A statistical method for detecting genomic aberrations in heterogeneous tumour samples from single nucleotide polymorphism genotyping data
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE23785 | GEO | 2010/08/25
SECONDARY ACCESSION(S): PRJNA130727
REPOSITORIES: GEO
Similar Datasets
Project description:SNP array analysis of microdissected tumor vs stroma vs surgically obtained tumor DNA was extracted from total tumor material, from microdissected stroma and microdissected, pure tumor tissue
2010-08-25 | E-GEOD-23785 | biostudies-arrayexpress
Project description:We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normal-cancer cell lines and real primary tumours.
| S-EPMC2965384 | biostudies-literature
Project description:BackgroundThe September 11, 2001 attacks on the World Trade Center and the Pentagon increased the concern about the potential for terrorist attacks on many vulnerable sectors of the US, including agriculture. The concentrated nature of crops, easily obtainable biological agents, and highly detrimental impacts make agroterrorism a potential threat. Although procedures for an effective criminal investigation and attribution following such an attack are available, important enhancements are still needed, one of which is the capability for fine discrimination among pathogen strains. The purpose of this study was to develop a molecular typing assay for use in a forensic investigation, using Wheat streak mosaic virus (WSMV) as a model plant virus.MethodThis genotyping technique utilizes single base primer extension to generate a genetic fingerprint. Fifteen single nucleotide polymorphisms (SNPs) within the coat protein and helper component-protease genes were selected as the genetic markers for this assay. Assay optimization and sensitivity testing was conducted using synthetic targets. WSMV strains and field isolates were collected from regions around the world and used to evaluate the assay for discrimination. The assay specificity was tested against a panel of near-neighbors consisting of genetic and environmental near-neighbors.ResultEach WSMV strain or field isolate tested produced a unique SNP fingerprint, with the exception of three isolates collected within the same geographic location that produced indistinguishable fingerprints. The results were consistent among replicates, demonstrating the reproducibility of the assay. No SNP fingerprints were generated from organisms included in the near-neighbor panel, suggesting the assay is specific for WSMV. Using synthetic targets, a complete profile could be generated from as low as 7.15 fmoles of cDNA.ConclusionThe molecular typing method presented is one tool that could be incorporated into the forensic science tool box after a thorough validation study. This method incorporates molecular biology techniques that are already well established in research and diagnostic laboratories, allowing for an easy introduction of this method into existing laboratories.Keywordssingle nucleotide polymorphisms, genotyping, plant pathology, viruses, microbial forensics, Single base primer extension, SNaPshot Multiplex Kit.
| S-EPMC3488013 | biostudies-literature
Project description:Single-nucleotide polymorphisms (SNPs) are the most abundant type of genetic variations; they provide the genetic fingerprint of individuals and are essential for genetic biomarker discoveries. Accurate detection of SNPs is of great significance for disease prevention, diagnosis and prognosis, and for prediction of drug response and clinical outcomes in patients. Nevertheless, conventional SNP genotyping methods are still limited by insufficient accuracy or labor-, time-, and resource-intensive procedures. Microfluidics has been increasingly utilized to improve efficiency; however, the currently available microfluidic genotyping systems still have shortcomings in accuracy, sensitivity, throughput and multiplexing capability. To address these challenges, we developed a multi-step SNP genotyping microfluidic device, which performs single-base extension of SNP specific primers and solid-phase purification of the extension products on a temperature-controlled chip. The products are ready for immediate detection by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), providing identification of the alleles at the target loci. The integrated device enables efficient and automated operation, while maintaining the high accuracy and sensitivity provided by MS. The multiplex genotyping capability was validated by performing rapid, accurate and simultaneous detection of 4 loci on a synthetic template. The microfluidic device has the potential to perform automatic, accurate, quantitative and high-throughput assays covering a broad spectrum of applications in biological and clinical research, drug development and forensics.
