Perm-seq: Mapping Protein-DNA Interactions in Segmental Duplication and Highly Repetitive Regions of Genomes with Prior-Enhanced Read Mapping

Abstract

Segmental duplications and other highly repetitive regions of genomes contribute significantly to cells’ regulatory programs. Advancements in next generation sequencing enabled genome-wide profiling of protein-DNA interactions by chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq). However, interactions in highly repetitive regions of genomes have proven difficult to map since short reads of 50–100 base pairs (bps) from these regions map to multiple locations in reference genomes. Standard analytical methods discard such multi-mapping reads and the few that can accommodate them are prone to large false positive and negative rates. We developed Perm-seq, a prior-enhanced read allocation method for ChIP-seq experiments, that can allocate multi-mapping reads in highly repetitive regions of the genomes with high accuracy. We comprehensively evaluated Perm-seq, and found that our prior-enhanced approach significantly improves multi-read allocation accuracy over approaches that do not utilize additional data types. The statistical formalism underlying our approach facilitates supervising of multi-read allocation with a variety of data sources including histone ChIP-seq. We applied Perm-seq to 64 ENCODE ChIP-seq datasets from GM12878 and K562 cells and identified many novel protein-DNA interactions in segmental duplication regions. Our analysis reveals that although the protein-DNA interactions sites are evolutionarily less conserved in repetitive regions, they share the overall sequence characteristics of the protein-DNA interactions in non-repetitive regions.

Generative modeling of multi-mapping reads with mHi-C advances analysis of Hi-C studies

“…In addition to discovery of novel TADs (Figure 5—figure supplement 3) by filling in the gaps in the contact matrix and boosting the domain signals, mHi-C also refines TAD boundaries (Figure 5—figure supplements 4 and 5), and eliminates potential false positive TADs that are split by the contact depleted gaps in Uni-setting (Figure 5—figure supplements 6–8). The novel, adjusted, and eliminated TADs are largely supported by CTCF signal identified using both uni- and multi-reads ChIP-seq datasets (Zeng et al, 2015) as well as convergent CTCF motifs (Figure 5—figure supplement 2D), providing support for mHi-C driven modifications to these TADs and revealing a slightly lower false discovery rate for mHi-C compared to Uni-setting (Figure 5C, Figure 5—figure supplement 2E, and Figure 5—figure supplement 9).…”

“…Such reads from repetitive regions can be aligned to multiple positions (Figure 1A) and are referred to as multi-mapping reads or multi-reads for short. The critical drawbacks of discarding multi-reads have been recognized in other classes of genomic studies such as transcriptome sequencing (RNA-seq) (Li and Dewey, 2011), chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) (Chung et al, 2011; Zeng et al, 2015), as well as genome-wide mapping of protein-RNA binding sites (CLIP-seq or RIP-seq) (Zhang and Xing, 2017). More recently, (Sun et al, 2018) and (Cournac et al, 2016) argued for a fundamental role of repeat elements in the 3D folding of genomes, highlighting the role of higher order chromatin architecture in repeat expansion disorders.…”

Generative Modeling of Multi-mapping Reads with mHi-C Advances Analysis of High Throughput Genome-wide Conformation Capture Studies

“…29-31). The novel, adjusted, and eliminated TADs are largely supported by CTCF ChIP-seq signal 21 as well as convergent CTCF motifs ( Supplementary Fig. 25d), providing evidence for mHi-C driven modifications to these TADs and revealing a lower false discovery rate for mHi-C compared to Uni-setting ( Fig.…”

“…1a -left) and are referred to as multi-mapping reads or multi-reads for short. The critical drawbacks of discarding multi-reads have been recognized in other classes of genomic studies such as transcriptome sequencing (RNA-seq) 19 , chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) 20,21 , as well as genome-wide mapping of protein-RNA binding sites (CLIP-seq or RIP-seq) 22 . In this work, we developed mHi-C, a hierarchical model that probabilistically allocates Hi-C multi-reads to their most likely genomic origins by utilizing specific characteristics of the paired-end reads of the Hi-C assay.…”

Rapid, Paralog-Sensitive CNV Analysis of 2457 Human Genomes Using QuicK-mer2

“…One method focused on detecting structural variation using discordant read pairs includes VariationHunter, which constructs consistent clusters of reads, including probabilistic assignment of reads that have multiple mappings [24]. Similarly, probabilistic approaches have been used to call ChIP-seq peaks from multiply-mapped reads [25,26]. These approaches are geared toward discovering genetic variation or functional genomics signals in repetitive sequences, and generally work by modeling the distribution of signals among multiple read placements.…”