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Journal Article

De Novo ChIP-seq Analysis


Schulz,  Marcel H.
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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He, X., Cicek, A. E., Wang, Y., Schulz, M. H., Le, H.-S., & Bar-Joseph, Z. (2015). De Novo ChIP-seq Analysis. Genome Biology, 16(1): 205. doi:10.1186/s13059-015-0756-4.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0028-DD0C-3
ABSTRACT: Methods for the analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data start by aligning the short reads to a reference genome. While often successful, they are not appropriate for cases where a reference genome is not available. Here we develop methods for de novo analysis of ChIP-seq data. Our methods combine de novo assembly with statistical tests enabling motif discovery without the use of a reference genome. We validate the performance of our method using human and mouse data. Analysis of fly data indicates that our method outperforms alignment based methods that utilize closely related species.