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

Program

Installation instructions

Tutorial

Manual

Description

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. //