Microarray-based optimization to detect genomic deletion mutations.
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ABSTRACT:
SUBMITTER: Belfield EJ
PROVIDER: S-EPMC4526893 | biostudies-literature | 2014 Dec
REPOSITORIES: biostudies-literature
Publications
Microarray-based optimization to detect genomic deletion mutations.
Belfield Eric J EJ Brown Carly C Gan Xiangchao X Jiang Caifu C Baban Dilair D Mithani Aziz A Mott Richard R Ragoussis Jiannis J Harberd Nicholas P NP
Genomics data 20141201
We performed array comparative genome hybridization (aCGH) analyses of five <i>Arabidopsis thaliana</i> mutants with genomic deletions ranging in size from 4 bp to > 5 kb. We used the Roche NimbleGen <i>Arabidopsis</i> CGH 3 × 720 K whole genome custom tiling array to optimize deletion detection. Details of the microarray design and hybridization data have been deposited at the NCBI GEO repository with accession number GSE55327. ...[more]
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Project description:BackgroundOligonucleotide microarray-based comparative genomic hybridization (CGH) offers an attractive possible route for the rapid and cost-effective genome-wide discovery of deletion mutations. CGH typically involves comparison of the hybridization intensities of genomic DNA samples with microarray chip representations of entire genomes, and has widespread potential application in experimental research and medical diagnostics. However, the power to detect small deletions is low.ResultsHere we use a graduated series of Arabidopsis thaliana genomic deletion mutations (of sizes ranging from 4 bp to ~5 kb) to optimize CGH-based genomic deletion detection. We show that the power to detect smaller deletions (4, 28 and 104 bp) depends upon oligonucleotide density (essentially the number of genome-representative oligonucleotides on the microarray chip), and determine the oligonucleotide spacings necessary to guarantee detection of deletions of specified size.ConclusionsOur findings will enhance a wide range of research and clinical applications, and in particular will aid in the discovery of genomic deletions in the absence of a priori knowledge of their existence.
| S-EPMC3998191 | biostudies-literature
Project description:The use of a graduated series of Arabidopsis thaliana genomic deletion mutations (of sizes ranging from 4 bp to ~5 kb) to optimize CGH-based genomic deletion detection
2014-02-25 | GSE55327 | GEO
Project description:The use of a graduated series of Arabidopsis thaliana genomic deletion mutations (of sizes ranging from 4 bp to ~5 kb) to optimize CGH-based genomic deletion detection Microarray-based ultra-high resolution discovery of genomic deletion mutations
2014-02-25 | E-GEOD-55327 | biostudies-arrayexpress
Project description:BACKGROUND: Microarray comparative genomic hybridization (CGH) is currently one of the most powerful techniques to measure DNA copy number in large genomes. In humans, microarray CGH is widely used to assess copy number variants in healthy individuals and copy number aberrations associated with various diseases, syndromes and disease susceptibility. In model organisms such as Caenorhabditis elegans (C. elegans) the technique has been applied to detect mutations, primarily deletions, in strains of interest. Although various constraints on oligonucleotide properties have been suggested to minimize non-specific hybridization and improve the data quality, there have been few experimental validations for CGH experiments. For genomic regions where strict design filters would limit the coverage it would also be useful to quantify the expected loss in data quality associated with relaxed design criteria. RESULTS: We have quantified the effects of filtering various oligonucleotide properties by measuring the resolving power for detecting deletions in the human and C. elegans genomes using NimbleGen microarrays. Approximately twice as many oligonucleotides are typically required to be affected by a deletion in human DNA samples in order to achieve the same statistical confidence as one would observe for a deletion in C. elegans. Surprisingly, the ability to detect deletions strongly depends on the oligonucleotide 15-mer count, which is defined as the sum of the genomic frequency of all the constituent 15-mers within the oligonucleotide. A similarity level above 80% to non-target sequences over the length of the probe produces significant cross-hybridization. We recommend the use of a fairly large melting temperature window of up to 10 degrees C, the elimination of repeat sequences, the elimination of homopolymers longer than 5 nucleotides, and a threshold of -1 kcal/mol on the oligonucleotide self-folding energy. We observed very little difference in data quality when varying the oligonucleotide length between 50 and 70, and even when using an isothermal design strategy. CONCLUSION: We have determined experimentally the effects of varying several key oligonucleotide microarray design criteria for detection of deletions in C. elegans and humans with NimbleGen's CGH technology. Our oligonucleotide design recommendations should be applicable for CGH analysis in most species.
| S-EPMC2577661 | biostudies-literature
Project description:BackgroundAn important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types.Methodology/principal findingsWe apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described.Conclusions/significanceThe performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.
