Measuring KRAS Mutations in Circulating Tumor DNA by Droplet Digital PCR and Next-Generation Sequencing

Measuring total cell-free DNA (cfDNA) or cancer-specific mutations herein has presented as new tools in aiding the treatment of cancer patients. Studies show that total cfDNA bears prognostic value in metastatic colorectal cancer (mCRC) and that measuring cancer-specific mutations could supplement biopsies. However, limited information is available on the performance of different methods. Blood samples from 28 patients with mCRC and known KRAS mutation status were included. cfDNA was extracted and quantified with droplet digital polymerase chain reaction (ddPCR) measuring Beta-2 Microglobulin. KRAS mutation detection was performed using ddPCR (Bio-Rad) and next-generation sequencing (NGS, Ion Torrent PGM). Comparing KRAS mutation status in plasma and tissue revealed concordance rates of 79% and 89% for NGS and ddPCR. Strong correlation between the methods was observed. Most KRAS mutations were also detectable in 10-fold diluted samples using the ddPCR. We find that for detection of KRAS mutations in ctDNA ddPCR was superior to NGS both in analysis success rate and concordance to tissue. We further present results indicating that lower amount of plasma may be used for detection of KRAS mutations in mCRC.     

Parental somatic mosaicism for CNV deletions – A need for more sensitive and precise detection methods in clinical diagnostics settings

To further assess the scale and level of parental somatic mosaicism, we queried the CMA database at Baylor Genetics. We selected 50 unrelated families where clinically relevant apparent de novo CNV-deletions were found in the affected probands. Parental blood samples screening using deletion junction-specific PCR revealed four parents with somatic mosaicism. Droplet digital PCR (ddPCR), qPCR, and amplicon-based next-generation sequencing (NGS) were applied to validate these findings. Using ddPCR levels of mosaicism ranged from undetectable to 18.5%. Amplicon-based NGS and qPCR for the father with undetectable mosaicism was able to detect mosaicism at 0.39%. In one mother, ddPCR analysis revealed 15.6%, 10.6%, 8.2%, and undetectable levels of mosaicism in her blood, buccal cells, saliva, and urine samples, respectively. Our data suggest that more sensitive and precise methods, e.g. CNV junction-specific LR-PCR, ddPCR, or qPCR may allow for a more refined assessment of the potential disease recurrence risk for an identified variant.     

multiSNV: a probabilistic approach for improving detection of somatic point mutations from multiple related tumour samples

Somatic variant analysis of a tumour sample and its matched normal has been widely used in cancer research to distinguish germline polymorphisms from somatic mutations. However, due to the extensive intratumour heterogeneity of cancer, sequencing data from a single tumour sample may greatly underestimate the overall mutational landscape. In recent studies, multiple spatially or temporally separated tumour samples from the same patient were sequenced to identify the regional distribution of somatic mutations and study intratumour heterogeneity. There are a number of tools to perform somatic variant calling from matched tumour-normal next-generation sequencing (NGS) data; however none of these allow joint analysis of multiple same-patient samples. We discuss the benefits and challenges of multisample somatic variant calling and present multiSNV, a software package for calling single nucleotide variants (SNVs) using NGS data from multiple same-patient samples. Instead of performing multiple pairwise analyses of a single tumour sample and a matched normal, multiSNV jointly considers all available samples under a Bayesian framework to increase sensitivity of calling shared SNVs. By leveraging information from all available samples, multiSNV is able to detect rare mutations with variant allele frequencies down to 3% from whole-exome sequencing experiments.     

Exome Sequencing in Classic Hairy Cell Leukaemia Reveals Widespread Variation in Acquired Somatic Mutations between Individual Tumours Apart from the Signature BRAF V(600)E Lesion

