Dataset Information

Combining microarray-based genomic selection (MGS) with the Illumina Genome Analyzer platform to sequence diploid target regions.

ABSTRACT: Novel methods of targeted sequencing of unique regions from complex eukaryotic genomes have generated a great deal of excitement, but critical demonstrations of these methods efficacy with respect to diploid genotype calling and experimental variation are lacking. To address this issue, we optimized microarray-based genomic selection (MGS) for use with the Illumina Genome Analyzer (IGA). A set of 202 fragments (304 kb total) contained within a 1.7 Mb genomic region on human chromosome X were MGS/IGA sequenced in ten female HapMap samples generating a total of 2.4 GB of DNA sequence. At a minimum coverage threshold of 5X, 93.9% of all bases and 94.9% of segregating sites were called, while 57.7% of bases (57.4% of segregating sites) were called at a 50X threshold. Data accuracy at known segregating sites was 98.9% at 5X coverage, rising to 99.6% at 50X coverage. Accuracy at homozygous sites was 98.7% at 5X sequence coverage and 99.5% at 50X coverage. Although accuracy at heterozygous sites was modestly lower, it was still over 92% at 5X coverage and increased to nearly 97% at 50X coverage. These data provide the first demonstration that MGS/IGA sequencing can generate the very high quality sequence data necessary for human genetics research. All sequences generated in this study have been deposited in NCBI Short Read Archive (http://www.ncbi.nlm.nih.gov/Traces/sra, Accession # SRA007913).

SUBMITTER: Okou DT

PROVIDER: S-EPMC2729809 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Combining microarray-based genomic selection (MGS) with the Illumina Genome Analyzer platform to sequence diploid target regions.

Okou David T DT Locke Adam E AE Steinberg Karyn M KM Hagen Katie K Athri Prashanth P Shetty Amol C AC Patel Viren V Zwick Michael E ME

Annals of human genetics 20090701 Pt 5

Novel methods of targeted sequencing of unique regions from complex eukaryotic genomes have generated a great deal of excitement, but critical demonstrations of these methods efficacy with respect to diploid genotype calling and experimental variation are lacking. To address this issue, we optimized microarray-based genomic selection (MGS) for use with the Illumina Genome Analyzer (IGA). A set of 202 fragments (304 kb total) contained within a 1.7 Mb genomic region on human chromosome X were MGS ...[more]

PMID: 19573206

Similar Datasets

Project description:BackgroundMicroarray-based pooled DNA experiments that combine the merits of DNA pooling and gene chip technology constitute a pivotal advance in biotechnology. This new technique uses pooled DNA, thereby reducing costs associated with the typing of DNA from numerous individuals. Moreover, use of an oligonucleotide gene chip reduces costs related to processing various DNA segments (e.g., primers, reagents). Thus, the technique provides an overall cost-effective solution for large-scale genomic/genetic research. However, few publicly shared tools are available to systematically analyze the rapidly accumulating volume of whole-genome pooled DNA data.ResultsWe propose a generalized concept of pooled DNA and present a user-friendly tool named Microarray Pooled DNA Analyzer (MPDA) that we developed to analyze hybridization intensity data from microarray-based pooled DNA experiments. MPDA enables whole-genome DNA preferential amplification/hybridization analysis, allele frequency estimation, association mapping, allelic imbalance detection, and permits integration with shared data resources online. Graphic and numerical outputs from MPDA support global and detailed inspection of large amounts of genomic data. Four whole-genome data analyses are used to illustrate the major functionalities of MPDA. The first analysis shows that MPDA can characterize genomic patterns of preferential amplification/hybridization and provide calibration information for pooled DNA data analysis. The second analysis demonstrates that MPDA can accurately estimate allele frequencies. The third analysis indicates that MPDA is cost-effective and reliable for association mapping. The final analysis shows that MPDA can identify regions of chromosomal aberration in cancer without paired-normal tissue.ConclusionMPDA, the software that integrates pooled DNA association analysis and allelic imbalance analysis, provides a convenient analysis system for extensive whole-genome pooled DNA data analysis. The software, user manual and illustrated examples are freely available online at the MPDA website listed in the Availability and requirements section.

Project description:The 27k Illumina Infinium Methylation Beadchip is a popular high-throughput technology that allows the methylation state of over 27,000 CpGs to be assayed. While feature selection and classification methods have been comprehensively explored in the context of gene expression data, relatively little is known as to how best to perform feature selection or classification in the context of Illumina Infinium methylation data. Given the rising importance of epigenomics in cancer and other complex genetic diseases, and in view of the upcoming epigenome wide association studies, it is critical to identify the statistical methods that offer improved inference in this novel context.Using a total of 7 large Illumina Infinium 27k Methylation data sets, encompassing over 1,000 samples from a wide range of tissues, we here provide an evaluation of popular feature selection, dimensional reduction and classification methods on DNA methylation data. Specifically, we evaluate the effects of variance filtering, supervised principal components (SPCA) and the choice of DNA methylation quantification measure on downstream statistical inference. We show that for relatively large sample sizes feature selection using test statistics is similar for M and ?-values, but that in the limit of small sample sizes, M-values allow more reliable identification of true positives. We also show that the effect of variance filtering on feature selection is study-specific and dependent on the phenotype of interest and tissue type profiled. Specifically, we find that variance filtering improves the detection of true positives in studies with large effect sizes, but that it may lead to worse performance in studies with smaller yet significant effect sizes. In contrast, supervised principal components improves the statistical power, especially in studies with small effect sizes. We also demonstrate that classification using the Elastic Net and Support Vector Machine (SVM) clearly outperforms competing methods like LASSO and SPCA. Finally, in unsupervised modelling of cancer diagnosis, we find that non-negative matrix factorisation (NMF) clearly outperforms principal components analysis.Our results highlight the importance of tailoring the feature selection and classification methodology to the sample size and biological context of the DNA methylation study. The Elastic Net emerges as a powerful classification algorithm for large-scale DNA methylation studies, while NMF does well in the unsupervised context. The insights presented here will be useful to any study embarking on large-scale DNA methylation profiling using Illumina Infinium beadarrays.

Dataset Information

Combining microarray-based genomic selection (MGS) with the Illumina Genome Analyzer platform to sequence diploid target regions.

Publications

Combining microarray-based genomic selection (MGS) with the Illumina Genome Analyzer platform to sequence diploid target regions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets