A simple optimization can improve the performance of single feature polymorphism detection by Affymetrix expression array: Transcript and Genome Hybridizations
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PROVIDER: PRJNA115273 | ENA |
REPOSITORIES: ENA
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Project description:The publicly available genome sequence information of two rice strains, japonica cultivar Nipponbare and indica cultivar 93-11, opens a great opportunity for investigation of performances DNA genotyping by high-density oligonucleotide arrays. Here, we compare single feature polymorphism (SFP) detection performances between whole genome hybridization and transcript hybridization using Affymetrix Rice Expression Array and the two rice cultivars.
2010-06-01 | GSE16341 | GEO
Project description:BACKGROUND: High-density oligonucleotide arrays are effective tools for genotyping numerous loci simultaneously. In small genome species (genome size: < approximately 300 Mb), whole-genome DNA hybridization to expression arrays has been used for various applications. In large genome species, transcript hybridization to expression arrays has been used for genotyping. Although rice is a fully sequenced model plant of medium genome size (approximately 400 Mb), there are a few examples of the use of rice oligonucleotide array as a genotyping tool. RESULTS: We compared the single feature polymorphism (SFP) detection performance of whole-genome and transcript hybridizations using the Affymetrix GeneChip Rice Genome Array, using the rice cultivars with full genome sequence, japonica cultivar Nipponbare and indica cultivar 93-11. Both genomes were surveyed for all probe target sequences. Only completely matched 25-mer single copy probes of the Nipponbare genome were extracted, and SFPs between them and 93-11 sequences were predicted. We investigated optimum conditions for SFP detection in both whole genome and transcript hybridization using differences between perfect match and mismatch probe intensities of non-polymorphic targets, assuming that these differences are representative of those between mismatch and perfect targets. Several statistical methods of SFP detection by whole-genome hybridization were compared under the optimized conditions. Causes of false positives and negatives in SFP detection in both types of hybridization were investigated. CONCLUSIONS: The optimizations allowed a more than 20% increase in true SFP detection in whole-genome hybridization and a large improvement of SFP detection performance in transcript hybridization. Significance analysis of the microarray for log-transformed raw intensities of PM probes gave the best performance in whole genome hybridization, and 22,936 true SFPs were detected with 23.58% false positives by whole genome hybridization. For transcript hybridization, stable SFP detection was achieved for highly expressed genes, and about 3,500 SFPs were detected at a high sensitivity (> 50%) in both shoot and young panicle transcripts. High SFP detection performances of both genome and transcript hybridizations indicated that microarrays of a complex genome (e.g., of Oryza sativa) can be effectively utilized for whole genome genotyping to conduct mutant mapping and analysis of quantitative traits such as gene expression levels.
| S-EPMC2885369 | biostudies-literature
Project description:Background: As costs decline, the size and scope of microarray experiments have increased. In multi-centre studies there is a need to ensure consistency of data pre-processing across centres. Similarly, in smaller scale studies the evolution of microarray platforms means that there is often a need to compare data generated on earlier microarrays to that generated on newer ones. It is important in such studies to ensure that platform-dependent biases are removed so that meta-analysis of different datasets can be performed reliably. In both these cases the optimal scenario is to have a small subset of samples repeated at each site or on each platform. These replicates can then be used to learn a relationship between probe intensities on the two platforms. Results: I introduce here a simple, linear-modelling-based method for normalizing data from multiple-platforms by using replicate hybridizations. A dataset of 20 rat liver samples is used as a benchmark. Eight samples are hybridized to two separate versions of Affymetrix microarrays, while the other 12 are hybridized to one, for a total of 28 arrays. Our linear modelling method removes platform bias as assessed using both unsupervised machine-learning and two-group statistical analyses. The method is computationally efficient and works well for data pre-processed by the GCRMA, RMA and MAS5 algorithms and using either default or alternative probe-mappings. The method is very stable towards the number of replicate samples used, with even two replicates greatly reducing platform-specific bias. Conclusions: A simple linear-modelling method can remove platform-specific bias independent of the pre-processing algorithm and ProbeSet-mapping used. This technique can readily be extended to multi-site experiments, and suggests the benefits of including a small number of replicate hybridizations in each new study as a normalization control. Twenty rats livers were processed, eight on both RAE230-A and RAE230-2 arrays, 8 on only RAE230-A arrays, and 4 on RAE230-2 arrays only.