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Project description:Comparative genomic hybridization was used to compare genetic diversity of five strains of Leptospira (Leptospira interrogans serovars Bratislava, Canicola, and Hebdomadis and Leptospira kirschneri serovars Cynopteri and Grippotyphosa). The array was designed based on two available sequenced Leptospira reference genomes, those of L. interrogans serovar Copenhageni and L. interrogans serovar Lai. A comparison of genetic contents showed that L. interrogans serovar Bratislava was closest to the reference genomes while L. kirschneri serovar Grippotyphosa had the least similarity to the reference genomes. Cluster analysis indicated that L. interrogans serovars Bratislava and Hebdomadis clustered together first, followed by L. interrogans serovar Canicola, before the two L. kirschneri strains. Confirmed/potential virulence factors identified in previous research were also detected in the tested strains.
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Project description:PurposeLeber hereditary optic neuropathy (LHON) is a mitochondrial inherited disease characterized by bilateral vision problems, such as reduced visual acuity, dyschromatopsia, and central or centrocecal scotoma. Of these cases, 95% are caused by three mutations in mitochondrial DNA (mtDNA): m.G11778A, followed by m.T14484C and m.G3460A. The remaining 5% of cases of LHON are caused by rare mutations also present in mtDNA. Although conventional molecular tools for molecular screening of LHON are becoming popular, in most cases these tools are still expensive and time-consuming and are difficult to reproduce. Therefore, to meet the need for more accurate, faster, and cheaper techniques for molecular screening, as well as make it more accessible, we used the high-throughput method TaqMan® OpenArray™ Genotyping platform for developing a customized high-throughput assay for the three main mutations related to LHON.MethodsThe assay was performed for 87 individuals diagnosed with LHON or acquired optic neuropathy of unknown origin. The three main mutations were screened using the customized assay with the TaqMan® OpenArray™ Genotyping platform, and all reactions were performed in triplicate. The positive and negative results were revalidated with restriction fragment length polymorphism PCR (RFLP-PCR) and Sanger sequencing.ResultsThe main mutations related to LHON were detected in 34 patients with genotyping reactions, of which 27 cases had the m.G11778A mutation, and seven had the m.T14484C mutation.ConclusionsThe TaqMan® OpenArray™ Genotyping platform was shown to be an effective tool for molecular screening of the most common mutations related to LHON without presenting false positive or negative results for the analyzed mutations. In addition, this tool can be considered a cheaper, faster, and more accurate alternative for molecular screening of LHON mutations than PCR and Sanger sequencing, as 94 genotyping reactions can be performed within 6 h and specific TaqMan probes are used.
| S-EPMC5524431 | biostudies-literature
Project description:Herein, a detailed protocol for a random mutation capture (RMC) assay to measure nuclear point mutation frequency in mouse tissue is described. This protocol is a simplified version of the original method developed for human tissue that is easier to perform, yet retains a high sensitivity of detection. In contrast to assays relying on phenotypic selection of reporter genes in transgenic mice, the RMC assay allows direct detection of mutations in endogenous genes in any mouse strain. Measuring mutation frequency within an intron of a transcribed gene, we show this assay to be highly reproducible. We analyzed mutation frequencies from the liver tissue of animals with a mutation within the intrinsic exonuclease domains of the two major DNA polymerases, δ and ε. These mice exhibited significantly higher mutation frequencies than did wild-type animals. A comparison with a previous analysis of these genotypes in Big Blue mice revealed the RMC assay to be more sensitive than the Big Blue assay for this application. As RMC does not require analysis of a particular gene, simultaneous analysis of mutation frequency at multiple genetic loci is feasible. This assay provides a versatile alternative to transgenic mouse models for the study of mutagenesis in vivo.
| S-EPMC3113589 | biostudies-literature
Project description:IntroductionCassava (Manihot esculenta) is an annual root crop which provides the major source of calories for over half a billion people around the world. Since its domestication ~10,000 years ago, cassava has been largely clonally propagated through stem cuttings. Minimal sexual recombination has led to an accumulation of deleterious mutations made evident by heavy inbreeding depression.MethodsTo locate and characterize these deleterious mutations, and to measure selection pressure across the cassava genome, we aligned 52 related Euphorbiaceae and other related species representing millions of years of evolution. With single base-pair resolution of genetic conservation, we used protein structure models, amino acid impact, and evolutionary conservation across the Euphorbiaceae to estimate evolutionary constraint. With known deleterious mutations, we aimed to improve genomic evaluations of plant performance through genomic prediction. We first tested this hypothesis through simulation utilizing multi-kernel GBLUP to predict simulated phenotypes across separate populations of cassava.ResultsSimulations showed a sizable increase of prediction accuracy when incorporating functional variants in the model when the trait was determined by<100 quantitative trait loci (QTL). Utilizing deleterious mutations and functional weights informed through evolutionary conservation, we saw improvements in genomic prediction accuracy that were dependent on trait and prediction.ConclusionWe showed the potential for using evolutionary information to track functional variation across the genome, in order to improve whole genome trait prediction. We anticipate that continued work to improve genotype accuracy and deleterious mutation assessment will lead to improved genomic assessments of cassava clones.
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