Comparative ChIP-seq (Comp-ChIP-seq): a practical guideline for experimental design and a novel computational methodology

Abstract

In order to evaluate cell-and disease-specific changes in the interacting strength of chromatin targets, ChIP-seq signal across multiple conditions must undergo robust normalization. However, this is not possible using the standard ChIP-seq scheme, which lacks a reference for the control of biological and experimental variabilities. While several studies have recently 20proposed different solutions to circumvent this problem, substantial technical and analytical differences among methodologies could hamper the experimental reproducibility and the quantitative accuracy. Here we propose a local regression strategy to accurately normalize ChIP-seq data in a genome-wide manner. Overall, our proposed experimental and computational standard for comparative ChIP-seq (Comp-ChIP-seq) will increase 25 experimental reproducibility, thereby reducing this major confounding factor in interpreting ChIP-seq results.

Mutant EZH2 Induces a Pre-malignant Lymphoma Niche by Reprogramming the Immune Response

“…Resulting .fastq files were aligned to mouse mm10 and Drosophila dm6 genomes using bwa-mem function of the BWA suite. ChIPseq data was normalized to dm6 spike-in reads using CompChIPseq algorithm, an analysis approach developed by (Blanco et al, 2019), which performs local cyclic Loess regression of spike-in reads to accurately normalize ChIP-seq data in a genomewide manner. Read density heat maps were generated using DeepTools suite (Ramirez et al, 2014).…”

siQ-ChIP: A reverse-engineered quantitative framework for ChIP-sequencing

“…These blues result from concerns regarding antibody behavior (specificity and IP efficiency) and from a perceived challenge regarding the ability to quantitatively plot ChIP-seq results. Regarding the latter, a host of methods have been introduced in efforts to add a mean-* Correspondence: bradley.dickson@vai.org, scott.rothbart@vai.org Center for Epigenetics, Van Andel Research Institute, 333 Bostwick Ave., 49503 Grand Rapids, MI, U.S.A. Full list of author information is available at the end of the article ingful "y-axis" to ChIP-seq datasets [2,3,4,5,6,7], calls to arms have been issued [6,8,9,10], and additional ChIP methods presenting newer solutions are introduced regularly [11]. Herein, we apply physics-based mathematical modeling to derive a quantitative framework for ChIP.…”