2010-06-25 | E-GEOD-16458 | biostudies-arrayexpress
Project description:Simple inheritance, complex regulation: supergene-mediated fire ant social polymorphism
| PRJNA629802 | ENA
Project description:One of the methods used to search for molecular markers is DNA hybridization to microarrays. Microarray hybridization of DNA from different individuals can reveal differences between them (single-feature polymorphisms (SFPs)). These SFPs can be used as markers for breeding purposes, or they can be converted to conventional markers by sequencing. This method has been utilized in a few different plants to discover genetic variation using Affymetrix arrays that exist for only a few organisms. We applied this approach, with some modifications, for marker discovery in melon.
2010-08-01 | GSE17673 | GEO
Project description:activated sludge simple Raw sequence reads
| PRJNA825387 | ENA
Project description:The size and scope of microarray experiments continue to increase. However, datasets generated on different platforms or at different centres contain biases. Improved techniques are needed to remove platform- and batch-specific biases. One experimental control is the replicate hybridization of a subset of samples at each site or on each platform to learn the relationship between the two platforms. To date, no algorithm exists to specifically use this type of control. LTR is a linear-modelling-based algorithm that learns the relationship between different microarray batches from replicate hybridizations. LTR was tested on a new benchmark dataset of 20 samples hybridized to different Affymetrix microarray platforms. Before LTR, the two platforms were significantly different; application of LTR removed this bias. LTR was tested with six separate data pre-processing algorithms, and its effectiveness was independent of the pre-processing algorithm. Sample-size experiments indicate that just three replicate hybridizations can significantly reduce bias. An R library implementing LTR is available.
2009-11-24 | GSE16458 | GEO
Project description:Natural populations of the fruit fly, Drosophila melanogaster, segregate genetic variation that leads to cardiac disease phenotypes. Drosophila is well-known as a model for studying the mechanisms by which human disease genes cause pathology, including heart disease, but it is less well appreciated that they may also model the genetic architecture of disease, since flies presumably also have diseases that have a genetic basis. It is reasoned that most of these aberrant inbred line effects would be due to capture of rare variants of large effect as homozygotes, allowing the variants to be mapped rapidly using contemporary genomic approaches. In order to map the genetic variants in flies, we used single feature polymorphism (SFP) analysis to contrast the genome-wide genotype frequencies between pools of flies with aberrant and normal heart phenotype. SFP analysis is an indirect method for genome-wide genotyping that utilizes differential hybridization of genomic DNA to probes on a DNA chip that was initially designed for gene expression profiling, but can be used for species where genotyping chips are not available.
2013-03-15 | GSE45123 | GEO
Project description:Natural populations of the fruit fly, Drosophila melanogaster, segregate genetic variation that leads to cardiac disease phenotypes. Drosophila is well-known as a model for studying the mechanisms by which human disease genes cause pathology, including heart disease, but it is less well appreciated that they may also model the genetic architecture of disease, since flies presumably also have diseases that have a genetic basis. It is reasoned that most of these aberrant inbred line effects would be due to capture of rare variants of large effect as homozygotes, allowing the variants to be mapped rapidly using contemporary genomic approaches. In order to map the genetic variants in flies, we used single feature polymorphism (SFP) analysis to contrast the genome-wide genotype frequencies between pools of flies with aberrant and normal heart phenotype. SFP analysis is an indirect method for genome-wide genotyping that utilizes differential hybridization of genomic DNA to probes on a DNA chip that was initially designed for gene expression profiling, but can be used for species where genotyping chips are not available. DNA was prepared from three independent pools of 15 flies for each of the two types, as well as from the two parental lines. The samples were sheared and labeled with biotin, then hybridized to Affymetrix Drosophila expression microarray chips. Mismatch hybridization, namely a significant difference in the hybridization intensity between the parental lines, was detected from all perfect match (PM) probes, located in over 9,000 probes with an estimated False Discovery Rate of 11%.
2013-03-15 | E-GEOD-45123 | biostudies-arrayexpress