In classic Hairy cell leukaemia (HCLc), a single case has thus far been interrogated by whole exome sequencing (WES) in a treatment naive patient, in which BRAF V(600)E was identified as an acquired somatic mutation and confirmed as occurring near-universally in this form of disease by conventional PCR-based cohort screens. It left open however the question whether other genome-wide mutations may also commonly occur at high frequency in presentation HCLc disease. To address this, we have carried out WES of 5 such typical HCLc cases, using highly purified splenic tumour cells paired with autologous T cells for germline. Apart from BRAF V(600)E, no other recurrent somatic mutation was identified in these HCLc exomes, thereby excluding additional acquired mutations as also prevalent at a near-universal frequency in this form of the disease. These data then place mutant BRAF at the centre of the neoplastic drive in HCLc. A comparison of our exome data with emerging genetic findings in HCL indicates that additional somatic mutations may however occur recurrently in smaller subsets of disease. As mutant BRAF alone is insufficient to drive malignant transformation in other histological cancers, it suggests that individual tumours utilise largely differing patterns of genetic somatic mutations to coalesce with BRAF V(600)E to drive pathogenesis of malignant HCLc disease.     

Identification of trunk mutations in gastric carcinoma: a case study

Background

Intratumor heterogeneity (ITH) poses an urgent challenge for cancer precision medicine because it can cause drug resistance against cancer target therapy and immunotherapy. The search for trunk mutations that are present in all cancer cells is therefore critical for each patient.

Case presentation

In this study, we aimed to evaluate the efficiency of multiregional sequencing for the identification of trunk mutations present in all regions of a tumor as a case study. We applied multiregional whole-exome sequencing (WES) to investigate the genetic heterogeneity and homogeneity of a case of gastric carcinoma. Approximately 83% of common missense mutations present in two samples and approximately 89% of common missense mutations present in three samples were trunk mutations. Notably, trunk mutations appeared to have higher variant allele frequencies (VAFs) than non-trunk mutations.

Conclusions

Our results indicate that small-scale multiregional sampling and subsequent screening of low VAF somatic mutations might be a cost-effective strategy for identifying the majority of trunk mutations in gastric carcinoma.

     

Genotype‐free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data

Restriction‐site associated DNA sequencing (RADSeq) facilitates rapid generation of thousands of genetic markers at relatively low cost; however, several sources of error specific to RADSeq methods often lead to biased estimates of allele frequencies and thereby to erroneous population genetic inference. Estimating the distribution of sample allele frequencies without calling genotypes was shown to improve population inference from whole genome sequencing data, but the ability of this approach to account for RADSeq‐specific biases remains unexplored. Here we assess in how far genotype‐free methods of allele frequency estimation affect demographic inference from empirical RADSeq data. Using the well‐studied pied flycatcher (Ficedula hypoleuca) as a study system, we compare allele frequency estimation and demographic inference from whole genome sequencing data with that from RADSeq data matched for samples using both genotype‐based and genotype free methods. The demographic history of pied flycatchers as inferred from RADSeq data was highly congruent with that inferred from whole genome resequencing (WGS) data when allele frequencies were estimated directly from the read data. In contrast, when allele frequencies were derived from called genotypes, RADSeq‐based estimates of most model parameters fell outside the 95% confidence interval of estimates derived from WGS data. Notably, more stringent filtering of the genotype calls tended to increase the discrepancy between parameter estimates from WGS and RADSeq data, respectively. The results from this study demonstrate the ability of genotype‐free methods to improve allele frequency spectrum‐ (AFS‐) based demographic inference from empirical RADSeq data and highlight the need to account for uncertainty in NGS data regardless of sequencing method.     

Identification of novel mutations in FFPE lung adenocarcinomas using DEPArray sorting technology and next-generation sequencing

Formalin-fixed paraffin-embedded (FFPE) tissues are utilized as the standard diagnostic method in pathology laboratories. However, admixture of unwanted tissues and shortage of normal samples, which can be used to detect somatic mutation, are considered critical factors to accurately diagnose cancer. To explore these challenges, we sorted the pure tumor cells from 22 FFPE lung adenocarcinoma tissues via Di-Electro-Phoretic Array (DEPArray) technology, a new cell sorting technology, and analyzed the variants with next-generation sequencing (NGS) for the most accurate analysis. The allele frequencies of the all gene mutations were improved by 1.2 times in cells sorted via DEPArray (tumor suppressor genes, 1.3–10.1 times; oncogenes, 1.3–2.6 times). We identified 16 novel mutations using the sequencing from sorted cells via DEPArray technology, compared to detecting 4 novel mutation by the sequencing from unsorted cells. Using this analysis, we also revealed that five genes (TP53, EGFR, PTEN, RB1, KRAS, and CTNNB1) were somatically mutated in multiple homogeneous lung adenocarcinomas. Together, we sorted pure tumor cells from 22 FFPE lung adenocarcinomas by DEPArray technology and identified 16 novel somatic mutations. We also established the precise genomic landscape for more accurate diagnosis in 22 lung adenocarcinomas with mutations detected in pure tumor cells. The results obtained in this study could offer new avenues for the treatment and the diagnosis of squamous cell lung cancers.     

