An automated analysis pipeline for a large set of ChIP-seq data: AutoChIP

Abstract

Although there are many applications available for the analysis of chromatin immunoprecipitation with massively parallel DNA sequencing (ChIP-seq), users need some knowledge about the installation, alignment, and peak calling procedures prior to the analysis. Here, we present an easy-to-use application for ChIP-seq analysis called AutoChIP. With AutoChIP, installation of necessary programs, alignment of unmapped reads to a reference genome, and identification of genome-wide binding sites can be done in a single step with a large set of ChIP-seq data. Evaluation of the cocktail algorithm implemented in AutoChIP showed that it outperformed a single ChIP-seq tool in terms of the ratio of motif occurrences and the average height of normalized read density over the identified peaks. In addition, annotation of the identified peaks with the known gene and repeat elements information provides a comprehensive picture of the genome-wide binding sites of given proteins. Overall, AutoChIP provides a comprehensive platform to analyze a large set of ChIPseq data in one step.

Meeting report: The biology of genomes and proteomes

“…The cancer tissue showed higher levels of arginine, betaine, glutamate, lysine, and taurine and lower levels of glutamine, hypoxanthine, isoleucine, lactate, methionine, pyruvate, and tyrosine relative to normal tissue. In another example, Dr. Kim carried out HR-MAS NMR experiments to distinguish metabolic changes in human hepatocellular carcinomas (HCCs) and colorectal liver metastases (CRLMs) and then confirmed that each tissue sample was clearly classified into different groups by multivariate analysis (Kim et al 2014a). Good separation among tumors and non-tumorous hepatic parenchyma was observed in OPLS-DA loading plots.…”