Computational Analysis of Spatial Organization of Chromosomes
Session Number
B05
Advisor(s)
Gamze Gursoy, University of Illinois at Chicago Jie Liang, University of Illinois at Chicago
Location
B-125 Tellabs
Start Date
28-4-2016 10:40 AM
End Date
28-4-2016 11:05 AM
Abstract
Understanding the spatial organization of genome is key to gaining insights into nuclear events that cause diseases such as cancer. Experimental techniques such as chromosome conformation capture and its derivatives (3C/4C/5C and Hi-C) provide a wealth of information on organization of genome in the nucleus by quantifying the frequency of DNA interactions between restriction enzyme fragments in a population of cells. By the analysis of the outcome of these techniques, one can compare the specific gene interactions in different cell types such as cancer cell vs. normal cells. Unfortunately, current computational analysis techniques suffer from the biases in the experiments that substantially affect the understanding of the experimental data, including the distance between restriction sites. Here, we investigated how these biases affect the identification of important chromatin interactions by calculating the distribution of restriction enzyme sites in the genome. We further used the available software to detect the topologically associated domains of chromosome 19 and showed how identification of these domains depends on the quality and quantity of the experimental data. These results will help to improve the analysis of current chromosome conformation capture data and correct discovery of gene interactions that are important for cellular functions.
Computational Analysis of Spatial Organization of Chromosomes
B-125 Tellabs
Understanding the spatial organization of genome is key to gaining insights into nuclear events that cause diseases such as cancer. Experimental techniques such as chromosome conformation capture and its derivatives (3C/4C/5C and Hi-C) provide a wealth of information on organization of genome in the nucleus by quantifying the frequency of DNA interactions between restriction enzyme fragments in a population of cells. By the analysis of the outcome of these techniques, one can compare the specific gene interactions in different cell types such as cancer cell vs. normal cells. Unfortunately, current computational analysis techniques suffer from the biases in the experiments that substantially affect the understanding of the experimental data, including the distance between restriction sites. Here, we investigated how these biases affect the identification of important chromatin interactions by calculating the distribution of restriction enzyme sites in the genome. We further used the available software to detect the topologically associated domains of chromosome 19 and showed how identification of these domains depends on the quality and quantity of the experimental data. These results will help to improve the analysis of current chromosome conformation capture data and correct discovery of gene interactions that are important for cellular functions.