Seqclock: Analysis Pipeline for Time Course Genomic Sequencing Experiments

• Activity Number: 190
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Xuekui Zhang*+
• Companies: Johns Hopkins University
• Keywords: Time-course ; RNA-seq ; ChIP-seq ; Segmentation ; Clustering ; Feature extraction

Abstract

Most cell activities (such as the regulation of gene expression) are dynamic processes, hence it is important to identify and characterize pattern changes on genome over time. Time-course ChIP-seq experiments are conducted for this goal. While such data becomes available, there is no analysis pipeline for analyzing them.

We proposed an analysis pipeline. It segment the genome according to pattern of genomic data, clustering genomic regions according to their time-course patterns, fit smoothing curves and extract features of such patterns. We will illustrate our method using a study containing both RNA-seq data and ChIP-seq data.