SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip
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PROVIDER: PRJNA115105 | ENA |
REPOSITORIES: ENA
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Project description:Nucleotide polymorphisms can potentially influence the hybridization of mRNA to 25-mer oligonucleotides. Because Affymetrix Rice Genome Array was designated mainly for Nipponbare genome of Oryza sativa, the expression level of other varieties could not be estimated correctly. We tried to apply new approaches to estimate expression level by discerning the probe-level differential hybridization.
2009-06-09 | GSE16265 | GEO
Project description:Nucleotide polymorphisms can potentially influence the hybridization of mRNA to 25-mer oligonucleotides. Because Affymetrix Rice Genome Array was designated mainly for Nipponbare genome of Oryza sativa, the expression level of other varieties could not be estimated correctly. We tried to apply new approaches to estimate expression level by discerning the probe-level differential hybridization. Nipponbare and 93-11 replicates were analyzed.
2009-06-09 | E-GEOD-16265 | biostudies-arrayexpress
Project description:BACKGROUND: High-density short oligonucleotide microarrays are useful tools for studying biodiversity, because they can be used to investigate both nucleotide and expression polymorphisms. However, when different strains (or species) produce different signal intensities after mRNA hybridization, it is not easy to determine whether the signal intensities were affected by nucleotide or expression polymorphisms. To overcome this difficulty, nucleotide and expression polymorphisms are currently examined separately. RESULTS: We have developed SNEP, a new method that allows simultaneous detection of both nucleotide and expression polymorphisms. SNEP involves a robust statistical procedure based on the idea that a nucleotide polymorphism observed at the probe level can be regarded as an outlier, because the nucleotide polymorphism can reduce the hybridization signal intensity. To investigate the performance of SNEP, we used three species: barley, rice and mice. In addition to the publicly available barley data, we obtained new rice and mouse data from the strains with available genome sequences. The sensitivity and false positive rate of nucleotide polymorphism detection were estimated based on the sequence information. The robustness of expression polymorphism detection against nucleotide polymorphisms was also investigated. CONCLUSION: SNEP performed well regardless of the genome size and showed a better performance for nucleotide polymorphism detection, when compared with other previously proposed methods. The R-software 'SNEP' is available at http://www.ism.ac.jp/~fujisawa/SNEP/.
| S-EPMC2706822 | biostudies-other
Project description:Primary outcome(s): To investigate the single nucleotide polymorphisms correlated with oxaliplatin-induced neuropathy
| 2616724 | ecrin-mdr-crc
Project description:BackgroundMicroarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondary data analyses rapidly increasing, we expect this rate to continue. Various initiatives such as the External RNA Control Consortium (ERCC) and the MicroArray Quality Control (MAQC) project have explored ways to provide standards for the technology. For microarrays to become generally accepted as a reliable technology, statistical methods for assessing quality will be an indispensable component; however, there remains a lack of consensus in both defining and measuring microarray quality.ResultsWe begin by providing a precise definition of microarray quality and reviewing existing Affymetrix GeneChip quality metrics in light of this definition. We show that the best-performing metrics require multiple arrays to be assessed simultaneously. While such multi-array quality metrics are adequate for bench science, as microarrays begin to be used in clinical settings, single-array quality metrics will be indispensable. To this end, we define a single-array version of one of the best multi-array quality metrics and show that this metric performs as well as the best multi-array metrics. We then use this new quality metric to assess the quality of microarry data available via the Gene Expression Omnibus (GEO) using more than 22,000 Affymetrix HGU133a and HGU133plus2 arrays from 809 studies.ConclusionsWe find that approximately 10 percent of these publicly available arrays are of poor quality. Moreover, the quality of microarray measurements varies greatly from hybridization to hybridization, study to study, and lab to lab, with some experiments producing unusable data. Many of the concepts described here are applicable to other high-throughput technologies.
| S-EPMC3097162 | biostudies-literature
Project description:Cross-species Detection in Barley1 GeneChip Array
| PRJNA149123 | ENA
Project description:Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.
| S-EPMC3431718 | biostudies-literature
Project description:Previously, cooperative binding to DNA between the bZIP domain of CREB1 and the ETS domain of GABPα was observed for the composite ETS-CRE motif (A0C1C2G3G4A5A6G7T8G9A10C11G12T13C14A15). Single nucleotide polymorphisms (SNPs) at the beginning and end of the ETS motif (ACCGGAAGT) increased cooperative binding. Here, we use a microarray containing all double nucleotide polymorphisms (DNPs) of the ETS-CRE motif to explore GABPα and CREB1 binding to their canonical motifs and their cooperative binding to the ETS-CRE motif. For GABPα, binding to SNPs predicted binding the DNPs. In contrast, CREB1 binding to DNPs showed cooperativity. Similar results were observed for other ETS and bZIP family members. Cooperative binding between GABPα and CREB1 was typically weaker than expected except for DNPs containing A7 and SNPs at the beginning of the ETS motif.
2020-01-01 | GSE125613 | GEO
Project description:Affymetrix GeneChip miRNA 4.0 Array for meningioma recurrence profilling
| PRJNA522376 | ENA