Dataset Information

Generalizing moving averages for tiling arrays using combined p-value statistics.

ABSTRACT: High density tiling arrays are an effective strategy for genome-wide identification of transcription factor binding regions. Sliding window methods that calculate moving averages of log ratios or t-statistics have been useful for the analysis of tiling array data. Here, we present a method that generalizes the moving average approach to evaluate sliding windows of p-values by using combined p-value statistics. In particular, the combined p-value framework can be useful in situations when taking averages of the corresponding test-statistic for the hypothesis may not be appropriate or when it is difficult to assess the significance of these averages. We exhibit the strengths of the combined p-values methods on Drosophila tiling array data and assess their ability to predict genomic regions enriched for transcription factor binding. The predictions are evaluated based on their proximity to target genes and their enrichment of known transcription factor binding sites. We also present an application for the generalization of the moving average based on integrating two different tiling array experiments.

SUBMITTER: Kechris KJ

PROVIDER: S-EPMC2942027 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Generalizing moving averages for tiling arrays using combined p-value statistics.

Kechris Katerina J KJ Biehs Brian B Kornberg Thomas B TB

Statistical applications in genetics and molecular biology 20100806

High density tiling arrays are an effective strategy for genome-wide identification of transcription factor binding regions. Sliding window methods that calculate moving averages of log ratios or t-statistics have been useful for the analysis of tiling array data. Here, we present a method that generalizes the moving average approach to evaluate sliding windows of p-values by using combined p-value statistics. In particular, the combined p-value framework can be useful in situations when taking ...[more]

PMID: 20812907

Similar Datasets

Project description:Array comparative genomic hybridization is a fast and cost-effective method for detecting, genotyping, and comparing the genomic sequence of unknown bacterial isolates. This method, as with all microarray applications, requires adequate coverage of probes targeting the regions of interest. An unbiased tiling of probes across the entire length of the genome is the most flexible design approach. However, such a whole-genome tiling requires that the genome sequence is known in advance. For the accurate analysis of uncharacterized bacteria, an array must query a fully representative set of sequences from the species' pan-genome. Prior microarrays have included only a single strain per array or the conserved sequences of gene families. These arrays omit potentially important genes and sequence variants from the pan-genome.This paper presents a new probe selection algorithm (PanArray) that can tile multiple whole genomes using a minimal number of probes. Unlike arrays built on clustered gene families, PanArray uses an unbiased, probe-centric approach that does not rely on annotations, gene clustering, or multi-alignments. Instead, probes are evenly tiled across all sequences of the pan-genome at a consistent level of coverage. To minimize the required number of probes, probes conserved across multiple strains in the pan-genome are selected first, and additional probes are used only where necessary to span polymorphic regions of the genome. The viability of the algorithm is demonstrated by array designs for seven different bacterial pan-genomes and, in particular, the design of a 385,000 probe array that fully tiles the genomes of 20 different Listeria monocytogenes strains with overlapping probes at greater than twofold coverage.PanArray is an oligonucleotide probe selection algorithm for tiling multiple genome sequences using a minimal number of probes. It is capable of fully tiling all genomes of a species on a single microarray chip. These unique pan-genome tiling arrays provide maximum flexibility for the analysis of both known and uncharacterized strains.

Project description:Whole genome tiling arrays provide a high resolution platform for profiling of genetic, epigenetic, and gene expression polymorphisms. In this study we surveyed natural genomic variation in cytosine methylation among Arabidopsis thaliana wild accessions Columbia (Col) and Vancouver (Van) by comparing hybridization intensity difference between genomic DNA digested with either methylation-sensitive (HpaII) or -insensitive (MspI) restriction enzyme. Single Feature Polymorphisms (SFPs) were assayed on a full set of 1,683,620 unique features of Arabidopsis Tiling Array 1.0F (Affymetrix), while constitutive and polymorphic CG methylation were assayed on a subset of 54,519 features, which contain a 5'CCGG3' restriction site. 138,552 SFPs (1% FDR) were identified across enzyme treatments, which preferentially accumulated in pericentromeric regions. Our study also demonstrates that at least 8% of all analyzed CCGG sites were constitutively methylated across the two strains, while about 10% of all analyzed CCGG sites were differentially methylated between the two strains. Within euchromatin arms, both constitutive and polymorphic CG methylation accumulated in central regions of genes but under-represented toward the 5' and 3' ends of the coding sequences. Nevertheless, polymorphic methylation occurred much more frequently in gene ends than constitutive methylation. Inheritance of methylation polymorphisms in reciprocal F1 hybrids was predominantly additive, with F1 plants generally showing levels of methylation intermediate between the parents. By comparing gene expression profiles, using matched tissue samples, we found that magnitude of methylation polymorphism immediately upstream or downstream of the gene was inversely correlated with the degree of expression variation for that gene. In contrast, methylation polymorphism within genic region showed weak positive correlation with expression variation. Our results demonstrated extensive genetic and epigenetic polymorphisms between Arabidopsis accessions and suggested a possible relationship between natural CG methylation variation and gene expression variation.

Project description:BackgroundRNAi screens via pooled short hairpin RNAs (shRNAs) have recently become a powerful tool for the identification of essential genes in mammalian cells. In the past years, several pooled large-scale shRNA screens have identified a variety of genes involved in cancer cell proliferation. All of those studies employed microarray analysis, utilizing either the shRNA's half hairpin sequence or an additional shRNA-associated 60 nt barcode sequence as a molecular tag. Here we describe a novel method to decode pooled RNAi screens, namely barcode tiling array analysis, and demonstrate how this approach can be used to precisely quantify the abundance of individual shRNAs from a pool.ResultsWe synthesized DNA microarrays with six overlapping 25 nt long tiling probes complementary to each unique 60 nt molecular barcode sequence associated with every shRNA expression construct. By analyzing dilution series of expression constructs we show how our approach allows quantification of shRNA abundance from a pool and how it clearly outperforms the commonly used analysis via the shRNA's half hairpin sequences. We further demonstrate how barcode tiling arrays can be used to predict anti-proliferative effects of individual shRNAs from pooled negative selection screens. Out of a pool of 305 shRNAs, we identified 28 candidate shRNAs to fully or partially impair the viability of the breast carcinoma cell line MDA-MB-231. Individual validation of a subset of eleven shRNA expression constructs with potential inhibitory, as well as non-inhibitory, effects on the cell line proliferation provides further evidence for the accuracy of the barcode tiling approach.ConclusionsIn summary, we present an improved method for the rapid, quantitative and statistically robust analysis of pooled RNAi screens. Our experimental approach, coupled with commercially available lentiviral vector shRNA libraries, has the potential to greatly facilitate the discovery of putative targets for cancer therapy as well as sensitizers of drug toxicity.

Dataset Information

Generalizing moving averages for tiling arrays using combined p-value statistics.

Publications

Generalizing moving averages for tiling arrays using combined p-value statistics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets