Comparing multiple ChIP-sequencing experiments. J Bioinform Comput Biol 2011 Apr;9(2):269-82
Date
04/28/2011Pubmed ID
21523932Pubmed Central ID
PMC4289603DOI
10.1142/s0219720011005483Scopus ID
2-s2.0-80052045458 (requires institutional sign-in at Scopus site) 1 CitationAbstract
New high-throughput sequencing technologies can generate millions of short sequences in a single experiment. As the size of the data increases, comparison of multiple experiments on different cell lines under different experimental conditions becomes a big challenge. In this paper, we investigate ways to compare multiple ChIP-sequencing experiments. We specifically studied epigenetic regulation of breast cancer and the effect of estrogen using 50 ChIP-sequencing data from Illumina Genome Analyzer II. First, we evaluate the correlation among different experiments focusing on the total number of reads in transcribed and promoter regions of the genome. Then, we adopt the method that is used to identify the most stable genes in RT-PCR experiments to understand background signal across all of the experiments and to identify the most variable transcribed and promoter regions of the genome. We observed that the most variable genes for transcribed regions and promoter regions are very distinct. Gene ontology and function enrichment analysis on these most variable genes demonstrate the biological relevance of the results. In this study, we present a method that can effectively select differential regions of the genome based on protein-binding profiles over multiple experiments using real data points without any normalization among the samples.
Author List
Ozer HG, Huang YW, Wu J, Parvin JD, Huang TH, Huang KMESH terms used to index this publication - Major topics in bold
AlgorithmsBreast Neoplasms
Cell Line
Cell Line, Tumor
Chromatin Immunoprecipitation
Computational Biology
Epigenesis, Genetic
Female
Genome, Human
Humans
Protein Binding
