Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation.

Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn's disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.

GenomeDISCO: a concordance score for chromosome conformation capture experiments using random walks on contact map graphs.

Motivation: The three-dimensional organization of chromatin plays a critical role in gene regulation and disease. High-throughput chromosome conformation capture experiments such as Hi-C are used to obtain genome-wide maps of three-dimensional chromatin contacts. However, robust estimation of data quality and systematic comparison of these contact maps is challenging due to the multi-scale, hierarchical structure of chromatin contacts and the resulting properties of experimental noise in the data. Measuring concordance of contact maps is important for assessing reproducibility of replicate experiments and for modeling variation between different cellular contexts. Results: We introduce a concordance measure called DIfferences between Smoothed COntact maps (GenomeDISCO) for assessing the similarity of a pair of contact maps obtained from chromosome conformation capture experiments. The key idea is to smooth contact maps using random walks on the contact map graph, before estimating concordance. We use simulated datasets to benchmark GenomeDISCO's sensitivity to different types of noise that affect chromatin contact maps. When applied to a large collection of Hi-C datasets, GenomeDISCO accurately distinguishes biological replicates from samples obtained from different cell types. GenomeDISCO also generalizes to other chromosome conformation capture assays, such as HiChIP. Availability and implementation: Software implementing GenomeDISCO is available at https://github.com/kundajelab/genomedisco. Supplementary information: Supplementary data are available at Bioinformatics online.

Proteome-Wide Identification of RNA-dependent proteins and an emerging role for RNAs in Plasmodium falciparum protein complexes.

Ribonucleoprotein complexes are composed of RNA, RNA-dependent proteins (RDPs) and RNA-binding proteins (RBPs), and play fundamental roles in RNA regulation. However, in the human malaria parasite, Plasmodium falciparum, identification and characterization of these proteins are particularly limited. In this study, we use an unbiased proteome-wide approach, called R-DeeP, a method based on sucrose density gradient ultracentrifugation, to identify RDPs. Quantitative analysis by mass spectrometry identifies 898 RDPs, including 545 proteins not yet associated with RNA. Results are further validated using a combination of computational and molecular approaches. Overall, this method provides the first snapshot of the Plasmodium protein-protein interaction network in the presence and absence of RNA. R-DeeP also helps to reconstruct Plasmodium multiprotein complexes based on co-segregation and deciphers their RNA-dependence. One RDP candidate, PF3D7_0823200, is functionally characterized and validated as a true RBP. Using enhanced crosslinking and immunoprecipitation followed by high-throughput sequencing (eCLIP-seq), we demonstrate that this protein interacts with various Plasmodium non-coding transcripts, including the var genes and ap2 transcription factors.