CHANCE - A Comprehensive and Easy-to-use Software for ChIP-seq QC

CHANCE - A Comprehensive and Easy-to-use Software for ChIP-seq QC

Reader Aaron Diaz from Jun S Song’s lab in UCSF alerted us about their recently published paper on new software for quality control and validation of ChIP-seq data. We never wrote about ChIP-seq and thought this would be a good opportunity to mention few other key papers as well.

CHANCE Download


Installation instructions




Based on description in the paper, the program developed by Aaron stands out in two respects - (a) in its sheer number of new features compared to other programs, (b) in its user-friendly graphical interface to make analysis easy.

From their abstract:

CHANCE (CHip-seq ANalytics and Condence Estimation) is a standalone package for ChIP-seq quality control and protocol optimization. Our user-friendly graphical software quickly estimates the strength and quality of immunoprecipitations, identies biases, compares the user’s data with ENCODE’s large collection of published datasets, performs multi-sample normalization, checks against qPCR-validated control regions, and produces informative graphical reports.

However, the best description of CHANCE’s features comes from supplementary CHANCE feature comparison table and Fig. 8 in the paper, as shown below.

Song lab at UCSF

We also checked the webpage of Song lab at UCSF and found it to be a collection of brilliant people, all from theoretical physics and mathematics background. Those interested in systems biology of transcription regulation may enjoy the following notes from Dr. Song’s lecture.

Key ChIP-seq Analysis Papers

While on the topic of ChIP-seq, we wanted to list few old and new papers useful for data analysis.

[Genome-Wide Mapping of in Vivo Protein-DNA Interactions, David S. Johnson, Ali Mortazavi, Richard M. Myers, Barbara Wold, Science 2007.

It was the earliest ChIP-seq paper. Following its links at google scholar is a good method to track the history of the field.

Model-based Analysis of ChIP-Seq (MACS), Yong Zhang et al. Genome Biology 2008.

It is among the most cited papers for analyzing ChIP-seq data. Shirley Liu’s group developed programs for analysis of microarray data, and this one extended the methods.

Computation for ChIP-seq and RNA-seq studies, Shirley Pepke, Barbara Wold & Ali Mortazavi, Nature Methods 6, S22 - S32 (2009).

It is a good review paper we do not like to cite for its $32 cost. We wanted to move on to the next one, but thanks to the Chinese, found a free copy for you.

Evaluation of Algorithm Performance in ChIP-Seq Peak Detection, Elizabeth G. Wilbanks, Marc T. Facciotti, PLOS One, 2010

Absolutely fascinating paper. Authors not only compared all existing ChIP-seq analysis tools available in 2010, but had enough sense to publish their findings in PLOS One so that others can read.

Other ChIP-seq Analysis Papers

Choices of the following papers are arbitrary. They do not provide a full list of all relevant papers, but only a subset. Please feel free to add in comment section, if you think we missed a relevant and interesting paper that our readers may enjoy.

A signalnoise model for significance analysis of ChIP-seq with negative control, Han Xu et al. Bioinformatics, 2010.

A co-localization model of paired ChIP-seq data using a large ENCODE data set enables comparison of multiple samples, Kazumitsu Maehara et al. Nucl. Acids Res. (2012).

Picking ChIP-seq peak detectors for analyzing chromatin modification experiments, Mariann Micsinai et al. Nucl. Acids Res. (2012).

ChIP-Seq: technical considerations for obtaining high-quality data, Benjamin L Kidder, Nature Immunology (2011).

Detecting differential binding of transcription factors with ChIP-seq, Kun Liang et al. Bioinformatics (2012).

An integrated software system for analyzing ChIP-chip and ChIP-seq data, Hongkai Ji et al. Nature Biotechnology 26, 1293 - 1300 (2008).

PeakRanger: A cloud-enabled peak caller for ChIP-seq data, Xin Feng, Robert Grossman and Lincoln Stein, BMC Bioinformatics (2011).

Our Paper

Finally, our modest contribution to the world of ChIP-chip and ChIP-seq:

High resolution mapping of Twist to DNA in Drosophila embryos: Efficient functional analysis and evolutionary conservation, Anil Ozdemir et al. Genome Research (2011).

The above research was done by the group of Angelike Stathopoulos, who is a very smart biologist at Caltech working on figuring out the transcriptional network in Drosophila.

Written by M. //