ChIP-chip versus ChIP-seq: Lessons for experimental design and data analysis

BackgroundChromatin immunoprecipitation (ChIP) followed by microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) allows genome-wide discovery of protein-DNA interactions such as transcription factor bindings and histone modifications. Previous reports only compared a small number of profiles, and little has been done to compare histone modification profiles generated by the two technologies or to assess the impact of input DNA libraries in ChIP-seq analysis. Here, we performed a systematic analysis of a modENCODE dataset consisting of 31 pairs of ChIP-chip/ChIP-seq profiles of the coactivator CBP, RNA polymerase II (RNA PolII), and six histone modifications across four developmental stages of Drosophila melanogaster.ResultsBoth technologies produce highly reproducible profiles within each platform, ChIP-seq generally produces profiles with a better signal-to-noise ratio, and allows detection of more peaks and narrower peaks. The set of peaks identified by the two technologies can be significantly different, but the extent to which they differ varies depending on the factor and the analysis algorithm. Importantly, we found that there is a significant variation among multiple sequencing profiles of input DNA libraries and that this variation most likely arises from both differences in experimental condition and sequencing depth. We further show that using an inappropriate input DNA profile can impact the average signal profiles around genomic features and peak calling results, highlighting the importance of having high quality input DNA data for normalization in ChIP-seq analysis.ConclusionsOur findings highlight the biases present in each of the platforms, show the variability that can arise from both technology and analysis methods, and emphasize the importance of obtaining high quality and deeply sequenced input DNA libraries for ChIP-seq analysis.

[1]  References , 1971 .

[2]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[3]  John J. Wyrick,et al.  Genome-wide location and function of DNA binding proteins. , 2000, Science.

[4]  Nicola J. Rinaldi,et al.  Transcriptional Regulatory Networks in Saccharomyces cerevisiae , 2002, Science.

[5]  Madeline A. Crosby,et al.  FlyBase: genes and gene models , 2004, Nucleic Acids Res..

[6]  Michel Bellis,et al.  Mapping the distribution of chromatin proteins by ChIP on chip. , 2006, Methods in enzymology.

[7]  Ernest Fraenkel,et al.  High-resolution computational models of genome binding events , 2006, Nature Biotechnology.

[8]  Clifford A. Meyer,et al.  Model-based analysis of tiling-arrays for ChIP-chip , 2006, Proceedings of the National Academy of Sciences.

[9]  Giovanni Parmigiani,et al.  Pre-processing Agilent microarray data , 2007, BMC Bioinformatics.

[10]  Allen D. Delaney,et al.  Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing , 2007, Nature Methods.

[11]  Wei Li,et al.  Model-based analysis of two-color arrays (MA2C) , 2007, Genome Biology.

[12]  Dustin E. Schones,et al.  High-Resolution Profiling of Histone Methylations in the Human Genome , 2007, Cell.

[13]  Yijun Ruan,et al.  Mapping of transcription factor binding regions in mammalian cells by ChIP: comparison of array- and sequencing-based technologies. , 2007, Genome research.

[14]  N. D. Clarke,et al.  Integration of External Signaling Pathways with the Core Transcriptional Network in Embryonic Stem Cells , 2008, Cell.

[15]  Clifford A. Meyer,et al.  Model-based Analysis of ChIP-Seq (MACS) , 2008, Genome Biology.

[16]  H. Stunnenberg,et al.  Genome-wide profiling of PPARgamma:RXR and RNA polymerase II occupancy reveals temporal activation of distinct metabolic pathways and changes in RXR dimer composition during adipogenesis. , 2008, Genes & development.

[17]  Mark Gerstein,et al.  Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets. , 2008, Genome research.

[18]  P. Park,et al.  Design and analysis of ChIP-seq experiments for DNA-binding proteins , 2008, Nature Biotechnology.

[19]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[20]  Zhaohui S. Qin,et al.  HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data , 2010, BMC Bioinformatics.

[21]  Ann E. Loraine,et al.  The Integrated Genome Browser: free software for distribution and exploration of genome-scale datasets , 2009, Bioinform..

[22]  Wing-Kin Sung,et al.  Inherent Signals in Sequencing-Based Chromatin-ImmunoPrecipitation Control Libraries , 2009, PloS one.

[23]  Michael Y Tolstorukov,et al.  Comparative analysis of H2A.Z nucleosome organization in the human and yeast genomes. , 2009, Genome research.

[24]  M. Gerstein,et al.  Unlocking the secrets of the genome , 2009, Nature.

[25]  Raymond K. Auerbach,et al.  PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls , 2009, Nature Biotechnology.

[26]  P. Park.  ChIP–seq: advantages and challenges of a maturing technology , 2009, Nature Reviews Genetics.

[27]  Raymond K. Auerbach,et al.  Mapping accessible chromatin regions using Sono-Seq , 2009, Proceedings of the National Academy of Sciences.

[28]  B. Ren,et al.  Genome-wide prediction of transcription factor binding sites using an integrated model , 2010, Genome Biology.

[29]  T. Laajala,et al.  A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments , 2009, BMC Genomics.

[30]  M. Eisen,et al.  Impact of Chromatin Structures on DNA Processing for Genomic Analyses , 2009, PloS one.

[31]  Nathaniel D. Heintzman,et al.  Histone modifications at human enhancers reflect global cell-type-specific gene expression , 2009, Nature.

[32]  E. Birney,et al.  Heritable Individual-Specific and Allele-Specific Chromatin Signatures in Humans , 2010, Science.

[33]  Mazhar Adli,et al.  Genome-wide chromatin maps derived from limited numbers of hematopoietic progenitors , 2010, Nature Methods.

[34]  M. Facciotti,et al.  Evaluation of Algorithm Performance in ChIP-Seq Peak Detection , 2010, PloS one.

[35]  Kai Tan,et al.  Discover regulatory DNA elements using chromatin signatures and artificial neural network , 2010, Bioinform..

[36]  Raphael Gottardo,et al.  rMAT - an R/Bioconductor package for analyzing ChIP-chip experiments , 2010, Bioinform..

[37]  A. Regev,et al.  Chromatin profiling by directly sequencing small quantities of immunoprecipitated DNA , 2010, Nature Methods.

[38]  M. Gerstein,et al.  Variation in Transcription Factor Binding Among Humans , 2010, Science.