Use of stereotypical mutational motifs to define resolution limits for the ultra-deep resequencing of mitochondrial DNA.

Massively parallel resequencing of mitochondrial DNA (mtDNA) has led to significant advances in the study of heteroplasmic mtDNA variants in health and disease, but confident resolution of very low-level variants ( C, from patient with MNGIE, mitochondrial neurogastrointestinal encephalomyopathy) and comparing mutational pattern distribution with healthy mtDNA by ligation-mediated deep resequencing (Applied Biosystems SOLiD). We empirically derived mtDNA-mutant heteroplasmy detection limits, demonstrating that the presence of stereotypical mutational motif could be statistically validated for heteroplasmy thresholds ≥ 0.22% (P = 0.034). We therefore provide empirical evidence from biological samples that very low-level mtDNA mutants can be meaningfully resolved by massively parallel resequencing, confirming the utility of the approach for studying somatic mtDNA mutation in health and disease. Our approach could also usefully be employed in other settings to derive platform-specific deep resequencing resolution limits

SAMQA: error classification and validation of high-throughput sequenced read data

<p>Abstract</p> <p>Background</p> <p>The advances in high-throughput sequencing technologies and growth in data sizes has highlighted the need for scalable tools to perform quality assurance testing. These tests are necessary to ensure that data is of a minimum necessary standard for use in downstream analysis. In this paper we present the SAMQA tool to rapidly and robustly identify errors in population-scale sequence data.</p> <p>Results</p> <p>SAMQA has been used on samples from three separate sets of cancer genome data from The Cancer Genome Atlas (TCGA) project. Using technical standards provided by the SAM specification and biological standards defined by researchers, we have classified errors in these sequence data sets relative to individual reads within a sample. Due to an observed linearithmic speedup through the use of a high-performance computing (HPC) framework for the majority of tasks, poor quality data was identified prior to secondary analysis in significantly less time on the HPC framework than the same data run using alternative parallelization strategies on a single server.</p> <p>Conclusions</p> <p>The SAMQA toolset validates a minimum set of data quality standards across whole-genome and exome sequences. It is tuned to run on a high-performance computational framework, enabling QA across hundreds gigabytes of samples regardless of coverage or sample type.</p

Fast and accurate mutation detection in whole genome sequences of multiple isogenic samples with IsoMut

Background: Detection of somatic mutations is one of the main goals of next generation DNA sequencing. A wide range of experimental systems are available for the study of spontaneous or environmentally induced mutagenic processes. However, most of the routinely used mutation calling algorithms are not optimised for the simultaneous analysis of multiple samples, or for non-human experimental model systems with no reliable databases of common genetic variations. Most standard tools either require numerous in-house post filtering steps with scarce documentation or take an unpractically long time to run. To overcome these problems, we designed the streamlined IsoMut tool which can be readily adapted to experimental scenarios where the goal is the identification of experimentally induced mutations in multiple isogenic samples. Methods: Using 30 isogenic samples, reliable cohorts of validated mutations were created for testing purposes. Optimal values of the filtering parameters of IsoMut were determined in a thorough and strict optimization procedure based on these test sets. Results: We show that IsoMut, when tuned correctly, decreases the false positive rate compared to conventional tools in a 30 sample experimental setup; and detects not only single nucleotide variations, but short insertions and deletions as well. IsoMut can also be run more than a hundred times faster than the most precise state of art tool, due its straightforward and easily understandable filtering algorithm. Conclusions: IsoMut has already been successfully applied in multiple recent studies to find unique, treatment induced mutations in sets of isogenic samples with very low false positive rates. These types of studies provide an important contribution to determining the mutagenic effect of environmental agents or genetic defects, and IsoMut turned out to be an invaluable tool in the analysis of such data. © 2017 The Author(s)

Detailed molecular characterisation of acute myeloid leukaemia with a normal karyotype using targeted DNA capture.

