Integrative Analysis of -Seq Data Sets for a Comprehensive Understanding of Regulatory Roles of Repetitive Regions

• Activity Number: 649
• Type: Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Xin Zeng*+ and Sunduz Keles
• Companies: University of Wisconsin - Madison and University of Wisconsin Madison
• Keywords: multi-read ; chip-seq ; dnase-seq

Abstract

A fundamental question in molecular biology is how cell type specific gene expression programs are established and maintained through gene regulation. The NIH funded ENCODE project has thus far generated vasts amounts of sequencing data towards this goal. A major impediment to comprehensive understanding of these data is the lack of statistical and computational methods required to identify functional elements in repetitive regions. The data analysis efforts by the ENCODE projects have far focused on mappable regions with unique sequence contents. This is a highly critical barrier to the advancement of ENCODE data because significant fractions of complex genomes are composed of repetitive regions. The current method of multi-read allocation is heavily influenced by the regional uni-read content within an experiment which might lose the specificity in repetitive regions with high sequence similarity. DNase-seq and FAIRE-seq are high-resolution techniques for mapping active regulatory elements across the genome. We developed a probabilistic model that effectively utilizes data from related experiments as prior which significantly advances multi-read analysis of ENCODE datasets.