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Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements [HuGene2.0st]

ABSTRACT: Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements [HuGene2.0st]

PROVIDER: PRJNA451410 | ENA |

REPOSITORIES: ENA

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Project description:MotivationA synoptic view of the human genome benefits chiefly from the application of nucleic acid sequencing and microarray technologies. These platforms allow interrogation of patterns such as gene expression and DNA methylation at the vast majority of canonical loci, allowing granular insights and opportunities for validation of original findings. However, problems arise when validating against a "gold standard" measurement, since this immediately biases all subsequent measurements towards that particular technology or protocol. Since all genomic measurements are estimates, in the absence of a "gold standard" we instead empirically assess the measurement precision and sensitivity of a large suite of genomic technologies via a consensus modelling method called the row-linear model. This method is an application of the American Society for Testing and Materials Standard E691 for assessing interlaboratory precision and sources of variability across multiple testing sites. Both cross-platform and cross-locus comparisons can be made across all common loci, allowing identification of technology- and locus-specific tendencies.ResultsWe assess technologies including the Infinium MethylationEPIC BeadChip, whole genome bisulfite sequencing (WGBS), two different RNA-Seq protocols (PolyA+ and Ribo-Zero) and five different gene expression array platforms. Each technology thus is characterised herein, relative to the consensus. We showcase a number of applications of the row-linear model, including correlation with known interfering traits. We demonstrate a clear effect of cross-hybridisation on the sensitivity of Infinium methylation arrays. Additionally, we perform a true interlaboratory test on a set of samples interrogated on the same platform across twenty-one separate testing laboratories.Availability and implementationA full implementation of the row-linear model, plus extra functions for visualisation, are found in the R package consensus at https://github.com/timpeters82/consensus.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:Recent genome-scale ChIP-chip studies of transcription factors have shown that a low percentage of experimentally determined binding sites contain the consensus motif for the immunoprecipitated factor. In most cases, differences between in vivo target sites that contain or lack a consensus motif have not been explored. We have previously shown that most sites to which E2F family members are bound in vivo do not contain E2F consensus motifs. The main purpose of this study was to develop an understanding of how E2F binding specificity is achieved in vivo. In particular, we have addressed how E2F family members are recruited to core promoter regions that lack a consensus motif and are excluded from other regions that contain a consensus motif. Using promoter and ENCODE arrays, we have shown that the predominant factors specifying whether E2F is recruited to an in vivo binding site are a) the site must be in a core promoter and b) the promoter region must be utilized as a promoter by the transcriptional machinery in that particular cell type. We have tested three models for recruitment of E2F to core promoters lacking a consensus site, including a) indirect recruitment, b) looping to the core promoter mediated by an E2F bound to a distal consensus motif, and c) assisted binding of E2F to a site that weakly resembles an E2F consensus motif within the core promoter. To test these models, we developed a new in vivo assay, termed eChIP, which allows analysis of transcription factor binding to isolated promoter fragments. Our findings suggest that in vivo a) the presence of a consensus motif is not sufficient to recruit E2Fs, b) E2Fs can bind to isolated regions that lack a consensus motif, and c) binding can require regions other than the best match to the E2F PWM in the core promoter. Keywords: E2F, ChIP-chip, transcription factor binding, consensus motifs 37 ChIP-chip arrays (of these, 14 array sets are biological duplicates). 22 samples are included in this series, the rest can be found in supplementary info to the following papers: Xu 2007, Jin 2006, Komashko 2008

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Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements [HuGene2.0st]

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