Ultrasensitive amplicon barcoding for next-generation sequencing facilitating sequence error and amplification-bias correction.
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ABSTRACT:
SUBMITTER: Ahmed I
PROVIDER: S-EPMC7324614 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:An amendment to this paper has been published and can be accessed via a link at the top of the paper.
| S-EPMC7538963 | biostudies-literature
Project description:BackgroundNext-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing.ResultsIn this paper, we present two new efficient error correction algorithms optimized for viral amplicons: (i) k-mer-based error correction (KEC) and (ii) empirical frequency threshold (ET). Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate their relative performance on 24 experimental datasets obtained by 454-sequencing of amplicons with known sequences. All three algorithms show similar accuracy in finding true haplotypes. However, KEC and ET were significantly more efficient than SHORAH in removing false haplotypes and estimating the frequency of true ones.ConclusionsBoth algorithms, KEC and ET, are highly suitable for rapid recovery of error-free haplotypes obtained by 454-sequencing of amplicons from heterogeneous viruses.The implementations of the algorithms and data sets used for their testing are available at: http://alan.cs.gsu.edu/NGS/?q=content/pyrosequencing-error-correction-algorithm.
| S-EPMC3382444 | biostudies-literature
Project description:Gene synthesis, the process of assembling gene-length fragments from shorter groups of oligonucleotides (oligos), is becoming an increasingly important tool in molecular and synthetic biology. The length, quality and cost of gene synthesis are limited by errors produced during oligo synthesis and subsequent assembly. Enzymatic error correction methods are cost-effective means to ameliorate errors in gene synthesis. Previous analyses of these methods relied on cloning and Sanger sequencing to evaluate their efficiencies, limiting quantitative assessment. Here, we develop a method to quantify errors in synthetic DNA by next-generation sequencing. We analyzed errors in model gene assemblies and systematically compared six different error correction enzymes across 11 conditions. We find that ErrASE and T7 Endonuclease I are the most effective at decreasing average error rates (up to 5.8-fold relative to the input), whereas MutS is the best for increasing the number of perfect assemblies (up to 25.2-fold). We are able to quantify differential specificities such as ErrASE preferentially corrects C/G transversions whereas T7 Endonuclease I preferentially corrects A/T transversions. More generally, this experimental and computational pipeline is a fast, scalable and extensible way to analyze errors in gene assemblies, to profile error correction methods, and to benchmark DNA synthesis methods.
| S-EPMC5587813 | biostudies-literature
Project description:BackgroundRecent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown.ResultsIn this paper, we evaluate the ability of error correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error-correction methods.ConclusionsIn terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity.
| S-EPMC7079412 | biostudies-literature
Project description:"Barcode-tagged" PCR primers used for multiplex amplicon sequencing generate a thus-far-overlooked amplification bias that produces variable terminal restriction fragment length polymorphism (T-RFLP) and pyrosequencing data from the same environmental DNA template. We propose a simple two-step PCR approach that increases reproducibility and consistently recovers higher genetic diversity in pyrosequencing libraries.
| S-EPMC3209180 | biostudies-other
Project description:Next-generation sequencing technologies can be used to analyse genetically heterogeneous samples at unprecedented detail. The high coverage achievable with these methods enables the detection of many low-frequency variants. However, sequencing errors complicate the analysis of mixed populations and result in inflated estimates of genetic diversity. We developed a probabilistic Bayesian approach to minimize the effect of errors on the detection of minority variants. We applied it to pyrosequencing data obtained from a 1.5-kb-fragment of the HIV-1 gag/pol gene in two control and two clinical samples. The effect of PCR amplification was analysed. Error correction resulted in a two- and five-fold decrease of the pyrosequencing base substitution rate, from 0.05% to 0.03% and from 0.25% to 0.05% in the non-PCR and PCR-amplified samples, respectively. We were able to detect viral clones as rare as 0.1% with perfect sequence reconstruction. Probabilistic haplotype inference outperforms the counting-based calling method in both precision and recall. Genetic diversity observed within and between two clinical samples resulted in various patterns of phenotypic drug resistance and suggests a close epidemiological link. We conclude that pyrosequencing can be used to investigate genetically diverse samples with high accuracy if technical errors are properly treated.
| S-EPMC2995073 | biostudies-literature
Project description:BackgroundThe popularity of new sequencing technologies has led to an explosion of possible applications, including new approaches in biodiversity studies. However each of these sequencing technologies suffers from sequencing errors originating from different factors. For 16S rRNA metagenomics studies, the 454 pyrosequencing technology is one of the most frequently used platforms, but sequencing errors still lead to important data analysis issues (e.g. in clustering in taxonomic units and biodiversity estimation). Moreover, retaining a higher portion of the sequencing data by preserving as much of the read length as possible while maintaining the error rate within an acceptable range, will have important consequences at the level of taxonomic precision.ResultsThe new error correction algorithm proposed in this work - NoDe (Noise Detector) - is trained to identify those positions in 454 sequencing reads that are likely to have an error, and subsequently clusters those error-prone reads with correct reads resulting in error-free representative read. A benchmarking study with other denoising algorithms shows that NoDe can detect up to 75% more errors in a large scale mock community dataset, and this with a low computational cost compared to the second best algorithm considered in this study. The positive effect of NoDe in 16S rRNA studies was confirmed by the beneficial effect on the precision of the clustering of pyrosequencing reads in operational taxonomic units.ConclusionsNoDe was shown to be a computational efficient denoising algorithm for pyrosequencing reads, producing the lowest error rates in an extensive benchmarking study with other denoising algorithms.
| S-EPMC4403973 | biostudies-literature
Project description:Next-generation sequencing of circulating tumor DNA presents a promising approach to cancer diagnostics, complementing conventional tissue-based diagnostic testing by enabling minimally invasive serial testing and broad genomic coverage through a simple blood draw to maximize therapeutic benefit to patients. LiquidHALLMARK® is an amplicon-based next-generation sequencing assay developed for the genomic profiling of plasma-derived cell-free DNA (cfDNA). The comprehensive 80-gene panel profiles point mutations, insertions/deletions, copy number alterations, and gene fusions, and further detects oncogenic viruses (Epstein-Barr virus (EBV) and hepatitis B virus (HBV)) and microsatellite instability (MSI). Here, the analytical and clinical validation of the assay is reported. Analytical validation using reference genetic materials demonstrated a sensitivity of 99.38% for point mutations and 95.83% for insertions/deletions at 0.1% variant allele frequency (VAF), and a sensitivity of 91.67% for gene fusions at 0.5% VAF. In non-cancer samples, a high specificity (≥99.9999% per-base) was observed. The limit of detection for copy number alterations, EBV, HBV, and MSI were also empirically determined. Orthogonal comparison of epidermal growth factor receptor (EGFR) variant calls made by LiquidHALLMARK and a reference allele-specific polymerase chain reaction (AS-PCR) method for 355 lung cancer specimens revealed an overall concordance of 93.80%, while external validation with cobas® EGFR Mutation Test v2 for 50 lung cancer specimens demonstrated an overall concordance of 84.00%, with a 100% concordance rate for EGFR variants above 0.4% VAF. Clinical application of LiquidHALLMARK in 1,592 consecutive patients demonstrated a high detection rate (74.8% circulating tumor DNA (ctDNA)-positive in cancer samples) and broad actionability (50.0% of cancer samples harboring alterations with biological evidence for actionability). Among ctDNA-positive lung cancers, 72.5% harbored at least one biomarker with a guideline-approved drug indication. These results establish the high sensitivity, specificity, accuracy, and precision of the LiquidHALLMARK assay and supports its clinical application for blood-based genomic testing.
| S-EPMC9053827 | biostudies-literature
Project description:mRNA abundance microarrays are the most widely used high-throughput technology for functional genomics. But despite this extensive characterization, and despite their wide-spread adoption in discovery research, the uptake of microarrays as clinical diagnostic tools remains relatively limited due to sample degradation. Recently, the NuGEN WT-Ovation Pico kit was developed to resolve this problem. Here, we comprehensively evaluate its utility on two separate biological problems. First, we evaluate its effect on 12 malignant and 2 normal cervix samples. Second, we evaluate its effect on 2 normal kidney samples. In each case, we directly compare aliquots of the same RNA sample that are prepared by standard and low-input/low-RIN protocols to address three key questions. First, does the use of a low-input/degraded-sample protocol alter transcriptional profiles either globally or of specific genes? Second, do these changes alter biological conclusions drawn from the dataset? Third, are these changes biased towards specific classes of genes, either in terms of their sequence characteristics (e.g. length, GC-content) or their biological function?
2014-01-01 | GSE42764 | GEO
Project description:mRNA abundance microarrays are the most widely used high-throughput technology for functional genomics. But despite this extensive characterization, and despite their wide-spread adoption in discovery research, the uptake of microarrays as clinical diagnostic tools remains relatively limited due to sample degradation. Recently, the NuGEN WT-Ovation Pico kit was developed to resolve this problem. Here, we comprehensively evaluate its utility on two separate biological problems. First, we evaluate its effect on 12 malignant and 2 normal cervix samples. Second, we evaluate its effect on 2 normal kidney samples. In each case, we directly compare aliquots of the same RNA sample that are prepared by standard and low-input/low-RIN protocols to address three key questions. First, does the use of a low-input/degraded-sample protocol alter transcriptional profiles either globally or of specific genes? Second, do these changes alter biological conclusions drawn from the dataset? Third, are these changes biased towards specific classes of genes, either in terms of their sequence characteristics (e.g. length, GC-content) or their biological function? To achieve these goals, we selected 12 frozen cervix cancers and 2 unmatched normal samples. RNA was extracted and was split into two aliquots: one was prepared using the Nugen WT-Ovation Pico kit and the other using the standard Affymetrix Express array method. This approach allows us to directly assess the effect of sample preparation, in isolation of any other effects. These data were then pre-processed and the filtered genes were subject to linear-modeling to evaluate technical and biological variability. The resulting gene sets were then subject to pathway and sequence analysis, and LTR analysis. Lastly, to generalize these conclusions we repeated this experimental approach identically in two normal kidney samples and one RNA mixture control sample.
2014-01-01 | E-GEOD-42764 | biostudies-arrayexpress