Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

BioTile Identification of Differentially Methylated Regions in Tiling Microarray Data

ABSTRACT: This submission includes simulated data generated to test the BioTile algorithm's capability of identifying differentially methylated regions (DMR) located within an actual tiling array dataset. The data submitted was generated in silico to closely resemble DNA methylation profiles using Affymetrix Mouse Promoter 1.0R Tiling arrays. This simulated dataset of 40 arrays was generated using the "rnorm" function in R to create probe-wise distributions with the same mean and variance as our previously measured dataset (GSE43460).

ORGANISM(S): Mus musculus

SUBMITTER: Jerry Guintivano

PROVIDER: E-GEOD-43462 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

BioTile, a Perl based tool for the identification of differentially enriched regions in tiling microarray data.

Guintivano Jerry J Arad Michal M Tamashiro Kellie L K KL Gould Todd D TD Kaminsky Zachary A ZA

BMC bioinformatics 20130303

<h4>Background</h4>Genome-wide tiling array experiments are increasingly used for the analysis of DNA methylation. Because DNA methylation patterns are tissue and cell type specific, the detection of differentially methylated regions (DMRs) with small effect size is a necessary feature of tiling microarray 'peak' finding algorithms, as cellular heterogeneity within a studied tissue may lead to a dilution of the phenotypically relevant effects. Additionally, the ability to detect short length DMR ...[more]

PMID: 23452827

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Project description:This submission includes the sample data for a protocol covering differential expression analysis with TopHat and Cufflinks. The protocol also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-Seq analysis results. While the procedure assumes basic informatics skills, these tools assume little to no background with RNA-Seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The example data was generated in silico to closely resemble a real experiment in Drosophila melanogaster. First, expression values in cultured S2 cells were calculated for FlyBase 5.2 transcripts. These values were used to generate 3 sequencing replicates for condition "C1", with underlying variability in expression across replicates simulated by fitting a negative binomial model through the real S2 read count data. A second simulated condition "C2" was generated by perturbing expression for 300 randomly selected genes. Genes were perturbed by selecting the most highly expressed isoform and increasing its relative expression by three fold. Three replicates of this condtion were sequenced as above. Simulated sequencing was performed by picking a transcript from the FlyBase transcriptome with equal to its abundance, choosing a fragment length from a normal distribution with mean = 180bp and standard deviation = 20bp, and then choosing a start point for the fragment within the transcript uniformly at random. Total sequencing yield for each replicate was chosen to match that of the real S2 data. Each replicate was mapped to the fly genome with TopHat v 1.3.1 seperately. The replicates were assembled seperately with Cufflinks v 1.1.0. The replicate assemblies were merged with Cuffmerge. This merged assembly was then analysed for differentially expressed and regulated genes with Cuffdiff.

Project description:The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and spike-ins comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. This SuperSeries is composed of the following subset Series: GSE9732: Spike-in Experiment for ChIP-chip Simulation GSE9842: Systematic evaluation of variability in simulated ChIP-chip experiments GSE9848: Systematic evaluation of variability in simulated ChIP-chip experiments Kevin_Encode GSE9849: Systematic evaluation of variability in simulated ChIP-chip experiments Myles_Encode GSE10004: ENCODE Spike-In, Yale Group GSE10076: ENCODE spikein, amplified DNA samples, NimbleGen arrays GSE10090: ENCODE spikein, nonamplified DNA samples, NimbleGen arrays GSE10112: Systematic evaluation of variability in simulated ChIP-chip experiments Keywords: SuperSeries For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Refer to individual Series

Dataset Information

BioTile Identification of Differentially Methylated Regions in Tiling Microarray Data

Publications

BioTile, a Perl based tool for the identification of differentially enriched regions in tiling microarray data.

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