4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments.

Ramya Raviram,Sana Badri,Emily R Miraldi,Charlotte Proudhon,Christian L Müller,Richard Bonneau,Pedro P Rocha,Valentina Snetkova,Jane A Skok,Yi Fu,Emily Swanzey,Alexandre V Morozov

doi:10.1371/journal.pcbi.1004780

Abstract

4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or “bait”) that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

Highlights

Understanding the 3D organization of the genome and the intricacies of chromatin dynamics has been the focus of studies aimed at characterizing gene regulation in physiological processes and disease states [1, 2]
Circularized chromosome conformation capture, or 4C-Seq is a technique developed to identify regions of the genome that are in close spatial proximity to a single locus of interest (‘bait’)
Existing methods for 4C-Seq data analysis do not comprehensively identify interactions across the entire genome due to biases in the technique that are related to the decrease in 4C signal that results from increased 3D distance from the bait. To compensate for these weaknesses in existing methods we developed 4C-ker, a method that explicitly models these biases to improve the analysis of 4C-Seq to better understand the genome wide interaction profile of an individual locus

Summary

Introduction

Understanding the 3D organization of the genome and the intricacies of chromatin dynamics has been the focus of studies aimed at characterizing gene regulation in physiological processes and disease states [1, 2]. The development of chromosome conformation capture (3C) transformed the field of nuclear organization enabling identification of chromatin interactions at the molecular level and at the same time opening the door to high-throughput, genomewide techniques [5]. Hi-C, for example, captures all pairwise interactions in the nucleus and has revealed that chromosomes segregate into two distinct spatial compartments (A and B) depending on their transcriptional and epigenetic status [6]. These compartments are further subdivided into Topological Associated Domains (TADs), which are highly self-interacting megabase scale structures [7,8,9]. Circular chromosome conformation capture combined with massive parallel sequencing (4C-Seq) is currently the best option for obtaining the highest resolution interaction signal for a particular region of interest

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