| S-EPMC4651459 | biostudies-literature
Project description:Establishing low-cost, high-throughput, simple, and accurate single nucleotide polymorphism (SNP) genotyping techniques is beneficial for understanding the intrinsic relationship between individual genetic variations and their biological functions on a genomic scale. Here, a straightforward and reliable single-molecule approach is demonstrated for precise SNP authentication by directly measuring the fluctuations in electrical signals in an electronic circuit, which is fabricated from a high-gain field-effect silicon nanowire decorated with a single hairpin DNA, in the presence of different target DNAs. By simply comparing the proportion difference of a probe-target duplex structure throughout the process, this study implements allele-specific and accurate SNP detection. These results are supported by the statistical analyses of different dynamic parameters such as the mean lifetime and the unwinding probability of the duplex conformation. In comparison with conventional polymerase chain reaction and optical methods, this convenient and label-free method is complementary to existing optical methods and also shows several advantages, such as simple operation and no requirement for fluorescent labeling, thus promising a futuristic route toward the next-generation genotyping technique.
| S-EPMC5700462 | biostudies-literature
Project description:BackgroundCopy number alterations (CNA) play a key role in cancer development and progression. Since more than one CNA can be detected in most tumors, frequently co-occurring genetic CNA may point to cooperating cancer related genes. Existing methods for co-occurrence evaluation so far have not considered the overall heterogeneity of CNA per tumor, resulting in a preferential detection of frequent changes with limited specificity for each association due to the high genetic instability of many samples.MethodWe hypothesize that in cancer some linkage-independent CNA may display a non-random co-occurrence, and that these CNA could be of pathogenetic relevance for the respective cancer. We also hypothesize that the statistical relevance of co-occurring CNA may depend on the sample specific CNA complexity. We verify our hypotheses with a simulation based algorithm CDCOCA (complexity dependence of co-occurring chromosomal aberrations).ResultsApplication of CDCOCA to example data sets identified co-occurring CNA from low complex background which otherwise went unnoticed. Identification of cancer associated genes in these co-occurring changes can provide insights of cooperative genes involved in oncogenesis.ConclusionsWe have developed a method to detect associations of regional copy number abnormalities in cancer data. Along with finding statistically relevant CNA co-occurrences, our algorithm points towards a generally low specificity for co-occurrence of regional imbalances in CNA rich samples, which may have negative impact on pathway modeling approaches relying on frequent CNA events.
| S-EPMC3061884 | biostudies-literature
Project description:We investigated the application of an oligonucleotide microarray to (i) specifically detect Cryptosporidium spp., (ii) differentiate between closely related C. parvum isolates and Cryptosporidium species, and (iii) differentiate between principle genotypes known to infect humans. A microarray of 68 capture probes targeting seven single-nucleotide polymorphisms (SNPs) within a 190-bp region of the hsp70 gene of Cryptosporidium parvum was constructed. Labeled hsp70 targets were generated by PCR with biotin- or Cy3-labeled primers. Hybridization conditions were optimized for hybridization time, temperature, and salt concentration. Two genotype I C. parvum isolates (TU502 and UG502), two C. parvum genotype II isolates (Iowa and GCH1), and DNAs from 22 non-Cryptosporidium sp. organisms were used to test method specificity. Only DNAs from C. parvum isolates produced labeled amplicons that could be hybridized to and detected on the array. Hybridization patterns between genotypes were visually distinct, but identification of SNPs required statistical analysis of the signal intensity data. The results indicated that correct mismatch discrimination could be achieved for all seven SNPs for the UG502 isolate, five of seven SNPs for the TU502 isolate, and six of seven SNPs for both the Iowa and GCH1 isolates. Even without perfect mismatch discrimination, the microarray method unambiguously distinguished between genotype I and genotype II isolates and demonstrated the potential to differentiate between other isolates and species on a single microarray. This method may provide a powerful new tool for water utilities and public health officials for assessing point and nonpoint source contamination of water supplies.