Somatic Activating Mutations in GNAQ and GNA11 Are Associated with Congenital Hemangioma

Congenital hemangioma is a rare vascular tumor that forms in utero. Postnatally, the tumor either involutes quickly (i.e., rapidly involuting congenital hemangioma [RICH]) or partially regresses and stabilizes (i.e., non-involuting congenital hemangioma [NICH]). We hypothesized that congenital hemangiomas arise due to somatic mutation and performed massively parallel mRNA sequencing on affected tissue from eight participants. We identified mutually exclusive, mosaic missense mutations that alter glutamine at amino acid 209 (Glu209) in GNAQ or GNA11 in all tested samples, at variant allele frequencies (VAF) ranging from 3% to 33%. We verified the presence of the mutations in genomic DNA using a combination of molecular inversion probe sequencing (MIP-seq) and digital droplet PCR (ddPCR). The Glu209 GNAQ and GNA11 missense variants we identified are common in uveal melanoma and have been shown to constitutively activate MAPK and/or YAP signaling. When we screened additional archival formalin-fixed paraffin-embedded (FFPE) congenital cutaneous and hepatic hemangiomas, 4/8 had GNAQ or GNA11 Glu209 variants. The same GNAQ or GNA11 mutation is found in both NICH and RICH, so other factors must account for these tumors’ different postnatal behaviors.     

A Somatic MAP3K3 Mutation Is Associated with Verrucous Venous Malformation

Verrucous venous malformation (VVM), also called “verrucous hemangioma,” is a non-hereditary, congenital, vascular anomaly comprised of aberrant clusters of malformed dermal venule-like channels underlying hyperkeratotic skin. We tested the hypothesis that VVM lesions arise as a consequence of a somatic mutation. We performed whole-exome sequencing (WES) on VVM tissue from six unrelated individuals and looked for somatic mutations affecting the same gene in specimens from multiple persons. We observed mosaicism for a missense mutation ({"type":"entrez-nucleotide","attrs":{"text":"NM_002401.3","term_id":"42794764","term_text":"NM_002401.3"}}NM_002401.3, c.1323C>G; {"type":"entrez-protein","attrs":{"text":"NP_002392","term_id":"42794765","term_text":"NP_002392"}}NP_002392, p.Iso441Met) in mitogen-activated protein kinase kinase kinase 3 (MAP3K3) in three of six individuals. We confirmed the presence of this mutation via droplet digital PCR (ddPCR) in the three subjects and found the mutation in three additional specimens from another four participants. Mutant allele frequencies ranged from 6% to 19% in affected tissue. We did not observe this mutant allele in unaffected tissue or in affected tissue from individuals with other types of vascular anomalies. Studies using global and conditional Map3k3 knockout mice have previously implicated MAP3K3 in vascular development. MAP3K3 dysfunction probably causes VVM in humans.     

Paired analysis of tumor mutation burden calculated by targeted deep sequencing panel and whole exome sequencing in non-small cell lung cancer

