A Peak-Finder Meta Server for ChIP-Seq Analysis

Abstract: Chromatin immunoprecipitation (ChIP) coupled with ultra high-throughput parallel sequencing (ChIP-seq) is widely used to study transcriptional regulation on a genome wide scale. Numerous computational tools have been developed to identify transcription factor (protein) binding sites from large ChIP-seq datasets. The diversity of the datasets and the algorithm dependencies make it hard to get a satisfactory result. Many studies have compared the performance and accuracy of the algorithms using empirical datasets. It is shown that selecting the best algorithm to analyze a ChIP-seq dataset for detecting binding sites of a specific transcription factor depends on the dataset conditions. A systematic solution to compare the results of multiple algorithms to produce the best putative binding sites is still lacking. In this thesis project a new software package was introduced to provide a single interface for several state-of-the-art algorithms. A voting mechanism and a scoring mechanism were implemented to identify a set of the best predicted transcription factor binding sites (peaks) by normalizing and comparing the predicted peaks of the selected algorithms. The methods were applied on some publicly available datasets and the results were validated by comparing them to the results of the selected algorithms and their corresponding binding motifs. The discovered motifs showed a very high similarity to the consensus motifs of the selected transcription factors.