Advances in sequencing technologies are giving unprecedented insights into the spectrum of somatic mutations underlying acute myeloid leukaemia with a normal karyotype (AML-NK). It is clear that the prognosis of individual patients is strongly influenced by the combination of mutations in their leukaemia and that many leukaemias are composed of multiple subclones, with differential susceptibilities to treatment. Here, we describe a method, employing targeted capture coupled with next-generation sequencing and tailored bioinformatic analysis, for the simultaneous study of 24 genes recurrently mutated in AML-NK. Mutational analysis was performed using open source software and an in-house script (Mutation Identification and Analysis Software), which identified dominant clone mutations with 100% specificity. In each of seven cases of AML-NK studied, we identified and verified mutations in 2-4 genes in the main leukaemic clone. Additionally, high sequencing depth enabled us to identify putative subclonal mutations and detect leukaemia-specific mutations in DNA from remission marrow. Finally, we used normalised read depths to detect copy number changes and identified and subsequently verified a tandem duplication of exons 2-9 of MLL and at least one deletion involving PTEN. This methodology reliably detects sequence and copy number mutations, and can thus greatly facilitate the classification, clinical research, diagnosis and management of AML-NK

Cell Dispersal Influences Tumor Heterogeneity and Introduces a Bias in NGS Data Interpretation

Short and long distance cell dispersal can have a marked effect on tumor structure, high cellular motility could lead to faster cell mixing and lower observable intratumor heterogeneity. Here we evaluated a model for cell mixing that investigates how short-range dispersal and cell turnover will account for mutational proportions. We show that cancer cells can penetrate neighboring and distinct areas in a matter of days. In next generation sequencing runs, higher proportions of a given cell line generated frequencies with higher precision, while mixtures with lower amounts of each cell line had lower precision manifesting in higher standard deviations. When multiple cell lines were co-cultured, cellular movement altered observed mutation frequency by up to 18.5%. We propose that some of the shared mutations detected at low allele frequencies represent highly motile clones that appear in multiple regions of a tumor owing to dispersion throughout the tumor. In brief, cell movement will lead to a significant technical (sampling) bias when using next generation sequencing to determine clonal composition. A possible solution to this drawback would be to radically decrease detection thresholds and increase coverage in NGS analyses. © 2017 The Author(s)

High throughput mutagenesis for identification of residues regulating human prostacyclin (hIP) receptor

The human prostacyclin receptor (hIP receptor) is a seven-transmembrane G protein-coupled receptor (GPCR) that plays a critical role in vascular smooth muscle relaxation and platelet aggregation. hIP receptor dysfunction has been implicated in numerous cardiovascular abnormalities, including myocardial infarction, hypertension, thrombosis and atherosclerosis. Genomic sequencing has discovered several genetic variations in the PTGIR gene coding for hIP receptor, however, its structure-function relationship has not been sufficiently explored. Here we set out to investigate the applicability of high throughput random mutagenesis to study the structure-function relationship of hIP receptor. While chemical mutagenesis was not suitable to generate a mutagenesis library with sufficient coverage, our data demonstrate error-prone PCR (epPCR) mediated mutagenesis as a valuable method for the unbiased screening of residues regulating hIP receptor function and expression. Here we describe the generation and functional characterization of an epPCR derived mutagenesis library compromising >4000 mutants of the hIP receptor. We introduce next generation sequencing as a useful tool to validate the quality of mutagenesis libraries by providing information about the coverage, mutation rate and mutational bias. We identified 18 mutants of the hIP receptor that were expressed at the cell surface, but demonstrated impaired receptor function. A total of 38 non-synonymous mutations were identified within the coding region of the hIP receptor, mapping to 36 distinct residues, including several mutations previously reported to affect the signaling of the hIP receptor. Thus, our data demonstrates epPCR mediated random mutagenesis as a valuable and practical method to study the structurefunction relationship of GPCRs. © 2014 Bill et al

Comparing mutation calls in fixed tumour samples between the Affymetrix OncoScan® Array and PCR based next-generation sequencing

Background: The importance of accurate and affordable mutation calling in fixed pathology samples is becoming increasingly important as we move into the era of personalised medicine. The Affymetrix OncoScan® Array platform is designed to produce actionable mutation calls in archival material. Methods: We compared calls made using the OncoScan platform with calls made using a custom designed PCR panel followed by next-generation sequencing (NGS), in order to benchmark the sensitivity and specificity of the OncoScan calls in a large cohort of fixed tumour samples. 392 fixed, clinical samples were sequenced, encompassing 641 PCR regions, 403 putative positive calls and 1528 putative negative calls. Results: A small number of mutations could not be validated, either due to large indels or pseudogenes impairing parts of the NGS pipeline. For the remainder, if calls were filtered according to simple quality metrics, both sensitivity and specificity for the OncoScan platform were over 98%. This applied even to samples with poorer sample quality and lower variant allele frequency (5–10%) than product claims indicated. Conclusions: This benchmarking study will be useful to users and potential users of this platform, who wish to compare technologies or interpret their own results