ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions

Rashid, Naim U.; Giresi, Paul G.; Ibrahim, Joseph G.; Sun, Wei; Lieb, Jason D.

doi:10.1186/gb-2011-12-7-r67

Cited by 170 publications

(187 citation statements)

References 43 publications

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“…This implies that it can be integrated with peak algorithms that use bins as raw data (Ji et al 2008;Kharchenko et al 2008;Zhang et al 2008;Rashid et al 2011). Here, we demonstrate the advantages of our approach by adapting the peak detection algorithm used by ENCODE, namely the ChIP-seq processing pipeline (SPP) (Kharchenko et al 2008).…”

Section: Adjusting Binding Quantification For Gc-bias Reduces Batch Ementioning

confidence: 97%

“…One reason is that most peak calling algorithms operate on bin level information. Specifically, these algorithms define bins, compute coverage in these bins, and then peaks are inferred from these coverage measurements (Ji et al 2008;Kharchenko et al 2008;Zhang et al 2008;John et al 2011;Rashid et al 2011). Here we develop a method that makes use of an approximation that permits the adaptation of published peak calling algorithms so that they adjust for GC-content bias.…”

Section: Mixture Model Estimates Gc-content Effect For Background Andmentioning

confidence: 99%

“…The regions reported by this analysis are referred to as peaks due to the appearance of the coverage plots (Pepke et al 2009). Several competing peak detection algorithms have been described in the literature (Ji et al 2008;Jothi et al 2008;Kharchenko et al 2008;Valouev et al 2008;Zhang et al 2008;Rozowsky et al 2009;John et al 2011;Rashid et al 2011). Although details of these competing approaches vary, most follow similar general principles.…”

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confidence: 99%

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Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Teng

Irizarry

2017

Genome Res.

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The main application of ChIP-seq technology is the detection of genomic regions that bind to a protein of interest. A large part of functional genomics' public catalogs is based on ChIP-seq data. These catalogs rely on peak calling algorithms that infer protein-binding sites by detecting genomic regions associated with more mapped reads (coverage) than expected by chance, as a result of the experimental protocol's lack of perfect specificity. We find that GC-content bias accounts for substantial variability in the observed coverage for ChIP-seq experiments and that this variability leads to false-positive peak calls. More concerning is that the GC effect varies across experiments, with the effect strong enough to result in a substantial number of peaks called differently when different laboratories perform experiments on the same cell line. However, accounting for GC content bias in ChIP-seq is challenging because the binding sites of interest tend to be more common in high GC-content regions, which confounds real biological signals with unwanted variability. To account for this challenge, we introduce a statistical approach that accounts for GC effects on both nonspecific noise and signal induced by the binding site. The method can be used to account for this bias in binding quantification as well to improve existing peak calling algorithms. We use this approach to show a reduction in false-positive peaks as well as improved consistency across laboratories.

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Section: Adjusting Binding Quantification For Gc-bias Reduces Batch Ementioning

confidence: 97%

Section: Mixture Model Estimates Gc-content Effect For Background Andmentioning

confidence: 99%

mentioning

confidence: 99%

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Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Teng

Irizarry

2017

Genome Res.

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“…We also analyzed the accuracy of the widely used peak-finding algorithms MACS 5 , FindPeaks 14 , F-Seq 6 , ZINBA 7 and SICER 15 on the same data. True positives were defined as RefSeq promoters with abovemedian gene expression.…”

Section: Signal Detection Using Dfiltermentioning

confidence: 99%

“…For comparison, we evaluated two other methods, F-Seq 6 and ZINBA 7 , that were designed for open chromatin detection. DNase-seq and FAIRE-seq data from 'Tier 1' human cell lines of the ENCODE project (http://genome.ucsc.edu/encode/cellTypes.html) were used in this analysis 3 .…”

Section: A N a Ly S I Smentioning

confidence: 99%

Uniform, optimal signal processing of mapped deep-sequencing data

et al. 2013

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A Detailed Protocol for Formaldehyde‐Assisted Isolation of Regulatory Elements (FAIRE)

Simon

Giresi

Davis

et al. 2013

CP Molecular Biology

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Nucleosome displacement is a key event in the regulation of gene expression in the eukaryotic genome. This unit details an approach called Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE) for isolating nucleosome-depleted regions. FAIRE does not rely on the use of antibodies or enzymes, and has proven successful in most eukaryotic cells and tissues. The set of regulatory elements enriched by FAIRE is similar to those identified through DNase hypersensitivity. The enriched fragments can be detected by quantitative PCR, tiling DNA microarrays, or next-generation sequencing. Although the signal-to-noise ratio is typically lower than that observed for DNase assays, FAIRE has high sample-to-sample reproducibility, requires very low amounts of input material, is inexpensive, is amenable to high-throughput adaptations, and is a relatively simple procedure with a high rate of success, even for those without extensive experience in molecular biology protocols.

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ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions

Cited by 170 publications

References 43 publications

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Uniform, optimal signal processing of mapped deep-sequencing data

A Detailed Protocol for Formaldehyde‐Assisted Isolation of Regulatory Elements (FAIRE)

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