Quasispecies Analyses of the HIV-1 Near-full-length Genome With Illumina MiSeq

Ode, Hirotaka; Matsuda, Masakazu; Matsuoka, Katsunari; Hachiya, Atsuko; Hattori, Junko; Kito, Yumiko; Yokomaku, Yoshiyuki; Iwatani, Yoshinori; Sugiura, Wataru

doi:10.3389/fmicb.2015.01258

Cited by 51 publications

(56 citation statements)

References 61 publications

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“…Subtypes A, C, CRF01_AE, and CRF02_AG were the most common non-B subtypes, accounting for 224 (26.2%) of sequences. Plasma HIV-1 RNA levels were available for all sequenced samples in three studies [21, 23, 24].…”

Section: Resultsmentioning

confidence: 99%

Analysis of unusual and signature APOBEC-mutations in HIV-1polnext-generation sequences

Tzou

Pond

Ávila‐Ríos

et al. 2019

Preprint

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IntroductionAt low mutation-detection thresholds, next generation sequencing (NGS) for HIV-1 genotypic resistance testing is susceptible to artifactual detection of mutations arising from PCR error and APOBEC-mediated G-to-A hypermutation.MethodsWe analyzed published HIV-1 pol Illumina NGS data to characterize the distribution of mutations at eight NGS thresholds: 20%, 10%, 5%, 2%, 1%, 0.5%, 0.2%, and 0.1%. At each threshold, we determined the proportion of amino acid mutations that were unusual (defined as having a prevalence <0.01% in HIV-1 group M sequences) or were signature APOBEC mutations.ResultsEight studies, containing 855 samples, in the NCBI Sequence Read Archive were analyzed. As detection thresholds were lowered, there was a progressive increase in the proportion of positions with both usual and unusual mutations and in the proportion of all mutations that were unusual. The median proportion of positions with an unusual mutation increased gradually from 0% at the 20% threshold to 0.3% at the 1% threshold and then exponentially to 1.3% (0.5% threshold), 6.9% (0.2% threshold), and 23.2% (0.1% threshold). In two of three studies with available plasma HIV-1 RNA levels, the proportion of positions with unusual mutations was negatively associated with virus levels. Although the complete set of signature APOBEC mutations was much smaller than that of unusual mutations, the former outnumbered the latter in one-sixth of the samples at the 0.5%, 1%, and 2% thresholds.ConclusionsThe marked increase in the proportion of positions with unusual mutations at thresholds below 1% and in samples with lower virus loads suggests that, at low thresholds, many unusual mutations are artifactual, reflecting PCR error or G-to-A hypermutation. Profiling the numbers of unusual and signature APOBEC pol mutations at different NGS thresholds may be useful to avoid selecting a threshold that is too low and poses an unacceptable risk of identifying artifactual mutations.

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Section: Resultsmentioning

confidence: 99%

Analysis of unusual and signature APOBEC-mutations in HIV-1polnext-generation sequences

Tzou

Pond

Ávila‐Ríos

et al. 2019

Preprint

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“…nucleic acid extraction, reverse transcription, PCR, template amplicon preparation for NGS and NGS sequencing) and NGS data processing . The gross error rates generated from short‐read NGS platforms ranges from approximately 1 to 10 errors per 1000 bases leading to increased false positive detection of minority variants when their prevalence falls below approximately 1% . The additional variant QC strategies significantly improve the reliability of calling variants of low abundance, undetectable by Sanger sequencing.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatic data processing pipelines in support of next‐generation sequencing‐based HIV drug resistance testing: the Winnipeg Consensus