Owing to rapid advancements in NGS (next generation sequen-cing), genomic alteration is now considered an essential pre-dictive biomarkers that impact the treatment decision in many cases of cancer. Among the various predictive biomarkers, tumor mutation burden (TMB) was identified by NGS and was con-sidered to be useful in predicting a clinical response in cancer cases treated by immunotherapy. In this study, we directly com-pared the lab-developed-test (LDT) results by target sequencing panel, K-MASTER panel v3.0 and whole-exome sequencing (WES) to evaluate the concordance of TMB. As an initial step, the reference materials (n = 3) with known TMB status were used as an exploratory test. To validate and evaluate TMB, we used one hundred samples that were acquired from surgically resected tissues of non-small cell lung cancer (NSCLC) patients. The TMB of each sample was tested by using both LDT and WES methods, which extracted the DNA from samples at the same time. In addition, we evaluated the impact of capture re-gion, which might lead to different values of TMB; the evalu-ation of capture region was based on the size of NGS and target sequencing panels. In this pilot study, TMB was evalu-ated by LDT and WES by using duplicated reference samples; the results of TMB showed high concordance rate (R² = 0.887). This was also reflected in clinical samples (n = 100), which showed R² of 0.71. The difference between the coding sequence ratio (3.49%) and the ratio of mutations (4.8%) indicated that the LDT panel identified a relatively higher number of mutations. It was feasible to calculate TMB with LDT panel, which can be useful in clinical practice. Furthermore, a customized approach must be developed for calculating TMB, which differs according to cancer types and specific clinical settings.     

Estimation of population allele frequencies from next‐generation sequencing data: pool‐versus individual‐based genotyping

Molecular markers produced by next‐generation sequencing (NGS) technologies are revolutionizing genetic research. However, the costs of analysing large numbers of individual genomes remain prohibitive for most population genetics studies. Here, we present results based on mathematical derivations showing that, under many realistic experimental designs, NGS of DNA pools from diploid individuals allows to estimate the allele frequencies at single nucleotide polymorphisms (SNPs) with at least the same accuracy as individual‐based analyses, for considerably lower library construction and sequencing efforts. These findings remain true when taking into account the possibility of substantially unequal contributions of each individual to the final pool of sequence reads. We propose the intuitive notion of effective pool size to account for unequal pooling and derive a Bayesian hierarchical model to estimate this parameter directly from the data. We provide a user‐friendly application assessing the accuracy of allele frequency estimation from both pool‐ and individual‐based NGS population data under various sampling, sequencing depth and experimental error designs. We illustrate our findings with theoretical examples and real data sets corresponding to SNP loci obtained using restriction site–associated DNA (RAD) sequencing in pool‐ and individual‐based experiments carried out on the same population of the pine processionary moth (Thaumetopoea pityocampa). NGS of DNA pools might not be optimal for all types of studies but provides a cost‐effective approach for estimating allele frequencies for very large numbers of SNPs. It thus allows comparison of genome‐wide patterns of genetic variation for large numbers of individuals in multiple populations.     

Efficient Genotyping of KRAS Mutant Non-Small Cell Lung Cancer Using a Multiplexed Droplet Digital PCR Approach

Droplet digital PCR (ddPCR) can be used to detect low frequency mutations in oncogene-driven lung cancer. The range of KRAS point mutations observed in NSCLC necessitates a multiplex approach to efficient mutation detection in circulating DNA. Here we report the design and optimisation of three discriminatory ddPCR multiplex assays investigating nine different KRAS mutations using PrimePCR™ ddPCR™ Mutation Assays and the Bio-Rad QX100 system. Together these mutations account for 95% of the nucleotide changes found in KRAS in human cancer. Multiplex reactions were optimised on genomic DNA extracted from KRAS mutant cell lines and tested on DNA extracted from fixed tumour tissue from a cohort of lung cancer patients without prior knowledge of the specific KRAS genotype. The multiplex ddPCR assays had a limit of detection of better than 1 mutant KRAS molecule in 2,000 wild-type KRAS molecules, which compared favourably with a limit of detection of 1 in 50 for next generation sequencing and 1 in 10 for Sanger sequencing. Multiplex ddPCR assays thus provide a highly efficient methodology to identify KRAS mutations in lung adenocarcinoma.     

