidence that they will identify CXCR4-usage in both CXCR4-exclusive and dual tropic variants.