Gideon Shaked
Pronouns: he/him
Research Mentor(s): Matthew Patrick
Research Mentor School/College/Department: Department of Dermatology / Medicine
Program: UROPF
Session: Session 4 (1:40pm – 2:30pm)
Authors: Gideon Shaked, Matthew Patrick, Haihan Zhang, Lam Tsoi
Abstract
The three-dimensional organization of the genome plays a crucial role in regulating the transcription of genes. To that end, “loops†of chromatin can facilitate regulatory interactions between distant genomic sites along the linear genome by bringing them into close proximity in 3D space. Chromosome conformation capture techniques like Hi-C and promoter capture Hi-C (pcHi-C) can provide information on the organization of such chromatin loops in 3D space. However, in order to effectively analyze the results of Hi-C and pcHi-C experiments, researchers need the ability to determine whether chromatin loop data contains overlaps with regions of interest and measure the statistical significance of any such overlaps. In this study, we developed LoopSim, a tool that analyzes Hi-C and pcHi-C chromatin loop data and returns information about overlaps with regions of interest. Furthermore, LoopSim determines whether any such overlaps with regions of interest are statistically significant by randomly simulating an empirical background distribution of chromatin loop datasets. Researchers can determine whether experimental chromatin loop data has statistically significant overlaps with regions of interest by comparing experimentally derived chromatin loop data with the simulated distribution of chromatin loop data. In addition to its purely scientific benefits, LoopSim performs a series of checks on inputted data to verify its integrity. LoopSim produces error or warning messages if issues are found, and in certain cases LoopSim automatically removes erroneous data. LoopSim is also simple to use and has a user-friendly interface, making it accessible to a wide range of researchers, including those with limited computational expertise. Moreover, it is efficient and can process large amounts of data in a relatively short amount of time, being 20% faster than our previous approach. LoopSim can conduct a statistically robust analysis of Hi-C data, and its ease of use and efficiency make it accessible to a wide range of researchers.