Comprehensive Analysis to Improve the Validation Rate for Single Nucleotide Variants Detected by Next-Generation Sequencing

Next-generation sequencing (NGS) has enabled the high-throughput discovery of germline and somatic mutations. However, NGS-based variant detection is still prone to errors, resulting in inaccurate variant calls. Here, we categorized the variants detected by NGS according to total read depth (TD) and SNP quality (SNPQ), and performed Sanger sequencing with 348 selected non-synonymous single nucleotide variants (SNVs) for validation. Using the SAMtools and GATK algorithms, the validation rate was positively correlated with SNPQ but showed no correlation with TD. In addition, common variants called by both programs had a higher validation rate than caller-specific variants. We further examined several parameters to improve the validation rate, and found that strand bias (SB) was a key parameter. SB in NGS data showed a strong difference between the variants passing validation and those that failed validation, showing a validation rate of more than 92% (filtering cutoff value: alternate allele forward [AF]≥20 and AF<80 in SAMtools, SB<–10 in GATK). Moreover, the validation rate increased significantly (up to 97–99%) when the variant was filtered together with the suggested values of mapping quality (MQ), SNPQ and SB. This detailed and systematic study provides comprehensive recommendations for improving validation rates, saving time and lowering cost in NGS analyses.     

Mining statistically-solid k-mers for accurate NGS error correction

Background

NGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid k-mers. A solid k-mer is a k-mer frequently occurring in NGS reads. The other k-mers are called weak k-mers. A solid k-mer does not likely contain errors, while a weak k-mer most likely contains errors. An intensively investigated problem is to find a good frequency cutoff f₀ to balance the numbers of solid and weak k-mers. Once the cutoff is determined, a more challenging but less-studied problem is to: (i) remove a small subset of solid k-mers that are likely to contain errors, and (ii) add a small subset of weak k-mers, that are likely to contain no errors, into the remaining set of solid k-mers. Identification of these two subsets of k-mers can improve the correction performance.

Results

We propose to use a Gamma distribution to model the frequencies of erroneous k-mers and a mixture of Gaussian distributions to model correct k-mers, and combine them to determine f₀. To identify the two special subsets of k-mers, we use the z-score of k-mers which measures the number of standard deviations a k-mer’s frequency is from the mean. Then these statistically-solid k-mers are used to construct a Bloom filter for error correction. Our method is markedly superior to the state-of-art methods, tested on both real and synthetic NGS data sets.

Conclusion

The z-score is adequate to distinguish solid k-mers from weak k-mers, particularly useful for pinpointing out solid k-mers having very low frequency. Applying z-score on k-mer can markedly improve the error correction accuracy.

     

High-depth sequencing of over 750 genes supports linear progression of primary tumors and metastases in most patients with liver-limited metastatic colorectal cancer

BackgroundColorectal cancer with metastases limited to the liver (liver-limited mCRC) is a distinct clinical subset characterized by possible cure with surgery. We performed high-depth sequencing of over 750 cancer-associated genes and copy number profiling in matched primary, metastasis and normal tissues to characterize genomic progression in 18 patients with liver-limited mCRC.ResultsHigh depth Illumina sequencing and use of three different variant callers enable comprehensive and accurate identification of somatic variants down to 2.5% variant allele frequency. We identify a median of 11 somatic single nucleotide variants (SNVs) per tumor. Across patients, a median of 79.3% of somatic SNVs present in the primary are present in the metastasis and 81.7% of all alterations present in the metastasis are present in the primary. Private alterations are found at lower allele frequencies; a different mutational signature characterized shared and private variants, suggesting distinct mutational processes. Using B-allele frequencies of heterozygous germline SNPs and copy number profiling, we find that broad regions of allelic imbalance and focal copy number changes, respectively, are generally shared between the primary tumor and metastasis.ConclusionsOur analyses point to high genomic concordance of primary tumor and metastasis, with a thick common trunk and smaller genomic branches in general support of the linear progression model in most patients with liver-limited mCRC. More extensive studies are warranted to further characterize genomic progression in this important clinical population.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0589-1) contains supplementary material, which is available to authorized users.     

Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes

De novo mutations affect risk for many diseases and disorders, especially those with early-onset. An example is autism spectrum disorders (ASD). Four recent whole-exome sequencing (WES) studies of ASD families revealed a handful of novel risk genes, based on independent de novo loss-of-function (LoF) mutations falling in the same gene, and found that de novo LoF mutations occurred at a twofold higher rate than expected by chance. However successful these studies were, they used only a small fraction of the data, excluding other types of de novo mutations and inherited rare variants. Moreover, such analyses cannot readily incorporate data from case-control studies. An important research challenge in gene discovery, therefore, is to develop statistical methods that accommodate a broader class of rare variation. We develop methods that can incorporate WES data regarding de novo mutations, inherited variants present, and variants identified within cases and controls. TADA, for Transmission And De novo Association, integrates these data by a gene-based likelihood model involving parameters for allele frequencies and gene-specific penetrances. Inference is based on a Hierarchical Bayes strategy that borrows information across all genes to infer parameters that would be difficult to estimate for individual genes. In addition to theoretical development we validated TADA using realistic simulations mimicking rare, large-effect mutations affecting risk for ASD and show it has dramatically better power than other common methods of analysis. Thus TADA''s integration of various kinds of WES data can be a highly effective means of identifying novel risk genes. Indeed, application of TADA to WES data from subjects with ASD and their families, as well as from a study of ASD subjects and controls, revealed several novel and promising ASD candidate genes with strong statistical support.     

The Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations

Preliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations.     

Limited evidence that cancer susceptibility regions are preferential targets for somatic mutation

BackgroundGenome wide-association studies have successfully identified several hundred independent loci harboring common cancer susceptibility alleles that are distinct from the more than 110 cancer predisposition genes. The latter are generally characterized by disruptive mutations in coding genes that have been established as ‘drivers’ of cancer in large somatic sequencing studies. We set out to determine whether, similarly, common cancer susceptibility loci map to genes that have altered frequencies of mutation.ResultsIn our analysis of the intervals defined by the cancer susceptibility markers, we observed that cancer susceptibility regions have gene mutation frequencies comparable to background mutation frequencies. Restricting analyses to genes that have been determined to be pleiotropic across cancer types, genes affected by expression quantitative trait loci, or functional genes indicates that most cancer susceptibility genes classified into these subgroups do not display mutation frequencies that deviate from those expected. We observed limited evidence that cancer susceptibility regions that harbor common alleles with small estimated effect sizes are preferential targets for altered somatic mutation frequencies.ConclusionsOur findings suggest a complex interplay between germline susceptibility and somatic mutation, underscoring the cumulative effect of common variants on redundant pathways as opposed to driver genes. Complex biological pathways and networks likely link these genetic features of carcinogenesis, particularly as they relate to distinct polygenic models for each cancer type.     

Targeted Next-Generation Sequencing for Clinical Diagnosis of 561 Mendelian Diseases

BackgroundTargeted next-generation sequencing (NGS) is a cost-effective approach for rapid and accurate detection of genetic mutations in patients with suspected genetic disorders, which can facilitate effective diagnosis.Conclusions/SignificanceResults of the evaluation showed that targeted NGS enabled to detect disease-causing mutations with high accuracy, stability, speed and throughput. Thus, the technology can be used for the clinical diagnosis of 561 Mendelian diseases.     

The Quantification of Representative Sequences pipeline for amplicon sequencing: case study on within‐population ITS1 sequence variation in a microparasite infecting Daphnia

Next generation sequencing (NGS) platforms are replacing traditional molecular biology protocols like cloning and Sanger sequencing. However, accuracy of NGS platforms has rarely been measured when quantifying relative frequencies of genotypes or taxa within populations. Here we developed a new bioinformatic pipeline (QRS) that pools similar sequence variants and estimates their frequencies in NGS data sets from populations or communities. We tested whether the estimated frequency of representative sequences, generated by 454 amplicon sequencing, differs significantly from that obtained by Sanger sequencing of cloned PCR products. This was performed by analysing sequence variation of the highly variable first internal transcribed spacer (ITS1) of the ichthyosporean Caullerya mesnili, a microparasite of cladocerans of the genus Daphnia. This analysis also serves as a case example of the usage of this pipeline to study within‐population variation. Additionally, a public Illumina data set was used to validate the pipeline on community‐level data. Overall, there was a good correspondence in absolute frequencies of C. mesnili ITS1 sequences obtained from Sanger and 454 platforms. Furthermore, analyses of molecular variance (amova ) revealed that population structure of C. mesnili differs across lakes and years independently of the sequencing platform. Our results support not only the usefulness of amplicon sequencing data for studies of within‐population structure but also the successful application of the QRS pipeline on Illumina‐generated data. The QRS pipeline is freely available together with its documentation under GNU Public Licence version 3 at http://code.google.com/p/quantification-representative-sequences .