Comparative study on ChIP-seq data: normalization and binding pattern characterization

Taslim, Cenny; Wu, Jiejun; Yan, Pearlly S.; Singer, Gregory A. C.; Parvin, Jeffrey D.; Huang, Tim Hui-Ming; Lin, Shili; Huang, Kun

doi:10.1093/bioinformatics/btp384

Cited by 57 publications

(68 citation statements)

References 26 publications

(35 reference statements)

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“…S1 A and B). A few methods can compare binding signals from two conditions (6)(7)(8)(9), but they only analyze one protein at a time and do not consider replicate samples. Recently, several methods for analyzing multiple ChIP datasets have been developed for improving peak calling (10)(11)(12) and identifying combinatorial binding patterns of multiple proteins within a cell type (13)(14)(15)(16).…”

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confidence: 99%

Differential principal component analysis of ChIP-seq

Wang

Yang

2013

Proc. Natl. Acad. Sci. U.S.A.

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We propose differential principal component analysis (dPCA) for analyzing multiple ChIP- sequencing datasets to identify differential protein–DNA interactions between two biological conditions. dPCA integrates unsupervised pattern discovery, dimension reduction, and statistical inference into a single framework. It uses a small number of principal components to summarize concisely the major multiprotein synergistic differential patterns between the two conditions. For each pattern, it detects and prioritizes differential genomic loci by comparing the between-condition differences with the within-condition variation among replicate samples. dPCA provides a unique tool for efficiently analyzing large amounts of ChIP-sequencing data to study dynamic changes of gene regulation across different biological conditions. We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein–DNA interactions.

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confidence: 99%

Differential principal component analysis of ChIP-seq

Wang

Yang

2013

Proc. Natl. Acad. Sci. U.S.A.

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“…It was shown in mRNA-Seq studies that normalization is a primary factor affecting the test of DE (Taslim et al 2009;Anders and Huber 2010;Bullard et al 2010;Robinson and Oshlack 2010). We therefore also used the results in DE to reflect the performance of the normalization methods.…”

Section: Test Of Differential Expressionmentioning

confidence: 99%

“…We therefore also used the results in DE to reflect the performance of the normalization methods. Based on previous studies (Taslim et al 2009;Bullard et al 2010;Robinson and Oshlack 2010), we used three different tests of DE, namely x 2 test, Poisson distribution, and binomial distribution. We define significantly changed microRNAs as those that have P-values <0.05 after Bonferroni corrections, and we present the DE test results in Figure 5.…”

Section: Test Of Differential Expressionmentioning

confidence: 99%

Evaluation of normalization methods in mammalian microRNA-Seq data

Garmire

Subramaniam²

2012

RNA

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Simple total tag count normalization is inadequate for microRNA sequencing data generated from the next generation sequencing technology. However, so far systematic evaluation of normalization methods on microRNA sequencing data is lacking. We comprehensively evaluate seven commonly used normalization methods including global normalization, Lowess normalization, Trimmed Mean Method (TMM), quantile normalization, scaling normalization, variance stabilization, and invariant method. We assess these methods on two individual experimental data sets with the empirical statistical metrics of mean square error (MSE) and Kolmogorov-Smirnov (K-S) statistic. Additionally, we evaluate the methods with results from quantitative PCR validation. Our results consistently show that Lowess normalization and quantile normalization perform the best, whereas TMM, a method applied to the RNA-Sequencing normalization, performs the worst. The poor performance of TMM normalization is further evidenced by abnormal results from the test of differential expression (DE) of microRNA-Seq data.Comparing with the models used for DE, the choice of normalization method is the primary factor that affects the results of DE. In summary, Lowess normalization and quantile normalization are recommended for normalizing microRNA-Seq data, whereas the TMM method should be used with caution.

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“…via ChIP-Seq). Examples of these approaches include the ENCODE Cluster Aggregation Tool (CAGT) [3], or the clustering of genes based on PolII binding profiles performed in [9]. Local approaches have to address two challenging problems: aligning the peaks to a reference, and standardising the peaks so that they can be represented as vectors of equal dimensions.…”

Section: Introductionmentioning

confidence: 99%

“…transcription start sites (TSS) [9] or transcription factor binding sites from auxiliary ChIP-Seq experiments [3]). The regions are then standardised either by rescaling to a fixed gene length [9] or by applying windows of fixed length either side of the anchor points [3] irrespective of the true extent of the local enrichment. These strategies may be plausible for certain applications.…”

Section: Introductionmentioning

confidence: 99%

DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks

2013

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Background: Functional genomic and epigenomic research relies fundamentally on sequencing based methods like ChIP-seq for the detection of DNA-protein interactions. These techniques return large, high dimensional data sets with visually complex structures, such as multi-modal peaks extended over large genomic regions. Current tools for visualisation and data exploration represent and leverage these complex features only to a limited extent. Results: We present DGW, an open source software package for simultaneous alignment and clustering of multiple epigenomic marks. DGW uses Dynamic Time Warping to adaptively rescale and align genomic distances which allows to group regions of interest with similar shapes, thereby capturing the structure of epigenomic marks. We demonstrate the effectiveness of the approach in a simulation study and on a real epigenomic data set from the ENCODE project. Conclusions: Our results show that DGW automatically recognises and aligns important genomic features such as transcription start sites and splicing sites from histone marks. DGW is available as an open source Python package.

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Comparative study on ChIP-seq data: normalization and binding pattern characterization

Abstract: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/.

Cited by 57 publications

References 26 publications

Differential principal component analysis of ChIP-seq

Differential principal component analysis of ChIP-seq

Evaluation of normalization methods in mammalian microRNA-Seq data

DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks

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