Format

Send to

Choose Destination
Genome Biol. 2015 Sep 23;16:205. doi: 10.1186/s13059-015-0756-4.

De novo ChIP-seq analysis.

Author information

1
Department of Human Genetics, The University of Chicago, 920 E. 58th Street, CLSC, Chicago, IL, 60637, USA. xinhe@uchicago.edu.
2
Computational Biology Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA. cicek@cs.cmu.edu.
3
Department of Computer Engineering, Bilkent University, Ankara, 06800, Turkey. cicek@cs.cmu.edu.
4
Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, MIT, Cambridge, MA, 02139, USA. yuhaow@mit.edu.
5
Multimodal Computing and Interaction, Saarland University & Max Planck Institute for Informatics, Saarbr├╝cken, 66123, Saarland, Germany. mschulz@mmci.uni-saarland.de.
6
Computational Biology Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA. hple+@cs.cmu.edu.
7
Computational Biology Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA. zivbj@cs.cmu.edu.

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.

PMID:
26400819
PMCID:
PMC4579611
DOI:
10.1186/s13059-015-0756-4
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center