Enns

Brumme

et al. 2018

J Intern AIDS Soc

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IntroductionNext‐generation sequencing (NGS) has several advantages over conventional Sanger sequencing for HIV drug resistance (HIVDR) genotyping, including detection and quantitation of low‐abundance variants bearing drug resistance mutations (DRMs). However, the high HIV genomic diversity, unprecedented large volume of data, complexity of analysis and potential for error pose significant challenges for data processing. Several NGS analysis pipelines have been developed and used in HIVDR research; however, the absence of uniformity in data processing strategies results in lack of consistency and comparability of outputs from different pipelines. To fill this gap, an international symposium on bioinformatic strategies for NGS‐based HIVDR testing was held in February 2018 in Winnipeg, Canada, convening laboratory scientists, bioinformaticians and clinicians involved in four recently developed, publicly available NGS HIVDR pipelines. The goal of this symposium was to establish a consensus on effective bioinformatic strategies for NGS data management and its use for HIVDR reporting.DiscussionEssential functionalities of an NGS HIVDR pipeline were divided into five analytic blocks: (1) NGS read quality control (QC)/quality assurance (QA); (2) NGS read alignment and reference mapping; (3) HIV variant calling and variant QC; (4) NGS HIVDR reporting; and (5) extended data applications and additional considerations for data management. The consensuses reached among the participants on all major aspects of these blocks are summarized here. They encompass not only recommended data management and analysis strategies, but also detailed bioinformatic approaches that help ensure accuracy of the derived HIVDR analysis outputs for both research and potential clinical use.ConclusionsWhile NGS is being adopted more broadly in HIVDR testing laboratories, data processing is often a bottleneck hindering its generalized application. The proposed standardization of NGS read QC/QA, read alignment and reference mapping, variant calling and QC, HIVDR reporting and relevant data management strategies in this “Winnipeg Consensus” may serve as a starting guideline for NGS HIVDR data processing that informs the refinement of existing pipelines and those yet to be developed. Moreover, the bioinformatic strategies presented here may apply more broadly to NGS data analysis of microbes harbouring significant genomic diversity.

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“…Better than mapping just to a standard reference may be to map once to a standard reference, call the consensus, then use this as the reference for one or more rounds of remapping [12,[21][22][23][24]. Remapping is expected to be more accurate, because the consensus initially called is expected to be closer to the true consensus than the standard reference is.…”

Section: Mapping Reads: Problems and Solutionsmentioning

confidence: 99%

“…[26]). Remapping to contigs [11,12,22,27,28] settles ambiguity at positions spanned by multiple contigs which disagree, corrects positions where assembly did not call the most common base, and provides minority variant information.…”

Section: Mapping Reads: Problems and Solutionsmentioning

confidence: 99%

Easy and Accurate Reconstruction of Whole HIV Genomes from Short-Read Sequence Data

Wymant

Blanquart

Gall

et al. 2016

Preprint

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Next-generation sequencing has yet to be widely adopted for HIV. The difficulty of accurately reconstructing the consensus sequence of a quasispecies from reads (short fragments of DNA) in the presence of rapid between-and within-host evolution may have been a deterrent. In particular, mapping (aligning) reads to a reference sequence leads to biased loss of information; this bias can distort epidemiological and evolutionary conclusions. De novo assembly avoids this bias by effectively aligning the reads to themselves, producing a set of sequences called contigs. However contigs provide only a partial summary of the reads, misassembly may result in their having an incorrect structure, and no information is available at parts of the genome where contigs could not be assembled. To address these problems we developed the tool shiver to preprocess reads for quality and contamination, then map them to a reference tailored to the sample using corrected contigs supplemented with existing references sequences. Run with two commands per sample, it can easily be used for large heterogeneous data sets. We use shiver to reconstruct the consensus sequence and minority variant information from paired-end short read data produced with the Illumina platform, for 65 existing publicly available samples and 50 new samples. We show the systematic superiority of mapping to shiver's constructed reference over mapping the same reads to the standard reference HXB2: an average of 29 bases per sample are called differently, of which 98.5% are supported by higher coverage. We also provide a practical guide to working with imperfect contigs.

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Quasispecies Analyses of the HIV-1 Near-full-length Genome With Illumina MiSeq

Cited by 51 publications

References 61 publications

Analysis of unusual and signature APOBEC-mutations in HIV-1polnext-generation sequences

Analysis of unusual and signature APOBEC-mutations in HIV-1polnext-generation sequences

Bioinformatic data processing pipelines in support of next‐generation sequencing‐based HIV drug resistance testing: the Winnipeg Consensus

Easy and Accurate Reconstruction of Whole HIV Genomes from Short-Read Sequence Data

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