| S-EPMC123883 | biostudies-literature
Project description:Single nucleotide polymorphisms (SNPs) have emerged as the genetic marker of choice for mapping disease loci and candidate gene association studies, because of their high density and relatively even distribution in the human genomes. There is a need for systems allowing medium multiplexing (ten to hundreds of SNPs) with high throughput, which can efficiently and cost-effectively generate genotypes for a very large sample set (thousands of individuals). Methods that are flexible, fast, accurate and cost-effective are urgently needed. This is also important for those who work on high throughput genotyping in non-model systems where off-the-shelf assays are not available and a flexible platform is needed.We demonstrate the use of a nanofluidic Integrated Fluidic Circuit (IFC) - based genotyping system for medium-throughput multiplexing known as the Dynamic Array, by genotyping 994 individual human DNA samples on 47 different SNP assays, using nanoliter volumes of reagents. Call rates of greater than 99.5% and call accuracies of greater than 99.8% were achieved from our study, which demonstrates that this is a formidable genotyping platform. The experimental set up is very simple, with a time-to-result for each sample of about 3 hours.Our results demonstrate that the Dynamic Array is an excellent genotyping system for medium-throughput multiplexing (30-300 SNPs), which is simple to use and combines rapid throughput with excellent call rates, high concordance and low cost. The exceptional call rates and call accuracy obtained may be of particular interest to those working on validation and replication of genome- wide- association (GWA) studies.
| S-EPMC2789104 | biostudies-literature
Project description:Single-nucleotide polymorphisms (SNPs) constitute the bulk of human genetic variation and provide excellent markers to identify genetic factors contributing to complex disease susceptibility. A rapid, sensitive, and inexpensive assay is important for large-scale SNP scoring. Here we report the development of a multiplex SNP detection system using silicon chips coated to create a thin-film optical biosensor. Allele-discriminating, aldehyde-labeled oligonucleotides are arrayed and covalently attached to a hydrazinederivatized chip surface. Target sequences (e.g., PCR amplicons) then are hybridized in the presence of a mixture of biotinylated detector probes, one for each SNP, and a thermostable DNA ligase. After a stringent wash (0.01 M NaOH), ligation of biotinylated detector probes to perfectly matched capture oligomers is visualized as a color change on the chip surface (gold to blue/purple) after brief incubations with an anti-biotin IgG-horseradish peroxidase conjugate and a precipitable horseradish peroxidase substrate. Testing of PCR fragments is completed in 30-40 min. Up to several hundred SNPs can be assayed on a 36-mm2 chip, and SNP scoring can be done by eye or with a simple digital-camera system. This assay is extremely robust, exhibits high sensitivity and specificity, and is format-flexible and economical. In studies of mutations associated with risk for venous thrombosis and genotyping/haplotyping of African-American samples, we document high-fidelity analysis with 0 misassignments in 500 assays performed in duplicate.
| S-EPMC208797 | biostudies-literature
Project description:BackgroundCopy number aberrations (CNAs) are an important molecular signature in cancer initiation, development, and progression. However, these aberrations span a wide range of chromosomes, making it hard to distinguish cancer related genes from other genes that are not closely related to cancer but are located in broadly aberrant regions. With the current availability of high-resolution data sets such as single nucleotide polymorphism (SNP) microarrays, it has become an important issue to develop a computational method to detect driving genes related to cancer development located in the focal regions of CNAs.ResultsIn this study, we introduce a novel method referred to as the wavelet-based identification of focal genomic aberrations (WIFA). The use of the wavelet analysis, because it is a multi-resolution approach, makes it possible to effectively identify focal genomic aberrations in broadly aberrant regions. The proposed method integrates multiple cancer samples so that it enables the detection of the consistent aberrations across multiple samples. We then apply this method to glioblastoma multiforme and lung cancer data sets from the SNP microarray platform. Through this process, we confirm the ability to detect previously known cancer related genes from both cancer types with high accuracy. Also, the application of this approach to a lung cancer data set identifies focal amplification regions that contain known oncogenes, though these regions are not reported using a recent CNAs detecting algorithm GISTIC: SMAD7 (chr18q21.1) and FGF10 (chr5p12).ConclusionsOur results suggest that WIFA can be used to reveal cancer related genes in various cancer data sets.
| S-EPMC3114745 | biostudies-literature