Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
BMC Bioinformatics. 2021 Apr 20;22(1):201. doi: 10.1186/s12859-021-04097-5.
A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq.
We present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips .
ChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed.
评估定量 ChIP-seq 分析（如峰调用和差异结合）的主要挑战是缺乏可靠的真实数据。准确模拟 ChIP-seq 数据可以减轻这一挑战，但现有的框架要么过于繁琐而无法应用于全基因组，要么无法模拟 ChIP-seq 中的许多重要实验条件。
我们提出了 ChIPs，这是一个使用关键实验步骤的统计模型快速模拟 ChIP-seq 数据的工具包。我们展示了 ChIPs 可用于一系列应用，包括基准分析工具和评估各种实验参数的影响。ChIPs 是用 C++编写的独立命令行程序实现的，可从 https://github.com/gymreklab/chips 获得。
ChIPs 是一个高效的 ChIP-seq 模拟框架，它可以在灵活的实验条件范围内生成逼真的数据集。它可以作为各种需要真实数据的 ChIP-seq 分析的重要组成部分。
