4C-ker: A method to reproducibly identify genome-wide interactions captured by 4C-Seq experiments

Abstract

Abstract4C-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. PLOS Computational Biology |

Epigenetic drift during long-term culture of cells in vitro

“…Bait-specific primers for the circularized inverse PCR are listed in Supplemental Table S7. Amplicons were sequenced on a HiSeq2500 platform (Illumina), mapped to the reference genome (hg19) and analyzed with the Hidden-Markov-Model-based tool 4Cker (Raviram et al 2016). This tool corrects for increasing signal noise in trans chromosomal interactions and far-cis chromosomal interactions by adaptive window sizes.…”

Alternative splicing is a developmental switch for hTERT expression

“…Sequencing library was prepared as described using Expand Long Template polymerase (Roche) and Illumina adaptor sequence-containing primers unique to the hTERT fragend listed in Supplementary Table 1. Sequencing was performed by the NYU Genome Technology Core on Illumina HiSeq 4000 sequencer. Sequencing data was analyzed using the 4Cker software package described in (14) using the recommended parameters.…”

“…Active REs are known to directly contact the promoters they regulate and have increased chromatin accessibility, we therefore, performed Circular Chromosomal Conformation Capture (4C) and ATAC-seq in H7 human embryonic stem cells (ESC) and adult retinal pigment epithelial (ARPE) cells that are proficient for p53 and have a stable diploid karyotype. We used a 1.1 kb fragment spanning the hTERT promoter as bait and captured sequences that are in close proximity by 4C (13), using 4Cker software to analyze Illumina sequencing reads (Figure S1B-C) (14). Our analysis identified loci that contact the hTERT promoter exclusively in telomerase positive cells (H7) or in telomerase negative cells (ARPE), as well as loci that contact the TERT promoter in both cases.…”

The impact of endogenous retroviruses on nuclear organization and gene expression

“…Visual inspection of the interactions of this RLTR4 copy with the rest of the genome, revealed that the regions of higher interaction belonged to the A compartment. The 4C-ker pipeline [60] was used to quantify this and determine which regions on chromosome 3 interact at high frequency with the MuLV integration site. As shown in Fig.…”

“…Single-end reads were mapped to a reduced genome of unique 25bp fragments adjacent to DpnII sites (mm10) using bowtie2 [79] as per the details described in 4C-ker [60]. Reads that map to the Encode blacklist regions were removed from downstream analysis [80].…”