# COPAR: A ChIP-Seq Optimal Peak Analyzer

Authors: Binhua Tang; Xihan Wang; Victor X. Jin
Journal: BioMed Research International
 (2017)

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## Abstract

Sequencing data quality and peak alignment efficiency of ChIP-sequencing profiles are directly related to the reliability and reproducibility of NGS experiments. Till now, there is no tool specifically designed for optimal peak alignment estimation and quality-related genomic feature extraction for ChIP-sequencing profiles. We developed open-sourced COPAR, a user-friendly package, to statistically investigate, quantify, and visualize the optimal peak alignment and inherent genomic features using ChIP-seq data from NGS experiments. It provides a versatile perspective for biologists to perform quality-check for high-throughput experiments and optimize their experiment design. The package COPAR can process mapped ChIP-seq read file in BED format and output statistically sound results for multiple high-throughput experiments. Together with three public ChIP-seq data sets verified with the developed package, we have deposited COPAR on GitHub under a GNU GPL license.


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## Body

## 1. Introduction

Next-generation sequencing (NGS) integrated with ChIP technology provides a genome-wide perspective for biomedical research and clinical diagnosis applications [1–3].Data quality and peak alignment of ChIP-sequencing profiles are directly related to the reliability and reproducibility of analysis results. For example, ChIP-seq data characterize alteration evidence for transcription factor (TF) binding activities in response to chemical or environmental stimuli, but if the ChIP-seq alignment is poorly selected, any follow-up analysis may lead to inaccurate TF binding results and inevitable loss of biological meanings [4, 5].The mostly investigated items in ChIP-seq peak calling procedures are peak number, false discovery rate (FDR), corresponding bin-size, and other statistical thresholds selected in each analysis. Without exception, such arguments form impenetrable barriers for biologists and bioinformaticians to choose a suitable pair condition for analyzing experimental results.And to our knowledge, few literatures or application notes focus on such topics; thus herein we propose a flexible package based on feature extraction and signal processing algorithms for solving such an argument-selection optimization problem in optimal peak alignment.In summary, the package COPAR can quantitatively measure NGS/ChIP-seq experiment quality through global peak alignment comparison and extract genomic features based on spectrum method for in-depth analysis of ChIP-sequencing profiles.


## 2. Materials and Methods

### 2.1. Optimal Peak Alignment Estimation

For determining optimal ChIP-seq alignment, we need to analyze peak numbers under specific argument constraints. Thus we acquire optimal peak numbers by constraining specific arguments, which can be formalized as a class of optimal track analysis, illustrated as(1)argmaxiPi,i∈Ns.t.fi≤χ,bi=β,pi≤δ,where Pi denotes a set of optimal peak numbers under corresponding argument constraints, fi stands for argument FDR, bi stands for bin-size, pi denotes p value threshold, and χ, β, and δ represent the presupposed argument values, respectively.


### 2.2. Spectrum-Based Genomic Feature Extraction

For a finite random variable sequence, its power spectrum is normally estimated from its autocorrelation sequence by use of discrete-time Fourier transform (DTFT), denoted as [6–8](2)Pω=12π∑n=-∞∞Cxxne-jnω,where Cxx denotes autocorrelation sequence of a discrete signal xn, defined as(3)Cxxi,j=EXi-μiXj-μjσiσj,where μ and σ stand for mean and variance, respectively.In our study, for consideration of the ChIP-seq data characteristics, we use 128 sampling points to calculate discrete Fourier transform, with the related sampling frequency 1 KHz.


## 2.1. Optimal Peak Alignment Estimation

For determining optimal ChIP-seq alignment, we need to analyze peak numbers under specific argument constraints. Thus we acquire optimal peak numbers by constraining specific arguments, which can be formalized as a class of optimal track analysis, illustrated as(1)argmaxiPi,i∈Ns.t.fi≤χ,bi=β,pi≤δ,where Pi denotes a set of optimal peak numbers under corresponding argument constraints, fi stands for argument FDR, bi stands for bin-size, pi denotes p value threshold, and χ, β, and δ represent the presupposed argument values, respectively.


## 2.2. Spectrum-Based Genomic Feature Extraction

For a finite random variable sequence, its power spectrum is normally estimated from its autocorrelation sequence by use of discrete-time Fourier transform (DTFT), denoted as [6–8](2)Pω=12π∑n=-∞∞Cxxne-jnω,where Cxx denotes autocorrelation sequence of a discrete signal xn, defined as(3)Cxxi,j=EXi-μiXj-μjσiσj,where μ and σ stand for mean and variance, respectively.In our study, for consideration of the ChIP-seq data characteristics, we use 128 sampling points to calculate discrete Fourier transform, with the related sampling frequency 1 KHz.


## 3. Results

The COPAR package was developed and open-sourced for academic biologists, and it uses built-in functions for determining optimal peak alignment candidate and extracting genomic features from ChIP-seq dataset.The package is designed to handle BED-formatted ChIP-seq data as input [9], and it can process single ChIP-seq for optimal peak alignment and feature extraction analysis, together with the capability to perform genome-wide statistical comparison for multiple ChIP-seq samples. The analysis flowchart for the package is given in Figure 1.Figure 1
Flowchart for optimal peak alignment estimation and genomic feature analysis with COPAR. The package can perform optimal peak estimation based on global alignment of ChIP-seq data; then it can utilize the frequency spectrum approach for genomic feature extraction and carries out statistical comparison for multiple ChIP-seq samples.It can automatically determine the optimal peak alignment with statistically meaningful FDR through fast global alignment comparison; the global comparison is subject to two statistical arguments, namely, bin-size andp value threshold.The functionalities of our developed package are largely complementary to and extend current tools used for ChIP-seq data analysis. The optimal peak alignment estimation is shown in Figures2(a) and 2(b); and the spectrum-based feature extraction is given in Figures 2(c) and 2(d). Figures 2(a) and 2(b) utilize heatmap to represent peak number and corresponding FDR candidate subject to each argument pair, bin-size (vertical axis), and p value threshold (horizontal axis), respectively; Figure 2(c) denotes the spectrum distribution of the global peak alignment candidate sequence, normalized with its frequency range [0,500] Hz and magnitude within [-40,-3] dB; Figure 2(d) denotes the randomized case.Figure 2
Global optimal peak analysis result subject to the arguments bin-size and FDR. (a) Global distributions for peak number candidates and (b) corresponding false discovery rate, subject to bin-size (vertical axis, from 100 through 500 bp) and p value threshold (horizontal axis, from 0.951 to 0.999), respectively; (c) genomic feature extraction based on spectrum distribution for global peak number candidates identified from COPAR; (d) spectrum distribution for the randomized sequence.
(a)
(b)
(c)
(d)


## 4. Conclusions

Based on global peak alignment, COPAR optimizes the argument selection in ChIP-seq analysis; meanwhile, COPAR utilizes the signal spectrum processing method to further extract genomic features and statistically compare multiple ChIP-seq samples for NGS high-throughput experiments.In summary, our developed package COPAR can process mapped read file in BED format and output statistically sound results for diverse high-throughput sequencing experiments; we further verified the package with three GEO ChIP-seq datasets as study cases, and we included the analysis results into the package manual. The developed package COPAR is currently available under a GNU GPL license fromhttps://github.com/gladex/COPAR.



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*Source: 5346793-2017-03-05.xml*