A novel statistical method for quantitative comparison of multiple ChIP-seq datasets

Chen, Li; Wang, Chi; Qin, Zhaohui S.; Wu, Hao

doi:10.1093/bioinformatics/btv094

Cited by 48 publications

(49 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ChIPComp [29] Using a generalized linear model with the Poisson distribution to detect differential peaks…”

Section: Namementioning

confidence: 99%

An introduction to computational tools for differential binding analysis with ChIP‐seq data

Tu¹,

Shao²

2017

Quant. Biol.

View full text Add to dashboard Cite

Background: Gene transcription in eukaryotic cells is collectively controlled by a large panel of chromatin associated proteins and ChIP-seq is now widely used to locate their binding sites along the whole genome. Inferring the differential binding sites of these proteins between biological conditions by comparing the corresponding ChIP-seq samples is of general interest, yet it is still a computationally challenging task. Results: Here, we briefly review the computational tools developed in recent years for differential binding analysis with ChIP-seq data. The methods are extensively classified by their strategy of statistical modeling and scope of application. Finally, a decision tree is presented for choosing proper tools based on the specific dataset. Conclusions: Computational tools for differential binding analysis with ChIP-seq data vary significantly with respect to their applicability and performance. This review can serve as a practical guide for readers to select appropriate tools for their own datasets.

show abstract

“…ChIPComp [29] Using a generalized linear model with the Poisson distribution to detect differential peaks…”

Section: Namementioning

confidence: 99%

An introduction to computational tools for differential binding analysis with ChIP‐seq data

Tu¹,

Shao²

2017

Quant. Biol.

View full text Add to dashboard Cite

show abstract

“…We selected two representative computational tools for group-level differential ChIP-seq analysis to compare with MAnorm2. The two tools, named ChIPComp [18] and PePr [28], represent two broad classes of methods for differential ChIP-seq analysis [6,14]. More specifically, ChIPComp requires its users to provide pre-defined peaks for each single ChIP-seq sample while PePr has no such requirement (we could see that MAnorm2 and…”

Section: By Conducting a Comparison Of The H3k4me3 Chip-seq Samples Bmentioning

confidence: 99%

“…It alleviates the problem of S/N ratio by using common peaks (genomic regions enriched with ChIPseq reads) of the two samples to infer a reference model for globally normalizing ChIPseq signals [17]. This strategy has also been exploited by later methods for differential ChIP-seq analysis [18]. In MAnorm2, we extended MAnorm to normalization of any number of ChIP-seq samples.…”

Section: Introductionmentioning

confidence: 99%

MAnorm2 for quantitatively comparing groups of ChIP-seq samples

Tan

et al. 2020

Preprint

View full text Add to dashboard Cite

Eukaryotic gene transcription is regulated by a large cohort of chromatin associated proteins, and inferring their differential binding sites between cellular contexts requires a rigorous comparison of the corresponding ChIP-seq data. We present MAnorm2, a new computational tool for quantitatively comparing groups of ChIP-seq samples. MAnorm2 uses a hierarchical strategy for ChIP-seq data normalization and performs differential analysis by assessing within-group variability of ChIP-seq signals under an empirical Bayes framework. In this framework, MAnorm2 considers the abundance of differential ChIP-seq signals between groups of samples and the possibility of different within-group variability between groups. When samples in each group are biological replicates, MAnorm2 can reliably identify differential binding events even between highly similar

show abstract

“…However, this approach ignores the quantitative differences of methylation levels and thus could lead to undesirable results. A number of methods have been developed to perform quantitative comparison of ChIP-seq data including, QChIPat[93], DBChIP[94], MAnorm [95], ChIPComp [96], diffReps [97], DIME [98], ChIPnorm [99] and MMDiff [100], all of which could be used for DMR calling from captured data. In particular, MEDIPS [101] is specifically designed for MeDIP-seq data, and implemented as an easy-to-use, well-documented Bioconductor package.…”

Section: Dna Methylationmentioning

confidence: 99%

Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data

Qin

Conneely

et al. 2016

Stat Biosci

Self Cite

View full text Add to dashboard Cite

With the rapid development of high throughput technologies such as array and next generation sequencing (NGS), genome-wide, nucleotide-resolution epigenomic data are increasingly available. In recent years, there has been particular interest in data on DNA methylation and 3-dimensional (3D) chromosomal organization, which are believed to hold keys to understand biological mechanisms, such as transcription regulation, that are closely linked to human health and diseases. However, small sample size, complicated correlation structure, substantial noise, biases, and uncertainties, all present difficulties for performing statistical inference. In this review, we present an overview of the new technologies that are frequently utilized in studying DNA methylation and 3D chromosomal organization. We focus on reviewing recent developments in statistical methodologies designed for better interrogating epigenomic data, pointing out statistical challenges facing the field whenever appropriate.

show abstract

A novel statistical method for quantitative comparison of multiple ChIP-seq datasets

Abstract: An R software package ChIPComp is freely available at http://web1.sph.emory.edu/users/hwu30/software/ChIPComp.html.

Cited by 48 publications

References 20 publications

An introduction to computational tools for differential binding analysis with ChIP‐seq data

An introduction to computational tools for differential binding analysis with ChIP‐seq data

MAnorm2 for quantitatively comparing groups of ChIP-seq samples

Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data

Contact Info

Product

Resources

About